For once, I don’t have all the answers. That’s why I said “aatsuu“. That is an Inuit (Inuktitut) word for “I don’t know.” We’re learning Inuit language today because I wonder how they would describe a recent event in Antarctica. You see, I had been told growing up that the Inuit had more than 30 different words for “snow”, so who better to describe the changing surface properties of snow and ice?
But, as it turns out, that is a controversial statement. It has led to what linguists refer to as “the Great Eskimo Vocabulary Hoax.” There are many other blogs and podcasts that have talked about this “myth“. Exactly how many Inuit words there are for snow (or ice) depends on a lot of factors. The two biggest factors are: What is an “Inuit” language? And, what is a word? “Inuit” used here is a blanket term used to describe the native people of the North American Arctic and a few groups in far-eastern Siberia, which includes distinct groups of people that call themselves Inuit, Inupiat, Yupik, and Alutiit, among others, and have a variety of different languages. One commonality is that they all have agglutinative languages. Simply put, they combine root words with modifiers to create complex words that take the place of phrases. It is summarized succinctly in this comic. So, we might describe snow as “wet and heavy” or “light and fluffy”, while an agglutinative language would say “snowwetandheavy” or “snowfluff” to mean the same thing.
If you focus only on the root words, you get a small number of words that is similar to the number of words in English. If you add in all the possible modifiers, you get a limitless number. (Some of these are amazingly specific, such as qautsaulittuq: “ice that breaks when its strength is tested using a harpoon.”)
As part of the International Polar Year 2007-2008, the Sea Ice Knowledge and Use (SIKU) project (“siku” is the Inuit root word for “ice”) combined the efforts of physical and social scientists to better characterize our collective understanding of ice behavior in the Arctic by studying the native Arctic residents’ understanding of ice behavior, in part, through their culture and language. The discussion on the variety of words for snow and ice takes up five chapters of this compilation of SIKU research. That’s where I learned that puttippoq means an ice surface that has become wet due to melting. (You can read their take on the Great Eskimo Vocabulary Hoax here.)
Didn’t think you’d see a discussion on linguistics in a blog about satellite meteorology, did you? So, let’s get to the satellite meteorology. We’ll start with a look at what I previously called the “mystery channel“, although a better name for it is the “snow band”, since it is very sensitive to the properties of snow and ice.
As always, it is best to view this video in full screen mode. What you are seeing is a compilation of VIIRS band M-08 (1.24 µm) images from both S-NPP and NOAA-20 from 12-13 February 2020, and there are two interesting things to note. First, the left half of the image is the high-elevation Antarctic Plateau, which contains a very bright feature that is very stationary. The right side of the image is low-elevation and contains the southernmost tip of the Ross Ice Shelf (outlined on the map). The Transantarctic Mountains (or, more specifically, the Queen Maud Mountains) in the middle separate the two regions. Pay attention to the expanding dark region on top of the ice shelf.
Since it is difficult to focus on more than one thing at a time, let’s focus on the ice shelf first. (Coincidentally, I haven’t found an Inuit word for “ice shelf”, but I did find sikuiuitsoq, which means “ice that doesn’t melt” – used to refer to ice that has been around a long time, which certainly applies to the Ross Ice Shelf.)
Animation of VIIRS M-08 images (12-13 February 2020)
This is an animated GIF that you will have to click to view. This feature shows up in the longer-wavelength bands, M-10 (1.61 µm) and M-11 (2.25 µm):
Animation of VIIRS M-10 images (12-13 February 2020)
Animation of VIIRS M-11 images (12-13 February 2020)
But, see if you can find it in the shorter-wavelength bands, M-07 (0.86 µm) and M-05 (0.67 µm):
Animation of VIIRS M-07 images (12-13 February 2020)
Animation of VIIRS M-05 images (12-13 February 2020)
At the shorter wavelengths, the feature only appears at certain times, suggesting a viewing angle dependence on the reflectance. That means the bidirectional reflectance distribution function (BRDF) is not uniform.
The explanation for this feature is pretty simple. The cold air over the Antarctic Plateau sinks down through the canyons in the Queen Maud Mountains, and as it descends, the air compresses and warms. These are called katabatic winds. In this case, the katabatic winds are aided by the synoptic scale flow as evidenced by the cloud motion. This relatively warm wind is likely melting the top surface of the Ross Ice Sheet, causing a drop in reflectance in the short-wave infrared (IR) similar to what we’ve seen before. In fact, the darkest regions of those canyons are where the howling katabatic winds have scoured away all the snow, leaving behind only the oldest glacial ice. And glacial ice has the largest grain sizes of any of the ice out there, which we know is a big factor on ice reflectivity in the shortwave-IR. (Watch those animations again and note that M-11 appears to provide the strongest signal of blowing snow coming out of those canyons. This is exploited by the Day Snow/Fog RGB.)
For comparison purposes, let’s look at the Natural Color RGB (also known as the Day Land Cloud RGB), made up of M-05 (blue), M-07 (green) and M-10 (red):
Animation of VIIRS Natural Color RGB composite of M-5, M-7, and M-10 (12-13 February 2020)
And, what we are calling the VIIRS “Snowmelt” RGB (M-05/blue, M-08/green, M-10/red):
Animation of VIIRS Snowmelt RGB images (12-13 February 2020)
And, finally, a variation of the “Snow” RGB developed by Météo-France (M-11/blue, M-08/green, M-07/red):
Animation of VIIRS MeteoFrance Snow RGB images (12-13 February 2020)
The inclusion of M-08 makes a big difference on the visibility of this feature. And, in contrast, this is one application where True Color imagery (M-03/0.48 µm/blue, M-04/0.55 µm/green, M-05/0.67 µm/red) is of no help at all:
Animation of VIIRS True Color images (12-13 February 2020)
As for the second region of interest from the original video, “Aatsuu”. We have a region of ice and/or snow in the Antarctic Plateau that is significantly brighter than its surroundings in the shortwave IR. The question is: why is it such a well-defined shape with a distinct edge to it? Here are all the same bands and RGBs as above:
Animation of VIIRS M-05 images (12-13 February 2020)
Animation of VIIRS M-07 images (12-13 February 2020)
Animation of VIIRS M-08 images (12-13 February 2020)
Animation of VIIRS M-10 images (12-13 February 2020)
Animation of VIIRS M-11 images (12-13 February 2020)
Animation of VIIRS True Color RGB images (12-13 February 2020)
Animation of VIIRS Natural Color RGB images (12-13 February 2020)
Animation of VIIRS Snowmelt RGB images (12-13 February 2020)
Animation of VIIRS MeteoFrance Snow RGB images (12-13 February 2020)
We know that smaller particle size leads to increased reflectivity in the shortwave IR. And, fresh snow typically fits that bill. But, fresh snow tends to appear more streaky (technical term) in satellite images. It’s the distinct edges that are so puzzling.
Anyone with more experience about the ice properties on the Antarctic Plateau out there? Or, experts at what makes snow and ice bright in the shortwave IR? If so, feel free to post a comment. (But, any theories involving UUSOs or UUIOs [Unidentified Under Ice Objects] will be placed in this blog’s trash.)
If not, isn’t this what graduate students are for?
You youngins are not old enough to remember, but we took a look at Gough Island before. Well, not directly, but as part of the British territory of Saint Helena, Ascension and Tristan da Cunha eight years ago. We also did a special feature on Saint Helena and Ascension four years ago. So, why are we re-visiting a group of tiny islands in the middle of the South Atlantic Ocean for a third time? Because of the great view that VIIRS provided earlier this month, and because Gough Island is an interesting place.
For starters, it rhymes with “scoff” and not with “dough” despite the spelling. So now you know. It is also home to one of the more unique jobs in meteorology. It has no permanent residents, but every year a group of 5-10 people are brought in to run the weather station on it for the South African Weather Service and study the biology of the island for the South African National Antarctic Programme (SANAP) even though it is a British island. (At least one member of the team has to be a doctor, since there are no hospitals within 400 km and boats only stop by a couple of times per year.) From the pictures and video, it certainly looks like unique place to spend a year.
Now, on to the interesting satellite imagery. We begin our visit to Gough Island with a loop from Meteosat-11, and its imager, SEVIRI (PDF document):
Note that Meteosat data was provided to NOAA by EUMETSAT and the video above shows their “Enhanced” Natural Color RGB. I can also take this opportunity to promote the fact that we are now allowed to share Meteosat imagery on our ultra-popular website, SLIDER, which is where the above loop came from.
Credits and advertising out of the way, did you see Gough Island? If not, you could try viewing the video in full-screen mode. Or, it might help if I zoomed in on the area, like this:
Meteosat-11 “Enhanced” Natural Color RGB (07-18 UTC, 5 January 2020)
The southernmost green dot is Gough Island. The other green dots are Tristan da Cunha, Inaccessible Island, and Nightingale Island. What caught my attention was two things: it’s rare to get such a clear view of these islands and the waves produced by Gough Island clearly impact clouds that never even passed over the island. Of course, having come from SEVIRI, this loop is limited to 3 km resolution (since the HRV band isn’t part of this RGB, and doesn’t normally cover this part of the world).
What if we had 375 m resolution? What would that look like? Well, on VIIRS, it looks like this:
NOAA-20 VIIRS Natural Color RGB composite of channels I-01, I-2 and I-3 (14:38 UTC 5 January 2020)
Click on the image to view the full resolution. It’s worth it.
It should be noted that I haven’t applied the same “enhanced” version of the Natural Color RGB that removes the cyan color of ice clouds and snow. Another difference is something that you don’t see in the SEVIRI loop: sun glint. That’s because Meteosat-11 isn’t viewing the scene from the same angle as VIIRS.
Look closely downwind (or leeward) of Gough Island and you’ll see from the sun glint that the island is producing waves not only in the atmosphere, but on the surface of the ocean:
Same image as above, only zoomed in on Gough Island
Of course, if you clicked on the sun glint link, you saw a more extreme example of this, and if you bothered to read the article, you also saw the explanation (written much more succinctly and accurately than I could without plagiarism).
That was only the NOAA-20 view. We also have the Suomi-NPP view, which covered this area before and after NOAA-20. Here are all three views combined:
Animation of VIIRS Natural Color RGB images (5 January 2020)
You have to click on the image above to see the animation play. Now you can see the motion of the clouds, yet the waves are nearly stationary. That’s because they are “tied” to the island that is producing them. This is an example of trapped lee waves. And pilots beware: as this case shows, these waves are present even where there are no clouds to reveal them.
What is perhaps more interesting is that the waves in the ocean show up in the mid-wave infrared (IR) thanks to the sun glint:
S-NPP VIIRS I-04 image (13:46 UTC, 5 January 2020)
This is I-04, the 375 m resolution channel at 3.7 µm, from the first S-NPP overpass (13:46 UTC, 5 January 2020). See the waves on the lee of both Gough Island and Tristan da Cunha? (Tristan da Cunha’s waves aren’t apparent in the clouds. Since these are trapped lee waves, they are occurring below the height of the cirrus clouds to the northwest.) Now, let’s animate the three overpasses:
Animation of VIIRS I-04 images (5 January 2020)
The impact of sun glint on the these images, especially the middle one (NOAA-20) is obvious. The last image from S-NPP (15:29 UTC) has no sun glint, so these waves are much harder to spot.
Now check out the high-resolution longwave IR (LWIR) band, I-05 (11.4 µm):
Animation of VIIRS I-05 images (5 January 2020)
Pay attention to the change in scaling as revealed by the color table. Three things stand out: with this combination of scaling and color table, you can see structure in the sea surface temperature, the waves downwind of Gough are still visible in the ocean even in the LWIR, and “limb cooling” is something to watch out for.
More detail on the items of note: the sea surface temperature (SST) structure is easier to spot in I-05 because it is not impacted by sun glint. This is because the Earth emits significantly more radiation in the LWIR than what it receives from the sun. In the midwave-IR, the contribution from the sun is significant (as these images show). The waves are still visible in I-05 because the winds on the downward portion of the wave are hitting the ocean surface and modifying the exchange of heat between the atmosphere and the ocean, leading to waves of warmer and cooler SST. And, third, “limb cooling” is the name given to the fact that, at high satellite viewing angles, the path length of the radiation through the atmosphere increases, and more radiation comes from higher up where temperatures are colder. (More on limb cooling may be found on slides 19-21 here.) Look to the clear sky areas on the left edge of the swath on the first I-05 image and compare it to the middle image. Then do the same for the right edge of the swath on the last image. The limb cooling effect is readily apparent.
There’s one more interesting thing from this same scene. Look at the True Color images from these three overpasses:
Animation of VIIRS True Color RGB images (5 January 2020)
See any variations in the color of the ocean not related to sun glint? That is phytoplankton, a source of life and death in the ocean. In fact, Gough Island’s location, where warmer sub-tropical water mingles with colder mid-latitude water is what makes it such a great nesting site for birds. The fish eat the phytoplankton and the birds eat the fish. Unfortunately, stowaway mice brought to Gough Island by accident are eating the birds.
All that interesting science from one tiny island in the middle of the South Atlantic Ocean.
There’s an ongoing serious situation in Australia: the bush in New South Wales and Queensland is on fire.
Here’s a look at what the Advanced Himawari Imager (AHI) on Himawari-8 saw on 8 November 2019: click here.
What you see in that loop is the “Natural Fire Color RGB” (known to American forecasters as the “Day Land Cloud Fire RGB”) on the left (link to PDF description here), and the “Fire Temperature RGB” on the right (link to PDF description here). These are precisely the products we debuted on this blog seven years ago when we first looked at fires in Australia. Except, now there is a difference: the “Natural Fire Color RGB” is now made with the 3.7 µm band as the red component (replacing the 2.25 µm band I used originally), since the 3.7 µm channel is even better at detecting fires. This also means that we can produce the VIIRS version using “I-band” resolution (375 m). AHI, used in the loop I linked to above, has 2 km resolution* for the mid- and shortwave infrared (IR) bands.
Along the coast, near the northern edge of the images is Brisbane, the third largest city in Australia. Near the southern edge of those images is Sydney, the largest city in Australia. As you can see from Himawari-8, much of the area between the two is on fire. And, this is not the “Outback” where very few people live. This region contains some of the highest population density in Australia, and it’s also prime habitat for koalas, which don’t live anywhere outside of eastern Australia (except in zoos).
It’s no secret that resolution plays in big role in fire detection from satellites. We’ve covered this many times before. But, to hammer the point home (bit of American slang), here’s the resolution difference between VIIRS and AHI in full view from 3:50 UTC on 7 November 2019:
Himawari-8 AHI Day Land Cloud Fire RGB composite of bands 2, 4, and 7 (03:50 UTC, 7 November 2019)
S-NPP VIIRS Day Land Cloud Fire RGB composite of bands I-1, I-2 and I-4 (03:49 UTC, 7 November 2019)
As always, click on each image to bring up the full resolution version. If you just look at the elephant-thumbnail-sized images above without clicking on them, you might get the impression that fires are easier to spot with AHI than with VIIRS. That’s because AHI makes it appear that the entire 2km-wide pixel* is full of fire, when a fire typically only fills a very small percentage of the total area of the pixel. With 375 m resolution**, VIIRS more accurately pinpoints the locations of fire activity. Although, it should be noted that even this is still a larger scale than most fire fronts. To be really accurate, you need something with the resolution of Landsat’s OLI, or a similar radiometer attached to an aircraft – except these high-resolution instruments don’t provide full global coverage multiple times daily like VIIRS, or hemispheric coverage every 10 minutes like AHI. (*On AHI [and ABI and AMI] pixels may be approximated as square-shaped solid angles that are projected onto the curved surface of the Earth from a point roughly 36,000 km above the Equator. 2 km is the width of an IR pixel at the sub-satellite point [on the Equator], where the resolutions are the highest. **VIIRS pixel resolutions vary across the swath by a factor of 2 between nadir and edge of scan, as we shall see. 375 m is the nadir value.)
For completeness, we can do the same comparison with the Fire Temperature RGB:
Himawari-8 AHI Fire Temperature RGB composite of bands 5, 6 and 7 (03:50 UTC, 7 November 2019)
S-NPP VIIRS Fire Temperature RGB composite of bands M-10, M-11 and M-12 (03:46 UTC, 7 November 2019)
This time, we’re comparing 2 km resolution (AHI) against 750 m resolution (VIIRS), so the differences aren’t as stark. But, this is a good opportunity to remind everyone that the Fire Temperature RGB provides information on fire intensity, while the Natural Fire Color (Day Land Cloud Fire) RGB provides information on fire detections (plus smoke and burn scars), and should be used more as a “fire mask”.
There’s another resolution difference that is easy to see from these fires, and it can be quite significant. I first noticed it when looking at this animation I made of the VIIRS Fire Temperature RGB from 1-11 November 2019:
Animated GIF of VIIRS Fire Temperature RGB images (1-11 November 2019)
You have to click on the animation to get it to play.
Did you notice the same thing I did? You probably noticed the explosive growth of the fires from 7-9 November, but that’s not what I’m talking about. (Hint: Pay close attention to the nighttime images.) At night, without any sunlight present, you lose information on clouds and the background land surface, and only the fires are visible (unless they are obscured by clouds). That’s where today’s feature of interest resides. I’ll zoom in on some of the fires from 5 November 2019 to make it easier to see:
Animated GIF of VIIRS Fire Temperature RGB images (5 November 2019)
The image from 14:01 UTC comes from S-NPP, while the image from 14:52 comes from NOAA-20. Is NOAA-20 better than S-NPP at detecting the fires? Well, the reverse happened two nights later:
Animated GIF of VIIRS Fire Temperature RGB images (7 November 2019)
This time, the fires appear hotter (brighter) in the 15:03 UTC image, which came from S-NPP. The 14:12 UTC image came from NOAA-20. Here’s a sequence of three images from 10 November where the NOAA-20 image is sandwiched by two S-NPP images:
Animated GIF of VIIRS Fire Temperature RGB images (10 November 2019)
So, why do the fires appear brighter in some images and not others? It’s possible that the fires are becoming more active in the middle image (due to an increase in winds, for example), but it’s more likely that you are seeing the direct result of resolution differences between the various overpasses. “But, I thought both VIIRS instruments had the same resolution,” you might say as though it were a question. And that statement would suggest that you forgot about the “bowtie-effect”. (Not the effect that has anything to do with diamonds, but the effect I wrote a whole chapter about here [PDF].) If you read the **above you would already know that the resolution of VIIRS degrades by a factor of two between nadir and the edge of scan. And, if you didn’t already know, NOAA-20 and S-NPP are positioned in space a half-orbit apart. This means that, in the time it takes between a NOAA-20 overpass and a S-NPP overpass, the Earth has rotated by half the width of the swath (approximately). So, when one VIIRS instrument views something at nadir, it will be close to the edge of scan on the other satellite (and have more coarse resolution as a result).
So, in the last animation, the first image (14:05 UTC) is S-NPP viewing the fires from the east near the edge of scan, the middle image (14:56 UTC) is NOAA-20 viewing the fires near nadir, and the third image is S-NPP viewing the fires from the west – even closer to the edge of scan. (Plus, the terrain is sloping away from S-NPP in the last image as well.)
Those factors contribute to the changing appearance of the fires. They also highlight the value of having two VIIRS instruments in space: if one satellite doesn’t get a good look at a fire, the other one likely will.
By the way, these fires have been producing a lot of smoke. Here is a loop of VIIRS True Color images from 6-11 November:
And the view from the ground is even more apocalyptic:
Australia so far has lost 970,000 hectares due to current bushfires.
“We are no longer the sunburnt country, we are the country on fire 🔥”#AUSTRALIAFIRES pic.twitter.com/NBQBoro1OA
— kel.lunn (@LunnKel) November 12, 2019
Australian bush fires need more media coverage and MORE people spreading the news about it. these fires are becoming much worse than the amazon, yet the rest of the world stays quiet. Please use your voice and help us spread it around!#Australiabushfires #AUSTRALIAFIRES#btsarmy pic.twitter.com/olKtVkKDoB
— ⍨ 𝙯𝙞𝙖 #blm ⁰⁶²⁶ (@ktaehzia) November 12, 2019
As we all know, the furthest south you can travel is to the South Pole – the Geographic South Pole, not the Magnetic South Pole or the Geomagnetic South Pole. When you get there, try to face east if you can. (This is easier to do at the “Ceremonial South Pole” than it is at the actual South Pole.)
The furthest south you can get by boat is an island off the coast* of Antarctica, called Ross Island. (*The term “coast” is used loosely here, since Ross Island is usually connected to Antarctica by the Ross Ice Shelf.) At the southern tip of Ross Island is the largest “city” in Antarctica: McMurdo Station. McMurdo is the port-of-entry for most visitors to Antarctica. It is also home to a ground station that receives data from NOAA-20 (and many other satellites). So, if you love the lower latency that comes with NOAA-20 VIIRS data, you have McMurdo Station to thank. (S-NPP data is only downlinked at Svalbard – once per orbit – while NOAA-20 is downlinked at both Svalbard and McMurdo.) This is the location of today’s resolved mystery.
The mystery began with the development of a new website for viewing global VIIRS imagery: Polar SLIDER*. (*Shameless self-promotion: I helped develop that website.) If you click on that link, choose “Southern Hemisphere” from the Sector menu to view Antarctica. With every product, you can zoom in anywhere on the globe* to view the full resolution data. (*Claim is void near the Equator.) Under the Product menu, you can choose between all 22 VIIRS channels (16 M-bands, 5 I-bands, and the Day/Night Band), or from a list of imagery products and cloud products. (And we are always working to add new products.) Since it’s perpetual night down there right now, you’ll notice that the visible and near-IR bands don’t give you much information – except the Day/Night Band, of course, which can provide images like this:
NOAA-20 VIIRS DNB image (14:25 UTC, 14 August 2019)
Ross Island is in the center of that image. That bright light at the southern tip of Ross Island is McMurdo Station. The second bright light south of that is the “airport“. Here’s an annotated image with the map plotted on it:
NOAA-20 VIIRS DNB image of Ross Island and surroundings (14:25 UTC, 14 August 2019)
As always, click on an image to see it in full resolution. Now that we have our bearings, let’s look at the high resolution mid-wave IR band (I-4/3.74 µm):
NOAA-20 VIIRS channel I-4 (14:25 UTC, 14 August 2019)
See that white dot in the middle of Ross Island? What is that? (Hint: it’s not part of the map.)
To make some sense of this, look at the color table plotted on the bottom of the image. White pixels on this scale (not counting the map) are +100°C (+373 K). In contrast, the dark turquoise color surrounding it is in the -25°C to -30°C range (243-248 K). What could be over 100°C in Antarctica in the winter? Did something catch on fire?
It turns out, it is a semi-permanent feature according to this animation collected from Polar SLIDER. (You have to click on the animation to see it play.)
Animation of VIIRS channel I-4 images (13 August 2019)
Looking at Day/Night Band images over the same time period, it also shows up as a bright spot:
Animation of VIIRS DNB images (13 August 2019)
Maybe it’s a nuclear reactor that powers all of McMurdo Station? (Nope. There was a nuclear power plant, but that was de-commissioned in 1972.) Maybe the fact that this bright (in the DNB), hot spot (mid-IR) is on top of a mountain has something to do with it? (Bingo!)
Ross Island is made up of volcanoes, the most prominent of which are Mt. Erebus and Mt. Terror (named for the ships on the original expedition that discovered them). Mt. Terror (the one on the right) is inactive. Mt. Erebus, on the other (left) hand, is the southernmost active volcano in the world. And, what’s relevant here is the fact that it is home to one of only five known lava lakes in the world. So, molten-hot liquid rock exists in an ice-covered environment where temperatures regularly dip down to -50°C or -60°C. And, it’s right next to the largest settlement in Antarctica. Sleep tight. (Since we’re less than a week away from the first sunrise of the spring, get your sleep while you can down there!)
As of the time of this writing, there is currently a humanitarian crisis in Mozambique caused by what was Tropical Cyclone Idai. Here’s the situation as of 25 March 2019.
Wikipedia actually has a pretty detailed history of Idai. Long story short, one of the worst (“worst” meaning large negative impact on humans) tropical cyclones in recorded history for the Southern Hemisphere formed just off the coast of Mozambique on 4 March 2019. It quickly headed inland as a tropical storm, where it dropped heavy rains on northern Mozambique and Malawi. Then, it turned back into the Mozambique Channel, headed for Madagascar, stopped, turned around, rapidly intensified, and then hit Mozambique a second time as a Category 2 cyclone. After making it on land a second time, it stalled out and dissipated, dropping more heavy rain in the process on central Mozambique and eastern Zimbabwe. Here is a long loop from Meteosat-8 showing much of the life cycle of Cyclone Idai as it appeared in the longwave infrared (IR).
Here’s a visible (True Color) loop from VIIRS that covers most of the month of March:
Animation of VIIRS True Color images from both S-NPP and NOAA-20 (1-25 March 2019)
This loop has been reduced in resolution to half of its original size to save on file size. Even with only 2-3 images per day (since we combined both S-NPP and NOAA-20 images), you can still clearly see the cyclone over Mozambique early in the loop head out to sea and then turn around and hit Mozambique again, where it dumped heavy rain for several days.
But, I want to draw your attention to several of the images in that loop: the beginning, the middle, and the end. On 1 March 2019, NOAA-20 got a pretty clear view of central Mozambique:
NOAA-20 VIIRS True Color composite image (11:32 UTC, 1 March 2019)
We’ll call this the “Before” image – and this one is full resolution (750 m). (NOTE: You have to click on it show it at full resolution.) We can also look at the Natural Color RGB (also known as the Day Land Cloud RGB and about a dozen other names), which we can make with the high resolution imagery bands I-1, I-2 and I-3:
NOAA-20 VIIRS Natural Color RGB composite image (11:32 UTC, 1 March 2019)
This is also at full resolution (375 m). (Again, only if you click on it.)
The worst of the flooding occurred with Idai’s second landfall on 14 March 2019, and both VIIRS got great views of Idai prior to landfall:
NOAA-20 Natural Color RGB composite image (10:47 UTC, 14 March 2019)
S-NPP Natural Color RGB composite image (11:38 UTC, 14 March 2019)
These images were taken ~50 min. apart. And, if you couldn’t already tell, they’re the high resolution Natural Color images. This is for two reasons: 1) who doesn’t want to see tropical cyclones at the highest resolution possible? and 2) the Natural Color RGB brings out details in the cloud structure you can’t see in True Color. As we’ve discussed before, Natural Color highlights ice clouds in a cyan color, while liquid clouds are nearly white. But, if you look closely in the above images, you will see lighter and darker cyan regions in the clouds above (or at the top of) the eyewall. This is due to differences in particle size. Larger ice particles appear more cyan, while smaller ice particles appear more white. (Of course, there is also some shadowing going on, which accounts for the darkest regions.)
Another thing to note is the first image comes from NOAA-20, which was to the east of Idai. This provides a great view of the sloped structure of the west side of the eyewall. (And, not much information on the east side of the eyewall.) The second image comes from Suomi-NPP, which was to the west of Idai, looking at the east side of the eyewall. The two satellites in tandem provide an almost 3D view of the clouds in the eyewall (separated by 50 minutes, of course).
Also, see that peninsula that is just to the west of the eyewall in the last two images? (Hint: you won’t see it unless you bring up the full resolution versions.) That’s where the city of Beira is (or was). Beira was home to half a million people, and was one of the major ports in Mozambique. It took a direct hit from the eyewall of Idai, which destroyed approximately 90% of the buildings there. Beira was also ground zero for the resulting flooding, and the pictures coming out are not pretty.
This is a good segue to talk about the images from the end of the loop. NOAA-20 captured a relatively cloud-free view of Mozambique on 25 March 2019:
NOAA-20 VIIRS True Color composite image (10:42 UTC, 25 March 2019)
NOAA-20 VIIRS Natural Color RGB composite image (10:47 UTC, 25 March 2019)
These images were collected 10 days after landfall, and the flooding is still evident. Don’t believe me? Compare these “After” images with the “Before” images shown earlier (zoomed in on Beira):
Animation comparing NOAA-20 True Color RGB composite images from 1 March 2019 and 25 March 2019
Notice the fertile, green agricultural land surrounding Beira in the “before” image that is covered by brown floodwater in the “after” image. Just like what we saw in the pictures from Beira.
But, there’s a lot flooding that is not so easy to see in the True Color that shows up better in the Natural Color RGB:
Animation comparing NOAA-20 Natural Color RGB images from 1 March 2019 and 25 March 2019
Since this VIIRS Natural Color imagery has twice the resolution of True Color, this animation is too large for WordPress to play it automatically. You have to click on it to see the animation play.
We’ve talked before about differences between True Color and Natural Color when it comes to flooding, and this example shows it quite well. You see, True Color can miss flooding, because water is pretty transparent at visible wavelengths. If the water is clear, you can see through it and, from the perspective of VIIRS, you see the ground underneath the water (as long as the water is relatively shallow). If the water is muddy, like most of this flooding, it’s easier to see (since radiation reflects off the particles in the water), but it can look the same as the mud (or bare ground) that isn’t covered by water.
Natural Color uses longer wavelengths, where water is much more absorbing, so water appears nearly black. That’s why it is typically easier to see flooding against a background of non-flooded land in Natural Color than True Color. But, the flooding around Beira is so muddy, the high reflectivity in the visible channel (which is the blue component of the RGB) starts to win out, and the floodwater appears more blue than black.
We can prove it by looking at the individual bands that make up these RGB composites. Remember to click to play the animations for the I-bands:
Comparison of NOAA-20 VIIRS channel I-1 (0.64 µm) images from 1 March and 25 March 2019
Comparison of NOAA-20 VIIRS channel I-2 (0.87 µm) images from 1 March and 25 March 2019
Comparison of NOAA-20 VIIRS channel I-3 (1.61 µm) images from 1 March and 25 March 2019
Note that the flooded areas look brighter in I-1 (thanks to the dirty water) and look darker in I-2 and I-3 (because they are less sensitive to the dirt in the water and more sensitive to the water itself).
The individual M-bands that comprise the True Color RGB, shown below, have been corrected for Rayleigh scattering and scaled the same as in the True Color images above:
Comparison of NOAA-20 VIIRS channel M-3 (0.48 µm) images from 1 March and 25 March 2019
Comparison of NOAA-20 VIIRS channel M-4 (0.55 µm) images from 1 March and 25 March 2019
Comparison of NOAA-20 VIIRS channel M-5 (0.67 µm) images from 1 March and 25 March 2019
It is quite difficult to detect the flooding using the visible channels (M-3, M-4, M-5 and I-1) alone. But, the flooded areas are generally brighter in the “after” images. However, the water is easy to see in the shortwave IR channels (I-2, and I-3 along with M-7 and M-10, which were not shown).
Of course, this was a very long-winded way of looking at the flooding. We could have just used the JPSS Program’s official Flood Product made with VIIRS, created by researchers at George Mason University. Here is a three day composite image (composited to reduce the impact of clouds), covering 19-22 March 2019:
NOAA-20 VIIRS Flood Detection Product using a 3-day cloud-free composite (19-22 March 2019). Image courtesy S. Li (GMU).
Red and yellow areas show where flooding is detected. Gray areas are areas that were cloudy all three days. As an interesting side note, this product is validated against the Natural Color RGB. For more on this product, click here. If you want to know how much precipitation actually fell, here is a loop provided by NASA made with observations from GPM (Global Precipitation Measurement Mission):
You get bonus points if you can read the scale below the images. But, even without a magnifying glass, you can probably guess: it’s a lot of rain!
A winter storm moved through the Northeast U.S. over the weekend of 19-20 January 2019. This Nor’easter was a tricky one to forecast. Temperatures near the coast were expected to be near (or above) freezing. Temperatures inland were expected to be much colder. Liquid-equivalent precipitation, at least according to the GFS, was predicted to be in the 1-3 inch (25-75 mm) range the day before. This could easily convert to 1-2 feet (30-60 cm) of snow. The question on everyone’s mind: who gets the rain, who gets the snow, and who gets the “wintry mix”? The fates of ~40 million people hang in the balance. This is one of the situations that meteorologists live for!
The difference between 71°F and 74°F is virtually meaningless. The difference between 31°F and 34°F (with heavy precipitation, at least) is the difference between closing schools or staying open. It’s the difference between bringing out the plows or keeping them in the garage; paying overtime for power crews to keep the electricity flowing or just another work day; shutting down public transportation or life as usual.
Of course, the obvious follow-up question is: what is the “wintry mix” going to be? Rain mixed with snow? Sleet? Freezing rain? It doesn’t take much to change from one to the other, but there can be a big difference on the resulting impacts based on what ultimately falls from the sky.
So, what happened? Here’s an article that does a good job of explaining it. And, here are PDF files of the storm reports from National Weather Service Forecast Offices in Albany, Boston (actually in Norton, MA) and New York City (actually in Upton, NY). The synopsis: some places received ~1.5 inches (~38 mm) of rain, some places received ~11 inches (~30 cm) of snow and some places were coated in up to 0.6 inches (15 mm) of ice.
Of particular relevance here are the locations that received the ice. If you took the locations listed in the storm reports that had more than 0.1 inches (2.5 mm) of ice (at least the ones in Connecticut) and plotted them on a map, they match up quite well with this map of power outages that came from the article I linked to:
Map of power outages in Connecticut as a result of an ice storm (19-20 January 2019). Image courtesy Eversource/NBC Connecticut.
Now, compare that map with this VIIRS image from 22 January 2019 (after the clouds cleared out):
VIIRS channel I-3 image from NOAA-20, 17:09 UTC 22 January 2019
As always, you can click on the image to bring up the full resolution version. This is the high-resolution imagery band, I-3, centered at 1.6 µm from NOAA-20. Notice that very dark band stretching from northern New Jersey into northern Rhode Island? That’s where the greatest accumulation of ice was. Notice how well it matches up with the known power outages across Connecticut!
The ice-covered region appears dark at 1.6 µm because ice is very absorbing at this wavelength and, hence, it’s not very reflective. And, since it is cold, it doesn’t emit radiation at this wavelength either (at least, not in any significant amount). This is especially true for pure ice, as was observed here (particularly the second image), since there aren’t any impurities in the ice to reflect radiation back to the satellite. The absorbing nature of snow and ice compared with the reflective nature of liquid clouds is what earned this channel the nickname “Snow/Ice Band” (PDF).
At shorter wavelengths (less than ~ 1 µm), ice and snow are reflective. (Note how a coating of ice makes everything sparkle in the sunlight.) This makes it nearly impossible to tell where the ice accumulation was in True Color images:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 from NOAA-20, 17:09 UTC 22 January 2019
The Natural Color RGB (which the National Weather Service forecasters know as the Day Land Cloud RGB (PDF file)) includes the 1.6 µm band, which is what makes it useful for discriminating clouds from snow and ice. And, as expected, the region of ice accumulation does show up (although it is tempered by the highly reflective nature of snow and ice in the visible and “veggie” bands that make up the other components of the RGB):
VIIRS Natural Color RGB composite of channels, I-1, I-2 and I-3 from NOAA-20 (17:09 UTC, 22 January 2019)
Another RGB composite popular with forecasters is the Day Snow/Fog RGB (PDF file), where blue is related to the brightness temperature difference between 10.7 µm and 3.9 µm, green is the 1.6 µm reflectance, and red is the reflectance at 0.86 µm (the “veggie” band). This shows the region of ice even more clearly than the Natural Color RGB:
VIIRS Day Snow/Fog RGB composite of channels (I-5 minus I-4), I-3 and I-2 from NOAA-20 (17:09 UTC, 22 January 2019)
Breaking things up into the individual components, we can see how the ice transitions from being reflective in the visible and near-infrared (near-IR) to absorbing in the shortwave-IR:
VIIRS high-resolution visible channel, I-1 (0.64 µm), from NOAA-20 (17:09 UTC, 22 January 2019)
VIIRS high-resolution “veggie” channel, I-2 (0.86 µm), from NOAA-20 (17:09 UTC, 22 January 2019)
VIIRS channel M-8 (1.24 µm) from NOAA-20 (17:09 UTC, 22 January 2019)
VIIRS channel M-11 (2.25 µm) from NOAA-20 (17:09 UTC, 22 January 2019)
Of course, the 1.6 µm image was already shown, so I didn’t bother to repeat it. If you squint, you can even see a hint of the ice signature at 1.38 µm, the “Cirrus Band“, where most of the surface signal is blocked by water vapor absorption in the atmosphere:
VIIRS “cirrus” channel, M-9 (1.38 µm), from NOAA-20 (17:09 UTC 22 January 2019)
If the ice had accumulated in southern New Jersey or Pennsylvania, though, it would not have shown up in this channel, since the air was too moist at this time to see all the way down to the surface. But, you can compare this image with the previous images to see why they call it the “cirrus band”, since the cirrus does show up much more clearly here.
So, mark this down as another use for VIIRS: detecting areas impacted by ice storms. And remember, even though ice storms may have a certain beauty, they are also dangerous. And, not just for the obvious reasons. This storm in particular came complete with ice missiles. So, for the love of everyone else on the road, scrape your car clean of ice before risking your life out there!
Oh, Yakutsk! It has been a long time – 2012, to be exact – since we last spoke about you. It was a different time back then, with me still referring to the Natural Color RGB as “pseudo-true color”. (Now, most National Weather Service forecasters know it as the “Day Land Cloud RGB”). VIIRS was a only a baby with less than one year on the job. Back then, the area surrounding the “Coldest City on Earth” was on fire. This time, we return to talk about ice.
You see, rivers near the Coldest City on Earth freeze during the winter, as do most rivers at high latitudes. Places like the Northwest Territories, the Yukon, Alaska and Siberia use this to their advantage. Rivers that are frozen solid can make good roads, a fact that has often been overly dramatized for TV. Transporting heavy equipment may be better done on solid ice in the winter than on squishy, swampy tundra in the summer. But, that comes with a cost: ice roads only work during the winter.
In remote places like these, with few roads, rivers are the lifeblood of transportation – acting as roads during the winter and waterways for boats during the summer. But, what about the transition period that happens each spring and fall? Every year there is a period of time where it is too icy for boats and not icy enough for trucks. Monitoring for the autumn ice-up is an important task. And, perhaps it is more important to monitor for the spring break-up of the ice, since the break up period is often associated with ice jams and flooding.
We’ve covered the autumn ice up before (on our sister blog), but VIIRS recently captured a great view of the spring break up near Yakutsk, that will be our focus today.
We will start with the astonishing video captured by VIIRS’ geostationary cousin, the Advanced Himawari Imager (AHI) on Himawari-8 from 18 May 2018:
The big river flowing south to north in the center of the frame is the Lena River. (Yakutsk is on that river just south of the easternmost bend.) The second big river along the right side of the frame is the Aldan River, which turns to the west and flows into the Lena in the center of the frame.
Now that you are oriented, take a look at that video again in full screen mode. If you look closely, you will see a snake-like section of ice flowing from the Aldan into the Lena. This is exactly the kind of thing river forecasters are supposed to be watching for during the spring!
Of course, this is a geostationary satellite, which provides good temporal resolution, but not as good spatial resolution. The video is made from 1-km resolution imagery, but we are looking at high latitudes on an oblique angle, so the resolution is more like 3-4 km here. So, how does this look from the vantage point of VIIRS, which provides similar imagery at 375 m resolution? See for yourself:
(You will have to click on the image to get the animation to play.)
Animation of VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (18 May 2018)
This animation includes both Suomi NPP and NOAA-20 VIIRS. That gives us ~50 min. temporal resolution to go with the sub-kilometer spatial resolution. Eagle-eyed viewers can see how the resolution changes over the course of the animation, as the rivers start out near the left edge of the VIIRS swath (~750 m resolution), then on subsequent orbits, the rivers are near nadir (~375 m resolution) and then on the right edge of the swath (~750 m resolution again). In any case, this is better spatial resolution than AHI can provide at this latitude.
One thing you can do with this animation is calculate how fast the ice was moving. I estimated the leading edge of the big “ice snake” moved about 59 pixels (22.3 km at 375 m resolution) during the 3 hour, 21 minute duration of the animation. That works out to an average speed of 6.7 km/hr (3.6 knots), which doesn’t seem unreasonable. Counting up pixels also indicates our big “ice snake” is at least 65 km long, and the Aldan River is nearly 3 km wide in its lower reaches when it meets the Lena River. That is in the neighborhood of 200 km2 of ice!
That much ice moving at 3 knots can do a lot of damage. Just look at what the ice on this much smaller river did to this bridge:
(Make sure you watch it all the way to the end!)
Roughly one month ago, social media (and, later, more conventional media) outlets were inundated with numerous reports of orange snow in eastern Europe and western Asia – reports like this one, this one and this one. Of course, it wouldn’t really be a hit with the media unless someone could claim it was “apocalyptic”. And of course, the apocalypse didn’t happen. It was simply Saharan dust picked up by high winds from an intense mid-latitude cyclone and deposited far away. We’ve seen this before with VIIRS.
These reports focused on Sochi, Russia, home of the 2014 Winter Olympics. Unfortunately, every time I looked for it in VIIRS imagery, it was cloudy in Sochi. But, the plume of Saharan dust that caused this event was clearly visible over the Mediterranean:
NOAA-20 VIIRS true color composite of channels M-3, M-4 and M-5 (10:03 UTC 25 March 2018)
This image came from our new NOAA-20 VIIRS, which, at this point, is not operational and undergoing additional testing. If you look closer, you might also notice smoke or smog over Poland in the image above (upper left corner). If you really zoom in (click on the image to get to the full resolution version), you may notice a brownish tint to the snow along the north shore of the Black Sea – where the BBC report I linked to listed additional sightings of orange snow. But, the dust-covered snow shows up more clearly in this “before and after” image courtesy of S-NPP VIIRS and the @NOAASatellites twitter account:
“Before” and “After” S-NPP VIIRS true color images from 22 March 2018 (left) and 25 March 2018 (right).
(As an aside: differences in technique used to produce these true color images are likely larger than the differences between S-NPP VIIRS and NOAA-20 VIIRS, so don’t read too much into the fact that the dust-on-snow appears more clearly in the @NOAASatellites image than in my own.)
But, dust-on-snow is not limited to areas within a few thousand kilometers of the Sahara Desert. (It is limited to areas within 40,000 km of the Sahara [in the horizontal dimension, at least], since that is roughly the circumference of the Earth – and assuming you ignore dust storms on Mars.) Dust on snow can happen anywhere you have snow within striking distance of a source of dust. Another example was captured by a new Landsat-like micro-satellite, Venµs, and its non-microsat predecessor, Sentinel-2B, Landsat’s European cousin. A more dramatic example happened last week right here in Colorado. Here is a VIIRS true color image of Colorado from S-NPP VIIRS, taken on 14 April 2018:
S-NPP VIIRS true color composite of channels M-3, M-4 and M-5 (19:45 UTC 14 April 2018)
Here are similar images from NOAA-20 and S-NPP from 18 April 2018:
NOAA-20 VIIRS true color composite of channels M-3, M-4 and M-5 (19:20 UTC 18 April 2018)
S-NPP VIIRS true color composite of channels M-3, M-4 and M-5 (20:11 UTC 18 April 2018)
The trick is to compare these two images with the image from 14 April. The other trick is to know where you’re supposed to be looking. (Hint: we’re looking at the Sangre de Cristo mountains in southern Colorado.) Here’s a “before” and “after” image overlay trick I’ve used before. (You may have to refresh the page before it will work.) Both of these images are the S-NPP VIIRS ones, for simplicity:
If you slide the bar left to right, you should notice the snow is more brown in the mountains just right of center in the 18 April image. There are other areas where the snow melted between the two images, plus a couple of small clouds that add to the differences. Of course, this is only 750 m resolution. We get a better view with the 375m-resolution visible channel, I-1:
We lose the color information, of course, since we are looking at a single channel, but it is obvious the snow became less reflective in the 18 April image. And, we can prove that this was a result of dust. Here are the visible, true color, Dust RGB, “Blue Light Dust” and DEBRA Dust images from S-NPP on 17 April 2018, courtesy Steve M.:
S-NPP VIIRS true color composite of channels M-3, M-4 and M-5 (20:26 UTC 17 April 2018)
S-NPP VIIRS Dust RGB image (20:26 UTC 17 April 2018)
S-NPP VIIRS Blue Light Dust image (20:26 UTC 17 April 2018)
S-NPP VIIRS DEBRA Dust image (20:26 UTC 17 April 2018)
If you are unfamiliar with them, we’ve looked at the Dust RGB, Blue Light Dust and DEBRA before, here and here. As seen in the above images, this was not a difficult to detect dust case. Even Landsat-8 captured this event, which is surprising given the narrow swath and 16-day orbit repeat cycle. (Sure, it’s higher resolution than VIIRS, but will it be overhead when you need it?)
So now we get to why dust-on-snow is important. There is a growing body of research (e.g. this paper) that shows dust-on-snow has a big impact on water resources in places like the Rocky Mountains. You see, dirty snow is less reflective than clean snow. That means it absorbs more solar radiation. This, in turn, means it heats up and melts faster, leading to earlier spring run-off. The end result is less water later in the season, which opens the door to wildfires and more severe droughts. This article that, coincidentally, was published as I was writing this, sums things up nicely. It is so important, the Center for Snow and Avalanche Studies has formed CODOS: the Colorado Dust on Snow Program, whose purpose is to monitor dust on snow and provide weekly updates.
As for why you shouldn’t eat orange snow, that should be obvious. You shouldn’t eat any snow that isn’t pure white (and even that might be risky). But, feel free to eat colorful ice, as long as you know where it came from.
Today, we’re going to take a look at another less-covered VIIRS channel on this blog: M-9, also known as the “cirrus band”. (Disambiguation: if you’re looking for the electronic musical group “Cirrus (band)“, you’re in the wrong place.) We don’t use M-9 on this blog much because it doesn’t often provide amazing images. But, it is used for a lot of practical applications, so it is worth knowing about. We are also going to say “Hello!” to Suomi-NPP’s baby brother, NOAA-20, and welcome a new VIIRS instrument in space!
Unlike M-8, the “cirrus band” (PDF) is on nearly all of the new geostationary satellites (except Himawari). It’s also on MODIS, Landsat, and several other polar-orbiting satellite imagers. The “cirrus band” is unique in that it is highly sensitive to water vapor, but is located in the near-IR (1.38 µm) where emission from the Earth is minimal. (So, contrary to popular belief, VIIRS does have a water vapor channel. It just doesn’t behave like the typical mid-wave IR water vapor channels most people are used to.)
Electromagnetic radiation at 1.38 µm is absorbed by water vapor. But, the Earth and its atmosphere are too cold to emit much at this wavelength. (Thankfully, or we would have all melted by now.) Of course, the sun is hot enough. This means the 1.38 µm radiation coming from the sun is absorbed by water vapor in our atmosphere, and the only* radiation making its way back to VIIRS is what is reflected off of clouds above the water vapor. This makes channels centered at 1.38 µm particularly useful at identifying thin cirrus that would otherwise blend in with the background on other channels. Hence, the name “cirrus band”. (* Of course, reflection off of high clouds is not the only source, as we shall see. That’s the reason for this blog post.)
So, high clouds are white and the background is black – this is the assumption when looking at VIIRS’s cirrus band (unless you’re using a funky color table). But, take a look at this image that S-NPP VIIRS took on 17 January 2018:
S-NPP VIIRS channel M-9 (“cirrus band”) image from 18:34 UTC, 17 January 2018
On my monitor, viewing angle makes a big difference as to how bright the features appear. If you are viewing this on a laptop or tablet, your screen is much easier to adjust that than my Jumbotron if it’s hard to see. You can also move your head around and see if anyone else looks at you funny. (This is also a good way to test out a TV in the store before you buy it. Will people sitting off to the side get the same view as someone directly in front of the TV? You might want to know that if hosting a party for the big game this weekend.)
Let’s zoom in on the area in question:
S-NPP VIIRS channel M-9 image from 18:34 UTC, 17 January 2018
And, give the image maximum contrast:
S-NPP VIIRS channel M-9 image displayed with maximum contrast (18:34 UTC, 17 January 2018)
There is a feature in that image that looks awfully like the coastline of the Gulf of Mexico stretching from Louisiana to the Florida panhandle. It sure looks like you can see the Mississippi River, the Tennessee River, and all the “lakes” in eastern Texas. But, I thought water vapor was supposed to absorb all the radiation before it made it to the surface! And, this is Louisiana we’re talking about. The entire coastal region of the state is a big swamp – I mean a collection of bayous. So, there should be plenty of water vapor around.
One would expect to see all the way to the surface in high-altitude arid areas, like the Bolivian Altiplano and the upper elevations of the Atacama Desert. And, you do:
S-NPP VIIRS channel M-9 image from 18:32 UTC, 1 June 2017.
But, one does not expect to see the surface of Louisiana at 1.38 µm, since it is so close to sea level and it is one of the most humid parts of the United States. Maybe something is wrong with S-NPP VIIRS? Let’s look at our new baby, NOAA-20 VIIRS:
NOAA-20 VIIRS channel M-9 image (19:25 UTC, 17 January 2018)
And, once again, with maximum contrast:
NOAA-20 VIIRS channel M-9 image displayed with maximum contrast (19:25 UTC, 17 January 2017)
Note that NOAA-20 was launched back in November 2017, and is still undergoing post-launch testing and checkout, so it has not been declared operational just yet. But, this is a good test for the new VIIRS. It can see the same surface features S-NPP did 50 minutes earlier. And, it means that both instruments are working. So, why can we see all the way to the surface of Louisiana in the “cirrus band”? Because, the atmosphere was incredibly dry.
Here’s the sounding from Slidell, LA (on the other side of Lake Pontchartrain from New Orleans) on 12 UTC 17 January 2018. Notice the precipitable water value (“PWAT”) is 2.47, which is reported on soundings in mm. That’s just less than 0.1 inches. The nearby soundings taken at Shreveport and Lake Charles reported PWATs of 2.45 mm and 2.77 mm, respectively. Normal for this time of year is about 7 times greater! (Note that he corrected his typo.)
To put this into perspective, this was drier than the Sahara Desert was a few days later:
NOAA-20 VIIRS channel M-9 image displayed with maximum contrast (12:40 UTC, 22 January 2018)
Notice you can’t see the surface of the Sahara, indicating there was more water vapor in the air over the desert than there was over Louisiana. The only thing you can see are the cirrus clouds and other clouds that made it to the upper atmosphere. This is more typical of the “cirrus band”.
Now, back to Louisiana: the dry, Arctic airmass resulted in a number of record low temperatures. Plus, this was accompanied by snow, as seen by both S-NPP and NOAA-20:
S-NPP VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (18:34 UTC 17 January 2018)
NOAA-20 VIIRS True Color composite of channels M-3, M-4 and M-5 (19:25 UTC, 17 January 2018)
Snow was reported all the way to Gulf Coast, and you can see evidence of it in the images around Houston, TX, which is pretty rare. But, wait! Why didn’t we see snow in the M-9 “cirrus band” images? Because snow is not very reflective at 1.38 µm, and it blends in with the background. To show what an interesting winter it has been, here’s a map put out by the Weather Prediction Center from 18 January 2018, showing estimated total snowfall accumulations for this winter (so far). Note that an area of Mississippi and Louisiana has had approximately the same amount of snow as most of Iowa and southern Wisconsin (and even here in Northern Colorado!). All 48 contiguous United States have received measurable snowfall!
Fun fact: you can open one of the True Color images in a new browser tab, and the other image in this tab and toggle back and forth between them. This allows you to see the clouds move, and the edges of the snowfield melt. If you have eagle eyes, you can also see that the S-NPP image is sharper on the east side of the image (close to its nadir), while the NOAA-20 image is sharper on the west side (close to its nadir). The satellites are both in the same orbit, but on opposite sides of the Earth. Since the Earth is constantly rotating underneath them, and the VIIRS swath is designed to fill all the gaps at the Equator (unlike MODIS), their ground tracks at low and mid-latitudes are separated by half the width of a VIIRS swath. Nadir for one VIIRS is near the edge of the swath of the other VIIRS. (But, not at high latitudes.) The distance from Tallahassee, Florida to Houston, Texas is a pretty good rule of thumb for the spatial distance between the two satellites when they’re over the United States. Fifty minutes is a good rule of thumb for the temporal distance between them (and this is true all over the globe).
So, for once, Louisiana was colder than the Arctic (Ocean, at least) and drier than the Sahara Desert!
I wrote the first post on this blog more than 5.5 years ago. Since then, I have covered a multitude of instances where VIIRS imagery has helped us learn about the world we live on. But, during that time there has been one channel on VIIRS that has never been mentioned. Not once. And, what may be even more surprising is that this channel is not featured on any of the next generation geostationary satellites. It’s not on the GOES-R Program’s ABI, not on Himawari’s AHI, not on the upcoming Meteosat Third Generation FCI. Those with photographic memories will know exactly which channel I’m talking about. The rest of you will just have to guess, or go back through the archives and use the process of elimination to figure it out.
So, is this channel useless? Why is it on VIIRS, but not ABI? Which one is it? The suspense is killing me! I can’t answer that second question, but I can definitely answer the third and give some insights to #1. (The short answer to #1 is “No” – otherwise we wouldn’t be here.) But, to do this, we have to remember why Lake Mille Lacs disappeared earlier this year. It might also be good to remember our earlier posts on Greenland, because that is the location of our most recent mystery.
We begin with the view of Greenland from GOES-16 back at the end of July 2017:
This video covers the period of time from 0700 UTC 27 July to 2345 UTC 28 July. If you follow this blog, you already know that this the “Natural Color” RGB composite, which in GOES-16 ABI terms is made of bands 2 (0.64 µm), 3 (0.86 µm) and 5 (1.61 µm). Notice the whitish coloration over the central portion of Greenland. This is the feature of interest.
We know from experience (and earlier blog posts) that snow and ice are not very reflective at 1.6 µm, which is why it takes on that cyan appearance in Natural Color imagery. Whitish colors are indicative of liquid clouds. But, the feature of interest doesn’t appear to move over this two day period. (If you look closely, it does appear to shrink a little, though.) It’s hard to believe a cloud could be that stationary over a two day period.
Let’s isolate the 1.6 µm band by itself to see if we can tell what’s going on:
Shortly after the first sunrise, you can see a patch of liquid clouds over the ice that quickly dissipate, leaving our feature of interest exposed. Clouds appear again near the first sunset, and late in the second day (28 July). The feature of interest isn’t as bright as those clouds, but is brighter than the rest of the ice and snow on Greenland.
At shorter wavelengths, nearly all of Greenland is bright, so our feature of interest isn’t as noticeable. Here’s the 0.86 µm band from ABI:
But, it shows up at the two longer shortwave IR bands. Here’s the 2.25 µm band:
The same is true for 3.9 µm, but I won’t waste time showing it.
So, what is going on? What is our feature of interest?
Well, the problem is, Greenland is way off on the limb from the perspective of GOES-16’s current location. Perhaps we need a better view from something that passes directly overhead of Greenland. Hmmm. What could that be?
This is a VIIRS blog after all, so I think you know the answer to my rhetorical question. Let’s start with our good old friend, Natural Color, which we should all be familiar with:
S-NPP VIIRS Natural Color RGB composite of bands M-5, M-7 and M-10 (14:40 UTC 27 July 2017)
You can tell by the shadows cast where the clouds are, even if they are a similar color to the background of snow and ice on Greenland. But, the feature of interest isn’t very obvious. There appears to be an area of lighter cyan over the central portions of the ice sheet, but it definitely doesn’t look like a cloud. Let’s break it up into single channels, like we did with ABI, starting with M-7 (0.86 µm):
S-NPP VIIRS channel M-7 (14:40 UTC 27 July 2017)
Again, it’s all bright. How about M-10 (1.61 µm)?
S-NPP VIIRS channel M-10 (14:40 UTC 27 July 2017)
Now, Greenland appears all dark. For completeness, let’s look at M-11 (2.25 µm):
S-NPP VIIRS channel M-11 (14:40 UTC 27 July 2017)
It’s subtle, but you can see a hint of brightening over the south-central portion of the ice sheet. (In case you’re wondering why it looks so much darker in VIIRS than ABI, it’s because our visible and near-IR GOES-16 imagery uses “square root scaling” by default. In image processing terms, it’s the same as a gamma correction of 2. The VIIRS images don’t have that.) Now, for the ace up my sleeve – the one channel that has never appeared before on this blog:
S-NPP VIIRS channel M-8 (14:40 UTC 27 July 2017)
This is M-8, centered at 1.24 µm. Its primary use is listed in the JPSS Program literature as “cloud particle size.” Based on reading the documentation for the cloud products, it appears M-8 is used operationally only as a backup for M-5 (0.67 µm) in the cloud optical thickness and effective particle size retrievals under certain conditions, or when M-5 fails to converge on solution. One of those conditions is the retrieval of cloud properties over snow and ice. As we shall see, however, M-8 is very good at determining the properties of the snow and ice itself.
M-8 shows quite clearly the bright central portion of Greenland (our feature of interest) surrounded by dark at the edges of the ice sheet (even without any gamma correction). Snow-free areas appear brighter than the edge of the ice sheet because, much like M-7/0.86 µm, vegetation is also highly reflective at 1.24 µm.
This example shows what we’ve long known. Snow and ice are highly reflective in the visible (and very near IR) portions of the electromagnetic spectrum. In the short- and mid-wave IR, snow and ice are absorbing and cold. This means they don’t emit or reflect much radiation at these wavelengths. That’s why they appear dark at 1.61 and 2.25 µm. M-8 straddles the boundary of these regions as exemplified by this graph:
Reflectance spectra of snow. The highlighted portion shows the bandwidth of VIIRS channel M-8.
The information in this graph comes from the ASTER Spectral Library created by NASA. Note that the reflectance of snow in M-8 is highly variable and a function of the snow grain size. This may explain why the central portion of Greenland’s ice sheet appears so bright, while the edges are so dark in M-8. Another explanation is that, much like in Minnesota, snow melt causes a drop in reflectance. Slush just isn’t as reflective as fresh snow, and M-8 is highly sensitive to this.
The last week in July was a very warm one for Greenland. The capitol, Nuuk, recorded highs in the 60s (°F), or upper-teens (°C), peaking at 71°F (22°C) on 29 July 2017. Normal for that time of year is 52°F (11°C).
Since Greenland is pretty far north, we can take advantage of the multiple VIIRS overpasses per day and really capture this snowmelt:
Animation of daytime VIIRS M-8 images (27-29 July 2017)
This animation, which you may have to click on to get it to play, covers the three day period 27-29 July 2017. Here’s it is obvious what impact the heat wave is having on Greenland’s ice and snow. Our “feature of interest” really shrinks over this period of time.
In early August, the snow and ice start to recover and become more reflective again. Here’s an extended animation that includes the relatively clear days of 17 July, 20 July and the entire period from 30 July to 3 August 2017:
Animation of VIIRS M-8 (17 July – 3 August 2017)*
Our “feature of interest” is unmelted snow/ice on Greenland’s ice sheet.
Now, this is the VIIRS Imagery Team Blog. We can do a better job of highlighting this snowmelt by combining it with other channels in an RGB composite. One way is to replace M-7 with M-8 in the Natural Color RGB:
Animation of VIIRS Natural Color imagery composites of channels, M-5, M-8 and M-10 (17 July – 3 August 2017)*
Fresh, fine snow has the cyan color we’re all familiar with, but now coarse snow and melting snow are a deeper, more vivid blue color.
Another option takes a page out of the EUMETSAT Snow playbook. Here’s one with M-8 as the blue component, M-7 as the green component and M-5 as the red component:
Animation of VIIRS RGB composite using channels, M-8, M-7 and M-5 (17 July – 3 August 2017)*
Now the fresh, fine snow is pale yellow, while the coarse snow and snowmelt are a darker yellow-orange. The question is: which one do you like better?
So, I have now talked about every band on VIIRS. And, I learned that the last time I looked at melting on Greenland, I should have been looking at M-8 from the very beginning.
In the fast paced world of weather, Hurricane Irma is old news. There’s already a Wikipedia page on it. But, people that were in Irma’s path are still cleaning up (at least at the time I’m writing this). In case you’ve already forgotten, or were living in a Faraday cage underground, here’s a quick recap. Among the factoids: Irma was the strongest hurricane ever recorded in the Atlantic basin and it was a Category 5 (the highest the scale goes) for the longest period of time of any Atlantic hurricane. The island of Barbuda took a direct hit from Irma and is now desolate and decimated. Jacksonville, which did not take a direct hit, received record flooding due to winds blowing the St. Johns River inland, while heavy rains inland were trying to flow out to sea. And, the hearing impaired mocked Manatee County, Florida for using a sign language interpreter that didn’t know sign language. Just in the U.S. alone, 26 people died.
Satellite imagers with higher resolution than VIIRS captured the damage. First, Landsat (~30 m spatial resolution) showed how vegetation was stripped from the soil in Antigua, Barbuda and the Virgin Islands. And, Worldview-4 (~30 cm resolution!) captured images of damaged structures in the Florida Keys and other islands in the Caribbean for Digital Globe (not a paid advertisement or endorsement). Our newest satellite, GOES-16, monitored Irma all the way from birth to death. (Shout out to my collegues at CIRA who provided the imagery used in that article!) And, of course, the VIIRS Day/Night Band showed the extent of power outages in Florida, which I won’t talk about further because I’ve already been beaten to it.
But, VIIRS works during the day, too. And it captured an aspect of Irma’s impact not mentioned above. We’ll start by taking a look at a VIIRS True Color image from 31 August 2017:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (1840 UTC 31 August 2017)
Remember, you can click on an image to bring up the full resolution version. Let’s compare this “before” image with one taken after Irma hit:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (1813 UTC 12 September 2017)
Notice anything different between the two images?
Apart from all the clouds (which are always different between two images), it shouldn’t take long to notice a change in the water surrounding Florida and, to a lesser extent, the Bahamas. You see, hurricanes bring with them heavy rains, high winds and waves and storm surge. The winds and waves churn up sediment at the bottom of the ocean – like this guy, only more, at least in shallow areas like the Florida Keys and the Bahamas. The storm surge causes beach erosion and flooding along the coasts while the heavy rains cause inland flooding (of both the “flash” and “river” variety). And, when was the last time you saw crystal clear floodwater? Floodwater is filled with dirt from the soils it eroded. Plus, there’s often garbage, raw sewage and toxic chemicals that may make it as hazardous as the hurricane itself. And, let’s not mention floating fire ant colonies because no one want to think about those – except I just did.
If you look closely, you may even see this sediment and pollution beginning to be entrained in currents in the Gulf of Mexico as well as on the Atlantic side of Florida. And, remember that the Atlantic side of Florida is home to the Gulf Stream (the current, not the aircraft).
Of course, we don’t have to just compare two days. We can monitor this sediment and pollution for as long as it’s there. Here’s a video showing both the before image (31 August 2017) and 6 days after (12-17 September 2017):
You might also notice the ocean around the Bahamas is always lighter in color. This is true even in the “before” image. This is because the water is very shallow in the Bahama Banks, and you can see all the way to the bottom. But, offshore on the west side of the largest island (Andros) the water becomes nearly white after Irma’s passage:
Comparison of VIIRS True Color images before and after Hurricane Irma (2017)
Go back to the video and see that it barely darkens over time. It is possible that, just like flood-induced erosion changes the landscape on the ground, the storm-induced waves and surge may have altered the underwater topography (“bathymetry”) of the Grand Bahama Bank and made the water even shallower. We’ll just have to wait and see how dark it gets.
Postscript: our VIIRS-like geostationary imager, the Advanced Baseline Imager (ABI) on GOES-16 also saw this sediment in the waters off the coast of Florida: click here. Remember, ABI doesn’t have a green wavelength visible band, but that’s no problem for CIRA’s Synthetic True Color imagery! [/end shameless plug]
It’s not often that a new discovery takes place that baffles the minds of lifelong scientists. This is a story about one that seems to have gone viral over the last few days. The abbreviated version (summarized from this article, this article, and this article, and many others like it) is as follows:
A group of dedicated aurora photographers noted a particular type of aurora that was different from what we normally think of. Instead of a rapidly changing curtain of light glowing green or red, it is a single arc of light, “purple” in color, with less apparent motion than a normal aurora. It doesn’t appear to move with the Earth’s magnetic field. The picture that accompanies every article about it is this one:
Photograph credited to Dave Markel Photography
The early guess was that it’s an example of a “proton arc” – a type of aurora caused by high energy protons rather than electrons. (Do a Google Image Search for “proton arc” and you’ll see many other examples.) However, the plot thickened when an expert on the aurora, Prof. Eric Donovan at the University of Calgary, debunked that guess based on the fact that proton arcs are not visible to the human eye. This was backed up by a graduate student at the University of Alaska-Fairbanks. Not knowing what else to call it, the dedicated aurora photographers named it Steve. No joke. (It comes from the animated movie, Over the Hedge.) The name has caught on, and now the internet is full of photographic examples of “Steve”. Here’s a time lapse video.
The Aurorasaurus Project has compiled a list of things we know about Steve. Our expert aurora professor matched up a known time and location of a Steve photograph with an overpass of the European Space Agency’s Swarm satellites and found this out:
“As the satellite flew straight though Steve, data from the electric field instrument showed very clear changes. The temperature 300 kilometres (185 miles) above Earth’s surface jumped by 3,000°C (5,400 degrees Fahrenheit) and the data revealed a 25 kilometre (15.5 mile) wide ribbon of gas flowing westwards at about 6 km/s (3.7 miles per second) compared to a speed of about 10 m/s (32.8 feet per second) either side of the ribbon.”
So, while we don’t exactly know what causes “Steve”, we do know that it is relatively common. (Do that Google Image Search for “proton arc” again for proof.) And we know it’s not a proton arc. Of course, the question that is relevant to us on this blog is: Can the VIIRS Day/Night Band see Steve?
There was a significant geomagnetic storm 22-23 April 2017 that may provide the answer. One of the Alberta Aurora Chasers (our dedicated group of aurora photographers) took this picture and, in the comments, noted the location (Lake Minnewanka, Alberta) and approximate time (“maybe 12:30” AM on the 22nd). Compare that with the nearest Day/Night Band image:
VIIRS Day/Night Band image (08:12 UTC 22 April 2017)
I put a gold star on there to indicate the location of Lake Minnewanka. Don’t see it? Here’s a close-up:
VIIRS Day/Night Band image above zoomed-in on Lake Minnewanka. The gold star indicates the location of the lake.
Unfortunately, Lake Minnewanka is outside the VIIRS swath. But, Aurorasaurus says Steve is often hundreds or thousands of miles long, and oriented east-west, so it should extend into the VIIRS swath. Now, this VIIRS image was taken at about 2:15 AM local time, almost two hours after the photograph was taken. Aurorasaurus also says Steve is visible on the order of minutes, “up to 20 minutes or more”. So, maybe Steve disappeared in the time between the two images. I certainly don’t see any straight or smooth arc of light near the star that resembles Steve. Although, just north of Calgary (the closest city within the VIIRS swath to Lake Minnewanka) there is faint evidence of aurora light, and it is on the equator-ward side of the aurora, which is consistent with previous observations.
The streaks of light visible near Calgary (and general streakiness across the whole aurora) are due to the way the VIIRS instrument scans the scene and the high-temporal variability of the aurora, which we’ve discussed before. But, as I mentioned, these streaks don’t extend for hundreds or thousands of miles.
Maybe, VIIRS had better luck on the next overpass (~3:55 AM local time):
VIIRS Day/Night Band image (09:53 UTC 22 April 2017)
Again, nothing jumps out to say, “Aha! That’s Steve!” So, was Steve there and VIIRS failed to see it? Or, was Steve not there at the time of the VIIRS overpass? The answer to that depends in part on the definition of “purple”.
Is Steve really “purple” as people describe? Or, is it violet? Wikipedia actually has a good section on this (at least, until someone edits it). There’s also the page discussing the “Line of Purples“. The problem stems from the fact that violet is a color similar to purple, but is physically very different. Violet is the name given to a specific wavelength range of light, specifically the visible portion of the spectrum less than 450 nm. Purple is a combination of blue and red wavelengths – blue being wavelengths between ~450 nm and ~495 nm and red being anything visible above ~620 nm. Violet and purple look similar to us because the cone cells in our eyes have a similar response to both colors. However, in the RGB color space of the computer you’re viewing this on, and in the color cameras used to take pictures of Steve, violet is impossible to duplicate. This is because violet is not a combination of red, green and blue – it’s its own wavelength. The red, green and blue light emitting diodes (or phosphors on a plasma screen) don’t emit violet wavelengths. Your camera stores the information it collects in RGB color space, too, and has to approximate violet the same way your computer does – by making it a bluer shade of purple. Depending on the camera, the detectors used may not even be sensitive to violet light.
So, what does this mean for VIIRS? The Day/Night Band is not sensitive to radiation at wavelengths shorter than ~500 nm, which includes blue and violet. But, it is sensitive to red and beyond – up to ~900 nm. So, if Steve really is purple, the Day/Night Band will only be sensitive to the red component of it. (It would be more faint, but VIIRS would likely be sensitive to it, given that it is sensitive to airglow, which is much more faint than the aurora.) If Steve is really violet, than the Day/Night Band won’t see it at all.
So, can the Day/Night Band detect Steve? I can’t answer that based on this information. We will have to wait for another dedicated aurora photographer to take a picture of Steve at a time and place when VIIRS is directly overhead. Feel cheated by that? Just enjoy the images of the aurora above. And, here are a few more from this event:
VIIRS Day/Night Band image (11:34 UTC 22 April 2017)
VIIRS Day/Night Band image (07:53 UTC 23 April 2017)
VIIRS Day/Night Band image (09:34 UTC 23 April 2017)
Don’t forget to click on them to see the full resolution!
UPDATE (13 October 2017): Over the years, I have looked at a number of Day/Night Band images of the aurora. During that time, I’ve noticed some “auroras” that appear to be very “Steve”-like. One example is shown in the image below from 17 January 2015.
VIIRS Day/Night Band image (13:09 UTC 17 January 2015)
The question is: is this an example of Steve? Or, just a less active aurora?
Of course, being over a remote part of northern Alaska, it’s unlikely anyone got a photograph to prove it was Steve. We’ll still have to wait for the perfect alignment of Steve, Steve-hunters and VIIRS to know if the Day/Night Band can (or cannot) detect them.
Two weeks ago, one of Minnesota’s 10,000 lakes disappeared, leaving them with only 9,999. And, it wasn’t a small one, either. It was the state’s second largest inland lake. But, this is not like Goose Lake, which actually did dry up. The lake in question simply became temporarily invisible. So, no need to panic, fishing and boating enthusiasts. But, as you’ll see, the term “invisible” can be just as ambiguous as the term “lake”.
Let’s start with the fact that Minnesota doesn’t have 10,000 lakes. Their slogan is a lie! Depending on how you define a lake, Minnesota has 21,871, or 15,291, or 11,842. But, Wisconsin might have more (or less) and likes to argue with Minnesota about that fact. Michigan might have way more (62,798) or way less (6,537). And, they all pale in comparison to the number of lakes in Alaska. Here is an article that explains the situation nicely.
With that out of the way, today’s story comes from “current GOES” and what one colleague noticed during a cursory examination of GOES Imager images. Here’s the GOES-13 visible image from 19:30 UTC 27 January 2017:
GOES-13 visible image, taken 19:30 UTC 27 January 2017
Compare that with the visible image from 19:15 UTC 2 February 2017:
GOES-13 visible image, taken 19:15 UTC 2 February 2017
Notice anything different between the two images over Minnesota? No? Then let’s flip back-and-forth between the two, with a giant, red arrow pointing to the area in question:
Animation of the above images. The red arrow points to Lake Mille Lacs.
The red arrow is pointing to the location of Lake Mille Lacs. You might know it as Mille Lacs Lake. (Either way, it’s name is redundant; “Mille Lacs” is French for “Thousand Lakes,” making it Thousand Lakes Lake.) As the above images show, on 27 January 2017 Lake Mille Lacs was not visible in the GOES image. On 2 February 2017, it was. They both look like clear days, so what happened? Why did Lake Mille Lacs disappear?
As I said before, the lake didn’t dry up. It simply became temporarily invisible. But, this requires a discussion about what it means to be “visible”. Lake Mille Lacs shows up in the image from 2 February 2017 because it appears brighter than the surrounding land. That’s because the lake is covered with snow. Aren’t the surrounding land areas also covered with snow? Yes. However, the surrounding lands also have trees which obscure the snow and shade the background surface, which is why forested areas appear darker even when there is snow.
That leads to this question: why does the lake appear darker on 27 January? Because it rained the week before. Want proof? Look at the almanac for Brainerd (NW of Lake Mille Lacs) for the period of 18-22 January 2017. Every day made it above freezing along with several days of rain. Much of the snow melted (including the snow on the lake). Want more proof? Here’s a video taken on the lake from 20 January 2017. See how Minnesotans drive around on frozen lakes – even in the rain? And, see how wet and slushy the surface of the ice is? This makes it appear darker than when there is fresh snow on top. If you’ve ever seen a pile of slush, you know it’s not bright white, but a dull gray color. The less reflective slush on the lake reduced the apparent brightness down to the level of the surrounding woodlands. That’s why the lake appeared to disappear.
Now, this is “current GOES” imagery. We can do better with VIIRS, since we have more channels to play with. And, as we all know, GOES-R successfully launched back in November 2016 and is now in orbit as GOES-16. This satellite has the first Advanced Baseline Imager (ABI) in space. The ABI has many of the same channels as VIIRS, so the following discussion applies to both instruments. “New” GOES will have up to 500 m resolution in the visible, which is much closer to VIIRS (375 m) than “current” GOES (1 km). That’s another thing to think about when we talk about what is visible and what isn’t.
Here are the VIIRS high-resolution visible (I-1) images that correspond to the GOES images above:
VIIRS high-resolution visible (I-1) image, taken 19:35 UTC 27 January 2017
VIIRS high-resolution visible (I-1) image, taken 19:22 UTC 2 February 2017
Although, we should probably focus on Minnesota. Here are the cropped images side-by-side:
Comparison between VIIRS high-resolution visible (I-1) images
Remember: you can click on any image to bring up the full resolution version.
Although Lake Mille Lacs is just barely visible in the image from 27 January, it’s much easier to see on 2 February. So, we get the same story from VIIRS that we got with GOES, which is good. That means we don’t have a major fault of a multi-million dollar satellite. It’s a “fault” of the radiative properties of slush, combined with the low resolution of the GOES images above.
Keep your eyes also on the largest inland lake in Minnesota: Red Lake. The Siamese twins of Upper and Lower Red Lake didn’t get as much rain as Lake Mille Lacs and its snow never fully melted, so its appearance doesn’t change much between the two images.
The GOES Imager also has a longwave infrared (IR) channel, and a mid-wave IR channel similar to VIIRS. Since the goal of this is not to compare GOES to VIIRS, but to show how these lakes appear at different wavelengths, we’ll stick to the VIIRS images. Here are the high-resolution VIIRS longwave IR images from the same times:
Comparison of VIIRS high-resolution longwave IR (I-5) images
In both images, the lakes are nearly invisible! This is because the longwave IR is primarily sensitive to temperature changes, and the slush is nearly the same temperature as the background land surface. With no temperature contrast to key on, the lake looks just like the surrounding land. Although, if you zoom in and squint, you might say that Lake Mille Lacs is actually more visible in the image from 27 January. 27 January was a warmer day (click back on that Brainerd almanac), and the surrounding land warmed up more than the slushy ice on the lake. 2 February was much colder on the lake and the land. But, let this be a lesson that, just because the lake doesn’t show up, it doesn’t mean the lake doesn’t exist!
Something interesting happens when you look at the mid-wave IR. All the lakes are visible, and take on a similar brightness, no matter how slushy they are:
Comparison of VIIRS high-resolution mid-wave IR (I-4) images
In this wavelength range, both reflection of solar energy and thermal emission are important. Snow, ice and slush are not reflective and they are cold, making the lakes appear darker than the surrounding land. The fact that the land surrounding Lake Mille Lacs and Red Lake is darker on 2 February than it is on 27 January is further proof that it was a colder day with more snow on the ground.
Here’s where we get to the advantage of VIIRS (and, soon, GOES-16): it has more channels in the shortwave and near-IR. The 1.6 µm “snow and ice” band has a lot of uses, and I expect it will be a popular channel on the ABI. Here’s what the high-resolution channel looks like from VIIRS:
Comparison of VIIRS high-resolution near-IR (I-3) images
Compare these with the visible images above. Now, the reverse is true: Lake Mille Lacs is easier to see in the first image than the second! You can’t call it invisible at all on 27 January! The presence of liquid water makes the slush very absorbing – more than even ice and snow – so it appears nearly black. In fact, it’s hard to tell the difference between the slushy ice-covered Lake Mille Lacs, and the open waters of Lake Superior, which has no ice or slush on it. On 2 February, we see the fresh layer of snow on Lake Mille Lacs has increased the lake’s reflectivity, but it’s still slightly darker than the surrounding snow covered land. This is for two reasons: snow and ice are absorbing at 1.6 µm and the surrounding woodlands are more reflective.
Here’s a better comparison between the “visible” and the “snow and ice” bands:
Comparison of VIIRS I-1 and I-3 images (animation)
You’ll have to click on the image to see it animate between the two.
Here’s an animation showing all five high-resolution bands on VIIRS for the two days:
Comparison of VIIRS high-resolution imagery channels (animation)
Again, you have to click on it to see it animate.
Now, we can combine channels into RGB composites that highlight the snow and ice. We’ve discussed several RGB composites for snow detection before. And, we have been looking at the Natural Color RGB for a long time. This composite combines the high-resolution bands I-1 (0.64 µm), I-2 (0.86 µm) and I-3 (1.6 µm) as the blue, green and red components of the image, respectively. Here’s what it looks like for these two days:
Comparison of VIIRS Natural Color RGB composites using high-resolution imagery bands
Lake Mille Lacs is visible on both days – first because it’s darker than the surroundings, then because it’s brighter. This composite demonstrates how vegetation can obscure the surface snow – it appears more brown in deciduous forests (and bare fields with no snow) and green in coniferous areas. But, the important point is that the wetter the snow and slush, the darker it appears. The fresher the snow, the brighter cyan color it has.
This is exaggerated in the “Snow RGB” that combines moderate resolution bands M-11 (2.25 µm), M-10 (1.6 µm) and M-7 (0.86 µm):
Comparison of VIIRS “Snow RGB” composites of channels M-11, M-10 and M-7
M-11 (2.25 µm) is sold as a “cloud particle size” band, but it also helps with snow and ice detection (and fires). The presence of water in melting snow enhances the darkening at 2.25 µm. In this RGB, that means melting snow appears more red, while fresh snow appears more pink. The slush on Lake Mille Lacs appears very dark – almost as dark as Lake Superior – so a Minnesotan might be forgiven if they see the image from 27 January and decide not to drive out on the lake to go ice fishing because they think the ice isn’t there.
Of course, VIIRS also gives us the True Color RGB – the most intuitive RGB composite – that combines the blue-, green- and red-wavelength visible bands: M-3 (0.48 µm), M-4 (0.55 µm) and M-5 (0.67 µm). If you’re curious, here’s what that looks like:
Comparison of VIIRS True Color RGB composite images
The slush on Lake Mille Lacs looks just like dirty slush and the fresh snow looks just like snow. (As it should!)
So, the second biggest lake in Minnesota never disappeared – it just changed its surface properties. And, it will always be “visible” to VIIRS in one channel or another – unless it’s cloudy (or it completely dries up).
By now, you probably know the drill: a little bit of discussion about a particular subject, throw in a few pop culture references, maybe a video or two, then get to the good stuff – high quality VIIRS imagery. Then, maybe add some follow-up discussion to emphasize how VIIRS can be used to detect, monitor, or improve our understanding of the subject in question. Not today.
You see, VIIRS is constantly taking high quality images of the Earth (except during orbital maneuvers or rare glitches). There isn’t enough time in a day to show them all, or go into a detailed discussion as to their relevance. And, nobody likes to read that much anyway. So, as we busily prepare for the upcoming holidays, we’re going to skip the in-depth discussion and get right to the good stuff.
Here then is a sample of interesting images taken by VIIRS over the years that weren’t featured on their own dedicated blog posts. Keep in mind that they represent the variety of topics that VIIRS can shed some light on. Many of these images represent topics that have already been discussed in great detail in previous posts on this blog. Others haven’t. It is important to keep in mind… See, I’m starting to write too much, which I said I wasn’t going to do. I’ll shut up now.
Without further ado, here’s a VIIRS Natural Color image showing a lake-effect snow event that produced a significant amount of the fluffy, white stuff back in November 2014:
VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (18:20 UTC 18 November 2014)
As always, click on the image to bring up the full resolution version. Did you notice all the cloud streets? How about the fact that the most vigorous cloud streets have a cyan color, indicating that they are topped with ice crystals? The whitish clouds are topped with liquid water and… Oops. I’m starting to discuss things in too much detail, which I wasn’t going to do today. Let’s move on.
Here’s another Natural Color RGB image using the high-resolution imagery bands showing a variety of cloud streets and wave clouds over the North Island of New Zealand:
VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (02:55 UTC 3 September 2016)
Here’s a Natural Color RGB image showing a total solar eclipse over Scandinavia in 2015:
VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (10:06 UTC 20 March 2015)
Here’s a VIIRS True Color image and split-window difference (M-15 – M-16) image showing volcanic ash from the eruption of the volcano Sangeang Api in Indonesia in May 2014:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (06:20 UTC 31 May 2014)
VIIRS split-window difference (M-15 – M-16) image (06:20 UTC 31 May 2014)
Here’s a VIIRS True Color image showing algae and blowing dust over the northern end of the Caspian Sea (plus an almost-bone-dry Aral Sea):
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (09:00 UTC 18 May 2014)
Here is a high-resolution infrared (I-5) image showing a very strong temperature gradient in the Pacific Ocean, off the coast of Hokkaido (Japan):
VIIRS I-5 (11.45 um) image (03:45 UTC 12 December 2016)
The green-to-red transition just southeast of Hokkaido represents a sea surface temperature change of about 10 K (18 °F) over a distance of 3-5 pixels (1-2 km). This is in a location that the high-resolution Natural Color RGB shows to be ice- and cloud-free:
VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (03:45 UTC 12 December 2016)
Here’s a high-resolution infrared (I-5) image showing hurricanes Madeline and Lester headed toward Hawaii from earlier this year:
VIIRS I-5 (11.45 um) image (22:55 UTC 30 August 2016)
Here are the Fire Temperature RGB (daytime) and Day/Night Band (nighttime) images of a massive collection of wildfires over central Siberia in September 2016:
VIIRS Fire Temperature RGB composite of channels M-10, M-11 and M-12 (05:20 UTC 18 September 2016)
VIIRS Day/Night Band image (19:11 UTC 18 September 2016)
Here is a 5-orbit composite of VIIRS Day/Night Band images showing the aurora borealis over Canada (August 2016):
Day/Night Band image composite of 5 consecutive VIIRS orbits (30 August 2016)
Here is a view of central Europe at night from the Day/Night Band:
VIIRS Day/Night Band image (01:20 UTC 21 September 2016)
And, finally, for no reason at all, here’s is a picture of Spain wearing a Santa hat (or sleeping cap) made out of clouds:
VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (13:05 UTC 18 March 2014)
There you have it. A baker’s ten examples showing a small sample of what VIIRS can do. No doubt it will be taking more interesting images over the next two weeks, since it doesn’t stop working over the holidays – even if you and I do.
Think of a snowflake. What happens when that snowflake hits the ground? Now, picture other snowflakes – millions of them – all hitting the ground and piling up on top of each other, crushing our first poor snowflake. Skiers love to talk (and dream) about “fresh powder.” But, what happens when the “powder” isn’t so fresh?
Those delicate, little snow crystals we imagine (or look at directly, if we click on links included in the text) undergo a transformation as soon as they hit the ground. Compression from the weight of the snow above, plus the occasional partial thaw and re-freeze cycle (when temperatures are in the right range), breaks up the snow flakes and converts the 6-pointed crystals into more circular grains of snow. As more and more snow accumulates on top, the air in between the individual snowflakes/grains (which is what helps make it a good insulator) gets squeezed out, making the snow more dense. If enough time passes and enough snow accumulates, individual snow grains can fuse together. These bonded snow grains are called “névé.” If this extra-dense snow can survive a whole summer without melting, then a second winter of this compaction and compression will squeeze out more air and fuse more snow grains, creating the more dense “firn.” After 20 or 30 years of this, what once was a collection of fragile snowflakes becomes a nearly solid mass of ice that we call a “glacier.” Glaciers can be made up of grains that are several inches in length.
But, you don’t need to hear me say it (or read me write it), you can watch a short video where a guy in a thick Scottish accent explains it. (Did you notice his first sentence was a lie? Snow is made of frozen water, so glaciers are made of frozen water, since they are made of snow. I think what he means is that glaciers aren’t formed the same way as a hockey rink, but the way he said it is technically incorrect.) At the end of the video, the narrator hints at why we are looking at glaciers today: glaciers have the power to grind down solid rock.
When a glacier forms on a non-level surface, gravity acts on it, pulling it down the slope. This mass of ice and friction from the motion acts like sandpaper on the underlying rock, converting the rock into a fine powder known as “glacial flour” or, simply, “rock flour.” In the spring and summer months, the meltwater from the glacier collects this glacial flour and transports it downstream, where it may be deposited on the river’s banks. During dry periods, it doesn’t take much wind to loft these fine particles of rock into the air, creating a unique type of dust storm that is not uncommon in Alaska. One that can be seen by satellites.
And, wouldn’t you know it, a significant event occurred at the end of October. Take a look at this VIIRS True Color image from 23 October 2016:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (21:24 UTC 23 October 2016)
See the big plume of dust over the Gulf of Alaska? Here’s a zoomed in version:
Zoomed in version of above image.
That plume of dust is coming from the Copper River delta. The Copper River is fed by a number of glaciers in Wrangell-St. Elias National Park, plus a few in the Chugach Mountains so it is full of glacial sediment and rock flour (as evidenced by this photo). And, it’s amazingly full of salmon. (How do they see where they’re going when they head back to spawn? And, that water can’t be easy for them to breathe.)
Notice also that we have the perfect set-up for a glacial flour dust event on the Copper River. You can see a low-pressure circulation over the Gulf of Alaska in the above picture, plus we have a cold, Arctic high over the Interior shown in this analysis from the Weather Prediction Center. For those of you familiar with Alaska, note that temperatures were some 30 °F warmer during the last week in October in Cordova (on the coast) than they were in Glennallen (along the river ~150 miles inland). That cold, dense, high-pressure air over the interior of Alaska is going to seek out the warmer, less dense, low-pressure air over the ocean – on the other side of the mountains – and the easiest route to take is the Copper River valley. The air being funneled into that single valley creates high winds, which loft the glacial flour from the river banks into the atmosphere.
Now, depending on your preferences, you might think that the dust shows up better in the Natural Color RGB composite:
VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (21:24 UTC 23 October 2016).
But, everyone should agree that the dust is even easier to see the following day:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (21:01 UTC 24 October 2016)
VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (21:01 UTC 24 October 2016)
You can also see a few more plumes start to show up to the southeast, closer to Yakutat.
Since Alaska is far enough north, we get more than one daytime overpass every day. Here’s the same scene on the very next orbit, about a 100 minutes later:
VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (22:42 UTC 24 October 2016)
Notice that the dust plume appears darker. What is going on? This is a consequence of the fact that glacial flour, like many aerosol particles, has a tendency to preferentially scatter sunlight in the “forward” direction. At the time of the first orbit (21:01 UTC), both the sun and the dust plume are on the left side of the satellite. The sunlight scatters off the dust in the same (2-dimensional) direction it was traveling and hits the VIIRS detectors. In the second orbit (22:42 UTC), the dust plume is now to the right of the satellite, but the sun is to the left. In this case, forward scattering takes the sunlight off to the east, away from the VIIRS detectors. With less backward scattering, the plume appears darker. This has a bigger impact on the Natural Color imagery, because the Natural Color RGB uses longer wavelength channels where forward scattering is more prevalent. Here’s the True Color image from the second orbit:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (22:42 UTC 24 October 2016)
The plume is a little darker than the first orbit, but not by as much as in the Natural Color imagery. Here are animations to show that:
Animation of VIIRS True Color images (24 October 2016)
Animation of VIIRS Natural Color images (24 October 2016)
There are many other interesting details you can see in these animations. For one, you can see turbid waters along the coast in the True Color images that move with the tides and currents. These features are absent in the Natural Color because the ocean is not as reflective at these longer wavelengths. You can also see the shadows cast by the mountains that move with the sun. Some of the mountains seem to change their appearance because VIIRS is viewing them from a different side.
The dust plumes were even more impressive on 25 October 2016, making this a three-day event. The same discussion applies:
VIIRS True Color composite of channels M-3, M-4 and M-5 (20:43 UTC 25 October 2016)
VIIRS True Color composite of channels M-3, M-4 and M-5 (22:26 UTC 25 October 2016)
VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (20:43 UTC 25 October 2016)
VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (22:26 UTC 25 October 2016)
Full disclosure, yours truly drove through a glacial flour dust storm along the Delta River on the north side of the Alaska Range back in 2015. Even though it was only about a mile wide, visibility was reduced to only a few hundred yards beyond the hood of my car. It felt as dangerous as driving through any fog. The dust event shown here was not a hazard to drivers, since it was out over the ocean, but it was a hazard to fisherman. Being in a boat near one of these river deltas means dealing with high winds and high waves. To forecasters, these dust plumes provide information about the wind on clear days (when cloud-track wind algorithms are no help), which is useful in a state with very few surface observing sites to take advantage of.
The last remaining issue for the day is one of terminology. You see, “glacial flour dust storm” is a mouthful, and acronyms aren’t always the best solution. (GFDS, anyone?) “Haboob” covers desert dust. “SAL” or “bruma seca” covers Saharan dust specifically. So, what should we call these dust events? Something along the lines of “rock flour”, only more proactive! And, Dusty McDustface is right out!
Q: When a tree falls in the forest and nobody is around to hear it, does it make a sound?
A: Yes.
That’s an easy question to answer. It’s not a 3000-year-old philosophical conundrum with no answer. Sound is simply a pressure wave moving through some medium (e.g. air, or the ground). A tree falling in the forest will create a pressure wave whether or not there is someone there to listen to it. It pushes against the air, for one. And it smacks into the ground (or other trees), for two. These will happen no matter who is around. As long as that tree doesn’t fall over in the vacuum of space (where there is nothing to transmit the sound waves and nothing to crash into), that tree will make “a sound”. (There are also sounds that humans cannot hear. Think of a dog whistle. Does that sound not exist because a human can’t hear it?)
What if it’s not a tree? What if it’s 120 million metric tons of rock falling onto a glacier? Does that make a sound? To quote a former governor, “You betcha!” It even causes a 2.9 magnitude earthquake!
That’s right! On 28 June 2016, a massive landslide occurred in southeast Alaska. It was picked up on seismometers all over Alaska. And, a pilot who regularly flies over Glacier Bay National Park saw the aftermath:
If you didn’t read the articles from the previous links, here’s one with more (and updated) information. And, according to this last article, rocks were still falling and still making sounds (“like fast flowing streams but ‘crunchier'”) four days later. That pile of fallen rocks is roughly 6.5 miles long and 1 mile wide. And, some of the rock was pushed at least 300 ft (~100 m) uphill on some of the neighboring mountain slopes.
Of course, who needs pilots with video cameras? All we need is a satellite instrument known as VIIRS to see it. (That, and a couple of cloud-free days.) First, lets take a look at an ultra-high-resolution Landsat image (that I stole from the National Park Service website and annotated):
Glacier Bay National Park as viewed by Landsat (courtesy US National Park Service)
Of course, you’ll want to click on that image to see it at full resolution. The names I’ve added to the image are the names of the major (and a few minor) glaciers in the park. The one to take note of is Lamplugh. Study it’s location, then see if you can find it in this VIIRS True Color image from 9 June 2016:
VIIRS True Color RGB composite image of channels M-3, M-4 and M-5 (20:31 UTC 9 June 2016), zoomed in at 200%.
Anything? No? Well, how about in this image from 7 July 2016:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (21:42 UTC 7 July 2016), zoomed in at 200%
I see it! If you don’t, try this “Before/After” image overlay, by dragging your mouse from side to side:
That dark gray area in the image from 7 July 2016 that the arrow is pointing to is the Lamplugh Glacier landslide! If the “Before/After” overlay doesn’t work, try refreshing the page, or look at this animated GIF:
Animation of VIIRS True Color images highlighting the Lamplugh Glacier landslide
Of course, with True Color images, it can be hard to tell what is cloud and what is snow (or glacier) and with VIIRS you’re limited to 750 m resolution. We can take care of those issues with the high-resolution (375 m) Natural Color images:
Animation of VIIRS Natural Color images of the Lamplugh Glacier landslide
Make sure you click on it to see the full resolution. If you want to really zoom in, here is the high-resolution visible channel (I-1) imagery of the event:
Animation of VIIRS high-resolution visible images of the Lamplugh Glacier landslide
You don’t even need an arrow to point it out. Plus, if you look closely, I think you can even see some of the dust coming from the slide.
That’s what 120 million metric tons of rock falling off the side of a mountain looks like, according to VIIRS!
You may have missed it in the news, but history was made last week:
A plane landed! Wow!
But, that’s not any old plane – that’s the first commercial airliner to land on St. Helena Island, which just completed the construction of their very first airport. That means there may be no more commercial sailing to this tiny island.
People prone to seasickness may be cheering the news. People afraid of flying might not. Did you notice it took three attempts to land that plane in the video above? The first pass was getting everything all lined up with no intention of landing. The landing gear wasn’t even down. The second – which looked like a roller coaster – was waived off due to the heavy crosswinds. The third time was the charm. However, it was such a shaky first landing, they’ve postponed the official opening of the airport.
So, where is St. Helena (pronounced Ha-LEEN-a), anyway? And why should I care?
Well, to answer the first question, it’s somewhere in this image:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (12:45 UTC 26 April 2016).
Did you find it? To help you with your bearings, Africa is just outside this VIIRS swath on the right side of the image. Two hints: click on the image to bring up the full resolution version. St. Helena is just northwest of the center of the image. It’s the only island in the image not covered by clouds. Fun fact: every island within this VIIRS swath is part of the British Overseas Territory of St. Helena, Ascension and Tristan da Cunha. We already looked more closely at Tristan da Cunha, so let’s take a look at the other two.
We can get a higher resolution look if we use the I-band Natural Color RGB composite:
VIIRS Natural Color RGB composite of channels I-01, I-02 and I-03 (12:45 UTC 26 April 2016).
Notice the island appears green in the center, surrounded by a ring of brown – just the way it looks on a really high resolution satellite image. VIIRS has the resolution to pick this out!
As for why you should care, I don’t know if I can answer that. If your first thought is to ask that question, you probably don’t care. But, there are a few interesting things to note about St. Helena (besides its new airport):
– It was once an important stopping point for ships sailing from Europe to India in search of spices. At least, until the Suez Canal opened.
– It later became a prison, housing those who fought against the British government and lost, including Napoleon Bonaparte, Dinuzulu, King of the Zulu Nation, and POWs from the Boer War.
– Along with Ascension Island, St. Helena helped inspire the modern environmental movement. And it was here that the first large scale experiments in weather modification took place. (Not counting rain dances, of course.)
After witnessing the effect of deforestation on the island in the late-1700s and early-1800s, it was believed that re-foresting would help keep moisture on the island, which would lead to more clouds and more rainfall. Ascension Island, which was essentially a barren wasteland when first discovered, was also planted with trees, creating it’s Green Mountain, which is clearly visible on very high resolution satellites.
Speaking of Ascension Island – where is that located? In the first image above, showing most of the Southern Atlantic, Ascension is near the upper left corner. It’s hard to see because it is covered by clouds. Just follow the 8 °S latitude line in from the left edge of the image.
Here it is at high resolution during a clear day:
VIIRS Natural Color RGB composite of channels I-01, I-02, and I-03 (14:03 UTC 20 April 2016).
If you look closely, you’ll see that there is a small cloud or two right over Green Mountain, so maybe the efforts of the early environmentalists paid off!
For completeness, Tristan da Cunha is in the lower left of the True Color image I posted at the top. While it is covered by clouds, you can tell it’s there because it is creating its own waves. Here it is on the next orbit, where it is closer to satellite nadir:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (15:24 UTC 26 April 2016).
If I’ve inspired you to visit these islands, ask the government to give me a commission. But, seriously, don’t forget to say “Hi!” to Jonathan. Or see the many other plants and animals that are found nowhere else on Earth.
UPDATE (16 October 2017): Reuters has reported that the airport is now officially open to commercial flights (only a year and half after I wrote the original blog post)!
As mentioned before on this blog, there are typhoons, hurricanes, and cyclones, and they’re all basically the same thing. They’re just given a different name depending on where they occur in the world. Similarly, there are many different names for winds (not counting the classification of wind speeds developed by a guy named Beaufort). There’s the Chinook, the Santa Ana, the bora, the föhn (or foehn), the mistral, the zonda, the zephyr and the brickfielder. (A more complete list is here.) Some of these winds are different names for the same phenomenon occurring in different parts of the world, like the föhn, the chinook, the zonda and the Santa Ana. Others are definitely different phenomena, with different characteristics (compare the mistral with the brickfielder), but they all have the same basic cause: the atmosphere is constantly trying to equalize its pressure.
The Mediterranean is home to wide variety of named winds, one of which is the sirocco (or scirocco). (Europe is home to wide variety of languages, so this wind is also known as “ghibli,” “jugo” [pronounced “you-go”], “la calima” and “xlokk” [your guess is as good as mine].) Sirocco is the name given to the strong, southerly or southeasterly wind originating over northern Africa that typically brings hot, dry air and, if it’s strong enough, Saharan dust to Europe. Of course, after picking up moisture from the Mediterranean, the wind becomes humid, making life unpleasant for people along the north shore. Hot, humid and full of dust. Perhaps it’s no surprise that the sirocco is believed to be a cause of insomnia and headaches.
Now, I don’t know how hot it was, but an intense low pressure system passed through the Mediterranean around Leap Day and, out ahead of it, strong, southerly winds carried quite a bit of dust from northern Africa into Italy. Here’s what it looked like in Algeria. And here’s what it looked like in Salento. See if you can see that dust in these True Color VIIRS images:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (12:09 UTC 28 February 2016).
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (11:48 UTC 29 February 2016)
No problem, right? With True Color imagery, the dust is usually easy to identify and distinguish from clouds and the ocean because it looks like dust. It’s the same color as the sky over Salento, Italy in that video I linked to. The top image shows multiple source regions of dust (mostly Libya, with a little coming from Tunisia) being blown out over the sea. The second image shows one concentrated plume being pulled into the clouds over the Adriatic Sea, headed for Albania and Greece.
By the way, this storm system brought up to 2 meters (6.5 feet) of snow to northern Italy, and even brought measurable snow to Algeria! Africa and Europe made a trade: you take some of my dust, and I’ll take some of your snow.
But, this wasn’t the worst dust event to hit Europe recently. Here’s what the VIIRS True Color showed over Spain and Portugal on 21 February 2016:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (12:40 UTC 21 February 2016).
And VIIRS wasn’t the only one to see this dust. Here’s a picture taken by Tim Peake, an astronaut on the International Space Station. Again, it’s easy to pick out the dust because it almost completely obscures the view of the background surface. But, what if the background surface is dust colored?
We switch now to the other side of the world and the Takla Makan desert in China, where the dust has been blowing for the better part of a week:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (07:11 UTC 4 March 2016).
Can you tell what is dust and what is the desert floor? Can you see the Indian Super Smog on the south side of the Himalayas? Here is the same scene on a clear (no dust) day:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (07:49 UTC 2 March 2016).
There is a subtle difference there, but you need good eyesight to see it. It might be easier to see if you loop the images:
Animation of VIIRS True Color images of the Takla Makan desert (1-7 March 2016).
You’ll have to click on the image to see it animate.
Did you notice the dark brown areas surrounding the Takla Makan? Those are areas that have green vegetation during the summer. Notice how they become completely obscured by the dust as the animation progresses. That’s one one way to tell that there’s dust there. But, as we have seen before, there are other ways to see the dust.
There’s EUMETSAT’s Dust RGB composite applied to VIIRS:
Animation of VIIRS EUMETSAT Dust RGB images of the Takla Makan desert (1-7 March 2016).
That’s another animation, by the way, so you’ll have to click on it to see it animate. The same is true for the Dynamic Enhanced Background Reduction Algorithm (DEBRA), which we also talked about before:
Animation of VIIRS DEBRA Dust Product images of the Takla Makan desert (1-7 March 2016)
But, there’s one more dust detection technique we have not discussed before: the “blue light absorption” technique:
Animation of VIIRS Blue Light Dust images of the Takla Makan desert (1-7 March 2016).
The Blue Light Dust detection algorithm keys in on the fact that many different kinds of dust absorb blue wavelengths of light more than the longer visible wavelengths. It uses this information to create an RGB composite where dust appears pastel pink, clouds and snow appear blueish and bare ground appears green. Of course, other features can absorb blue light as well, like the lakes near the northeast corner of the animation that show up as pastel pink. But, depending on your visual preferences and ability to distinguish color, the Blue Light Dust product gives another alternative to the hot pink of the EUMETSAT Dust RGB, the yellow of DEBRA, and the slightly paler tan of the True Color RGB.
One question you might ask is, “How come DEBRA shows a more vivid signal than the other methods?” In the True Color RGB, dust is slightly more pale than the background sand, because it’s made up of (generally) smaller sand particles (which are more easily lofted by the wind) that scatter light more effectively, making it appear lighter in color. In the EUMETSAT Dust RGB, dust appears hot pink because the “split window difference” (12 µm – 10.7 µm) is positive, while the difference in brightness temperatures between 10.7 µm and 8.5 µm is near zero and the background land surface is warm. In DEBRA, the intensity of the yellow is related to the confidence that dust is present in the scene based on a series of spectral tests. DEBRA is confident of the presence of dust even when the signals may be difficult to pick out in the other products, either because it’s a superior product, or because its confidence is misguided. (Hopefully, it’s the former and not the latter.)
By the way, the Takla Makan got its name from the native Uyghurs that live there. Takla Makan means “you can get in, but you can’t get out.” It has also been called the “Sea of Death.” I prefer to call it “China’s Big Bowl of Dust.” It’s a large area of sand dunes (bigger than New Mexico, but smaller than Montana) surrounded on most of its circumference by mountains between 5000 and 7000 m (~15,000-21,000+ feet!). The average annual rainfall is less than 1.5 inches (38 mm). That means when the wind blows it easily picks up the dusty surface, but that dust can’t go anywhere because it’s blocked by mountains (unless it blows to the northeast). The dust is trapped in its bowl.
The Takla Makan is also important historically, because travelers on the original Silk Road had to get around it. Notice on this map, there were two routes: one that skirted the northern edge of the Takla Makan and one that went around the southern edge. This part of Asia was the original meeting point between East and West.
CIRA produces all four imagery products over the Takla Makan desert in near-real time, which you can find here. And, in case you’re curious, you can check out how well DEBRA and the EUMETSAT Dust products compare for the dust-laden siroccos over southern Europe and northern Africa by clicking here and here (for the first event over Spain and Portugal) or here and here (for the second one over Italy and the Adriatic Sea).
Take a second to think about what would happen if Florida was hit by four hurricanes in one month.
Would the news media get talking heads from both sides to argue whether or not global warming is real by yelling at each other until they have to cut to a commercial? Would Jim Cantore lose his mind and say “I don’t need to keep standing out here in this stuff- I quit!”? Would we all lose our minds? Would our economy collapse? (1: yes. 2: every man has his breaking point. 3: maybe not “all”. 4: everybody panic! AHHH!)
It doesn’t have to just be Florida. It could be four tropical cyclones making landfall anywhere in the CONUS (and, maybe, Hawaii) in a 1-month period. The impact would be massive. But, what about Alaska?
Of course, Alaska doesn’t get “tropical cyclones” – it’s too far from the tropics. But, Alaska does get monster storms that are just as strong that may be the remnants of tropical cyclones that undergo “extratropical transition“. Or, they may be mid-latitude cyclones or “Polar lows” that undergo rapid cyclogenesis. When they are as strong as a hurricane, forecasters call them “hurricance force” (HF) lows. And guess what? Alaska has been hit by four HF lows in a 1-month period (12 December 2015 – 6 January 2016).
With very-many HF lows, some of which were ultra-strong, we might call them VHF or UHF lows. (Although, we must be careful not to confuse them with the old VHF and UHF TV channels, or the Weird Al movie.) In that case, let’s just refer to them as HF, shall we?
The first of these HF storms was a doozy – tying the record for lowest pressure ever in the North Pacific along with the remnants of Typhoon Nuri. Peak winds with system reached 122 mph (106 kt; 196 k hr-1; 54 m s-1) in Adak, which is equivalent to a Category 2 hurricane!
Since Alaska is far enough north, polar orbiting satellites like Suomi-NPP provide more than 2 overpasses per day. Here’s an animation from the VIIRS Day/Night Band, one of the instruments on Suomi-NPP:
Animation of VIIRS Day/Night Band images of the Aleutian Islands (12-14 December 2015).
It’s almost like a geostationary satellite! (Not quite, as I’ll show later.) This is the view you get with just 4 images per day. (The further north you go, the more passes you get. The Interior of Alaska gets 6-8 passes, while the North Pole itself gets all 15.) Seeing the system wrap up into a symmetric circulation would be a thing of beauty, if it weren’t so destructive. Keep in mind that places like Adak are remote enough as it is. When a storm like this comes along, they are completely isolated from the rest of Alaska!
Here’s the same animation for the high-resolution longwave infrared (IR) band (I-5, 11.5 µm):
Animation of VIIRS I-5 images of the Aleutian Islands (12-14 December 2015).
I’ve mentioned Himawari before on this blog. Well, Himawari’s field of view includes the Aleutian Islands. Would you like to see how this storm evolved with 10 minute temporal resolution? Of course you would.
Here is CIRA’s Himawari Geocolor product for this storm:
Here is a loop of the full disk RGB Airmass product applied to Himawari. Look for the storm moving northeast from Japan and then rapidly wrapping up near the edge of the Earth. This is an example of something you can’t do with VIIRS, because VIIRS does not have any detectors sensitive to the 6-7 µm water vapor absorption band, which is one of the components of the RGB Airmass product. The RGB Airmass and Geocolor products are very popular with forecasters, but they’re too complicated to go into here. You can read up on the RGB Airmass product here, or visit my collegue D. Bikos’ blog to find out more about this storm and these products.
You might be asking how we know what the central pressure was in this storm. After all, there aren’t many weather observation sites in this part of the world. The truth is that it was estimated (in the same way the remnants of Typhoon Nuri were estimated) using the methodology outlined in this paper. I’d recommend reading that paper, since it’s how places like the Ocean Prediction Center at the National Weather Service estimate mid-latitude storm intensity when there are no surface observations. I’ll be using their terminology for the rest of this discussion.
Less than 1 week after the first HF storm hit the Aleutians, a second one hit. Unfortunately, this storm underwent rapid intensification in the ~12 hour period where there were no VIIRS passes. Here’s what Storm #2 looked like in the longwave IR according to Himawari. And here’s what it looked like at full maturity according to VIIRS:
VIIRS DNB image (23:17 UTC 18 December 2015).
VIIRS I-5 image (23:17 UTC 18 December 2015).
Notice that this storm is much more elongated than the first one. Winds with this one were only in the 60-80 mph range, making it a weak Category 1 HF low.
Storm #3 hit southwest Alaska just before New Year’s, right at the same time the Midwest was flooding. This one brought 90 mph winds, making it a strong Category 1 HF low. This one is bit difficult to identify in the Day/Night Band. I mean, how many different swirls can you see in this image?
VIIRS DNB image (13:00 UTC 30 December 2015).
(NOTE: This was the only storm of the 4 to happen when there was moonlight available to the DNB, which is why the clouds appear so bright. The rest of the storms were illuminated by the sun during the short days and by airglow during the long nights.) The one to focus on is the one of the three big swirls closest to the center of the image (just above and right of center). It shows up a little better in the IR:
VIIRS I-5 image (13:00 UTC 30 December 2015).
The colder (brighter/colored) cloud tops are the clue that this is the strongest storm, since all three have similar brightness (reflectivity) in the Day/Night Band. If you look close, you’ll also notice that this storm was peaking in intensity (reaching mature stage) right as it was making landfall along the southwest coast of Alaska.
Storm #4 hit the Aleutians on 6-7 January 2016 (one week later), and was another symmetric/circular circulation. This storm brought winds of 94 mph (2 mph short of Category 2!) The Ocean Prediction Center made this animation of its development as seen by the Himawari RGB Airmass product. Or, if you prefer the Geocolor view, here’s Storm #4 reaching mature stage. But, this is a VIIRS blog. So, what did VIIRS see? The same storm at higher spatial resolution and lower temporal resolution:
Animation of VIIRS DNB images of the Aleutian Islands (6-7 January 2016).
Animation of VIIRS I-5 images of the Aleutian Islands (6-7 January 2016).
This storm elongated as it filled in and then retrograded to the west over Siberia. There aren’t many hurricanes that do that after heading northeast!
So, there you have it: 4 HF lows hitting Alaska in less than 1 month, with no reports of fatalities (that I could find) and only some structural damage. Think that would happen in Florida?
As we begin 2016, struggling to get back into the swing of things at work and vowing not to overeat or over-drink ever again, it’s appropriate to bid farewell to 2015 – not just for all the weird weather events that we covered on this blog over the year, but also for the weird, wacky weather that ruined many people’s holidays. I’m not sure of the exact number, but this article mentions 43 weather-related fatalities in the U.S. in the second half of December. Let’s see, between 23-30 December 2015, there were:
– 77 tornadoes (including 38 on the 23rd and 18 on the 27th);
– Parts of New Mexico and west Texas got over 2 ft (60 cm) of snow from a blizzard that created drifts upwards of 10 ft (3 m) on the 27th;
– Record warmth was observed in the Northeast before and during Christmas and the site of Snowvember went until 18 December before the first measurable snow of the season;
– Chicago received almost 2″ of sleet (48 mm) on the 29th when any accumulation of sleet is quite rare;
– And – what will be our focus here – St. Louis received over 3-months-worth of precipitation in three days (26-28 December), from a storm that flooded a large area of Missouri, Illinois and Arkansas. In fact, the St. Louis area had the wettest December on record, right after having the 7th wettest November on record, which put it over the top for wettest calendar year on record. Current estimates place 31 fatalities at the hands of this flooding, which caused the Mississippi River to reach its highest crest since the Great Flood of 1993.
What kind of satellite imager would VIIRS be if it couldn’t detect massive flooding on the largest river in North America? (Hint: not a very useful one. Or, a less useful one, if you’re not into hyperbole.) Hey, if it works in Paraguay, it works here – or it isn’t science!
I shouldn’t have to prove that the Natural Color RGB is useful for detecting flooding (since I have done it many, many, many, many, many, many times before), so we can go right to the imagery. Here’s what the Midwest looked like on 13 November 2015 – before the flooding began:
VIIRS Natural Color RGB composite of channels I-1, I-2, and I-3 (19:02 UTC 13 November 2015).
And, here’s what the same area looked like on New Year’s Day:
VIIRS Natural Color RGB composite of channels I-1, I-2, and I-3 (18:45 UTC 1 January 2016).
Notice anything different? This is actually the reverse of the last time we played “Spot the Differences” – we’re looking for where water is now that wasn’t there before, instead of searching for bare ground that used to have water on it.
Of course, the first thing to notice is the large area of snow covering Iowa, Nebraska and northwest Missouri that wasn’t there back in November. Next, we have more clouds over the southern and northern parts of the scene. Those are the easy differences to spot. Now look for the Missouri River in eastern Missouri, the Arkansas River in Arkansas, the Illinois River in Illinois, the Indiana River in Indiana… Wait! There is no Indiana River. I fooled you! (Although, there are rivers in Indiana that are flooded.)
The most significant areas of flooding are in northeast Arkansas and the “Bootheel” of Missouri (which I think looks more like a toe or a claw than a heel), and the Mississippi River along the border of Tennessee shows signs of significant flooding as well. (If only it were the Tennessee River!) Here’s a before and after comparison, zoomed in on that part of the region:
You may have to refresh the page to get this to work right.
There’s a lot more water in the image from 1 January 2016 than there was back in November 2015! Since we are looking at the high-resolution Imagery bands, our quick-and-dirty estimate of water volumes still applies like it did for California’s drought: multiply the number of water-filled pixels by the depth (in feet) of the flooding, and by 100 acres to get the floodwater volume in acre-feet. Then multiply that by 325,852 gallons per acre-foot to get the volume in gallons. Even though this estimate is not exact, you can see how the gallons of floodwater add up. And, if you live in California, you can dream of seeing that much water! If you live in Missouri and can think of an economical way to transport this water to California, you’d be rich.
Now, see how many other areas of flooding you can find when you compare the two images in animation form:
Click to view an animation of VIIRS Natural Color RGB images from 13 November 2015 and 1 January 2016.
You will have to click on the image to see the animation. You can click on the image again to see it in full resolution (with most web browsers).
One thing you might notice is that some of the floodwaters appear more blue than black. Take a look at the Arkansas River in particular. As we discussed with the Rio Paraná and Rio Paraguay, this is due to the increased sediment that increases the albedo of the water at visible wavelengths. In other places the floodwaters are shallow enough that VIIRS can see the ground underneath – again making the water appear more blue in this RGB composite.
Wouldn’t it be nice to identify areas of flooding without having to play a “Spot the Differences” game? Maybe something that would automatically detect flooded areas? Well, you’re in luck:
VIIRS-based Flood Map (18:48 UTC 1 January 2016). Image courtesy S. Li (GMU).
This image is an example of the VIIRS-based flood detection product being developed by the JPSS Program’s River Ice and Flooding Initiative. This initiative is a collaboration between university-based researchers and NOAA forecasters who use products like these to help save lives. Thanks to S. Li for developing the product for and providing the image!
If you want to know what the flooding looks like from the ground, here is a nice video. Or, you can look at some pictures here.
As a final note, the American Meteorological Society is holding its Annual Meeting in New Orleans next week. This event will be held at the Convention Center – right on the bank of the Mississippi River – right at the time the river is forecast to crest from these floodwaters. The world’s largest gathering of weather enthusiasts might be directly impacted by this flood. Let’s hope no one has to swim their way to any poster sessions or keynote speeches! (I don’t think local residents want to deal with any flooding, either.)
We’ve poked a lot of fun at China and their serious smog problem. (Just this week, Beijing schools had their very first “smog day.” It’s just like a “snow day”, except you can’t go outside and write your name in it.) But, as it turns out, China is not the only country to produce super-thick smog. India does it, too. And, from the point of view of human health, India’s smog may actually be worse!
The World Health Organization just released a list of the Top 20 smoggiest cities, and 13 of them are in India (plus 1 in Bangladesh and 3 in Pakistan). Not a single Chinese city was anywhere in the Top 20! I’d consider taking back some of things I’ve said about China, except that 1) I never lied (although I did quote Brian Williams), and 2) the Chinese government is now instituting “smog days” because the smog is so bad. What I will do is stop comparing every type of air pollution to Chinese smog. From now on (at least until they start making some positive changes), India is the paragon of poor air quality on this blog.
Since VIIRS has no trouble seeing Chinese smog, it should have no problem seeing Indian smog. And it doesn’t:
VIIRS True Color RGB composite of channels M-4, M-4 and M-5 (07:14 UTC 18 November 2015).
You guessed it: all that gray area is optically thick smog! Let’s not forget, too, that India is the seventh largest country in world (2.4% of the Earth’s total surface area!), which is quite a large area to be covered by smog.
In the True Color image above from 18 November 2015, you can see that the people of Tibet are grateful for the Himalayas, which are an effective barrier to the smog. They may not get much air up there on the highest plateau in the world, but what little there is is much cleaner than what’s down below!
If your respiratory system is sensitive to this kind of thing, you might not want to read any further. Consider this your trigger warning. For those few brave enough to continue – prepare yourself, because it gets worse!
Here’s another VIIRS True Color image from 14 November 2015:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (06:50 UTC 14 November 2015).
Now it’s even harder to see the background surface along the base of the Himalayas. And, it’s easy to compare India’s pollution with Burma’s – I mean Myanmar’s – clean air.
VIIRS passed over the center of India on 11 November 2015 and saw that almost the entire country was covered by smog, with the thickest smog near Delhi:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (07:46 UTC 11 November 2015).
November 11th was the night of Diwali, the Hindu, Sikh and Jain “Festival of Lights” celebrating the “triumph of goodness over evil and knowledge over ignorance.” If you clicked that link and thought, “that doesn’t look so bad,” then note that the first few pictures were taken in England. In India, it was much smokier. I guess lighting all those fireworks in India comes with this “pro”: they can light the way through the thick smog; and this “con”: they give off smoke that adds to the thick smog. And, while the smog didn’t stop people from celebrating Diwali, it did affect people’s plans. It also caused a huge increase in the market for air purifiers.
The super-smog was not confined to November or Diwali. It’s still going on! Here’s a VIIRS image from 5 December 2015:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (06:56 UTC 5 December 2015).
I assure you that India and Bangladesh are under there somewhere beneath all that gray muck!
As I mentioned in the previous post, we now have access to data from the new Japanese satellite, Himawari, which can be thought of as a geostationary version of VIIRS. Himawari-8 hangs out over the Equator at a longitude of 140 °E and it takes images of the full disk every 10 minutes. From its perspective, India is right on the edge of the Earth (which, in satellite meteorology is called “the limb”). This means Himawari’s line-of-sight to India has an extra long path through the atmosphere, and that makes the smog look even worse. Here’s a True Color/Geocolor loop of Himawari images of India’s “Worse-than-China” Super-Smog. You can find this and other amazing loops on our new “Himawari Loop of the Day” webpage. We also produce a lot of other Himawari imagery products, which we post here.
Shameless plugs aside, don’t forget: India’s smog is actually worse than China’s. And, unless you live in India, you probably didn’t think that was possible! (If you do live in India, get them to clean up the air!)
A Morning Glory is a lot of things: a flower, a town in Kentucky, a popular choice for song and album titles, and – what is most relevant for us – it’s a rare atmospheric phenomenon that is both beautiful and potentially deadly.
For glider pilots, it’s the atmospheric equivalent to catching a 40-wave off the North Shore of Oahu. Like surfing the North Shore, the thrill is in catching a powerful wave and going for a ride, which only happens if you position yourself in the right spot. And, just like surfing a monster wave, one misstep can result in being crushed downward into a pile of jagged rocks and swept out to sea. The difference is, a North Shore wave is 10-12 m high and only travels a 100 m or so until it hits land and stops. A Morning Glory wave is 500-1000 m high and can travel hundreds of kilometers over a period of several hours. Here’s a picture of one:
“MorningGloryCloudBurketownFromPlane” by Mick Petroff – Mick Petroff. Licensed under CC BY-SA 3.0 via Commons – https://commons.wikimedia.org/wiki/File:MorningGloryCloudBurketownFromPlane.jpg#/media/File:MorningGloryCloudBurketownFromPlane.jpg
Simply put, a Morning Glory is a solitary wave, or “soliton“. We talked about mesospheric bores before, which are another kind of soliton. In this case, however, the soliton propagates through (or along the top of) the atmosphere’s boundary layer. Sometimes, it produces a cloud or series of clouds that came to be known as a “Morning Glory” because these clouds commonly occur near sunrise in the one place on Earth where this event isn’t rare.
Enough talk. The Day/Night Band (DNB) on VIIRS just saw a one. Let’s see if you can see it:
VIIRS DNB image of Australia (15:24 UTC 26 October 2015)
This really is like “Where’s Waldo?” because the image covers a much larger area than the Morning Glory. Even I didn’t see it at first. But, zoom in to the corner of the image over the Gulf of Carpentaria. (You can click on any of these images to see the full resolution version.) Now do you see it?
VIIRS DNB image of the Gulf of Carpentaria (15:24 UTC 26 October 2015)
Once more on the zoom, and it’s obvious:
Same as above, but zoomed in on the Morning Glory.
But, this happened at ~1:30 AM local time – depending on where in that image you are looking – so maybe it’s a Middle-of-the-Night Glory instead of a Morning Glory. (Fun fact: Northern Territory and South Australia are on a half-hour time zone, GMT+9:30. Queensland and the rest of eastern Australia are at GMT+10:00. But, the southern states have Daylight Saving Time while the north and west do not. That means almost every state has it’s own time zone.)
The Gulf of Carpentaria is where Morning Glory clouds are most likely to form. And, this is the peak season for them. (The season runs from late August to mid-November.) What is rare is seeing them so clearly at night.
Since this image was taken one night before a full moon, there was plenty of moonlight available to the DNB to see the “roll clouds” that are indicative of the Morning Glory. You can even see ripples that extend beyond the endpoints of the clouds, which might be some kind of aerosol plume affected by the waves.
There is another way to see this Morning Glory, and it’s what we call the “low cloud/fog product”. The low cloud/fog product is simply the difference in brightness temperature between the longwave infrared (IR) (10.7 µm) and the mid-wave IR (3.9 µm). For low clouds, this difference is positive at night and negative during the day. Here is an example of the low cloud/fog product applied to a new geostationary satellite, Himawari-8:
Animation of AHI Low Cloud/Fog product images (10:00 – 22:50 UTC 26 October 2015)
The Advanced Himawari Imager (AHI) on Himawari-8 is similar to VIIRS, except it has water vapor channels in the IR and it doesn’t have the Day/Night Band. It also stays in the same place relative to the Earth and takes images of the “full disk” every 10 minutes. That’s what allows you to see – in impressive detail – the evolution of this Morning Glory. The low, liquid clouds switch from white to black after sunrise because, as I said, the signal switches from positive (white) to negative (black) at sunrise. Ice clouds (e.g. cirrus) always look black in this product.
Here’s a zoomed in version of the above animation:
As above, except zoomed in to highlight the Morning Glory
Of course, once the sun rises, the standard visible imagery from AHI captures the tail end of the Morning Glory:
Animation of AHI Band 3 images (20:00 – 23:30 UTC 26 October 2015)
And, once again, zoomed in:
As above, except zoomed in to highlight the Morning Glory
At this point, it really is a Morning Glory, since it appeared at sunrise. Of course, at night, only the VIIRS Day/Night Band under full moonlight can show it in “all of its glory”. (Pun definitely intended.)
Pilots take note: the waves can still exist even when the clouds evaporate, and they are a source of severe turbulence.
If you want to know more about the phenomenon, watch this video with a lot of information or this video with a lot of pretty pictures. And, while a lot of people believe the cause of the Morning Glory is still a mystery, one scientist in Germany thinks the cause is now known. You can read all about his and other’s research into the science behind these solitary waves at this webpage.
UPDATE (12/16/2016): We’ve seen more examples of Morning Glory waves and clouds with Himawari-8. The formation of two Morning Glory waves may be seen on our Himawari Loop-of-the-Day webpage here and here. Plus, there is an extended loop covering a two day period shown in this very large animated GIF (83 MB).
We mentioned India earlier this year due to a hellish heatwave. It’s only fair that we talk about one of the other cradles of civilization (human history) and another horrible weather-related h-word.
People have been living along the Nile River in northeastern Africa and on the Arabian Peninsula for thousands of years (dating back to the Paleolithic Era). And, every once in a while, a story comes along that makes you wonder why. I’m not talking about the never-ending human conflict that has plagued the region. I’m talking about the hostile climate. (Of course, it wasn’t always hostile. There have been periods of abundant moisture. Read this. Or this.)
If you’ve watched Raiders of the Lost Ark, you are no-doubt familiar with the ancient city of Tanis, and the story about it that was the basis of the whole plot of the movie. If you haven’t seen the movie: 1) shame on you; and, 2) watch this clip.
“The city of Tanis was consumed by the desert in a sandstorm that lasted a whole year.”
I hate to be the bearer of bad news but, that part of the story is false. No year-long sandstorm hit Tanis. And, despite rumors that the actual Ark is buried in Tanis, it has never been found. (Because it’s stored in a giant government warehouse! Duh!) Plus, Indiana Jones is a fictional character in a movie. But, the movie is not entirely false. According to this article, a major archaeological find did take place at Tanis right before World War II (led by a French archaeologist, no less), and very few people know about it because of the war. Plus, there really was an Egyptian Pharaoh named Shoshenq/Shishak.
Even if Tanis was not buried by a year-long sandstorm, that doesn’t mean nasty sandstorms don’t exist. In fact, most of the Middle East is still dealing with a massive sandstorm that lasted a whole week last week. This storm put Beijing’s air pollution to shame. In fact, the dust reached the highest concentrations ever recorded in Jerusalem since Israel became it’s own country in 1948. It was responsible for several fatalities. Here are some pictures. Here’s a video from Saudi Arabia. Here’s what it looked like in Jordan and Lebanon. And, of course, what follows is what the storm looked like in VIIRS imagery.
Since this dust storm lasted a whole week, we got plenty of VIIRS imagery of the event. It started on the afternoon of 6 September 2015, and here’s the first VIIRS True Color image of it:
VIIRS True Color image of channels M-3, M-4 and M-5 (10:06 UTC 6 September 2015)
Can you see it? (Click on the image to see the full resolution version.) A trained eye can spot it from this image alone. An untrained eye might have difficulty distinguishing it from the rest of the desert and sand. Look for the tan blob over Syria that is obscuring the view of the Euphrates river.
If you can see that, you can track it over the rest of the week:
Animation of VIIRS True Color images (6-12 September 2015)
This animation was reduced to 33% of it’s original size to limit the bandwidth needed to display it. It contains the afternoon overpasses (1 image per day) because you need sunlight to see things in true color. And, while it suffers from the fact that animated GIFs only allow 256 colors (instead of the 16,777,216 colors possible in the original images), you should be able to see the dust “explode” over Israel, Lebanon and Jordan over the next two days. It eventually advects over northwestern Saudi Arabia, Egypt and Cyprus during the rest of the week.
The last time we looked at a major dust storm, the dust was easy to see. It was blown out over the ocean, which is a nice, dark background to provide the contrast needed to see the dust. Here, the dust is nearly the same color as the background – because it is made out of what’s in the background. Is there a better way to detect dust in situations like this?
EUMETSAT developed an RGB composite explicitly for this purpose, and they call it the “Dust RGB.” And we’ve talked about it before. And, here’s what that looks like:
Animation of EUMETSAT Dust RGB images from VIIRS (6-12 September 2015)
Since this RGB composite uses only infrared (IR) channels, it works at night (although not as well) so you can get twice as many images over this time period. It also makes dust appear hot pink. The background appears more blue in the daytime images, so the dust does stand out. But, the background becomes more pink/purple at night, so the signal is harder to see at those times. Still, you can see the dust spread from Syria to Egypt over the course of the week.
My colleagues at CIRA have developed another way to identify dust: DEBRA. DEBRA is an acronym for Dynamic Enhanced Background Reduction Algorithm. As the name implies, DEBRA works by subtracting off the expected background signal, thereby reducing the background and enhancing the signal of the dust. So, instead of trying to see brown dust over a brown background (i.e. True Color RGB) or trying to see hot pink dust over a pinkish/purplish background (i.e. EUMETSAT Dust RGB) you get this:
Animation of VIIRS “DEBRA Dust” images (6-11 September 2015)
DEBRA displays dust as yellow over a grayscale background. The intensity of the yellow is related to the confidence that a given pixel contains dust. It could display dust as any color of the rainbow, but yellow was chosen specifically because there are fewer people that are colorblind toward yellow than any other type of colorblindness. That makes the dust very easy to see for nearly everyone. (Sorry, tritanopes and achromats.) One of the biggest complaints about RGB composites is that the 7-12% of the population that has some form of colorblindness have difficulty trying to see what the images are designed to show. (Since I’m so fond of RGB composites, I better check my white male trichromat privilege. Especially since, according to that last link, white males are disproportionately colorblind.) The point is: we now have a dust detection algorithm that works well with (most) colorblind people, and it makes dust easier to see even for people that aren’t colorblind. DEBRA also works at night, but I’ve only shown daytime images here to save on filesize.
The last two frames of the DEBRA animation show something interesting: an even more massive dust storm in northern Sudan and southern Egypt! Fortunately, fewer people live there, but anyone who was there at the time must have a story to tell about the experience. Here are closer up views of that Sudanese sandstorm (or should I say “haboob” since this is the very definition of the word?). First the True Color:
VIIRS True Color image (10:32 UTC 10 September 2015)
Next, the EUMETSAT Dust RGB:
VIIRS EUMETSAT Dust RGB image (10:32 UTC 10 September 2015)
And, finally DEBRA:
MSG-3 DEBRA Dust image (10:30 UTC 10 September 2015)
If you’re wondering why the DEBRA image doesn’t seem to line up with the other two, it’s because I cheated. The DEBRA image came from the third Meteosat Second Generation satellite (MSG-3), which is a geostationary satellite. The majority of the haboob was outside our normal VIIRS processing domain for DEBRA, so I grabbed the closest available MSG-3 image. It has much lower spatial resolution, but similar channels, so DEBRA works just as well. And, you don’t necessarily need high spatial resolution to see a dust storm that is ~ 1000 km across. What MSG-3 lacks in spatial resolution, it makes up for in temporal resolution. Instead of two images per day, you get 1 image every 15 minutes. Here is a long loop of MSG-3 images over the course of the whole week, where you can see both sandstorms: (WARNING: this loop may take a long time to load because it contains ~600 large images). Keep your eye on Syria early on, then on Egypt and Sudan. Both haboobs appear to be caused by the outflow of convective storms. Also, how many other dust storms are visible over the Sahara during the week? For comparison purposes, here’s a similar loop of EUMETSAT Dust images. (MSG-3 does not have True Color capability.)
These sandstorms have certainly made their impact: they’ve broken poor air quality records, killed people, made life worse for refugees, closed ports and airports, and even affected the Syrian civil war. Plus, the storms coincided with a heatwave. Having +100 °F (~40 °C) temperatures, high humidity and not being able to breathe because of the dust sounds awful. Correction: it is awful. And, life goes on in the Middle East.
UPDATE #1 (17 September 2015): Here’s a nice, zoomed-in, animated GIF of the Syrian haboob as seen by the DEBRA dust algorithm, made from MSG-3 images:
Click to view 59 MB Animated GIF
UPDATE #2 (17 September 2015): Steve M. also tipped me off to another – even more impressive – haboob that impacted Iraq at the beginning of the month (31 August – 2 September 2015). Here’s an animation of the DEBRA view of it:
Click to view 28 MB Animated GIF
This dust storm was even seen at night by the Day/Night Band, thanks to the available moonlight:
VIIRS Day/Night Band image of Iraq (22:43 UTC 31 August 2015)
Look at that cute little swirl. Well, it would be cute if it weren’t so hazardous.
We’ve covered mysteries before on this website. Well, here’s one from 150 years ago:
The emigrants, coming west on the Applegate Trail to Oregon in the 1870s, were puzzled. The trail was, of course, a seemingly unending set of wagon-wheel ruts stretching from the jumping-off points in the Midwest over deserts and mountains and all sorts of obstacles that seemed insurmountable, but weren’t.
But this one seemed impossible. Had the wagons before them really plunged directly into the enormous lake that lay before them? The ruts led directly into the water, and there was no sign of them having come out again.
It was miles across – the other side lay almost invisible on the horizon, much too far to float a caulked wagon. And yes, it was deep – far too deep to ford.
There was nothing for it but a trip around the lake, since the western sky lay on the other side. And so, around they went – making a detour of something like 100 miles.
On the other side, they found the wagon ruts again. They emerged from the water and headed on westward toward the Cascades. Once arrived at the West Coast, none of the previous emigrants knew anything about any lake there.
Was it aliens who came down to Earth to put a lake where there was none before? Did the earlier emigrants have covered wagon submarine technology (and very short term memories)? Maybe it was a very localized, very short-term Ice Age – a glacier snuck down from the Cascades and into the valley in the middle of the night and then melted without anyone noticing. What about that?
SPOILER ALERT: None of those theories is true. Anyone who would come up with these ridiculous ideas should be ashamed of themselves. Oh, wait – I came up with them. Hmmm. What I meant to say is: those are all good theories that are worthy of scientific exploration. Unfortunately, VIIRS wasn’t around in the 1870s. Plus, this mystery has already been solved. As our source explains:
It remained a mystery until, several years later, a drought struck and the lake dried up again.
What we’re talking about is Goose Lake, which is at times the largest lake that’s at least partially in Oregon. (In terms of surface area, not volume.) It’s right on the border between Oregon and California. When Goose Lake is at its fullest, it has a surface area of 147 square miles (380 km2), but it’s only 26 ft (8 m) deep. Maybe, if the emigrants weren’t so cowardly, they could have walked across it (although they might have gotten stuck in the mud). It would have saved 100 miles of extra walking (although they might have gotten stuck in the mud).
As you are probably well aware, California and Oregon are under a long-lasting, extreme drought. So, if you live near Goose Lake, it’s probably no surprise that the lake has dried up again. And, since this is 2015, VIIRS can tell us something about it this time.
Have you ever played one of those “spot the differences” games? (Don’t play them at work, or you’ll never get anything done.) Well, here’s a “spot the differences” game you can play at work – at least if your work involves detecting evidence of drought.
Here’s what Goose Lake looked like three years ago, according to VIIRS Natural Color imagery:
VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (20:40 UTC 15 July 2012)
Note that it’s not as dark in color as the other lakes because it is so shallow. Now, here’s the same scene just last week:
VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (21:40 UTC 16 July 2015)
Notice anything different? Now, for this spot-the-differences game, we’re going to ignore clouds, because they are always going to be different between the two images, difficult to count, and irrelevant to this discussion. (Except that clouds can obscure the view of a lake and can cast shadows that look like lakes.)
Since I labelled Goose Lake on those images, you have no excuse for not spotting that difference. Besides, if you can’t see that 147 square miles of lake surface are missing from the second image, you have no hope to see any of the other differences.
I counted at least 20 lakes or reservoirs that are present in the 2012 image that have dried up and vanished in the 2015 image. Plus, there are about as many lakes or reservoirs that have noticeably shrunk since 2012. Can you spot them all? Can you see more than I did?
After you’ve declared yourself done, compare your results with mine:
Comparison of the above VIIRS Natural Color images of Goose Lake.
As always, click on it to see the full resolution image. I’ve marked with red arrows those lakes that are visible in the 2012 image that are not visible in the 2015 image. Yellow arrows indicate the lake has lost surface area (but not totally vanished) between 2012 and 2015. And, there are a few spots that look like surface water visible in the 2015 image that are not present in 2012 – I’ve marked those with green arrows. There are a couple of lakes visible in the 2012 image that are covered by clouds in the 2015 image. Those are left unmarked. I’ve also labelled a burn scar left over from a pretty big wildfire in south-central Oregon visible in 2012 that has since disappeared. That’s the main non-lake, non-cloud related difference between the two images.
Most notably, Upper Alkali Lake (southeast of Goose Lake) dried up, which you should have noticed without me pointing it out. Drews Reservoir on the northwest side of Goose Lake in Oregon appears to have dried up, as does New Year Lake right across the border from Upper Alkali Lake in Nevada. Thompson Reservoir (the northernmost red arrow) looks bone dry and Gerber Reservoir (west of Drews Reservoir) has very little water left. The eastern half of Clear Lake Reservoir is now empty and the western half is significantly reduced in size. Three big reservoirs (lakes) on the southern edge of the image have also lost quite a bit of water (Trinity Lake, Shasta Lake and Eagle Lake).
Even if you don’t care that a bunch of salty, alkaline lakes in rural Jefferson (as they might prefer you to call it) have dried up, you should care about the reservoirs. And not just for the boating and other water recreation activities, which are now hazardous. When towns run out of water, prime agricultural land lays fallow, and Tom Selleck gets in trouble with the law, you know things are serious.
The reservoirs closer to central California are down quite a bit as well, and these impact a lot of people. Use your honed-in spot-the-difference skills in these VIIRS I-2 (0.865 µm) images from the same dates and times as the above images:
VIIRS I-2 image (20:40 UTC 15 July 2012)
VIIRS I-2 image (21:40 UTC 16 July 2015)
I-2 is one of the components of the Natural Color imagery (the green component). What makes it good for this purpose is that land and, particularly, vegetation are highly reflective at this wavelength, so they appear bright. Water is absorbing, so it appears black (or nearly so if the water’s dirty or shallow). It also has 375 m resolution at nadir. If you click to the full resolution versions of the above images, you can see that most of the reservoirs have lost quite a bit of surface area between 2012 and 2015.
If you’re too lazy, or have poor eyesight, click on this image below to better compare the two images:
Comparison of VIIRS I-2 images from the same dates and times as above
One more point that needs to be made: 375 m resolution at nadir is good for weather satellites like VIIRS, but the fact that you can see the loss of water in these images is testimony to how bad this drought is!
As you may or may not know, the resolution of VIIRS in these images degrades from 375 m at nadir to 750 m at the edge of the swath. As a reasonable approximation, that’s means each pixel is a quarter mile to a half mile wide. That means each pixel of missing water represents between 40 and 160 acres. We’ll say 100 acres, given that these images were taken roughly halfway between nadir and edge of scan. If the water was only 1 foot deep in these pixels, that would be a loss of 100 acre-feet. That’s 32.5 million gallons of water. (By the way, the average household uses between 0.5 and 1 acre-foot per year in water.)
Multiply the number of pixels that have lost water by 100 to get the area in acres. Multiply that by the average depth of the water lost to get the volume in acre-feet. And then multiply that by 325,852 gallons per acre-foot and that’s a lot of gallons of missing water!
(In case you’re interested, this PDF document says the average depth of Goose Lake is 8 ft. At 147 sq. mi. of surface area, that’s 245 billion gallons of water gone, give or take.)
Have you ever slept in a really hot room?
Of course, if you clicked on that link, keep in mind two things: perjury is a crime, and extreme heat is no joke. It is number one on the list of causes of weather-related fatalities. It may not capture the attention of the media like tornadoes, typhoons and tiger sharks but, exposure to extreme heat and extreme cold are routinely found to be the top two killers worldwide. (Well, that depends on the source of your information and how deaths are or are not attributed to weather. Some say extreme droughts and floods kill more.)
And of course, video footage of tornadoes and typhoons is more dramatic than frying an egg on the sidewalk or watching someone sweat inside a car. But, a recent heat wave in India is actually grabbing some attention from the media. Is it because there have been more than 2,200 documented fatalities? Or, the fact that it has been hot enough to make the roads melt?
Take a look at this hi/lo temperature calendar produced by the Weather Underground for Delhi, India during May 2015. If you’re paying attention, you’ll notice that only 4 days during the month had high temperatures less than 100 °F (38 °C). What is more concerning is that 18 out of the 31 days had low temperatures in the 80s. Look at May 18, 25 and 31: the lowest temperature recorded on each of those days was 87 °F (31 °C)! And take a look at the 10-day period in Hyderabad, India (May 20-29): highs near 110 °F everyday, with lows in the mid- to upper-80s.
And, for those of you in Phoenix or Death Valley, it is not a dry heat. According to this website, the automated weather station in Tirumala, Andhra Pradesh state recorded a temperature of 50 °C (122 °F) on May 31st. The day before, the high was 49 °C (120 °F), with a dew point of 24 °C (75 °F), which yields a heat index (or “feels like”) temperature of 59 °C (139 °F)!
Whether you side with Newman or Kramer on wanting to kill yourself after sleeping in a really hot room, with temperatures like this, it might not be your choice. If your body can’t cool down, you’ll be in trouble – especially if you don’t have air conditioning, like a lot of people in India.
You’ve probably guessed by now that VIIRS is capable of telling us something about this heatwave. And, you’re right! (Otherwise I wouldn’t be writing this.)
You should all know by now that the amount of radiation in the longwave infrared (IR) “window” (10-11 µm) is a function of the temperature of the object you’re looking at. We often refer to an object’s “brightness temperature,” which is the temperature that a black body would have if it emitted the same amount of radiation. With that in mind, here is the VIIRS longwave IR (M-15) image from 18 May 2015:
VIIRS IR (M-15) image from 08:06 UTC 18 May 2015. Colors correspond to brightness temperatures according to the scale at lower right.
The first thing to notice is: there aren’t many clouds out there to block out the sun. The second thing to notice is: that big, black area in west-central India is where the color-enhancement of the image has lead to “saturation”. The IR color table I like to use saturates at brightness temperatures of 330 K (57 °C), which isn’t usually a problem because most places around the globe don’t get that hot. Some pixels in this image reached 332 K (59 °C/139 °F)! (The detectors of M-15 don’t saturate unless the brightness temperature is higher than 380 K, so this is not a problem with VIIRS.)
To prove there weren’t many clouds, here’s the True Color RGB (M-3/M-4/M-5):
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 08:06 UTC 18 May 2015.
There is some smog and dust, though, if you look close but, it’s not quite the same thing. And wait! The observed temperatures were only 40-45 °C, not 59 °C! What gives?
Aha! You are now aware of the difference between “air temperature” and “skin temperature”. The satellite observes “skin temperature” – the temperature of the surface of the objects it’s looking at*. Thermometers measure the temperature of the air 2 m above the ground (assuming they follow the WMO standards [PDF]). As anyone who has ever tried to fry an egg on the sidewalk knows, the egg would never get cooked if you suspended it in the air 2 m above the ground. The ground heats up a lot more than the air does in this situation. One of the reasons is that the atmosphere doesn’t absorb radiation in this wavelength range*- and, if it did, it wouldn’t be an “atmospheric window”.
(* Not exactly. The atmosphere does have some effects in this wavelength range that have to be removed to get a true skin temperature. These effects increase with wavelength in the 11-12 µm range, which is why you may hear it called a “dirty window”.)
Another thing you should already know (even without cracking a few eggs) is that it’s much more comfortable to walk barefoot on grass in a park, than it is to walk barefoot in the parking lot (especially if it’s hot enough to make the asphalt melt). VIIRS can also tell you this.
Below, we’ve zoomed in on the area around Bombay (Mumbai) and the Gulf of Cambay. This is an image overlay that you might have to refresh your browser to see. Bombay is on the coast near the bottom of the images. As you drag the line back and forth, notice the areas with vegetation in the True Color image have a lower brightness temperature than the areas with bare ground.
I mentioned smog and dust earlier. Well, the haze, smog and dust were even worse over northwestern India on 20 May 2015:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 07:28 UTC 20 May 2015.
If you click on the image to see it in full resolution, you can see that the smog is trapped by the Himalayas. That means the people of Tibet are not only at more comfortable temperatures, they can also breathe fresh air.
In case you’re wondering, the dust does show up in the IR as well:
VIIRS IR (M-15) image, taken 07:28 UTC 20 May 2015.
Haze, smog, dust, unbearable heat and humidity: it’s no wonder why the people of India pray for the monsoon.
Think back to St. Patrick’s Day. Do you remember what you were doing? Hopefully you were wearing something green. And, hopefully, you didn’t leave anything green in the gutter behind the bar (e.g. undigested lunch or beverages or a mixture of the two). If you did, we don’t want to hear about it. It’s unpleasant enough that you had to read that and have that image in your mind. Apologies if you are eating.
If your mind was lucid enough that night, or the following night, did you remember to look up to the northern sky? Or, right above you, if you live far enough north? (Swap “north” for “south” if you live in the Southern Hemisphere. Everything is backwards there.) Was it a clear night?
If you answered “no” to the first two questions and “yes” to the third question, you missed out on an opportunity to see something green in the sky – one of the great atmospheric wonders of the world: the aurora. If you answered “yes” then “no”, tough luck. The lower atmosphere does not always cooperate with the upper atmosphere. If you answered “yes” on everything and still didn’t see the aurora, then you need to move closer to your nearest magnetic pole. Or, away from light pollution. (Although, truth be told, it is possible to live too far north or south to see the aurora. But, not many people live there. Those who do rarely have to worry about light pollution.)
If you forgot to look up at the night sky on 17-18 March 2015, you have no excuse. The media was hyping the heck out of it. That link is just one example of media predictions of the aurora being visible as far south as Dallas and Atlanta. While I couldn’t find any photographic evidence that that actually happened, there were people as far south as Ohio, Pennsylvania and New Jersey that saw the aurora. In the other hemisphere – the backwards, upside-down one – the aurora was seen as far north as Australia and New Zealand, which is a relatively rare occurrence for them. And there are no shortage of pictures and videos if you want proof: pictures, more pictures, even more pictures, video and pictures, video, and a couple more short videos here, here and here.
Now, we already know that VIIRS can see the aurora. We’ve covered both the aurora borealis and aurora australis before. This time, we’ll take a look at both at the same time – not literally, of course! – since the Day/Night Band viewed the aurora (borealis and australis) on every orbit for an entire 24 hour period, during which time it covered every part of the Earth. So, follow along as VIIRS circled the globe in every sense of the word during this event.
First, we start with the aurora australis over the South Pacific, south of Pitcairn Island, at 10:15 UTC on 17 March 2015. We then proceed westward, ending over the South Pacific, south of Easter Island at 08:16 UTC on 18 March 2015. Click on each image in the gallery to see the medium resolution version. Above each of those images is a link containing the dimensions of the high resolution version. Click on that to see the full resolution.
Notice how much variability there is in the spatial extent and shape of the aurora from one orbit to the next. Everything is represented, from diffuse splotches to well-defined ribbons (which are technical terms, of course, wink, wink). You can see just how close the aurora was to being directly over Australia and New Zealand. And, if you looked at the high resolution versions of all the images (which are very large), you might have seen this:
VIIRS DNB image of the aurora australis, 18:39 UTC 17 March 2015.
Just below center, the aurora is illuminating gravity waves forced by Heard Island. The aurora is also directly overhead of it’s “twin”, “Desolation Island” (aka Îles Kerguelen, upper-right corner right at the edge of the swath), although it looks too cloudy for the scientists and penguins living there to see it. (How many more Remote Islands can I mention that I’ve featured before?)
Now, I’m a sucker for animations, so I thought I’d combine all of these images into one and here it is (you can click on it to see the full-resolution version):
Animation of VIIRS DNB images of the aurora australis, 17-18 March 2015.
Here, it is easier to notice that the aurora is much further north (away from the South Pole) near Australia and New Zealand and further south (closer to the pole) near South America. This is proof that the geomagnetic pole does not coincide with the geographic pole. This also puts the southern tips of Chile and Argentina at a disadvantage when it comes to seeing the aurora, compared to Australia and New Zealand.
Now, repeat everything for the aurora borealis – beginning over central Canada (07:57 UTC 17 March 2015) and ending there ~24 hours later (07:40 UTC 18 March 2015):
Basically, if you were anywhere in Siberia where there were no clouds, you could have seen the aurora. (For those who are not impressed, Siberia is a big area.) Did you see the aurora directly over North Dakota? (I showed a video of that above.) Did you notice it was mostly south of Anchorage, Alaska? (Typically, it’s over Fairbanks.) It was pretty close to Moscow and Scotland, also. But, what about the sightings in Ohio, New Jersey, and Germany? It doesn’t look like the aurora was close to those places…
For one, the aurora doesn’t have to be overhead to see it. Depending on the circumstances (e.g. auroral activity, atmospheric visibility, light pollution, etc.), you can be 5 degrees or more of latitude away and it will be visible. Second, these are single snapshots of an aurora that is constantly moving. (We already know the aurora can move pretty fast.) It may have been closer to these places when VIIRS wasn’t there to see it.
Lastly, here’s an animation of the above images, moving in the proper clockwise direction, unlike in that backwards, upside-down hemisphere:
Animation of VIIRS DNB images of the aurora borealis, 17-18 March 2015.
If you want to know more about what causes the aurora, watch this video. If you want to know why auroras appear in different colors, read this. If you want to know why aboriginal Australians viewed the aurora as an omen of fire, blood, death and punishment, and why various Native American tribes viewed the aurora as dancing spirits that were happy, well, you have a lot more reading to do: link, link and link.
You may or may not have heard that a small town in Italy received 100 inches (250 cm; 2.5 m; 8⅓ feet; 8 x 10-17 parsecs) of snow in 18 hours just last week (5 March 2015). That’s a lot of snow! It’s more than what fell on İnebolu, Turkey back in the beginning of January. But, something else happened that week that is much more interesting.
All you skiers are asking, “What could be more interesting than 100 inches of fresh powder?” And all you weather-weenies are asking, “What could be more interesting than being buried under a monster snowstorm? I mean, that makes Buffalo look like the Atacama Desert!” The answer: well, you’ll have to read the rest of this post. Besides, VIIRS is incapable of measuring snow depth. (Visible and infrared wavelengths just don’t give you that kind of information.) So, looking at VIIRS imagery of that event isn’t that informative.
This is (or was, until I looked into it in more detail) another mystery. Not a spooky, middle-of-the-night mystery, but one out in broad daylight. (We can thus automatically rule out vampires.)
It started with a comparison between “True Color” and “Natural Color” images over Germany from 9 March 2015:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 11:54 UTC 9 March 2015.
VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 11:54 UTC 9 March 2015.
The point was to show, once again, how the Natural Color RGB composite can be used to differentiate snow from low clouds. That’s when I noticed it. Bright pixels (some white, some orange, some yellow, some peach-colored) in the Natural Color image, mostly over Bavaria. (Remember, you can click on the images, then click again, to see them in full resolution.) Thinking they might be fires, I plotted up our very own Fire Temperature RGB:
VIIRS Fire Temperature RGB composite of channels M-10, M-11 and M-12 from 11:54 UTC 9 March 2015.
I’ve gone ahead and drawn a white box around the area of interest. Let’s zoom in on that area for these (and future) images.
VIIRS True Color RGB (11:54 UTC 9 March 2015). Zoomed in and cropped to highlight the area of interest.
VIIRS Natural Color RGB (11:54 UTC 9 March 2015). Zoomed in and cropped to highlight the area of interest.
VIIRS Fire Temperature RGB (11:54 UTC 9 March 2015). Zoomed in and cropped to highlight the area of interest.
Now, these spots really show up. But, they’re not fires! Fires show up red, orange, yellow or white in the Fire Temperature composite (which is one of the benefits of it). They don’t appear pink or pastel blue. What the heck is going on?
Now, wait! Go back to the True Color image and look at it at full resolution. There are white spots right where the pastel pixels show up in the Fire Temperature image. (I didn’t notice initially, because white spots could be cloud, or snow, or sunglint.) This is another piece of evidence that suggests we’re not looking at fires.
For a fire to show up in True Color images, it would have to be about as hot as the surface of the sun and cover a significant portion of a 750-m pixel. Terrestrial fires don’t typically get that big or hot on the scale needed for VIIRS to see them at visible wavelengths. Now, fires don’t have to be that hot to show up in Natural Color images, but even then they appear red. Not white or peach-colored. If a fire was big enough and hot enough to show up in a True Color image, it would certainly show up in the high-resolution infrared (IR) channel (I-05, 11.45 µm), but it doesn’t:
VIIRS high-resolution IR (I-05) image (11:54 UTC 9 March 2015).
You might be fooled, however, if you looked at the mid-wave IR (I-04, 3.7 µm) where these do look like hot spots:
VIIRS high-resolution midwave-IR (I-04) image (11:54 UTC 9 March 2015).
What’s more amazing is I was able to see these bright spots all the way down to channel M-1 (0.412 µm), the shortest wavelength channel on VIIRS:
VIIRS “deep blue” visible (M-1) image (11:54 UTC 9 March 2015).
So, what do we know? Bright spots appear in all the bands where solar reflection contributes to the total radiance (except M-6 and M-9). I checked. (They don’t show up in M-6 [0.75 µm], because that channel is designed to saturate under any solar reflection so everything over land looks bright. They don’t show up in M-9 [1.38 µm] because solar radiation in that band is absorbed by water vapor and never makes it to the surface.) Hot spots do not coincide with these bright spots in the longer wavelength IR channels (above 4 µm).
What reflects a lot of radiation in the visible and near-IR but does not emit a lot in the longwave IR? Solar panels. That’s the answer to the mystery. VIIRS was able to see solar radiation reflecting off of a whole bunch of solar panels. That is the source of Germany’s “magic sparkle”.
Don’t believe me? First off, Germany is a world leader when it comes to producing electricity from solar panels. Solar farms (or “solar parks” auf Deutsch) are common – particularly in Bavaria, which produces the most solar power per capita of any German state.
Second: I was able to link specific solar parks with the bright spots in the above images using this website. (Not all of those solar parks show up in VIIRS, though. I’ll get to that.) And these solar parks can get quite big. Let’s take a look at a couple of average-sized solar parks on Google Maps: here and here. The brightest spot in the VIIRS Fire Temperature image (near 49° N, 11° E) matches up with this solar park, which is almost perfectly aligned with the VIIRS scans and perpendicular to the satellite track.
Third: it’s not just solar parks. A lot of people and businesses have solar panels on their roofs. Zoom in on Pfeffenhausen, and try to count the number of solar panels you see on buildings.
One more thing: if you think solar panels don’t reflect a lot of sunlight, you’re wrong. Solar power plants have been known reflect so much light they instantly incinerate birds*. (*This is not exactly true. See the update below.)
Another important detail is that all of the bright spots visible in the VIIRS images are a few degrees (in terms of satellite viewing angle) to the west of nadir. Given where the sun is in the sky this time of year (early March) and this time of day (noon) at this latitude (48° to 50° N), a lot of these solar panels are in the perfect position to reflect sunlight up to the satellite. But, not all of them. Some solar panels track the sun and move throughout the day. Other panels are fixed in place and don’t move. Only the solar panels in the right orientation relative to the satellite and the sun will be visible to VIIRS.
At these latitudes during the day, the sun is always to south and slightly to the west of the satellite. For the most part, solar panels to the east of the satellite will reflect light away from the satellite, which is why you don’t see any of those. If the panel is pointed too close to the horizon, or too close to zenith (or the sun is too high or too low in the sky), the sunlight will be reflected behind or ahead of the satellite and won’t be seen. You could say that this “sparkle” is actually another form of glint, like sun glint or moon glint – only this is “solar panel glint”.
Here’s a Natural Color image from the very next day (10 March 2015), when the satellite was a little bit further east and overhead a little bit earlier in the day:
VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 from 11:35 UTC 10 March 2015.
Notice the half-dozen-or-so bright spots over the Czech Republic. These are just west of the satellite track and in the same position relative to satellite and sun. (The bright spot near the borders of Austria and Slovakia matches up with this solar farm.) The bright spots over Germany are gone because they no longer line up with the sun and satellite geometry.
As for the pastel colors in the Natural Color and Fire Temperature RGBs, those are related to the proportional surface area of the solar panels relative to the size of each pixel as well as the background reflectivity of the ground surrounding the solar panels. The bright spots do generally appear more white in the high-resolution version of the Natural Color RGB from 9 March:
VIIRS high-resolution Natural Color (I-01, I-02, I-03) RGB image (11:54 UTC 9 March 2015).
See, we learned something today. Germany sparkles with green electricity and VIIRS can see it!
UPDATES (17 March 2015): Thanks to feedback from Renate B., who grew up in Bavaria and currently owns solar panels, we have this additional information: there is a push to add solar panels onto church roofs throughout Bavaria, since they tend to be the tallest buildings in town (not shaded by anything) and are typically positioned facing east, so the south-facing roof slopes are ideal for collecting sunlight. The hurdle is that churches are protected historical buildings that people don’t want to be damaged. Also, it’s not a coincidence that many solar parks have their solar panels facing southeast (and align with the VIIRS scan direction). They are more efficient at producing electricity in the morning, when the temperatures are lower, than they are in the afternoon when the panels are warmer. They face southeast to better capture the morning sun.
Also, to clarify (as pointed out by Ed S.): the solar power plant that incinerates birds generates electricity from a different mechanism than the photovoltaic (PV) arrays seen in these images from Germany. PV arrays (aka solar parks) convert direct sunlight to electricity. The “bird incinerator” uses a large array of mirrors to focus sunlight on a tower filled with water. The focused sunlight heats the water until it boils, generating steam that powers a turbine. Solar parks and solar panels on houses and churches do not cause birds to burst into flames.
Take a look at this VIIRS “Natural Color” image and see if you can find Pitcairn Island. It’s in there somewhere:
VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3, taken 22:25 UTC 10 April 2014
You’re definitely going to want to click through to the full resolution version. (Click on the image, then click again.) You won’t be able to see it otherwise. Take your time. Note: this is actually pretty similar to searching for fires.
Did you see it?
If you answered “no”: Good! That’s just what the early settlers of Pitcairn Island wanted: an island that no one could find! If you answered “yes”: I think you’re mistaken. You probably saw Henderson Island, which is bigger and easier to see.
Pitcairn is only 3.6 km across. That’s just 7 pixels in this composite of high-resolution (375 m at nadir; I-band) channels. It’s total land area is 4.6 km2. Henderson Island is 37.3 km2. There’s even a third island visible in this picture, but you need the eyes of an eagle to see it – Oeno at 0.65 km2. Look again and see if you see any green pixels.
If you give up, here’s the answer:
VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3, taken at 22:25 UTC 10 April 2014. The visible islands are labelled.
Now, you may have just clicked to the full-resolution version and are now wondering if I’m right about Oeno Island. Is there really anything there? Yes. Just look at that part of the image zoomed in by 800%:
VIIRS Natural Color image (10 April 2014) zoomed in on Oeno Island
See those three green pixels (not counting the latitude line drawn on there) that are surrounded by lighter blue pixels? That’s Oeno. It is one of the smallest islands you can say that VIIRS “saw”. Here’s what it looks like from a really high-resolution satellite. The light blue pixels surrounding it are the surrounding reef and lagoon of the atoll.
So, why all the interest in a couple of tiny islands in a remote part of the Pacific Ocean? First of all, there are winter storms battering both coasts of the United States, so it’s nice to enjoy a little bit of escapism. Now you can fantasize about being on a tropical island instead of facing the reality of shoveling another 2 feet of snow. Second, it’s fun to look for little islands that can’t be seen with current geostationary satellites (although it will be interesting to see if the high-resolution [0.5 km] visible channel on Himawari will be able to see it; it might be too far east, though). Plus, it’s been over two years since I last looked at remote islands – there may a whole new generation of viewers interested in this stuff who never knew this was part of the blog. Third, I don’t have to write as much and you don’t have to read as much as I fill my blog post quota for the month.
However, to barely keep things on the topic of atmospheric science and satellite meteorology, I will note that, in the images above, you can see a string of clouds streaming to the northwest from both Pitcairn and Henderson Islands. This is the visible manifestation of fluid dynamics which we have discussed before.
If you’ve heard of Pitcairn Island prior to this, it’s probably because you heard of the Mutiny on the Bounty. A group of mutineers who didn’t want to be hanged for their crime settled on Pitcairn Island and burned their ships so they could never leave and, hopefully, never be found. That is the very definition of “getting away from it all”. (Pitcairn is also known to stamp collectors who seek the very rare stamps from the far corners of the world. Selling stamps to tourists is actually a significant part of their economy.)
Today, the island is home to ~50 people – all but two of which are direct descendents of the mutineers. Oeno and Henderson Islands are uninhabited. Henderson Island is a UNESCO World Heritage Site that has been largely untouched by mankind. Oeno Island is a favorite “get-away” spot for Pitcairn Islanders for whom an island of 50 people is just too crowded!
If you want to know more about Pitcairn or you have an hour of free time to use up, check out this documentary on the island, its history, and the people who make it their mission to visit one of the world’s most remote islands:
Take a look at this image:
Photo credit: İskender Şengör via Severe Weather Europe on Facebook
Is this picture from A) the Keweenaw Peninsula of Michigan in 1978? B) Orchard Park, New York in November 2014 (aka “Snowvember”)? or C) İnebolu, Turkey from just last week?
If you pay attention to details, you will have noticed that I credited İskender Şengör with the picture and properly surmised that the answer is C. If you don’t pay attention to details, get off my blog! The details are where all the interesting stuff happens! You’d never be able to identify small fires or calculate the speed of an aurora or explain the unknown without paying attention to details.
If you follow the weather (or social media), you probably know about lake-effect snow. (Who can forget Snowvember?) But, have you heard of sea-effect snow?
Areas downwind of the Great Lakes get a lot more snow than areas upwind of the Lakes. I was going to explain why in great detail, but this guy saved me a lot of time and effort. (I have since been notified that much of the material in that last link was lifted from a VISIT Training Session put together by our very own Dan B. You can watch and listen to that training session here.) The physical processes that cause lake-effect snow are not limited to the Great Lakes, however. Anywhere you have a large body of relatively warm water (meaning it doesn’t freeze over) with episodes of very cold winds in the winter you get lake-effect or sea-effect snow.
When you think of the great snowbelts of the world, you probably don’t think of Turkey – but you should! Arctic air outbreaks associated with strong northerly winds blowing across the Black Sea can generate snow at the same rate as Snowvember or Snowpocalypse or Snowmageddon or any other silly name that the media can come up with that has “snow” in it (Snowbruary, Snowtergate aka Frozen-Watergate, Snowlloween, Martin Luther Snow Day, Snowco de Mayo, Snowth of July… Just remember, I coined all of these phrases if you hear them later). Plus, the Pontic Mountains provide a greater upslope enhancement than the Tug Hill Plateau in Upstate New York.
One such Arctic outbreak occurred from 7-9 January 2015, resulting in the picture above. Parts of Turkey received 2 meters (!) of snow (78 inches to Americans) in a 2-3 day period, as if you couldn’t tell from that picture or this one.
From satellites, sea-effect snow looks just like lake-effect snow. (Duh! It’s the same physical process!) Here’s a VIIRS “True Color” image of the lake-effect snow event that took place last week on the Great Lakes:
VIIRS “True Color” RGB composite, taken 19:24 UTC 7 January 2015.
Wait – that’s no good! We need to be able to distinguish the snow from the clouds. Let’s try that again with the “Natural Color” RGB composite:
VIIRS “Natural Color” RGB composite, taken 19:24 UTC 7 January 2015.
That’s better. Notice how the clouds are formed right over the lakes and how the clouds organize themselves into bands called “cloud streets“. The same features are visible in the sea-effect snow event over Turkey (from one day later):
VIIRS “Natural Color” RGB composite, taken 10:36 UTC 8 January 2015.
Look at how much of Turkey is covered by snow! (Most of that snow cover is from the low pressure system that passed over Turkey a couple days before the sea-effect snow machine kicked in.) And – *cough* attention to details *cough* – you can even see snow over Greece and more sea-effect snow on Crete. There’s also snow down in Syria, Lebanon and Israel (Israel is off the bottom of the image), which is bad news for Syrian refugees.The heavy snow has shut down thousands of roads, closed schools and businesses, and was even the source of a political scandal.
But, on the plus side, the Arctic outbreak in the Middle East brings a unique opportunity to see palm trees covered in snow. And, how often do you get to see the deserts of Saudi Arabia covered in snow? (EUMETSAT has provided more satellite images of this event at their Image Library.)
Take another look at that image over the Black Sea. See how the biggest snow band extends south (and curving to the southeast) from the southern tip of the Crimean Peninsula? That is an example of how topography impacts these snow events. Due to differences in friction, surface winds are slightly more backed over land than over water, therefore areas of enhanced surface convergence exist downwind of peninsulas. The snow bands are more intense in these regions of enhanced convergence. There are also bigger than normal snow bands downwind of the easternmost and westernmost tips of Crimea, and extending south from every major point along the west coast of the Black Sea. This is not a coincidence. Land-sea (or land-lake) interactions explain this. Go back and listen to the VISIT training session for more information.
Sea-effect snow affects other parts of the globe as well. It’s why the western half of Honshu (the big island of Japan) and Hokkaido are called “Snow Country“. Japan was also hit with a major sea-effect snowstorm last week and, of course, VIIRS caught it:
VIIRS “Natural Color” RGB composite, taken 03:48 UTC 8 January 2015.
See the clear skies over Korea and the cloud streets that formed over the Sea of Japan? Classic sea-effect clouds. You can even see snow all along the west coast of Honshu in between the breaks in the clouds. Topographic impacts are once again visible. Notice the intense snow band extending southeast from the southern tip of Hokkaido/northern tip of Honshu similar to the super-strength snow band off of Crimea. And there’s another one downwind of the straits between Kyushu and Shikoku. Another detail in this image you should have noticed is the impact that Jeju Island has on the winds and clouds. Those are classic von Kármán vortices which we have discussed before.
Fortunately, 8 January 2015 was near a full moon, so the Day/Night Band was able to capture a great image of these von Kármán vortices:
VIIRS Day/Night Band image, taken 18:09 UTC 7 January 2015.
So, to the people of the Great Lakes: Remember you’re not alone. There are people in Turkey and Japan who know what you go through every winter.
UPDATE #1: While I was aware (and now you are aware) that sea-effect snow can impact Cape Cod, it was brought to my attention that there is a sea-effect snow event going on there today (13 January 2015). Here’s what VIIRS saw:
VIIRS “Natural Color” RGB composite, taken 17:29 UTC 13 January 2015.
According to sources at the National Weather Service, some places have received 2-3 cm (~ 1 inch) of snow in a four-hour period. It’s not the same as shoveling off your roof in snow up to your neck, but it’s something!
Quick: what was the name of that Icelandic volcano that caused such a stir a few years ago? Oh, that’s right. You don’t remember. No one remembers. (Unless you live outside the U.S. in a place where you might have actually heard someone say the name correctly.) To Americans, it will forever be known as “That Icelandic Volcano” or “The Volcano That Nobody Can Pronounce” – even though it is possible to pronounce the name. Say it with me: Eye-a-Fiat-la-yo-could (Eyjafjallajökull).
Well, back at the end of August 2014 another volcano erupted in Iceland, and there is no excuse for not being able to pronounce this name correctly: Bárðarbunga. (OK, you have one excuse: use of the letter ð is uncommon outside of Iceland. In linguistics, ð is a “voiced dental fricative” which, in English, is a voiced “th”. “The” has a voiced “th”. “Theme” has an un-voiced “th” or, rather, “voiceless dental non-sibilant fricative“.) Look, you don’t want to offend any Icelanders, so say it right:
“Bowr-thar-Bunga.” See, it’s easy to say. (You may see people who are afraid of the letter ð refer to the recent eruption as Holuhraun [pronounced “Ho-lu-roin”], because Bárðarbunga is part of the Holuhraun lava field. So be aware of that.)
I know what you’re going to ask: “What is so special about this volcano? I haven’t heard anything about it up to this point, so why should I care?” You haven’t heard anything about it because you don’t live in Iceland or in Europe, which is downwind of Iceland. And, why should you care? Let me count the ways in the rest of this blog post.
You probably have heard of Kīlauea (and have no trouble pronouncing that name) and the lava flow that inched its way towards the town of Pahoa. Kīlauea has been continuously erupting since 1983. Bárðarbunga erupted on 29 August 2014 and has been spewing lava ever since, which at this point, is over 100 days of non-stop erupting. It’s Iceland’s version of Kīlauea. (Hopefully, it won’t continue to erupt for another 30 years.)
Just like Kīlauea, Bárðarbunga is attracting tourists from all over the world. It seems every wannabe photographer and videographer has gone (or wants to go) to Iceland to try to come up with the next viral video showing the breathtaking lava flows. Seriously, do a search for Bardarbunga or Holuhraun on YouTube or vimeo and see how many results show up. Here’s a pretty typical example (filmed by someone from Iceland):
Want to join in the fun? Just grab your camera, head to Iceland, hire an airplane or helicopter pilot, and find the most dramatic music you can think of to go along with your footage. Watch out, though – the airspace around the volcano can be rather crowded. As this video shows, it can be hard to film the volcano without other aircraft getting in the way.
If photography is more your thing, here are the latest images of the eruption on Twitter. (Look for the pictures of Beyonce and Jay-Z. If Twitter is correct, they flew over the volcano for his birthday. Viewing the eruption has gone mainstream! You’re too late, hipsters! Good luck getting to the next volcanic eruption before it becomes cool.)
Back to the matter at hand: why you should care about Bárðarbunga. After its first 100 days of erupting, it has created a field of new lava (76 km2) that is larger than the island of Manhattan (59 km2). The volcano has been creating a toxic plume of SO2 for the last 100 days that is making it difficult to breathe. (Here are some of the known health effects of breathing SO2.) SO2 can ultimately be converted into sulfuric acid (acid rain), depending on the chemistry in the air around the volcano. And while it may not be producing as much ash as Eyjafjallajökull did, VIIRS imagery shows it is producing ash, which is a threat to aircraft.
If you follow this blog, you know the best RGB composite for detecting ash is the True Color composite. This is because the visible wavelength channels that make the composite are sensitive to the scattering of light by small particles, like dust, smoke and ash. Iceland is a pretty cloudy place, so it’s not always easy to spot the ash plume, so here it is at its most visible:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 12:57 UTC 11 September 2014. The red arrow points to the location of Bárðarbunga.
Click on the image (or any other image) to see the full resolution version. The red arrow shows the location of Bárðarbunga. In case you’re wondering, the borders drawn inside the island are IDL’s knowledge of the boundaries of lakes and glaciers (jökull in Icelandic). The big one just south of the red arrow is Vatnajökull – the largest glacier in Europe and one of three national parks in Iceland. (If you want to go there, be aware of closures due to volcanic activity.)
See the ash plume extending from the red arrow to the east-northeast out over the Atlantic Ocean? Now, try to find the ash plume in this animation of True Color images from 29 August to 14 October 2014:
Animation of VIIRS True Color images of Iceland 29 August – 14 October 2014
As with most of my animations, I have selectively removed images where it was too cloudy to see anything. Sometimes, the steam from the volcano mixes with the ash to make its own clouds, much like a pyrocumulus. Watch for the ash to get blown to the northwest and then southwest in early October. In case you can’t see it, here’s a static example:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 12:15 UTC 10 October 2014. The red arrow shows the location of Reykjavik.
This time, the red arrow shows Reykjavik, the nation’s capitol and likely only city in Iceland you’ve heard of. The ash plume is pretty much right over Reykjavik!
Over the course of the first 100 days, no place in Iceland has been kept safe from the ash plume. But, that’s not the only threat from Bárðarbunga: I also mentioned SO2. If you recall from our look at Copahue (Co-pa-hway – say it right!) the EUMETSAT Dust algorithm is sensitive to SO2. So, can we detect the toxic sulfur dioxide plume from Bárðarbunga? Of course! But, it does depend on cloudiness and just how much (and how high) SO2 is being pumped into the atmosphere.
If you read my post on Copahue, you should have no trouble picking out the sulfur dioxide plume in this image of Bárðarbunga:
EUMETSAT Dust RGB composite applied to VIIRS, 12:57 UTC 11 September 2014
This image is from the same time as the first True Color image above, when the plume was very easy to see. Also note the large quantity of contrails (aka “chemtrails” to the easily misled). Those are the linear black streaks west of Iceland. If you’re confident in your ability to see the sulfur dioxide, see how often you can pick it out in this animation:
Animation of EUMETSAT Dust RGB images from VIIRS (29 August – 10 October 2014)
Some detail is lost because an RGB composite may contain as many as 16 million colors, while the .gif image standard only allows 256. But, you can still see the pastel-colored SO2 plume, which almost looks greenish under certain conditions due to interactions with clouds. Also note the volcano itself appears cyan – the hottest part of the image has a cool color! Unusual in a composite that makes almost everything appear red or pink.
If you want to see the volcano look more like a hot spot, here are animations of the shortwave IR (M-13, 4.0 µm) and the Fire Temperature RGB composite (which I promote whenever I can). I should preface these animations by saying I have not removed excessively cloudy images but, at least 80% of the days have two VIIRS afternoon overpasses and, to reduce filesizes, I have kept only one image per day:
Animation of VIIRS M-13 images of Iceland (29 August – 15 October 2014)
The Fire Temperature RGB is made up of M-10 (1.6 µm; blue), M-11 (2.25 µm; green) and M-12 (3.7 µm; red):
Animation of VIIRS Fire Temperature RGB images of Iceland (29 August – 15 October 2014)
No surprise, molten rock is quite hot! That area of lava has saturated my color table for M-13 and it saturated the Fire Temperature RGB. As I’ve said before, only the hottest fires show up white in the Fire Temperature RGB and lava is among the hottest things you’ll see with VIIRS. Sometimes, you can see the heat from the volcano through clouds (and certainly through the ash plume)! It’s also neat to watch the river of lava extend out to the northeast and then cool.
To quantify it a bit more, the first day VIIRS was able to see the hot spot of Bárðarbunga (31 August 2014), the M-13 brightness temperature was the highest I’ve seen yet: 631.99 K. The other midwave-IR channels (M-12 and I-4; 3.7 and 3.74 µm, respectively) saturate at 368 K. The Little Bear Fire (2012) peaked at 588 K and that fire was hot enough to show up in M-10 (1.6 µm) during the day, so it’s no wonder that we’ve saturated the Fire Temperature RGB.
There’s one more interesting way to look at Bárðarbunga using a new RGB composite. When I was first tipped to this event, I saw this image from NASA, which you can read more about here. That image was taken by the Operational Land Imager (OLI) from Landsat-8 and is a combination of “green, near-infrared and shortwave infrared” channels. Applying this to VIIRS, that combination becomes M-4 (0.55 µm), M-7 (0.87 µm) and M-11 (2.25 µm), which is similar to the Natural Color composite (M-5, 0.64 µm; M-7, 0.87 µm; M-10, 1.61 µm) except for a few notable differences. M-4 is more sensitive to smoke and ash and vegetation than M-5. And M-11 is more sensitive to fires and other hotspots than M-10.
The differences are subtle, but you can see them in this direct comparison:
Comparison between VIIRS “Natural Color” and “False Color with Shortwave IR” RGB composites (12:38 UTC 14 October 2014)
NASA calls this RGB composite “False Color with Shortwave Infrared,” although I’m sure there has to be a better name. Any suggestions?
Most of my images and loops have come from the first 45 days after eruption. This was a very active period for the volcano, and is where most of the previously mentioned videos came from. (And trust me, you and your browser couldn’t handle the massive animations that would have resulted from using all 100+ days of images.) To prove Bárðarbunga has gone on beyond that, here’s one of the new RGB composites from 17 November 2014:
VIIRS false color RGB composite of channels M-4, M-7 and M-11, taken 13:42 UTC 17 November 2014
This image really makes Iceland look like a land of fire and ice, which is exactly what it is!
OK, so there probably aren’t any “Canadatowns” in China like there are Chinatowns in Canada. (Now you’re probably wondering what a Canadatown in China would look like. Maybe stores and restaurants selling poutine and maple syrup? Hockey rinks and curling sheets everywhere? A Tim Hortons on every street corner?) But this isn’t about that!
Last time I made the comparison between Canada and China, it was because there were numerous fires, particularly in the Northwest Territories, that produced so much smoke that it choked the air, making it difficult to breathe. This smoke was visible all the way down to the Lower 48 United States. These huge smoke plumes looked a lot like Chinese super-smog. Today, we’re talking not about the smoke and smog… well, actually, smoke and smog will be mentioned… hmm. Uh, what I mean is we’re focusing on the zillions of fires that VIIRS saw over Manchuria – just like the zillions of fires in the Northwest Territories. Well, OK, not “just like” – those fires were caused by Mother Nature. These fires appear to be intentionally set by human beings and are much smaller.
A CIRA colleague was checking out a real-time loop of MTSAT 3.75 µm imagery over northeastern China and reported seeing bright spots (which are typically hot spots from fires) throughout the area for most of the last month. So what is going on there?
MTSAT has ~4 km spatial resolution, so it’s not the best for fire detection. (At the time of this writing, CIRA has access to MTSAT-2, aka Himawari-7, which has 4 km spatial resolution in the infrared channels. The Advanced Himawari Imager {AHI} was successfully launched on Himawari-8 on 7 October 2014 and, when the operational imagery becomes available, it will have 2 km resolution in this channel [and it will have many of the channels that VIIRS has]. CIRA has plans to acquire this data when it becomes available. Until then, you’ll have to deal with coarse spatial resolution.) To really see what is going on, you need the spatial resolution of VIIRS.
Of course, spatial resolution is not the only thing you need. Check out the VIIRS M-13 (4.0 µm) image below from 04:48 UTC 18 November 2014. How many hot spots can you see?
VIIRS M-13 image of northeastern China, taken 04:48 UTC 18 November 2014.
This image uses a color table specifically designed to highlight hot spots from fires. Any pixel above 317 K (44 °C or 111 °F) is colored. (As always, click on the image to see it in full resolution.) There aren’t that many colored pixels, even though we’re using a relatively low temperature threshold for fire detection. There are, however, a lot of nearly black pixels, which means they are warmer than the background, but not warm enough to be highlighted. (In case you’re not sure, I’m talking about the area between 45° and 48°N, 123° and 128°E.) If we used this temperature threshold in a summer scene, there would be a lot false alarm fire detections.
A situation like this is when the Fire Temperature RGB composite comes in handy. It can detect the small (or low temperature) fires with no problem, particularly since the background isn’t very warm. Try to count up all the red pixels in this image from the same time:
VIIRS Fire Temperature RGB composite of channels M-10, M-11 and M-12, taken 04:48 UTC 18 November 2014.
That’s a lot of fires! It’s probably because there are so many of them that they were visible in MTSAT. If you look closely at the full resolution image, there are two significant fires in North Korea, plus many more smaller fires/hot spots northwest and north of the Yellow Sea. Go back and compare the Fire Temperature RGB with the M-13 image. Admit it: fires in this scene are easier to see in the RGB composite.
If you don’t believe me, check out the M-13 and Fire Temperature RGB images that have been zoomed in on main concentration of fires. The Fire Temperature RGB has been lightened a little bit and the M-13 image has been darkened a little bit to highlight the hot spots better.
VIIRS M-13 image (as above) but zoomed in and slightly darkened.
VIIRS Fire Temperature RGB image (as above) but zoomed in and lightened slightly.
If you want to know why the Fire Temperature RGB composite works, go back and read this and this. Otherwise, stay put. If you’re familiar with the Fire Temperature RGB, because you are a loyal follower of this blog, you may be wondering why the overall image looks so dark.
All the previous cases where I’ve shown this RGB have been in the summer, typically under bright sunlight (since fires don’t tend to occur in winter). Here, it’s almost winter so there is less sunlight and the background surface is colder, which are going to make the image appear darker. Plus, there is some snow in the scene and snow appears black in this RGB composite. It’s not reflective at 1.61 µm (blue component) or 2.25 µm (green component) or at 3.74 µm (red component), plus it’s cold so it doesn’t emit much radiation at any of these wavelengths either.
The Natural Color RGB shows where the snow is. Look for the cyan signature of snow and ice here:
VIIRS Natural Color RGB composite of channels M-5, M-7, and M-10, taken 04:48 UTC 18 November 2014.
The Natural Color RGB shows that the fires are occurring in an area with a lot of lakes. Also, there isn’t a very strong green signature from vegetation in this area. So what is burning? Your guess is as good as mine. (Unless your guess is a bunch of Chinese children using magnifying glasses to burn ants. That’s not a very good guess – particularly because, as I said, there is less sunlight in the winter and it’s colder so the ants wouldn’t ignite easily. Also, that’s a cruel thing to suggest and my reasoned account of why that wouldn’t work should not be taken as an implicit admission that I ever did such a thing as a kid.)
A quick perusal of Google Maps reveals that it is an area full of agricultural fields. So my guess is that it’s some sort of end-of-year burning of agricultural waste. They are all small or low temperature fires and they’re not anything that made the news (I checked), so it’s doubtful that it’s a zillion uncontrolled fires.
How do we even know they’re fires? Besides the fact that they show up in the Fire Temperature RGB, we can also see the smoke. Check out this True Color RGB image and focus on the area where the majority of the fires are occurring:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken at 04:48 UTC 18 November 2014.
There are visible smoke plumes right where the greatest concentration of hot spots is located. There is also a long plume of gray along the base of the Changbai Mountains stretching southwest to the shores of the Yellow Sea, but it’s not clear if that is also smoke or simply smog. By the way, if you have respiratory ailments, don’t look at the southwest corner of the image (west of the Yellow Sea) because that’s definitely smog! The northern extent of that large area of smog is the Beijing metropolitan area.
What is most cough- and barf- inducing about that smog near Beijing is that it is thick enough to completely obscure the view of the surface. Last time we looked at that, it was record levels of smog that was receiving international attention. The thick, surface obscuring smog you see here isn’t record breaking or news-worthy – it’s simply a normal late fall day in eastern China!
If you can’t think of anything else to be thankful for on Thursday, you can be thankful that you don’t have to breathe air like that. Unless you live there. But, then, you wouldn’t be celebrating Thanksgiving anyway. And, if you live in Canada, you already had your Thanksgiving. You get to just sit back, relax, and watch Americans trample each other to death for discount electronics. Being able to avoid the Black Friday mob is something to be truly thankful for!
Have you noticed it? The seasons are changing (for the mid- and high latitudes, at least). Days are getting shorter (or longer if you live in the upside-down hemisphere). This time of year, if you live in Alaska or Scandinavia or similar high latitude locations, you lose about 5-10 minutes of available daylight each day. (That’s between a half and one hour per week!) You may have noticed by the fact that your neighbor no longer mows the lawn at 11:00 PM because it’s still bright outside and hey, why not? I wasn’t going to sleep anyway.
Closer to home – in the mid-latitudes – loss of daylight is more like 1-3 minutes per day, which isn’t as noticeable. But, one day, you watch the sun set and look at the clock and realize that it’s only 6:30 PM and you think, didn’t it used to be light out later than this?
That’s not the only way to tell the seasons are changing. For one, there’s the arrival of snow. (Although parts of Montana, Wyoming and South Dakota received snow earlier this year while it was still technically summer.) And, for two, there’s what VIIRS observed on 27 September 2014:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 17:57 UTC 27 September 2014
In case it’s not obvious, here’s what VIIRS saw earlier in the month (8 September 2014):
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 17:13 UTC 8 September 2014
Notice anything different between the two images? (Remember to click on the images, then on the “1735 x 1611” links below the banner to see the images in full resolution.)
That’s right – the loss of daylight leads to one of the benefits of autumn: fall foliage. VIIRS True Color imagery shows, quite clearly, that the leaves of New England and eastern Canada have changed color. Forests that were green in early September have turned orange, red and brown by the end of the month.
Another thing you may have noticed comparing those two images: the change from green to beige in the area around Montreal, Quebec. This is another sign of autumn: the fall harvest. This is a productive agricultural region in eastern Canada, and what you are seeing is the green vegetation (crops) being harvested, leaving behind bare dirt.
True Color imagery is useful for observing the changing foliage and the harvest because it is designed to reproduce what we humans observe on the ground. The red, green and blue components of the RGB composite are channels in the red (M-5, 0.67 µm), green (M-4, 0.55 µm) and blue (M-3, 0.48 µm) portions of the electromagnetic spectrum. When leaves change from green to red, the True Color RGB detects that.
Now, you’ve probably known since elementary school (or at least middle school) that leaves change color because of chlorophyll. And, unless you became a botanist, that is probably the limit of your knowledge on the subject. But, there’s a lot of interesting chemistry that goes on inside a leaf (and the whole tree) that determines it’s color.
Of course, leaves are green because they contain chlorophyll. Chlorophyll is necessary for plants to convert sunlight into sugar. Chlorophyll, by necessity due to it’s job, is highly absorbing of visible-wavelength radiation, although it is slightly less absorbing of green wavelengths. Green light is therefore preferentially reflected out of the leaves and into your eye, and the leaves appear green.
When the sunlight goes away and the air becomes cold, deciduous trees go into hibernation. They break down the chlorophyll in their leaves, and send the remaining nutrients down into the trunk and roots. This exposes the carotinoids that were in the leaves and these carotinoids have a yellow or orange color – they preferentially reflect yellow and/or orange wavelengths. Red colors come from a pigment called anthocyanin, which was recently discovered to be a sort of “plant sunscreen”.
Now, utilizing sunscreen when you get all your energy from the sun may sound silly but, recent studies have shown that anthocyanin protects the leaves from sun damage once the chlorophyll is gone so that the tree has time to extract all the nutrients out of the leaves before they fall off. Trees in poor soil conditions are more likely to turn red in the fall as a natural defense mechanism – they need to store all the nutrients they can from their leaves, since they aren’t getting them from the soil.
Oak and other leaves turn brown in the fall because of a buildup of tannin (link to PDF file), which is a waste product. Brown leaves are full of plant poo! Think about that the next time you go on a fall color driving tour.
Now, back to the satellite science before the biologists come after me for grossly oversimplifying leaf chemistry. I’ve often talked about the Natural Color RGB composite as being similar to the True Color RGB in many instances (except for the detection of ice and snow). So, what does that look like here?
Here’s the VIIRS Natural Color RGB from 8 September 2014:
VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 17:13 UTC 8 September 2014
And here’s the same RGB from 27 September 2014:
VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 17:57 UTC 27 September 2014
Why does the vegetation still appear green when the leaves have changed color? Because we’ve made vegetation artificially appear green. The Natural Color RGB uses the red wavelength visible channel (M-5, 0.67 µm) as the blue component. The green component is a near-infrared channel (M-7, 0.87 µm), where plants are their most reflective – leaves and other plant tissues don’t absorb radiation at this wavelength. The red component is a longer wavelength channel (M-10, 1.61 µm) where the water inside the leaves starts to absorb radiation and the reflectance goes down. Cellulose and lignin also weakly absorb at 1.61 µm. The bottom line is, plants are highly reflective at 0.87 µm regardless of how healthy the plant is, or what color the leaves are – so they will always appear green in the Natural Color images.
You might also note the one difference (apart from clouds) that shows up between the two Natural Color images is the lack of green surrounding Montreal in the 27 September image. This is another sign of the fall harvest: the highly reflective plants have been removed and all that’s left is dirt, which is not as reflective. That’s why those areas appear more brown in the later image.
If we look a bit further west in the True Color imagery from 27 September 2014, the fall color really stands out:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 17:57 UTC 27 September 2014
Fall colors are visible from the Adirondacks of Upstate New York and Quebec to the Upper Peninsula of Michigan. The most vivid fall color is in Ontario – both in the area of Sault Ste. Marie and in the area of Algonquin Provincial Park, the oldest provincial park in Canada. Every autumn, the Friends of Algonquin Park post pictures of the fall colors, including this shot from 27 September 2014 showing just what VIIRS was seeing. Amazing colors!
We have sunny days, cool nights and plant survival techniques to thank for that.
BONUS:
Here’s a desktop wallpaper that’s zoomed in on the above image and cropped to the most popular screen resolution (1366×768):
VIIRS True Color RGB Composite Desktop Wallpaper (17:57 UTC 27 September 2014). This image fits monitors with a 16:9 ratio and is optimized for 1366×768 screen resolutions.
Make sure you click on the image, then on the “1366 x 768” link below the banner to get the full resolution image. Then you can right-click on the image and choose “Set as desktop background” to save it as your new desktop wallpaper.
Conspiracy theorists will tell you that conspiracies exist everywhere; that they’re part of daily life; and that most people are ignorant of all the attempts by various governments around the world to covertly control every facet of your life. Only they know the truth. But, that’s just what they want you to believe! Conspiracy theorists are simply manipulating you in order to control you and create a New World Order! Wake up!
Full disclosure: I am subsidized by the U.S. government to inform people of the capabilities and uses of the satellite instrument called VIIRS and today I’ll show you how that satellite instrument can help separate fact from fiction when it comes to the latest conspiracy theory. (Of course, working for the government means I could be part of the conspiracy! Mwa ha ha!)
During the last week of August 2014, I was sent this link to a story from a pilot/photographer who captured “the creepiest thing so far” in his long flying career. I’ll quote his initial post again in its entirety here (for those of you too lazy to click on the links):
Last night [24 August 2014] over the Pacific Ocean, somewhere South of the Russian peninsula Kamchatka I experienced the creepiest thing so far in my flying career. After about 5 hours in flight we left Japan long time behind us and were cruising at a comfortable 34.000ft with about 4,5 hours to go towards Alaska.
We heard via the radio about earthquakes in Iceland, Chile and San Francisco, and since there were a few volcanos on our route that might or might not be going off during our flight, we double checked with dispatch if there was any new activity on our route after we departed from Hongkong.Then, very far in the distance ahead of us, just over the horizon an intense lightflash shot up from the ground. It looked like a lightning bolt, but way more intense and directed vertically up in the air. I have never seen anything like this, and there were no flashes before or after this single explosion of light.
Since there were no thunderstorms on our route or weather-radar, we kept a close lookout for possible storms that might be hiding from our radar and might cause some problems later on.
I decided to try and take some pictures of the night sky and the strange green glow that was all over the Northern Hemisphere. I think it was sort of a Northern Lights but it was much more dispersed, never seen anything like this before either. About 20 minutes later in flight I noticed a deep red/orange glow appearing ahead of us, and this was a bit strange since there was supposed to be nothing but endless ocean below us for hundreds of miles around us. A distant city or group of typical Asian squid-fishing-boats would not make sense in this area, apart from the fact that the lights we saw were much larger in size and glowed red/orange, instead of the normal yellow and white that cities or ships would produce.
The closer we got, the more intense the glow became, illuminating the clouds and sky below us in a scary orange glow. In a part of the world where there was supposed to be nothing but water.
The only cause of this red glow that we could think of, was the explosion of a huge volcano just underneath the surface of the ocean, about 30 minutes before we overflew that exact position.
Since the nearest possible airport was at least 2 hours flying away, and the idea of flying into a highly dangerous and invisible ash-plume in the middle of the night over the vast Pacific Ocean we felt not exactly happy. Fortunately we did not encounter anything like this, but together with the very creepy unexplainable deep red/orange glow from the ocean’s surface, we felt everything but comfortable. There was also no other traffic near our position or on the same routing to confirm anything of what we saw or confirm any type of ash clouds encountered.
We reported our observations to Air Traffic Control and an investigation into what happened in this remote region of the ocean is now started.
If you go back and click on the link, you’ll see he posted several pictures of the mysterious red lights along with more detailed information about where and when this occurred. To save you some time, here is a representative picture (taken at 11:21 UTC 24 August 2014). And here is the location of the aircraft when they saw the lights.
There are three parts to this story: 1) the bright flash of light that looked like lightning coming up from the surface; 2) the aurora-like features in the sky; and 3) the red and orange lights from the clouds below that appeared to be larger than ordinary ship lights.
Since the story was first posted, people from all over commented on what they thought the lights were and the pilot has been updating his webpage to cover the most common and/or most likely explanations. The media picked up the story and used it to claim the world was coming to an end. Existing theories range from UFOs (unidentified flying objects) and UUSOs (unidentified under-surface objects) operated by space aliens to covert military operations to spontaneously-combusting methane bubbling out of the ocean to “earthquake lights“. The pilot himself initially thought it was an underwater volcanic eruption.
So, can VIIRS shed light on what was going on? Yes – at least, on #2 and #3. VIIRS passed over the area in question at 15:35 UTC on 24 August, which is about 4 hours after the pilot took his pictures. This means VIIRS can’t say anything about the lightning-like flash that was observed. So #1 is unexplained.
As for #2 – the aurora-like features in the sky – those are simply airglow waves. We’ve discussed airglow and airglow waves before here and here.
Now, onto #3 where VIIRS is most informative: the mysterious surface lights. I mentioned the VIIRS overpass at 15:35 UTC on 24 August. Here’s what the Day/Night Band (DNB) saw:
VIIRS Day/Night Band image from 15:35 UTC 24 August 2014.
Look at 47.5°N latitude and 159°E longitude. (You can click on the image, then on the “4329 x 2342” link below the banner to see the full resolution image.) Those are the lights the pilot saw! (Note also that this night was near new moon, so any illumination of the clouds in that area comes from airglow. Light in the northeast corner of the image is twilight from the approaching sunrise.)
Now, VIIRS also has bands in the short-, mid- and long-wave infrared (IR). Surely, they must have seen the heat signature put out by a volcanic eruption, right? Not necessarily. The pilot’s photographs clearly show the lights shining through a layer of clouds, and it doesn’t take much cloud cover to obscure heat signatures at these wavelengths. But, for completeness, here are the observed brightness temperatures at 3.7 µm (channel M-12) and 10.7 µm (channel M-15):
VIIRS M-12 image from 15:35 UTC 24 August 2014
VIIRS M-15 image from 15:35 UTC 24 August 2014
I don’t see any hotspots in either of those images near the location of the lights. But, as I said, this doesn’t disprove the presence of flaming methane or volcanic activity because of possible obscuration by clouds. (Note that the clouds are easier to see in the DNB image than either of the IR images because there is no thermal contrast between the clouds and the open ocean for the IR images to take advantage of. There is, however, reflection of airglow light available to provide contrast in the DNB.)
What about the night before? The night after? Were the lights still there?
Here’s the DNB image from 15:54 UTC 23 August 2014 (aka the night before):
VIIRS DNB image from 15:54 UTC 23 August 2014
The light is there in pretty much the same place, although it looks like one big circle instead of a number of smaller lights. What is going on? Once again, it’s clouds. This time, the longwave IR shows we have optically thicker and/or an additional layer of high clouds over the lights:
VIIRS M-15 image from 15:54 UTC 23 August 2014
Optically thicker clouds scatter and diffuse the light more, and what you are seeing in the DNB image is the area of clouds surrounding the light source that scatter the light to the satellite. See how clouds scatter the city lights of the U.S. Midwest in this comparison between the DNB and M-15 from 07:42 UTC 2 September 2014:
(You may have to refresh the page if this before/after image trick doesn’t work.)
It’s not that Chicago, Illinois and Gary, Indiana extend that far out into Lake Michigan or that the map is not plotting correctly. It’s that the optically thicker clouds over the southern end of the lake scatter more of the light back to the satellite (and over a larger area than the lights themselves), making it appear that the light is coming from over the lake.
Similarly, scattering in the clouds makes the individual “mystery lights” over the Pacific Ocean appear to be one large area of light, instead of a number of smaller lights.
How do the lights look on 25 August 2014 (aka the night after)? Here’s the DNB image:
VIIRS DNB image from 15:18 UTC 25 August 2014
Did you notice that? The lights aren’t in the same place as before. They moved. In fact, I tracked these lights in the DNB for two weeks. And I got this result:
Do volcanoes move around from day to day? I think we can safely say the pilot was not observing a volcanic eruption.
Now, I don’t know much about spontaneously combusting methane bubbles in the ocean, but I doubt they are this frequent. The pilot found another pilot’s report of methane burning over the ocean from 9 April 1984 (which also occurred during a flight from Japan to Alaska) but, that was during the day and it was the resulting cloud that was spotted, not the actual flames. There is no evidence of clouds being produced by these lights over this two week period. There also isn’t much evidence from seismic activity over this period to justify earthquake lights.
Another theory put forth was meteorites but, again, it seems highly improbable that VIIRS would be capturing this many meteorites hitting this localized area of the Pacific Ocean every night for two weeks. Plus, they would have to be pretty large meteors to appear as large as these lights.
Unless you believe in UFOs (or UUSOs), that leaves only one question: why were the pilots of this flight so quick to dismiss ships? The DNB has seen ships on the ocean before, and they look a lot like this. (You can find examples of individual boats observed by the DNB here and an example of larger squid boat operations here.)
It is true that most squid boats use white or greenish light and the pictures clearly show red and orange lights coming up through the clouds. But military ships are known to use red lights at night, at least, according to Yahoo! Answers.
If it looks like a fleet of ships and moves like a fleet of ships, I’m guessing it’s a fleet of ships. Unless, of course, it’s a gam of sharks with freakin’ laser beams attached to their heads.
No, I’m not talking about Chinatown in Vancouver. Or Chinatown in Toronto. Or any other Chinatown in Canada. I’m talking about this. Or, more exactly, this. Poor air quality is making it difficult to breathe in Canada and elsewhere.
Unlike the situation in China, you can’t really blame the Canadians for their poor air quality. (Unless, of course, some serial arsonist is wreaking havoc unfettered.) You see, it has been an active fire season in western Canada, to put it mildly. Here’s a not-so-mild way to put it. That article, from 3 July 2014, put the number of fires in the Northwest Territories alone at 123, with most of them caused by lightning. But, after a check of the Northwest Territories’ Live Fire Map on 30 July 2014 it looks like there are more than that:
"Live Fire Map" from NWTFire, acquired 17:00 UTC 30 July 2014. This is a static image, not an interactive map.
I estimated 160-170 fires in that image (assuming I didn’t double count or miss any). How many fires can you count?
At one point earlier in July, it was estimated that battling the fires was costing $1 million per day! The fires have been impacting power plants, causing power outages, impacting cellular and Internet service, closing the few roads that exist that far north, and doubling the number of respiratory illnesses reported in Yellowknife, the territory’s capital.
It’s no secret that this area is sparsely populated. At last count, the territory had roughly 41,000 residents in 1.3 million km2. (Fun fact: the Northwest Territories used to make up 75% of the land area of Canada. It has since been split up among 5 provinces and into two other territories. With the formation of Nunavut in 1999, it was reduced to being only twice the size of Texas.) If so few people live there, why should we care if they have a few fires?
If you are so heartless as to ask that question, you are also short-sighted and selfish. For one, I already explained the damage that the fires are doing. For two, fires like these impact more than just the immediate area and more than just Canada. Let me explain that but, first, let me show you the fires themselves – as seen by VIIRS – over the course of the last month.
Animation of VIIRS Fire Temperature RGB images 24 June - 25 July 2014
You will have to click on the above image, then on the “933×700” link below the banner to see the animation at full resolution. It is 15 MB, so it may take a while to load if you have limited bandwidth. What you are looking at is the Fire Temperature RGB in the area of Great Slave Lake, the area hardest hit by this fire season. There are a lot of fires visible over the course of the month!
See how the larger fires spread out? They look like the large scale version of an individual flame spreading out on a piece of paper. (Don’t try to replicate it at home. I don’t want you catching your house on fire!) Of course, the spread of the fires is dependent on the winds, humidity, moisture content in the vegetation, and the firefighters – if they’re doing their job.
Now, these weren’t the only fires in Canada during this time. Check out this Fire Temperature RGB image from 15 July 2014 and see how many (rather large) fires there are in British Columbia and Saskatchewan:
VIIRS Fire Temperature RGB composite of channels M-10, M-11 and M-12, taken 21:08 UTC 15 July 2014
Make sure to click through to the full resolution version. I counted 9 large fires in British Columbia, 1 in Alberta (partially obscured by clouds) and 6 in Saskatchewan. If you look closely, you might also spot 3 small fires in Washington plus more small fires in Oregon. (“Small” here is compared to the fires in Canada.)
Now, all these fires means there must be smoke and, because VIIRS has channels in the blue and green portions of the visible spectrum, we can see the smoke clearly. This is one of the benefits of the True Color RGB (in addition to what we discussed last time). If I tried to create another animation, like I did above, showing the extent of the smoke plumes it would be so large it might crash the Internet. Instead, here are some of the highlights (or low-lights, depending on your point of view) from the last month.
On 6 July 2014, the smoke is largely confined to the area around Great Slave Lake:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 20:35 UTC 6 July 2014
The very next day (7 July 2014) the smoke is blown down into Alberta and Saskatchewan (almost as far south as Calgary and Saskatoon):
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 20:16 UTC 7 July 2014
One day later (8 July 2014) smoke is visible down into Montana, North Dakota and beyond the edge of the image in South Dakota (a distance of over 2000 km [1200 miles] from the source!):
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 19:57 UTC 8 July 2014
On the 12th of July, you could see a single smoke plume stretching from Great Slave Lake all the way into southwestern Manitoba (plus smoke over British Columbia from their fires):
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 20:23 UTC 12 July 2014
When the fires really get going in British Columbia a few days later, the smoke covers most of western Canada. On 15 July 2014, smoke is visible from the state of Washington to the southern reaches of Nunavut and Hudson Bay:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 19:27 UTC 15 July 2014
One day later (16 July 2014), and it appears that smoke covers 2/3 of Alberta, nearly all of Saskatchewan, all of western Manitoba, southern Nunavut, southeastern Northwest Territories, and most of Montana and North Dakota. There is also smoke over Washington, Oregon and northern Idaho:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 20:48 UTC 16 July 2014
A quick estimate puts the area of smoke in the above image at 2.5 million km2, which is roughly a third the size of the contiguous 48 states!
With renewed activity in the fires in the Northwest Territories last week, the smoke was still going strong over Canada, impacting Churchill, Manitoba (home of polar bears and beluga whales):
VIIRS True Color RGB composite of channels M-4, M-4 and M-5, taken 20:17 UTC 23 July 2014
I guess if the melting polar ice caps don’t kill off the polar bears, they can still get cancer from all this smoke. Maybe the “world’s saddest polar bear” will want to stay in Argentina.
I should add that some of my colleagues at CIRA and I have sensitive noses and were able to smell smoke right here in town (Fort Collins, Colorado) earlier this month. Plus, there were a few smoky/hazy sunsets. (Although it should be clarified that we don’t know if it was from the fires in Canada or the fires in Washington and Oregon. There weren’t any fires in Colorado at the time.) Nevertheless, the areal coverage and extent of the smoke from fires like these is immense, and can have impacts thousands of miles away from the source. And, it’s all carbon entering our atmosphere.
UPDATE (8/1/2014): Colleagues at CIMSS put together this image combining two orbits of data over North America from yesterday (31 July 2014), where you can see smoke stretching from Nunavut all the way down to Indiana, Ohio and West Virginia. There may even be some smoke over Kentucky and Tennessee. Witnesses at CIMSS reported very hazy skies across southern Wisconsin as a result.
When was the last time you heard anything about Paraguay? Nope – they weren’t in the World Cup, that was Uruguay. (Paraguay actually finished last out of all South American teams when it came to World Cup qualifying. Sorry to remind you, Paraguayans.) A quick perusal of the web indicates that the country has a history of isolationism, so it may not come as a surprise that news out of Paraguay is few and far between.
For you non-Paraguayans in the audience: How many of you knew that Paraguay was the richest nation in South America in the mid-1800’s? Paraguay held that title right up to the point that they tried to keep Brazilian influence out of a civil war in Uruguay. That kick-started the War of the Triple Alliance, which ultimately killed more than half the population of Paraguay, strengthened Argentina as a nation, and is credited with bringing about the end of slavery in Brazil. Paraguay has never been the same since. It became the poorest country in the region – a title it has held, pretty much, through today. This has caused one reporter to say (in one of the links above) that, to Paraguayans, success is a prelude to danger.
When the national football team scores, “it makes us nervous and we panic.”
But, this isn’t a metaphor for the title of this post. The title refers to Paraguay: the River (Rio Paraguay), which has brought the worst flooding in decades to Paraguay: the Country, and displaced more than 200,000 Paraguayans. Flooding has also occurred on the Rio Paraná – the second longest river in South America – and has impacted hundreds of thousands of people in Brazil and Argentina. (You won’t get me to say that it has impacted a Brazilian people – because that is an awful, overused joke. Oh, wait. Ignore what I said I wasn’t going to say.)
Just look at what the flooding did to Iguazú Falls – one of the wonders of the world you never heard about – on the border between Argentina and Brazil:
http://www.youtube.com/watch?v=76XfV42YvBI
There are more pictures of the flooding at the falls here. Iguazú Falls is located at the head of a narrow canyon called the Devil’s Throat, where water levels were reported to be 16 meters (52 feet) above normal! It is said that this is the worst flooding since 1982-1983. (That flood event killed 170 people.)
As shown before, VIIRS is capable of viewing widespread flooding. So, what does VIIRS tell us about this flood? As it turns out, both the “Natural Color” RGB composite and the “True Color” RGB composite provide unique information, so let’s take a closer look.
If you simply want to see where the water is, look no further than the “Natural Color” RGB composite. The “Natural Color” composite uses the high-resolution bands I-01 [0.64 µm; blue], I-02 [0.87 µm; green] and I-03 [1.61 µm; red]. At these wavelengths, water is not very reflective (it absorbs more than it reflects). So, with low reflectivity in all three channels, water appears nearly black. That allows one to identify water easily. Here’s a Natural Color image from a clear day before the worst of the flooding began (2 June 2014):
VIIRS "Natural Color" image, taken 17:28 UTC 2 June 2014
That’s Paraguay in the center of the image. Rio Paraguay is the north-south river that cuts Paraguay in half (OK, maybe 60-40). Rio Paraná is the big river that marks the eastern border between Paraguay and Argentina, and turns south after acquiring Rio Paraguay’s water. (Look for the big reservoir in the upper-right, and follow that river down to the bottom of the image, left of center.) Make sure you click on the image, then on the “3298 x 2345” link below the banner to see the full resolution version. Compare that with a similar image from the only clear day at the end of the month (30 June 2014):
VIIRS "Natural Color" image, taken 17:03 UTC 30 June 2014
At first glance, the most obvious flooding occurred along the Paraná in Argentina. But flooding is noticeable along the Rio Paraguay if we zoom in for a closer look. Here’s a “before” (2 June) and “after” (30 June) overlay for the area around Paraguay’s capital city, Asunción:
Drag the vertical bar over the images from left to right to compare the two. (If this “before/after” trick doesn’t work for you, try refreshing the page. It may not work at all if you’re using Google Chrome.) The flooding you see here near Asunción was associated with only a 2 m (6 ft) water rise.
Something interesting happens when we focus in on the Paraná at the Itaipú Reservoir, just upstream from Rio Iguazú:
VIIRS "Natural Color" images of Itaipu Reservoir, June 2014. These images have been brightened to highlight difference in reservoir color.
After the flooding, the reservoir no longer appears black. This is because the flooding washed an awful lot of dirt into the water. And it really shows up in the “True Color” RGB composite:
VIIRS "True Color" images of Itaipu Reservoir, June 2014.
The water appears more turquoise before the flood, and brown after the flood. This is because the True Color composite represents the true color of the objects in the image. It is made from channels in the blue [0.48 µm; M-3], green [0.55 µm; M-4] and red [0.67 µm; M-5] portions of the visible spectrum. Take a look again at the Iguazú Falls video above and notice how brown the water is. The True Color images capture this. The reason the water appears blue and not black in the Natural Color composite is that there is enough sediment in the water to make it reflective at 0.64 µm (the blue component of the image). The longer wavelengths in the green and red components are not sensitive to the sediment, whereas the shorter wavelengths in the True Color components are very sensitive to sediment. (This is the basis for Ocean Color retrievals.)
If we focus in on the Rio Paraná near where it meets the Rio Paraguay, we can see clearly that the Natural Color highlights where the flood waters are, and the True Color highlights the sediment in that water:
VIIRS Natural Color and True Color images of the Rio Parana, June 2014
Unfortunately, floods on the Paraguay and Paraná rivers are not uncommon, as a resident of Asunción explains:
BONUS: The NOAA/STAR JPSS group has put together a website on the flooding in Paraguay that features my Natural Color images along with a number of other VIIRS-based products that are being developed for flood detection. A lot of people from a number of different research groups played a part in this!
Not having full command of the German language, “sehr schweres Unwetter” seems like an understatement. It translates as “very bad thunderstorm,” which in this case is like calling the Titanic a “very big boat”. Of course, if you live in the Great Plains, you probably refer to a supercell thunderstorm as “a little bit of rain and wind” but the storms that hit Nordrhein-Westfalen (NRW) on 9-10 June 2014 rival anything the toughest Oklahoman has experienced (minus the tornadoes). Also, keep in mind that Germany and the Low Countries have nowhere near the wide-open spaces the U.S. Great Plains are known for. Take 5 times the population of Oklahoma and cram them into a land area the size of Maryland. (Or, if you’re from Maryland, multiply your state’s population by three to approximate the population density of the area we’re talking about. Then ponder how anyone in that part of Germany is able to spend less than 18 hours per day stuck in traffic like you would be if you were suddenly surrounded by three times as many people.)
Let me set the scene for you. (If you’ve ever lived in the Midwest, you know the drill.) The air is hot and unbelievably humid. The sky is overcast. There is no wind to speak of, but there is a certain “electricity” in the air that tells you that a violent end to the heatwave is coming. Off in the distance, clouds lower and darken. A gentle rumbling of thunder slowly builds as the storm approaches. Lightning appears and becomes ever more frequent. Right before the storm hits, the winds pick up out of nowhere and… Wait! I don’t need to describe it. I can show it to you:
http://www.youtube.com/watch?v=TFbsubhW5s0
http://www.youtube.com/watch?v=9sLfWkqoIq8
EDIT: I did need to describe it, because the videos are no longer available. If you weren’t able to see the videos before they were removed, they showed scary looking clouds and nearly constant lightning approaching Bochum. In fact, there were an estimated 113,000 lightning strikes across Germany from the storm.
Germany is, apparently, a land of iPhones and GoPros and all sorts of video recording equipment, and there is no shortage of video of the storm. There are videos of the storm approaching from different perspectives (here, here and here), the strong winds and heavy rains that are more reminiscent of a tropical storm (here, here and here), footage of the lightning in slow-motion and, because this is the Internet, a 30 min. montage of storm footage set to salsa music (although one commenter says the first footage is from a storm in 2010).
The aftermath is pretty impressive also – trees and large branches down everywhere blocking roads, crushing cars and stopping the never-late German train system. In fact, 6 people were killed – mostly by falling trees. Winds were observed in the 140-150 km h-1 range (approximately 85-90 miles per hour), which puts it just below a Category 2 hurricane according to the Saffir-Simpson scale. There were even reports of baseball sized hail, something that’s not unusual in Oklahoma, but is very rare in Europe. (Here is some pretty big hail in the town of Zülpich from earlier in the day.)
Now that you’ve used up the last 90 minutes looking at YouTube videos, let’s get down to business. What do satellites tell us about this storm?
EUMETSAT put together this animation of images from the geostationary satellite Meteosat-10:
Watch that video again, preferably in fullscreen mode. First, the white boxes highlight the supercell thunderstorms over Europe between 01:00 UTC 9 June 2014 and 08:15 UTC 10 June 2014. Right before sunset on 9 June, you can see a storm moving north out of France into Belgium that seems to explode as it heads towards the Netherlands and western Germany. This is our “schweres Unwetter”. The second thing to notice is where that storm is at 02:00 UTC on the 10th. That was the time that VIIRS passed overhead.
So, without any more bloviating, here’s the high-resolution infrared (I-5) image from VIIRS:
VIIRS I-5 image of severe thunderstorms over Europe from 02:07 UTC 10 June 2014
The storm that caused all the damage over Nordrhein-Westfalen has weakened and is now over northeastern Germany on its way to Poland. But, a second impressive supercell complex is pounding Belgium and the Netherlands, and taking aim at western Germany once again.
The coldest pixels are 196.5 K (-76.7 °C or -106 °F) in the storm over Benelux and 198.7 K (-74.5 °C or -102.1 °F) in the storm over northeast Germany. Another impressive thing about these storms is their size relative to the size of these countries. That Benelux storm looks like it’s at least five times the size of Luxembourg and as big as Belgium! (And I’m not counting the area of the anvil, which is even larger. I’m only counting the area containing overshooting tops.)
Since it’s nighttime, what did the Day/Night Band see? Well, the answer depends on how you display the data. You see, we’re approaching the Summer Solstice in the Northern Hemisphere, where the days are long and twilight encroaches the nighttime overpasses at these latitudes. If you try to scale the radiances from lowest = black to highest = white, you get something like this:
VIIRS Day/Night Band image, taken 02:07 UTC 10 June 2014. Radiance values are displayed and scaled according to text above.
That’s not very helpful because the radiance values vary by 6 orders of magnitude across the scene and we only have 256 colors to work with to relay that information. But, we can take advantage of the fact that the Day/Night Band radiance values are, to the first order, a function of the solar and lunar zenith angles, and use this as the basis for a “dynamic scaling” that compares the observed radiance with an expected maximum and minimum radiance value that is a function of those angles. (In case you’re interested, the dynamic scaling algorithm used here is based around the error function.) This allows you to produce something like this:
VIIRS Day/Night Band image, taken 02:07 UTC 10 June 2014. This image uses dynamic scaling as described in the text.
Here, we’ve lost some quantitative information (colors no longer represent specific radiance values) but we’ve gained valuable qualitative information. Now we can see where the storms are! Notice the shadows in the overshooting tops of our Benelux storm – right where the coldest pixels are in the infrared image. We can see some of the city lights, but not others, because the twilight encroaching from the northeast is brighter than the cities in that part of the image. (It is easy to pick out London and Paris, though.) If you read the previous post, you might be wondering why there are no mesospheric waves with these storms. That’s because there is too much twilight (and moonlight) to see the airglow. (There’s also the possibility that the stratosphere and mesosphere weren’t conducive for vertically propagating waves, but you wouldn’t be able to tell that under these lighting conditions.)
Some people like to combine the infrared with the Day/Night Band into a single image. This is done by changing the opacity of one of the images and overlaying it on the other. Here’s an example of what that looks like using the dynamically scaled Day/Night Band image:
VIIRS combined IR/DNB image from 02:07 UTC 10 June 2014
The light/shadow effect of the visible information adds a sort-of 3-D effect to the infrared images and, since this is the Day/Night Band, it can show where the storms are in relation to the urban areas. Here, it seems to work better for the Benelux storm than it does for the other one. (Of course, it would be better without the twilight. And, it works best with a full moon, which occurred three days later.)
Of course, if you have access to the Near Constant Contrast imagery, you don’t have to worry about scaling. The imagery is useful as-is:
VIIRS NCC image, taken at 02:07 UTC 10 June 2014
And the combined IR/NCC image looks like this:
Combined IR/NCC image from 02:07 UTC 10 June 2014
In case you’re interested, there are additional videos, animations and images of these storms from the Meteosat High Resolution Visible (HRV) channel at the EUMETSAT Image Library.
So far (*knock on wood*), it’s been a pretty quiet year for severe weather. If you only count tornadoes, there have been 81 tornado reports from 1 January to 4 April this year. (11 of those have come just this week.) This is a lot fewer than the previous three year average of 192 tornadoes by the end of March. For that, you can thank the dreaded, terrifying “Polar Vortex” you’ve heard so much about over the winter. Tornadoes don’t like to come out when it’s cold everywhere. (Although, there was a notable exception on 31 March 2014, when a tornado hit a farm in Minnesota when the area was under a blizzard warning.)
I just said that there have been 11 tornado reports this week. Eight of those came in the past 24 hours. At the southern end of the line that brought the tornadoes to Illinois, Missouri and Texas, the severe weather included golf ball-size hail and this:
25 FEET BY 30 FEET SHED ANCHORED 3 FEET INTO GROUND...TOTALLY RIPPED OUT AND IMPALED INTO A FENCE AND A ROOF OF NEIGHBORING HOUSE
That report came from the National Weather Service in Corpus Christi, TX and it was caused by non-tornadic straight-line winds in Orange Grove. Winds capable of ripping a shed out of the ground, combined with golf ball-sized hail – that’s one recipe for broken windows. And it’s not a pleasant way to be awakened at 4:30 in the morning.
A couple of hours earlier, VIIRS caught this severe storm as it was rapidly growing. Here’s what the storm looked like in the high-resolution infrared channel (I-5, 11.45 µm):
VIIRS high-resolution IR image (channel I-5), taken at 08:13 UTC 4 April 2013.
Make sure you click on the image, then on the “2999×2985” link below the banner to see the full resolution image, which, for some reason, is the only version where the colors display correctly.
The storm that hit Orange Grove is the southern-most storm, with what looks like a letter “C” imprinted on the top. (That kind of feature typically looks more like a “V” and makes this an “Enhanced-V” storm, which you can learn more about here. Enhanced-V storms are noted for their tendency to produce severe weather.) For those of you keeping score at home, the coldest pixel in this storm is 184.7 K (-88.5 °C).
Compare the image above with the Day/Night Band image below (from the same time):
VIIRS Day/Night Band image, taken at 08:13 UTC 4 April 2014
There are a few interesting features in this image. For one, there’s a lot of lightning over Louisiana, Arkansas and Mississippi. (Look for the rectangular streaks.) There’s even some lighting visible where our “Enhanced-V” is. Two, it takes a lot of cloudiness to actually obscure city lights: only the thickest storm clouds appear to be capable of blocking out light from the surface. Three: there are a lot of boats out in the Gulf of Mexico at 3 o’clock in the morning (and a few oil rigs as well). And four: notice what appear to be concentric rings circling the location where our severe storm is with its enhanced-V.
In this image, there is no moonlight (we’re before first quarter, so the moon isn’t up when VIIRS passes over at night). The light we’re seeing in those ripples is caused by “airglow”, which we’ve seen before. And the ripples themselves may be similar to what is called a “mesospheric bore.” If you don’t want to get too technical, a mesospheric bore is when this happens in the mesosphere. They are related to – but not exactly analogous to – undular bores, which you can read more about here.
Unlike the situation described for the undular bore in that last link, the waves here are caused by our severe storm. To put it simply, we have convection that has formed in unstable air in the troposphere. This convection rises until it hits the tropopause, above which the air is stable. This puts a halt to the rising motion of the convection but, some of the air has enough momentum to make it in to the stratosphere. This is called the “overshooting top“, and is where our -88°C pixels are located. (Look for the pinkish pixels in the middle of the “C” in the full-resolution infrared image.) The force of this overshooting top creates waves in the stable layer of air above (the stratosphere) that propagate all the way up into the mesosphere. The mesosphere is where airglow takes place, and these waves impact the optical path length through the layer where light is emitted. This of course, impacts the amount of light we see. The end result: a group of concentric rings of airglow light surrounding our storm.
You could make the argument that the waves we see in the Day/Night Band image are not an example of a bore. Bores tend to be more linear and propagate in one direction. These waves are circular and appear to propagate in all directions out from a central point. It may be better to describe them as “internal buoyancy waves“, which are similar to what happens when you drop a pebble into a pond. Only, in this case the pebble is a parcel of air traveling upwards, and the surface of the water is a stable layer of air. Compare the pebble drop scenario with this video of a bore traveling upstream in a river to see the difference.
In fact, if you look closer at the Day/Night Band image, in the lower-right corner (over the Gulf of Mexico) there is another group of more linear waves and ripples in the airglow that may actually be from a bore. It’s hard to say for sure, though, without additional information such as temperature, local air density, pressure and wind speeds way up in that part of the mesosphere.
By the way, you can see mesospheric bores and other waves in the airglow if you have sensitive-enough camera, like the one that took this image:
Photograph of a mesospheric bore. Image courtesy T. Ashcraft and W. Lyons (WeatherVideoHD.TV)
And, if you’re interested, the Arecibo Observatory has a radar and optical equipment set up to look at these upper-atmosphere waves (scroll down to Panel 2 on this page). The effect of these waves on atmospheric energy transport is a hot topic of research.
Golf ball-sized hail at the Earth’s surface is related to energy transport 100 km up in the atmosphere!
NOTE: This post has been updated since it was first written to clarify that the circular waves are likely not evidence of a bore, as was originally implied. They are more likely internal buoyancy waves, which are also known as gravity waves. For more information, consult your local library.
This has been some kind of winter. The media has focused a lot of attention on the super-scary “Polar Vortex” even though it isn’t that scary or that rare. (I wonder if Hollywood will make it the subject of the next big horror movie in time for Halloween.) Many parts of Alaska have been warmer than Georgia, with Lake Clark National Park tying the all-time Alaskan record high temperature for January (62 °F) on 27 January 2014. (Atlanta’s high on that date was only 58 °F.) Sacramento, California broke their all-time January record high temperature, reaching 79 °F three days earlier. In fact, many parts of California had record warmth in January, while everyone on the East Coast was much colder than average. Reading this article made me think of an old joke about statisticians: a statistician is someone who would say: if your feet are stuck in a freezer and your head is stuck in the oven, you are, on average, quite comfortable.
One consequence of the cold air in the eastern United States is that Hell froze over. No, not the Gates of Hell in Turkmenistan. This time I’m talking about Hell, Michigan. Hell is a nice, little town whose residents never get tired of people telling that joke.
It has been so cold in the region around Hell that the Great Lakes are approaching a record for highest percentage of surface area covered by ice. This article mentions some of the benefits of having ice-covered Lakes, including: less lake-effect snow, more sunshine and less evaporation from the Lakes, which would keep lake levels from dropping. Although, that is at the cost of getting ships stuck in the ice, and reducing the temperature-moderating effects of the Lakes, which allows for colder temperatures on their leeward side.
This article (and many other articles I found) uses MODIS “True Color” images to highlight the extent of the ice. Why don’t they show any VIIRS images? Well, I’m here to rectify that.
First off, I can copy all those MODIS images and show the “True Color” RGB composite from VIIRS:
VIIRS "True Color" RGB composite of channels M-3, M-4 and M-5, taken 17:27 UTC 11 February 2014
While it was a rare, sunny winter day for most of the Great Lakes region on 11 February 2014, it’s hard to tell that from the True Color imagery. I mean, look at this True Color MODIS image shown on NPR’s website. Can you tell what is ice and what is clouds?
There are ways of distinguishing ice from clouds, which I have talked about before but, it doesn’t hurt to look at these methods again and see how well they do here. First, let’s look at my modification of the EUMETSAT “Snow” RGB composite:
VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 17:27 UTC 11 February 2014
This “Snow” RGB composite differs by using reflectances at 2.25 µm in the place of the 3.9 µm channel that EUMETSAT uses. (Their satellite doesn’t have a 2.25 µm channel.) It’s easy to see where the clouds are now. Of course, now the snow and ice appear hot pink, which you may not find aesthetically pleasing. And it certainly isn’t reminiscent of snow and ice.
If you don’t like the “Snow” RGB, you may like the “Natural Color” RGB composite:
VIIRS "Natural Color" RGB composite of channels I-01, I-02 and I-03, taken 17:27 UTC 11 February 2014
This has the benefit of making snow appear a cool cyan color, and has the added benefit that you can use the high-resolution imagery bands (I-01, I-02 and I-03) to create it. There is twice the resolution in this image than in the Snow and True Color RGB images. Here’s another benefit you may not have noticed right away: the clouds, while still white, appear to be slightly more transparent in the Natural Color RGB. This makes it a bit easier to see the edge of the ice on the east side of Lake Michigan and the center of Lake Huron, for example.
If you’re curious as to how much ice is covering the lakes, here are the numbers put out by the Great Lakes Environmental Research Laboratory (which is about a 25 minute drive from Hell) from an article dated 13 February 2014:
Lake Erie: 96%; Lake Huron: 95%; Lake Michigan: 80%; Lake Ontario: 32% and Lake Superior: 95%. This gives an overall average of 88%, up from 80% the week before. The record is 95% set in 1979, although it should be said satellite measurements of ice on the Great Lakes only date back to 1973.
Why does Lake Ontario have such a low percentage? That last article states, “Lake Ontario has a smaller surface area compared to its depth, so it loses heat more slowly. It’s like putting coffee in a tall, narrow mug instead of a short, wide one. The taller cup keeps the coffee warmer.” Doesn’t heat escape from the sides of a mug as well as the top? And isn’t Lake Superior deeper than Lake Ontario? Another theory is that “Lake Ontario’s depth and the churning caused by Niagara Falls means that it needs long stretches of exceptionally cold weather to freeze.” Does Niagara Falls really have that much of an impact on the whole lake?
So, what is the correct explanation? I’m sorry, VIIRS can’t answer that. It can only answer “How Much?” It can’t answer “Why?”
BONUS UPDATE (17 February 2014):
It has come to my attention that the very next orbit provided better images of the Great Lakes, since they were no longer right at the edge of the swath. Here, then, are the True Color, Snow and Natural Color RGB composite images from 19:07 UTC, 11 February 2014:
VIIRS "True Color" composite of channels M-3, M-4 and M-5, taken 19:07 UTC 11 February 2014
VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 19:07 UTC 11 February 2014
VIIRS "Natural Color" composite of channels I-01, I-02, and I-03, taken 19:07 UTC 11 February 2014
UPDATE #2 (18 March 2014): The Great Lakes ice cover peaked at 92.2% on 6 March 2014, just short of the all-time record in the satellite era. March 6th also happened to be a clear day over the Great Lakes, and VIIRS captured these images:
VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 18:35 UTC 6 March 2014
VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 18:35 UTC 6 March 2014
Nope. This post is not about a warplane, an alcoholic beverage or a “New Wave” band from the 1970s. (Those are all B-52s.) And I’m not talking about a county road in Michigan or a New York City bus line. B-31 is the rather bland name given to the massive iceberg that just broke off from the Pine Island Glacier in Antarctica. (Of course, if you tried to name every chunk of ice floating around Antarctica, how long would it take you to run out of names and just switch to random letters and numbers?)
This particular chunk of ice is special, however, as it has been described as the size of a city. Now, as a scientist, I have to say that the size of a city is a terrible unit of measurement. How big a city are we talking about? I suspect people who live in one of the ten largest cities in the world would laugh at what the people of Wyoming call a “city”. And are we talking the size of the greater metropolitan area or just what is within the city limits?
The article that describes B-31 as the size of city mentioned that it was roughly the size of Singapore, or twice the size of Atlanta. Those seem like odd choices for comparison. How many of you have a good idea of what the land area is of Singapore? And twice the size of Atlanta? They could have used New York City, which has just over twice the land area of Atlanta and people are probably more familiar with New York City. In any case, all of these size estimates have errors.
The original estimate came from this NASA MODIS image and associated caption, which put the size of B-31 as 35 km x 20 km. Now, that’s 700 km2 assuming the iceberg is a perfect rectangle, which you can see in the image that it isn’t. Singapore has a land area of 714 km2, while New York City is 768 km2 and Atlanta is 341 km2 (these are “within the city limits” numbers, not the size of the greater metropolitan area). Since the iceberg is actually smaller than the 35 km x 20 km rectangle based on the widest and longest dimensions of the iceberg, maybe “twice the size of Atlanta” is the most accurate estimate.
Anyway, MODIS is not the only satellite instrument out there capable of viewing B-31. Landsat-8 saw it in much higher resolution in another post from NASA. And, of course this entire blog is about what VIIRS can see. Now, VIIRS doesn’t have the resolution of Landsat or the highest-resolution channels on MODIS, but VIIRS has the Day/Night Band, allowing us to see the iceberg both day and night (at visible wavelengths).
To show why that is important, take a look at the infrared image (M-15, 10.7 µm) below. Images in the “infrared window” (the N-band window, according to this site) used to be the only way to detect surface features and clouds at night. At these wavelengths, the amount of radiation detected by the satellite is a function of the temperature of the objects the instrument is looking at. As always, to see the high resolution version of the image, click on it, then on the “1660×1706” link below the banner.
VIIRS IR image (M-15) taken 23:34 UTC 7 November 2013
See that slightly darker gray area near the center of the image? That’s open water in Pine Island Bay, which is only slightly warmer than the ice and low clouds surrounding it. Otherwise, there isn’t much detail in this picture. What really stands out are the cold, high clouds that are highlighted by the color scale. Contrast this with a visible wavelength image from the same time (M-5, 0.67 µm):
VIIRS visible (M-5) image, taken 23:34 UTC 7 November 2013
The open water in Pine Island Bay shows up clear as day because, well, it is daytime and the ice and snow reflect a lot more sunlight back to the satellite than the open water does. Icebergs can easily be distinguished from the low clouds now. You can even see through some of the low clouds to identify individual icebergs that are not visible in the infrared image. The difference in reflectivity between the ice and water at visible wavelengths is a lot greater than the difference in brightness temperature in the 10-12 µm infrared wavelengths, and that contrast is what makes things more easily visible.
Now, it is summer down there and at these latitudes, the sun is up for most of the day (actually, all day for everywhere in this scene on the Summer Solstice, which occurred on 21 December 2013), so you could say that using the VIIRS Day/Night Band to look at this stuff is unnecessary. But, since VIIRS is on a polar-orbiting satellite, it views the poles a lot more frequently than where you or I live: every 101 minutes on average, instead of every 12 hours in the low and mid-latitudes. That means it may occasionally capture a nighttime image here or there during the short nights and will frequently capture images where the day/night terminator crosses through the scene and we still want to be able to see what’s going on then. And you need the Day/Night Band to do that.
For the first time on this blog, however, we’re not going to show the Day/Night Band data exactly. We’re going to show the Near Constant Contrast imagery product, which is produced from the Day/Night Band. You can read up more on the Near Constant Contrast product and how it’s related to the Day/Night Band here. At this point, we’ll refer to NCC and DNB rather than having to type out Near Constant Contrast and Day/Night Band all the time.
Here’s a NCC image from 7 November 2013 at 20:15 UTC where the Pine Island Glacier has been identified. B-31 is still attached to the glacier – it’s sticking out into the bay and, if you look at the high resolution version of the image, you may be able to see the crack where it has started to calve.
VIIRS Near Constant Contrast image from 20:15 UTC 7 November 2013. The Pine Island Glacier is identified.
Keep your eye on that spot as you watch this zoomed-in animation of NCC images starting from the above image to 03:06 UTC 18 November:
Animation of VIIRS NCC images of the Pine Island Glacier from 7-18 November 2013
I should say that the above animation does not include images from every orbit. I’ve subjectively removed images that were too cloudy to see anything as well as images where the VIIRS swath didn’t cover enough of the scene. This left 25 images over the 11 day period. Even so, VIIRS captured the moment of B-31 breaking free quite well.
Imagine the sound that this 600+ km2 chunk of ice made as it broke free. I bet it sounded something like this glacier calving event in Greenland:
One of the articles linked to above mentioned the importance of tracking such a large iceberg, because it could impact ships in the area. (Just this week a ship got stranded in ice off the coast of Antarctica.) So, I decided to see if VIIRS could track it. The results are in the MP4 video clip linked to below. You may need an appropriate browser plug-in or add-on (or whatever your browser calls it) to be able to view the video.
Animation of VIIRS NCC images from 7 November – 26 December 2013 (.mp4 file)
That’s 50 days of relatively cloud-free VIIRS NCC images (7 November – 26 December 2013), compressed down to 29 seconds. Go ahead, watch the video more than once. Each viewing uncovers additional details. Notice how B-31 doesn’t move much after 10 December. Notice how ice blocks the entrance to Pine Island Bay at the beginning of the loop, then clears out by the end of the loop. Notice all the icebergs near the shore that are pushed or pulled or blown out to sea from about 20 December through the end of the loop. Notice that B-31 isn’t even the biggest chunk of ice out there. Notice the large ice sheet on the west side of Pine Island Bay that breaks up right at the end of the loop. In fact, here’s another zoomed-in animated GIF to make sure you notice it:
Animation of VIIRS NCC images from 20-26 December 2013
That area of ice is much larger than B-31! (Dare I say, as large as the state of Rhode Island? Probably not, because then you’ll just think of how Rhode Island is the smallest US state, so it can’t be very impressive. It’s also not very accurate since that estimate is based on eye-balling it and thinking it looks like it could be four times the size of B-31.)
Of course, we are heading towards the middle of summer in the Antarctic when the ice typically reaches its minimum extent. So the ice breaking up isn’t unusual. Plus, large calving events occur on the Pine Island Glacier every few years. But, the B-31 event is noteworthy because Pine Island Glacier holds about 5% of the total freshwater contained on Antarctica. It’s also the site of an ongoing field experiment where researchers are investigating glacier-ocean interactions. You can read up on what it’s like to install instruments on a glacier while living in a tent on the coldest continent 1000 miles from any other human settlement in this article. (That article doesn’t say if any instruments are still stuck in B-31 and floating out to sea, though.) And, if you’re curious, Pine Island Glacier has its own Twitter account. So far, the conclusions are that Pine Island Glacier is thinning, receding and speeding up. Large calving events are just one piece of the puzzle, but an important piece to understand since they contribute to sea level rise.
The calving process of B-31 was first noticed by NASA researchers noticing a crack forming in Pine Island Glacier while flying over the area in October 2011 – before VIIRS was even launched. But, VIIRS was there to capture the end result of that crack two years later!
UPDATE (22 April 2014): B-31 has continued to drift towards the open ocean. Researchers at NASA have been monitoring the movement of the massive iceberg since it first calved, and have put together their own video here, which tracks B-31 from the time of my video above into mid-March 2014.
If you pay attention to tropical cyclones, that headline may be confusing. Unlike the Super Cyclone in the Indian Ocean we just looked at, Super Typhoons are not rare in the Pacific Ocean. There have been 5 of them this year. What is rare is a typhoon that is estimated to be one of the strongest storms ever recorded in human history. I am, of course, speaking about Typhoon Haiyan, which the Philippines will forever remember as Yolanda.
Animation of visible images from MTSAT of Super Typhoon Haiyan (Yolanda) from 7 November 2013. Courtesy Dan Lindsey (NOAA).
If you don’t pay that much attention to tropical cyclones, you should be asking, “How do we know it is one of the most intense tropical cyclones ever in recorded human history?” You may also be asking, “Why does it have two names?” And, “What is the difference between a typhoon and a hurricane and a tropical cyclone?”
I’ll answer those in reverse order. Typhoons, hurricanes and tropical cyclones are different names given to the same physical phenomenon. If it occurs in the Atlantic Ocean or the Pacific Ocean north of the Equator and east of the International Date Line, it is called a “hurricane”, a name that was derived from Huracan, the Mayan god of wind and storms. If it occurs in the Pacific Ocean north of the Equator and west of the International Date Line, it is called a “typhoon”, which may come from the Chinese “daaih-fùng” (big wind), Greek “typhōn” (wind storm) or Persian “ṭūfān” (a hurricane-like storm). Anywhere else and it is a “cyclone” – a term for rotating winds, which ultimately comes from the Greek “kyklos” (circle).
Why does it have two names (Haiyan and Yolanda)? Different parts of the world use different naming conventions. When it comes to typhoons, the United States uses the naming convention of the Japan Meteorological Agency and the World Meteorological Organization. The Philippines come up with their own name list. That’s why we know it as Haiyan, while Filipinos know it as Yolanda.
Now, was this really the most intense tropical cyclone in all of recorded human history? That question is more difficult to answer. It depends on how you define “intensity”. Is it the lowest atmospheric pressure at the Earth’s surface? Is it the highest 1-minute, 5-minute or 10-minute average wind speed at the Earth’s surface? Is it based on structural damage? Deaths?
The last two, damage and deaths, are better measures of the storm’s impact, rather than its physical strength. So, we’re going to focus on how one would measure the physical strength of the storm.
Barometers, used to measure pressure, have been around for about 400 years. Anemometers, which measure wind speed, have been around in their modern form for about 160 years. (It is also possible to estimate wind speeds from Doppler radar, technology that has been around since World War II, although these estimates are not as accurate as anemometers.) The primary issue is getting these instruments inside a super typhoon (and not having them be destroyed in the process).
It is possible to attach an anemometer and a barometer to an airplane, then fly the plane into the storm to measure the wind and pressure (which is done for almost every hurricane on a path to hit the United States), but not every country is wealthy enough to afford their own research aircraft. Plus, it’s tough to find anyone crazy enough to fly into a storm as strong as Haiyan. Here is a story of why “hurricane hunting” isn’t always a good idea.
Weather satellites, which have been around for 50 years, can view these storms from afar (with no risk of being damaged by them) and are the primary way to determine wind speeds and pressures (particularly when the storm is out over the ocean, where there aren’t many barometers and anemometers). The method to determine the strength of a storm from satellite is called the “Dvorak Technique”, developed by Vernon Dvorak in the 1970s, and discussed in detail here. Basically, the algorithm takes the current appearance of the storm in visible and infrared wavelengths (how symmetric it is about the eye, what is the brightness temperature in the warmest pixel in the eye, what is the brightness temperature of the coldest ring of clouds around the eye, and so on), along with the recent history of the storm’s appearance and relates that to a storm’s central pressure and maximum sustained wind speed based on an empirical relationship. For those storms that have been viewed by both satellite and aircraft, the Dvorak Technique has been shown to be pretty accurate: over 50% of storms have wind speed errors less than 5 knots, and overall root-mean-square errors of 11 knots.
The image loop from MTSAT above, and the VIIRS images below of Haiyan (Yolanda) highlight the relevant points the Dvorak Technique keys on when determining its intensity: a well defined eye with warm infrared brightness temperatures (up to +23 °C), a ring of cold clouds surrounding the eye (the purple color corresponds to temperatures less than -80 °C), and it’s hard to find a storm more symmetric than this one.
VIIRS infrared (I-5) image of Typhoon Haiyan (Yolanda), taken 16:39 UTC 6 November 2013. Image courtesy Dan Lindsey (NOAA). Brightness temperatures are given in K.
VIIRS infrared (I-5) image of Super Typhoon Haiyan (Yolanda) taken 16:16 UTC 7 November 2013. Image courtesy Dan Lindsey (NOAA). Brightness temperatures are given in degrees Celsius (on the same scale as the previous image).
As a quick aside about the power of VIIRS, Haiyan was right at the edge of the scan when the image above was taken. Look at the impressive detail even at the edge of scan! See if you can beat that, MODIS!
Using Dvorak’s method, Haiyan (Yolanda) achieved the maximum possible value on the “T-number” scale: 8.0. That puts the maximum sustained winds above 170 knots (315 km h-1 or 195 mph!) and the sea-level pressure below 900 mb (hPa), according to the scale. You can’t get any stronger than that because the data used to develop the empirical relationship doesn’t contain any storms stronger than that. We’ve reached signal saturation on the Dvorak “T-number” scale. (And the Saffir-Simpson scale, and the Beaufort scale.) All we can say is Haiyan right up there with the strongest tropical cyclones ever observed. We can also say that Haiyan was the only storm to make landfall as an 8.0 on the “T-number” scale. But, beyond that, we would need actual in situ observations to know just how strong Haiyan (Yolanda) really was.
As expected, one of the strongest typhoons ever to make landfall caused some power outages. The Day/Night Band on VIIRS captures it well:
Did you notice the vertical bar in the above image that you can click on? Slide it left to right to see the differences in the amount of city lights (and nocturnal fishing activities) before and after Haiyan (Yolanda) made landfall. Tacloban was, of course, one of the hardest hit heavily populated areas. As you can see from radar, it took a direct hit from the eyewall.
With winds estimated at 195 mph, Haiyan (Yolanda) was like an EF-4 tornado. A 30-mile wide EF-4 tornado that lasted for several hours.
UPDATE: I have been notified that the above sliding bar trick in the Day/Night Band images above doesn’t work in all browsers (or for all operating systems). If that’s the case for you, click on the image below, then on the “1000×1000” link below the banner to see the high resolution animation.
VIIRS Day/Night Band images highlighting power outages caused by Typhoon Haiyan (Yolanda) 2013. Images courtesy Steve Miller (CIRA).
The first two images in the animation show the Day/Night Band images from the nighttime overpasses on 31 October and 9 November 2013. The last two frames (one with the map plotted and one without) highlight the differences in these images by creating an RGB composite of the before and after images. Power outages show up as red in this composite. Areas that have kept their power show up a golden color. Areas with light after the storm, but not before the storm, show up green. In this case, green areas highlight where boats were after the storm, and where clouds scattered the city lights over a larger area than they appeared to be before the storm, when there were no clouds overhead. It’s another way to look at power outages in the Day/Night Band.
The Indian Ocean has just had its first Super Cyclone since 2007. The name of it is “Phailin” and I bet you just pronounced it incorrectly (unless you speak Thai). It’s closer to “PIE-leen” than it is to “FAY-lin”. The name was derived from the Thai word for sapphire. (If you go to Google Translate and translate “sapphire” into Thai, you can click on the “audio” icon {that looks like a speaker} in the lower right corner of the text box to hear a robotic voice pronounce it. You can also click on the fourth suggested translation below the text box and try to pronounce that as well.)
If you’re tired of reading about flooding in this blog, you’re probably going to want to avoid reading about Phailin. It already dumped up to 735 mm (28.9 inches) of rain on the Andaman Islands in a 72-hour period. Aside from the heavy rains, Phailin is a text-book example of “rapid intensification”, as official estimates of the storm’s intensity grew from 35 kt (65 km h-1 or 40 mph) when the storm was first named, to 135 kt (250 km h-1 or 155 mph!) just 48 hours later. Here’s a loop of what that rapid intensification looks like from the geostationary satellite, Meteosat-7. (Those are the Andaman Islands where the cyclone first forms.)
VIIRS being on a polar-orbiting satellite, it’s not possible to get an image of the cyclone every 30 minutes like you can with Meteosat-7. VIIRS only views a cyclone like Phailin twice per day. But, VIIRS can do things that Meteosat-7 can’t. The first is produce infrared (IR) imagery at 375 m resolution. (Meteosat-7 has 5 km resolution.) The image below is from the high resolution IR band, taken at 20:04 UTC 10 October 2013:
VIIRS high-resolution IR image of Super Cyclone Phailin, taken 20:04 UTC 10 October 2013
Look at the structure of the clouds surrounding the eye. (You’re definitely going to want to see it at full resolution by clicking on the image, then on the “3875×3019” link below the banner.) VIIRS is detecting wave features in the eyewall that other current IR sensors aren’t able to detect because they don’t have the resolution. The coldest cloud tops are found in the rainband to the west of the eyewall (look for that purple color) and are 179 K (-94 °C). That’s pretty cold!
Also notice the brightness temperature gradient on the west side of the eye is a lot sharper than on the east side of the eye. This is because the satellite is west of eye (the nadir line is along the left edge of the plotted data), looking down on the storm at an angle, revealing details about the side of the eyewall on the east side. Look down on the inside of a cardboard tube or a piece of pipe at an angle to replicate the effect. (Actually, the eye wall of a tropical cyclone slopes away from the center, so it’s more like funnel than a tube. If you go looking for a cardboard tube or a piece of pipe to look at, the results will be inaccurate. Grab a funnel instead.)
Another advantage of VIIRS is the Day/Night Band, a broadband visible channel that is sensitive to the low levels of light that occur at night. There is no geostationary satellite in space with this capability. The image below was taken from the Day/Night Band at the same time as the IR image above:
VIIRS Day/Night Band image of Super Cyclone Phailin, taken 20:04 UTC 10 October 2013
The Day/Night Band shows the eye clearly. Plus, being able to see the city lights gives an idea of the amount of people and infrastructure that are in the storm’s path.
Now, hold on a minute. 10 October 2013 was one day before first quarter moon, which means the moon was below the horizon when this image was taken. (Generally speaking, the moon is only up for nighttime VIIRS overpasses that occur from two days after first quarter to two days after last quarter.) If you want get more specific, India is one of the few places with a half-hour offset from most time zones (UTC +5:30), which means this image was taken at a local time of 1:34 AM 11 October 2013. Local moonrise time for the eastern coast of India for that date was 11:33 AM (10 hours later), while the moonset occurred 3.5 hours earlier (10:02 PM). This means you should be asking the obvious question: if there was no moonlight (and obviously no sunlight either, since this a nighttime image), why is VIIRS able to see the cyclone?
Was it the scattering of city lights off the clouds that allows you to see the clouds at night, like in this photo? No, because this cyclone is way out over the ocean, in the middle of the Bay of Bengal. Due to the curvature of the Earth, city lights won’t illuminate any clouds more than a few tens of kilometers away. The center of this storm is about 600 km away from any city lights and is still visible. At the most, only the very edges of the storm near cities would be illuminated if this were the case.
I can see at least two lightning strikes in the image, so is it lightning illuminating the cloud from the inside? No, it’s not that either. See how streaky the lightning appears? The whole storm would look like a series streaks, some brighter than others, depending on how close they were to the tops of the clouds (and how close the lightning was to the position of the VIIRS sensor’s field of view during each scan). The top of the storm is much too uniform in brightness for it to be caused by lightning.
So, if you’re so smart, what is the explanation, Mr. Smartypants? I’m glad you asked. It is a phenomenon called “airglow” (or sometimes “nightglow” when it occurs at night). You can read more about it here and here. The basic idea is that gas molecules in the upper atmosphere interact with ultraviolet (UV) radiation and emit light. Some of these light emissions head down toward the earth’s surface, are reflected back to space by the clouds, and detected by the satellite.
Really? Some tiny amount of gas molecules way up in the atmosphere emit a very faint light due to excitation by UV radiation, and you’re telling me VIIRS can see it? But, it’s nighttime! There’s no UV radiation at night! How do you explain that? The UV radiation breaks up the molecules into individual atoms during the day. At night, the atoms recombine back into molecules. That’s when they emit the light. Look, it’s in a peer-reviewed scientific journal if you don’t believe me. (A shortened press release about it is here.) Thanks to airglow (and the sensitivity of the Day/Night Band), VIIRS can see visible-wavelength images of storms at night even when there is no moon!
Getting back to the Super Cyclone, here’s what Phailin looked like in the high-resolution IR channel the next night (19:45 UTC 11 October 2012), right around the time where it reached its maximum intensity:
VIIRS channel I-05 image of Super Cyclone Phailin, taken 19:45 UTC 11 October 2013
Here, the cyclone is much closer to nadir (the nadir line passes through the center of the image), so you’re more-or-less looking straight down into the eye on this orbit. The corresponding Day/Night Band image is below:
VIIRS Day/Night Band image of Super Cyclone Phailin, taken 19:45 UTC 11 October 2013
Once again, the cyclone is illuminated by airglow. (Some of the outer rainbands are also being lit up by city lights, which are visible through the clouds.) The only question is, what is that bright thing off the coast of Burma (Myanmar) that shows up in both Day/Night Band images? It looks like a huge, floating city. According to Google Maps, there’s nothing there. That is one question I don’t have the answer to (*see Update #2*).
Any other questions about cyclones in India? Check out this FAQ guide put out by the India Meteorological Department.
With a peak intensity estimate at 140 kts (259 km h-1 or 161 mph), Phailin was one of the strongest cyclones ever in the Indian Ocean. (Only 2007’s Gonu – 145 kt – was stronger. Several other storms have been estimated at 140 kt.) The last time a cyclone of Phailin’s intensity hit India, over 10,000 people died. Credit must be given to the Indian government, who successfully evacuated 900,000 people from the coast (the article refers to 9.1 lakhs; one lakh is 100,000), and so far, only about 25 people have been confirmed dead. In fact, fewer people were killed by this cyclone than were killed by a panicked stampede outside a temple in central India the same weekend.
UPDATE #1 (15 October 2013): The Day/Night Band also captured the power outages caused by Phailin. Here is a side-by-side comparison of Day/Night Band images along the coast of the state of Odisha (also called Orissa), which took a direct hit from the cyclone – a zoomed in and labelled version of the 10 October image above (two days before landfall) against a similar image from 14 October 2013 (two days after landfall):
VIIRS Day/Night Band images from before and after Super Cyclone Phailin made landfall along the east coast of India.
Notice the lack of lights in and around the small city of Berhampur. That’s roughly where Phailin made landfall. Also, notice the difference in appearance of the metropolitan area of Calcutta. It almost appears as if the city was cut in two as a result of electricity being out in large parts of the city.
UPDATE #2 (15 October 2013): Thanks to Renate B., we’ve figured out the bright lights over the Bay of Bengal near the coast of Myanmar (Burma) are due to offshore oil and gas operations. Take a look at the map on this website. See the yellow box marked “A1 & A3”? That is a hotly contested area for gas and oil drilling, right where the bright lights are. It is claimed by Burma (Myanmar) and India, China and South Korea are all invested in it. China has built a pipeline out to the site that cuts right through Myanmar (Burma) that some of the locals are not happy about.
UPDATE #3 (16 October 2013): It was pointed out to me that the maximum IR brightness temperature in the eye of the cyclone in the 20:04 UTC 10 October 2013 image was 297.5 K (24.4 °C), which is pretty warm for a hurricane/cyclone/typhoon eye. It is rare for the observed IR brightness temperature inside the eye to exceed 25-26 °C. Of course, the upper limit is the sea surface temperature, which is rarely above 31-33 °C. And the satellite’s spatial resolution affects the observed brightness temperature, along with a number of other factors.
A warm eye is related to a lack of clouds in (or covering up) the eye, the eye being large enough to see all the way to the surface at the viewing angle of satellite, the satellite having high enough spatial resolution to identify pixels that don’t contain cloud, and the underlying sea surface temperature. Powerful, slow moving storms may churn the waters enough to mix cooler water from the thermocline up into the surface layer, reducing the sea surface temperature. Heavy rains and cloud cover from the storm may also lower the sea surface temperature. Phailin was generally over 28-29 °C water, and was apparently moving fast enough (or the warm water was deep enough) to not mix too much cool water from below (a process called upwelling).
It may or may not have any practical implications, but the high resolution IR imagery VIIRS is able to produce may break some records on warmest brightness temperature ever observed in a tropical cyclone eye.
A year’s worth of precipitation fell on parts of Colorado in one week’s time (9 September to 17 September 2013). As Colorado State Climatologist Nolan Doesken said, “Whenever you get your annual precip in a few days time, you’re in trouble.” So it is that this blog returns to flooding once again. Flooding that hit real close to home.
If you have an hour and a half available, you might want to watch this video with preliminary results and discussion about what happened given by scientists from the Colorado State University (CSU) Department of Atmospheric Science and CIRA (including Nolan Doesken and fellow JPSS Imagery Team member Dan Lindsey). If you don’t have an hour and a half, here’s an article with a good background on the events as they happened in Boulder (although if you’re a slow reader, it may not save you much time since it’s pretty comprehensive). A less comprehensive, 4-page summary of the event was put together by the University of Colorado-Boulder, the Colorado Climate Center (at CSU) and NOAA’s Earth System Research Laboratory (ESRL) which may be found here (PDF document).
The Colorado Climate Center and the Department of Atmospheric Science at CSU have put together this website to document the flood event. If you haven’t seen enough pictures of the flooding on the news or elsewhere on the internet, these two pages here and here give a good idea of the damage that resulted. By the end of September, 8 people were confirmed dead in Colorado as a result of the flooding.
Just to make sure that all of you have seen this, here are the precipitation totals (in inches) from various National Weather Service (NWS) Cooperative Observers, trained weather spotters, automated rain guages and CoCoRaHS members for the 7-day period ending on the 16 September 2013, put together by the Denver/Boulder NWS Forecast Office:
Preliminary rainfall totals (in inches) over Northern Colorado, 9-16 September 2013. Image courtesy NWS.
Remember to multiply those numbers by 25.4 if you’re used to using millimeters as the standard measure of rain. Also, keep in mind that this part of the world averages somewhere between 12 and 20 inches of precipitation per year.
From a satellite perspective, there really isn’t much (that isn’t classified) that can beat Digital Globe, a private company that specializes in high-resolution satellite imagery. Here’s what you can see with 0.5 m resolution. (Oh, how meteorologists would love to have data and forecast models on that kind of resolution – even if we’d all be drowning in yottabytes of data!)
In contrast, the high resolution imagery channels on VIIRS have ~350 m resolution, which is not enough to see each individual puddle, but it is enough to capture the flooding that occurred on the South Platte River subsequent to the 5-18 inches of rain that fell along the Front Range mountains.
Here’s what the “Natural Color” RGB composite of channels I-01 (0.64 µm, blue), I-02 (0.87 µm, green) and I-03 (1.61 µm, red) looked like before the flooding occurred:
VIIRS false-color RGB composite of channels I-01, I-02 and I-03, taken 19:49 UTC 7 September 2013
Click on the image, then on the “1172×866” link below the banner to see the full resolution version. Note that you can’t actually see the South Platte River before the flooding occurred, but you can see the dark olive color of the river valley (caused by the mixture of trees, other ground vegetation and rich soils along the river) and the swath of light green irrigated farmland on either side of the river.
The week that the flooding occurred, it was very cloudy (duh!), so VIIRS wasn’t able to see much. But, on the 14th (which people around here refer to as “that Saturday” because each day that week brought specific memories to those that lived through it) the clouds briefly broke enough for VIIRS to see that the South Platte River valley had begun to flood:
VIIRS false-color RGB composite of channels I-01, I-02 and I-03, taken 19:17 UTC 14 September 2013. The yellow arrow indicates the furthest east extent of flooding along the South Platte River.
Look for the dark, bluish-greenish color (scientific term) extending as far east as the yellow arrow. That arrow is pointing to the leading edge of the flood water, which was near the town of Weldona at this time. Places upriver from there all the way to the north side of Denver were experiencing significant (even record breaking) flooding.
Three days later (17 September 2013, about one week after the flooding began) it was a really clear day over Colorado, which made it easy to see that the flooding made it past Fort Morgan and Sterling out to little Sedgwick:
VIIRS false-color RGB composite of channels I-01, I-02 and I-03, taken 20:01 UTC 17 September 2013. The yellow arrow indicates the furthest east extent of flooding along the South Platte River at this time.
Two weeks after the flood began, flood waters made the South Platte River visible all the way to (and past) North Platte, Nebraska, another site of record flooding roughly 250 miles away from where the heavy rains occurred!
VIIRS false-color RGB composite of channels I-01, I-02 and I-03, taken 19:30 UTC 24 September 2013. Note that the South Platte River is visible from Denver, CO to North Platte, NE as a result of the flooding.
Here’s a short animation of this sequence of images:
Animation of VIIRS false-color composites of channels I-01, I-02 and I-03 from 7-24 September 2013
You have to click on the image, then on the “1172×866” link to see the images loop.
It should also be said that this event didn’t just affect Colorado. Parts of New Mexico reported over 12 inches of rain and at least 1 death. Cheyenne, Wyoming just recorded the second wettest month on record (dating back to the late 1800s). And, as mentioned above, the flooding made it down the Platte River all the way to central Nebraska. And, as a piece of good news, this flood water is being used to refill the Ogallala Aquifer, which has been low due to long-term, drier-than-normal conditions.
Events like this generally bring more questions than answers: Was it a “100-year flood” or a “1000-year flood”? Could the forecasts have been better? If the forecasts were better, would anyone have believed them? How do you prepare for unprecedented events?
Sorry, I couldn’t help myself with that title. Last time we looked at flooding in Russia, it was in the western parts – generally near Moscow and primarily along the Oka River – and caused by rapid melting of record spring snowfall. This time, flooding is occurring in Russia’s Far East, primarily along the Amur River, caused by heavy rainfall related to monsoon wind patterns in the region – record levels of flooding not seen before in the 160 years Russians have settled in the area.
Unfortunately, this natural disaster is affecting more than just Russia. In China, many people are dead or missing as the result of flooding. (The figure of “hundreds dead or missing” includes flooding caused by typhoons Utor and Trami in southeastern China, flash flooding in western China, and the subject of today’s post: river flooding in northeastern China and far east Russia.) The Chinese provinces of Liaoning, Jilin and Heilongjiang have been hit particularly hard with persistent, heavy rains since late July, as have areas just across the border in Amur Oblast, Khabarovsk Krai and the Jewish Autonomous Oblast in Russia.
A few more facts: Heilongjiang is the Chinese name for the Amur River. It translates to English as “Black Dragon”. The Mongols called it Kharamuren (“Black Water”), which, I assume, the early Russian settlers shortened to Amur. It is the longest undammed river in the Eastern Hemisphere and the home to the endangered Amur leopard and Amur tiger. Since 1850, the Amur River has been the longest piece of the border between China and Russia. Now, in 2013, the Amur River has reached the highest levels ever recorded.
Backing up a bit, here’s what the area looked like according to “Natural Color” or “pseudo-true color” VIIRS imagery back in the middle of July:
VIIRS false-color RGB composite of channels I-01, I-02 and I-03, taken 03:27 UTC 14 July 2013
As always, click on the image, then on the “2368×1536” link below the banner to see the full resolution version. Here’s what the same area looked like about a month later:
VIIRS false color RGB composite of channels I-01, I-02 and I-03, taken 03:14 UTC 21 August 2013
Notice anything different? The Amur River has overflowed its floodplain and is over 10 km (6 miles) wide in some places. Just downriver (northeast) from Khabarovsk, the flooded area is up to 30 km (18 miles) wide!
Pay attention to Khabarovsk. Back in 1897, the Amur River crested there with a stage of 6.42 m (about 21 feet in American units), which was the previous high water mark. On 22 August 2013, the river stage reached 7.05 m (23 feet) and was expected to keep rising to 7.8 m (25.6 feet) by the end of August. The map below (in Russian) shows the local river levels on 22 August 2013. It came from this website.
Amur River levels at various locations in Khabarovsk Krai, Russia on 22 August 2013.
Note that Khabarovsk in Cyrillic is Хабаровск (the black dot in the lower left), and Amur is Амур. The blue numbers represent the river stage in cm. Red numbers indicate the change in water level (in cm) over the last 24 hours. The colored dots indicate how high the river level is above flood stage according to the color scale (also in cm). The river at Khabarovsk is more than 4 meters (13 feet) above flood stage.
Not impressed by comparing a “before” and “after” image? Here’s an animation over that time period (14 July to 21 August 2013), with images from really cloudy days removed:
Animation of VIIRS false-color composites of channels I-01, I-02 and I-03. Click on the image, then on the "1184x768" link below the banner to view the animation.
You have to click through to the full resolution version before the loop will play. In order to not make the world’s largest animated GIF, the I-band images in the loop have been reduced in resolution by a factor of 2, making them the same resolution as if I had used M-5, M-7 and M-10 to make this “Natural Color” composite.
The Day/Night Band is not known for its ability to detect flooding at night, but it also saw how large the Amur River has become:
VIIRS Day/Night Band image, taken 17:27 UTC 20 August 2013
This image was taken on 20 August 2013, which just so happens to be the night of a full moon. The swollen rivers are clearly visible thanks to the moonlight (and general lack of clouds).
Khabarovsk is a city of over 500,000 people and would require a major evacuation effort if the river reached the expected 7.8 m level. Over 20,000 people have already been evacuated in Russia alone (and over a million people in China) according to this report. Oh, and at least two bears.
This heavy rain and flooding makes it all the more surprising that, a little further north and west in Russia, there have been numerous, massive wildfires. Check out this “True Color” image from VIIRS, taken on 16 August 2013:
VIIRS"True Color" composite of channels M-3, M-4 and M-5, taken 03:12 UTC 16 August 2013.
See the supersized swirling Siberian smoke spreading… OK, I’ll quit with the alliteration. Here’s the smoke plume on the very next overpass (about 90 minutes later) seen on a larger scale:
VIIRS "True Color" composite of channels M-3, M-4 and M-5, taken 04:52 UTC 16 August 2013.
A strong ridge of high pressure with its clockwise flow is trapping the smoke over the region. In this image you can see quite a few of the smoke sources where the fires are still actively burning. Look in the latitude/longitude box bounded by 98 °E to 105 °E and 59 °N to 61 °N. By the way, that’s Lake Baikal on the bottom of the image, just left of center.
A quick back-of-the-envelope calculation indicates that the area covered by smoke is roughly 500,000 km2. (Of course it is complicated by the fact that the smoke is mixing in with the clouds, so it is hard to define a true boundary for the smoke on the north and west sides.) That puts it in the size range of Turkmenistan, Spain and Thailand. If that’s not a good reference for you, how’s this? The smoke covers an area larger than California and smaller than Texas.
These fires have burned for more than a month. This article from NASA includes a MODIS image from 25 July 2013 containing massive smoke plumes and shows that areas of central Russia (particularly north of the Arctic Circle) have had a record heatwave this summer. And here are a few more images of the smoke from MODIS over the past few weeks.
Heatwaves and fires and floods? Russia is all over the map. Literally. I mean, look at a map of Asia – Russia is all over that place. It even spreads into Europe!
Hopefully, Google Translate didn’t steer me wrong on the meaning of “abafado”. “Bruma seca” is a term used by Portuguese and Spanish speakers that literally translates to “dry mist”. It is typically used to refer to thick haze or the brownish air caused by dust and, more specifically, to the Saharan Air Layer (scroll down a bit on this Weather Underground blog post for nice description of what that is).
We’re speaking Portuguese today because we are re-visiting Cape Verde, an island nation where people speak Portuguese. (Actually, many people speak a creole version called Kriolu kabuverdianu that has Western African elements added to the Portuguese.) Last time we visited Cape Verde, the islands were creating interesting waves and plumes in the atmosphere. This time, Cape Verde is buried under a plume – a plume of Saharan air that is so thick, you can barely see the islands:
VIIRS "True Color" RGB composite of channels M-03, M-04 and M-05, taken 15:07 UTC 30 July 2013
I had to plot the map boundaries on the image just to see where the islands are. Otherwise, they would be lost in a sea of brown dust. Also, without the map, it’s difficult to find the shoreline of western Africa because the dust looks just like the Sahara Desert where it came from.
This image is (and the images to follow are) a “True Color” RGB composite. (As always, click on the picture, then on the “2442×1920” link below the banner to see the full resolution image.) Unlike many previous true color images shown on this blog, these have been “Rayleigh corrected.” This means the impact of Rayleigh scattering by the molecules in the atmosphere has been removed. The reason for doing this is that it makes the surface easier to see and it better represents what people normally see when looking out of the window on an airplane. Dust particles, on the other hand, are Mie scatterers at visible wavelengths (refer back to that last link) so they still show up. In fact, this is one of the strengths of the True Color composite: it is quite sensitive to particulate matter in the atmosphere like smoke, smog, haze and dust.
The image above was taken on 30 July 2013, one day after the dust really started to be pushed off the African coast. It is not clear if the people of Cape Verde were forced indoors by this dust since I wasn’t able to find any news reports on it. The western edge of the dust plume (between 28° and 29° W longitude) almost looks like it is casting a shadow, which would indicate the dust is lofted pretty high in the troposphere in this image.
This dust plume pushed across the Atlantic Ocean over the following days. VIIRS passed over Cape Verde on 31 July 2013 (14:48 UTC) and captured this image:
VIIRS "True Color" RGB composite of channels M-03, M-04 and M-05, taken 14:48 UTC 31 July 2013
Here, the dust plume extends from one side of the swath to the other – over 3000 km. On the very next orbit (16:29 UTC 31 July 2013), the plume can be seen on four consecutive data granules, extending almost to the middle of the swath. (The satellite covers a distance of over 2000 km over four granules.)
VIIRS "True Color" RGB composite of channels M-03, M-04 and M-05, taken 16:29 UTC 31 July 2013
Hold on. What’s that strip of white-colored stuff extending north-northwest from 50° W longitude label? Some kind of white dust? That happens to be in a straight line? Nope. It’s what is called “sun glint” and it’s the same basic phenomenon as the glare you see looking out over a body of water without polarized sunglasses. The dust is all the brown stuff on the right side of the image. That’s South America and the Lesser Antilles on the left side of the image.
If you click to the full resolution version of the image above, you may find that the image doesn’t seem very big considering it is made of four granules. (Its pixel size is 1600×1536. In contrast, the image above that is only two granules, yet is 3200×1536 in size.) That’s because I had to reduce the resolution of the data in order to plot it all without running out of memory on my computer. VIIRS has twice the resolution of what is shown in the latter image. (And this high resolution requires a lot of computing power to display!)
On 1 August 2013, the plume pushed even closer to the Lesser Antilles (although they are off the left side of this image).
VIIRS "True Color" composite of channels M-03, M-4 and M-05, taken 16:10 UTC 1 August 2013
Again, the resolution has been degraded by a factor of two. It is interesting to note that one granule covers an area of the Earth about 3040 x 570 km in size (1.7 million sq km, or 669,000 sq mi), so four granules is about 6.9 million km2. That’s 2.6 million square miles. In comparison, the size of the lower 48 states is about 3.1 million square miles (3.7 million square miles if you add on Alaska and Hawaii). Now notice that the dust covers most of the last image. If you add on the area of the dust plume that stretches all the way back to Africa, you are talking about an area well over the size of the United States! By the time it arrives in the Caribbean, that dust better learn to speak Antillean Creole. It is a long way from Cape Verde.
So, what does all of this mean? It is often claimed that the presence of Saharan dust layers is bad for hurricane formation. Evidence for that claim is provided here and here. However, there are also scientists who refute that claim, which you can read about here. Scientists at the U.S. Geological Survey (USGS) have found that Saharan dust may be harmful to people and to coral reefs. According to this article in Nature, the dust is beneficial for the Amazon rainforest.
This event was also discussed on the Weather Channel. Compare his visible images to mine, which use only one color of the visible spectrum to my three color images. So, whether Saharan dust is good or bad, I think we can all agree that VIIRS is good!
UPDATE (5 August 2013): Remember the “split window difference”? It was mentioned the last time we visited Cape Verde. Here’s is a split window difference product produced at CIMSS that highlights the plume as it traveled across the Atlantic. This loop starts on 29 July and ends on 2 August 2013 and is made of data collected by the geostationary satellite MSG-3.
UPDATE (19 August 2013): Here’s another animation of the dust plume, made using observations from the Ozone Mapping and Profiler Suite (OMPS), one of the new instruments aboard Suomi NPP alongside VIIRS. (Actually, it’s on the opposite end of the satellite from VIIRS, so it’s not literally alongside VIIRS, but you get the idea.)
The last two posts covered flooding. Now, a month later, we are back to covering last year’s most common topic: wildfires. This time, we’ll make a game out of it. Keep in mind that, for many operational fire weather forecasters, this isn’t a game – it is information that could prove useful in saving lives or homes from destruction. If you have read the earlier posts on fire detection and haven’t forgotten what you’ve been told (here’s a good one to go back and read), this should be easy for you.
The following images are the unmapped data from three consecutive VIIRS granules over the Southwest U.S., starting at 20:36 UTC 11 June 2013. The “raw” data has been processed to produce the “True Color”, “Natural Fire Color” and “Fire Temperature” RGB composites. Plus, the brightness temperature data from channel M-13 (4.0 µm) has a color table applied to it to aid in fire detection. Satellite channels near 4 µm are the “industry standard”, so to speak, for detecting fires as they are highly sensitive to sub-pixel heat sources like fires. The “Natural Fire Color” and “Fire Temperature” composites are RGB composites developed just for VIIRS that both had their debut on this very blog.
The question is: how many fires can you see? Remember, you have to allocate resources (firefighters, helicopters, planes, etc.) based on your assessment. The media is hounding you for all the latest statistics on each blaze and they can’t wait until the 5:00 briefing. They need the scoop now to get higher ratings. Plus, the crew is loading fire retardant on the plane as you read this. Where should the pilot fly to? Everyone is counting on you! (Of course, you would never have just satellite data by itself in a real-life scenario – but, do you want to play this game, or just think of flaws?)
I’ll give you a hint: You won’t see any fires unless you view each image at full resolution. Click on the image, then on the “3200×2304” link below the banner to see the full resolution version. (You could even open each full resolution image in a new tab, and click between the tabs for easy comparison, assuming you’re not using some archaic version of Internet Explorer or another old browser that doesn’t allow tabs. When you would click on the “3200×2304” link, instead right-click and select “Open in New Tab”. Another option would be to save the images and open them in an image viewing software program that will allow you to zoom in more than 100% but, that is starting to sound like a lot of work and I’m not sure I want to play this game anymore. It’s too complicated. By the way, if that’s the way you feel, don’t become the manager of a fire incident team.)
I’ll give you another hint: Many of the hot spots that indicate fires are only 1-2 pixels in size. Be prepared to look for needles in the haystack, and make sure you have your reading glasses on, if you need them.
VIIRS "True Color" composite of channels M-03, M-04 and M-05, taken at 20:36 UTC 11 June 2013
VIIRS "Natural Fire Color" composite of channels M-05, M-07 and M-11, taken 20:36 UTC 11 June 2013
VIIRS "Fire Temperature" composite of channels M-10, M-11 and M-12, taken 20:36 UTC 11 June 2013
VIIRS channel M-13 image, taken 20:36 UTC 11 June 2013
So, did you see them all? You should have identified 12 fires. Did you find less than 12? Some of them are hard (or impossible) to see in some of the images. Did you find more than 12? The color scale used on the M-13 image led to false alarms, so you can be forgiven if that’s what caused you count too many.
This example shows some of the complicating factors when trying to identify fires from satellites. It also shows why fire managers never rely on satellite data alone. Now, having said that, VIIRS can and does provide useful information on fires.
First, here’s the answer (link goes to PDF) from the National Interagency Fire Center. They identified 15 active “large incident” fires on 12 June 2013. (They update their maps once per day, so all the fires that started on 11 June make it on the 12 June map.) But, there are differences between their map and what VIIRS saw.
First, the Mail Trail fire (#5 in the PDF) is outside the domain of these three VIIRS granules, so you couldn’t have found that in these images. Fires #3, 4 and 7 (Healy, Porcupine and Ferguson) are obscured by clouds, and/or were mostly contained, transitioning from active to inactive. The Tres Lagunas Fire (#13) started back in May and is undergoing mop up activities. The hot spots from that fire (if there are any left) aren’t visible in the images, but the burn scar is. That leaves the Stockade (#1), Crowley Creek (#2), Hathaway (#6), Fourmile (#8), Silver (#9), Thompson Ridge (#10), Jaroso (#11), Big Meadows (#12), Royal Gorge (#14), and Black Forest (#15) – 10 fires which are all visible in the VIIRS images. Plus, VIIRS saw two more fires that are not included on that list: one in southern California (near the Salton Sea) that I couldn’t find any information on, plus a pellet plant fire in Show Low, Arizona. (Small fires in towns are usually outside the scope of the National Interagency Fire Center, so they don’t bother to list those.)
I would argue that the “Fire Temperature” composite worked the best at identifying each of these fires, but all 4 images have their uses. Here’s the Fire Temperature RGB image with the visible fires identified:
VIIRS "Fire Temperature" composite of channels M-10, M-11 and M-12, taken 20:36 UTC 11 June 2013
Answer honestly. Which fires did you see, and which fires did you miss?
The Fire Temperature RGB takes advantage of the VIIRS channels in the portion of the electromagnetic spectrum ranging from the near-infrared (NIR) to the shortwave infrared (SWIR). The blue component is M-10 (1.61 µm), the green component is M-11 (2.25 µm) and the red component is M-12 (3.7 µm). As wavelength increases over this range, the contribution of the Earth’s emission sources increases and the contribution from the sun decreases. As a result, only the hottest hot spots show up in M-10, as they have to be seen over the large signal of radiation from the sun reflecting off the Earth’s surface. In M-12 (as in M-13), hot spots from fires produce more radiation at that wavelength than the amount of reflected solar radiation. M-11 is somewhere in the middle. That means relatively cool (e.g. smoldering) or small fires only show up in M-12, which makes those pixels appear red. Pixels containing fires hot enough or large enough to show up in M-11 will take on an orange to yellow color. Pixels containing fires hot enough or large enough to show up in all three channels will appear white.
You have to be careful, though, as some pixels in the Fire Temperature RGB appear red, even though there aren’t any fires in them. A few of these pixels show up red in the M-13 image, and are labelled as “not a fire/false alarm”:
VIIRS M-13 image, taken 20:36 UTC 11 June 2013
According to the color table used, any pixel with a brightness temperature above 340 K (67 °C) will be colored, with colors ranging from red to orange to pale yellow as temperature increases. Now, look at that area in the True Color image (or on Google Maps):
VIIRS "True Color" composite of channels M-03, M-04 and M-05, taken 20:36 UTC 11 June 2013
That area is very dark – almost black – volcanic rock with very little vegetation that has been baking in the sun all day. It has managed to acquire a brightness temperature that is higher than some of the active fire pixels. The Crowley Creek fire doesn’t show up as red in the M-13 image (the Stockade fire is the one with the yellow and orange pixels) and the Fourmile fire is barely visible. (It has two pixels warmer than 340 K, even though 10 pixels appear red in the Fire Temperature RGB). The color scale in the M-13 image could be applied to a different temperature range, but you’ll always have that trade-off: have the colors start at too high a temperature, and you’ll miss some fires; have the colors start at too low a temperature, and you’ll increase the false alarms.
The True Color image should have helped you identify 5 of the fires. The smoke plumes that show up are a dead giveaway. I’m talking about the Big Meadows, Royal Gorge, Jaroso, Thompson Ridge and Silver fires, of course. There may be smoke with the Hathaway fire, but it would be mixed in with the cirrus clouds and hard to see. Not all fires produce a lot of smoke, though. Having information on the ones that do aids in issuing air quality alerts, among other benefits.
Lastly, the Natural Fire Color image highlights most (but not all) of the fires. Look for the red pixels:
VIIRS "Natural Fire Color" composite of channels M-05, M-07 and M-11, taken 20:36 UTC 11 June 2013
The Natural Fire Color doesn’t show active hot spots at Crowley Creek, and the Hathaway and Fourmile fires are difficult to see, because they aren’t quite hot enough. (Generally speaking, any fire that shows up red in the Fire Temperature RGB is too cold to show up as red in the Natural Fire Color.) But, this composite has the advantage of showing burn scars in addition to the active fires. Burn scars appear dark brown. The Fourmile and Crowley Creek burn scars are visible. Plus, burn scars from last year’s fires still show up: The Whitewater-Baldy, High Park and Waldo Canyon scars are identified. The Tres Lagunas was mentioned above, and it’s burn scar is visible. If you look closely, I’m sure you could find more burn scars from last year’s long fire season.
Here are all four images, zoomed in on each fire at 800%, combined into an animation to highlight how each fire appears in each image:
Animation of M-13, True Color, Natural Fire Color and Fire Temperature imagery zoomed in each fire (20:36 UTC 11 June 2013)
For some reason, you have to click to the full resolution version of the image before the animation will display.
Hopefully, this exercise is useful in demonstrating the complications that arise when trying to detect fires from satellites in space, as well as the strengths and weaknesses of some of the various methods VIIRS has at it’s disposal to aid the fire weather community.
It seems that last year’s posts were all about fires. Fires in Colorado (multiple fires, in fact), the Canary Islands, Siberia, Australia – there was even that 40-year-old pit of burning natural gas that has been called the “Gates of Hell“. (It’s still burning, by the way.) Maybe this year’s theme will be all about flooding. We just looked at flooding in the U.S. Midwest. And now, we return back to Russia – the western part this time – where massive flooding has occurred this spring.
Moscow had 65 cm of snow on the ground on 1 April 2013. (That’s roughly 26 inches for any American readers.) That’s the most snow they’ve ever had on the ground that late in the spring, and it was all thanks to record snowfall during the month of March. This article from 26 March 2013 says they got 70 cm (28 inches) in a two day period, and forecasters were predicting another 8-10 cm by the end of the month.
What happens when record amounts of snow melt? It causes flooding. In this case, flooding that makes the Illinois River look like a creek you can hop across. The watershed of the Volga River has been hit especially hard. Here’s a picture that our resident Russian, Galina C., tells me is from near the city of Ryazan, so I assume it is the Oka River. (Refer back to the Volga River map I linked to.) There are more pictures here.
To bring this all together with VIIRS, here is what VIIRS saw on 28 March 2012, right after the region got 70 cm of snow:
False color RGB composite of VIIRS channels I-01, I-02 and I-03, taken 10:38 UTC 28 March 2013
Again, to see the full resolution image, click on it and then click on the “1793×2036 ” link below the banner. This is the false color combination that EUMETSAT refers to as “Natural Color“, where snow and ice appear cyan and liquid clouds appear white. The whole scene is snow, except for a few small clouds north of Moscow and anywhere there are trees sticking out above the snow, which appear green.
Notice that you can’t see any rivers. They’re all frozen over and covered with snow.
Here’s what VIIRS saw (same false color combination) a month later (29 April 2013):
False color composite of VIIRS channels I-01, I-02 and I-03, taken 10:39 UTC 29 April 2013
All the snow is gone. Plus, look at all the rivers you can see. The problem is that you shouldn’t normally be able to see all of these rivers. The flooding makes them visible.
What I think is more impressive is seeing a time-lapse loop of VIIRS images over this period:
Animation of false color composites of VIIRS channels I-01, I-02 and I-03 from 28 March 2013 to 2 May 2013.
Make sure you look at it in full resolution mode. Note that the time period between frames in the animation varies. Some days it was too cloudy to see anything, one or two days had missing data, etc., so this isn’t always one image per day.
The city of Ryazan is identified in the animation (remember the photo linked to earlier). To put it into perspective, check out the Google Maps satellite view of the city. The Oka River is normally ~200 m wide near the city. In the last two frames of the animation, the Oka River is over 10 km wide at its widest point near Ryazan! The same goes for a lot of the rivers visible at the end of the loop – rivers that are normally a few tens or hundreds of meters wide are up to a few kilometers wide.
The city of Tambov at 52°43′N, 41°26′E, which is outside of the domain of the animation, but in the southeastern portion of the larger static images, experienced its worst flooding in 130 years in early April. (That corner of the domain was the first to experience snowmelt.) One of the contributing factors at Tambov, according to that article, was that the ground below the snow was still frozen. The snowmelt occurred before the ground thawed. This meant that the meltwater couldn’t be absorbed into the ground – it simply collected in the low-lying areas or ran off into the rivers, which quickly filled as you can see.
Our resident Russian was also able to grab this plot of the Oka River stage at Novinky, just upstream of where the Oka empties into the Volga. The information comes from this website. This plot covers the time period from 7 April to 7 May 2013.
River stage of the Oka River at Novinky, Russia for April 2013. Data comes from gis.waterinfo.ru, with help from Galina Chirokova (CIRA).
The Oka River looks like it peaked at about 2.5 m above normal. (8 ft. for you Americans.)
All that water is going to end up in the Caspian Sea, whose water level is largely based on inflow from the Volga River’s watershed. Variations of sea level in the Caspian have been +/-3 m over the last century and, with this influx of snowmelt, it is sure to go up.
The week beginning on 14 April 2013 was a big week for weather across the United States. There were 30 reports of tornadoes. (Make sure you click on each link, and look at the filtered reports.) And, when our home base of Fort Collins, Colorado was in the middle of being buried under two feet of snow, large parts of the Midwest received 4-7 inches of rainfall. This is a lot of rain for an area with saturated ground caused by recent snowmelt. Unsurprisingly, it caused a lot of flooding – including a sinkhole in a Chicago neighborhood.
Now, we know VIIRS is good at detecting snow. But, flooding is a bit trickier, particularly river flooding. First, flooding usually occurs when it’s cloudy. (Not always, of course, since you can have flooding from snowmelt or heavy rains that occurred upstream or caused by ice jams when it isn’t cloudy. And, as we saw with Hurricane Isaac, flooding may linger long after the clouds are gone.) Second, flooding can have a huge impact over a small area that your satellite might not have the resolution to detect.
Well, I’m here to report that VIIRS has the resolution to detect the flooding that occurred over Illinois last week. And the flooding lasted until well after the clouds cleared. Take a look at the image below from 21 April 2013, where the flooding is visible:
VIIRS false color composite of channels I-01, I-02 and I-03, taken 18:13 UTC 21 April 2013
This is a “Natural Color” RGB composite of the high-resolution channels I-01 (0.64 µm, blue), I-02 (0.87 µm, green) and I-03 (1.61 µm, red). If you click on the image, then on the “3124×2152” link below the banner, you will see the full resolution image. If you’re wondering where the flooding is, notice the rivers I have labelled in the image. Now try to spot those rivers in this image from two weeks earlier (5 April 2013):
VIIRS false color composite of channels I-01, I-02 and I-03, taken 18:13 UTC 5 April 2013.
Those rivers are a lot more difficult to see. The Illinois, Sangamon, and Mississippi rivers are the only rivers easily visible in the before image. A lot more show up after the heavy rains because they grew beyond their banks and became big enough for VIIRS to see. You might also notice that the vegetation has become much greener over this two week period. To make it easier to compare, here are those images cropped and centered on the swollen rivers, side-by-side:
False-color RGB composites of VIIRS channels I-01, I-02 and I-03, taken on 5 April 2013 (left) and 21 April 2013 (right)
There are a couple of important things to note about these images that are related to how VIIRS and its satellite (Suomi-NPP) work. One is that Suomi-NPP has an orbit with a 16-day repeat cycle. Every 16 days it should (if it’s in its proper orbit) pass over the same spot on the Earth at the same time of day. The images above were taken 16 days apart, and as you can see in the captions, were taken at the same time of day. The only difference in the area included in the images is the result of the start time of the data granules being 13 seconds off. This means that VIIRS is viewing all the same spots at the same viewing angles.
This leads to point #2: the VIIRS instrument has a constant angular resolution (recall that it uses a constantly rotating mirror to detect radiation across the swath) which, when projected onto the surface of the Earth, means that it does not have a constant spatial resolution. (See slide 12 of this presentation.) The spatial resolution of the high resolution channels shown here is ~375 m at nadir, and it degrades to ~750 m resolution at the edge of the swath. In the images above, the center of the VIIRS swath (nadir) is near the right edge of the data plotted. The left edge of the data plotted is about 80% of the distance from nadir to the edge of the swath. The loss in resolution over this distance may be enough to prevent VIIRS from detecting all the flooding that is occurring. But, the important thing is that we are viewing all these rivers at the same angles and the same resolution. This gives the best comparison between the before and after images.
A few more things to notice in the above images: there is snow in the northern part of Michigan’s Lower Peninsula, with ice on Green Bay and Lake Winnebago (all of which are easier to see in the image from 5 April 2013). Does anyone living there still remember last year’s record heat wave? Many places in this region had already had a number of +80 and +90 °F days, but it seems like a distant memory now. This year, winter doesn’t want to end.
One last thing for today: If you focus on Michigan again you might notice another area of flooding. This one is large enough it wouldn’t be impacted by any resolution degradation (even though it is near the center of the swath where you wouldn’t be worried about that anyway). I’ve zoomed in on the area here:
False-color composites of VIIRS channels I-01, I-02 and I-03 from 5 April 2013 (left) and 21 April 2013 (right)
This is along the Shiawassee River near the Shiawassee National Wildlife Refuge, a few miles southwest of Saginaw. This area of flooding is confirmed by these aerial photographs taken on 22 April 2013.
Science fiction fanatics know it as “Middle-earth“. Abel Tasman, the Dutch explorer who became the first European to sail there, called it “Staten Landt“, which was later changed to Nieuw Zeeland, Nova Zeelandia, and, finally, New Zealand. The native Maori people call it “Aotearoa“, which loosely translates to “the land of the long, white cloud”.
A group of volcanic islands southeast of Australia, New Zealand is known for the Southern Alps, the locations where they filmed the Lord of the Rings trilogy and rugby, although I’m sure there’s more to the country than that. Residents of New Zealand refer to themselves as “kiwis”, although it is not clear if they prefer to be thought of as birds or fruit.
Being an island nation in the mid-latitudes with 17 peaks above 10,000 ft (3,000 m), you might expect there would be no shortage of moisture and uplift to form clouds and precipitation. There are sea breezes, mountain/valley circulations, orographic uplift of prevailing winds, periodic mid-latitude cyclones and the occasional tropical storm to get things started. But, that’s not the case this year.
The North Island is currently experiencing its worst drought in over 30 years. Many places have experienced less than half of normal precipitation this summer, according to NIWA (their version of NOAA). These are places that normally receive 40-80 inches of precipitation per year. (Remember, summer just ended down there and that 500 mm is roughly 20 inches.)
Wellington, the nation’s capital, has begun rationing water for the first time in recorded history (which covers about 170 years). The chair of the Wellington region’s committee in charge of the water supply was quoted as saying, “People should shower with a friend, if that’s an option . . . or put a brick in the toilet. If you know anyone who’s particularly adept at rain dances, then encourage them to get out there and do what they do.”
One of the previous links mentioned that the drought is so bad, it can be seen from space. They didn’t provide evidence to back up that claim, so I guess I have to do it. Here’s what VIIRS saw on 28 January 2013 (before the North Island went 4-6 weeks without any significant precipitation):
"True Color" RGB composite of VIIRS channels M-03, M-04 and M-05, taken 01:49 UTC 28 January 2013
And here is what VIIRS saw on 21 March 2013 (after 4-6 weeks without significant precipitation):
"True Color" RGB composite of VIIRS channels M-03, M-04, and M-05, taken 02:15 UTC 21 March 2012
The two images above are “true color” composites. If you look closely at the two images, you might notice significantly less green vegetation in the 21 March 2013 image, particularly in box that covers 39° to 40° S latitude and 174° to 176° E longitude. (Remember, you can see the full-resolution image by clicking on it, and then on the “1434×2120” link below the banner.)
Not convincing? Maybe it shows up a bit better in the “natural color” composite, which has a strong vegetation signal. Here are those images:
False color composite of VIIRS channels M-05, M-07 and M-10, taken 01:49 UTC 28 January 2013
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False color composite of VIIRS channels M-05, M-07 and M-10, taken 02:15 UTC 21 March 2012
And just to be clear, here are the images zoomed in on the west side of the North Island, where the drought has hit the hardest:
Drought impact on vegetation in the North Island of New Zealand between 28 January (left) and 21 March 2013 (right)
In the image on the left, from 28 January, light green areas represent grassland/pasture (backed up by this land use map) and dark green areas represent forests. In the image on the right, from 21 March, the grassy areas have turned brown while the forests have remained green. Six weeks with almost no rain will do that to grass.
While the “true color” and “natural color” RGB composites are only qualitative (and require viewers to be able to distinguish sometimes subtle changes in the amount of green in the images), there are ways to quantify the “greenness” of vegetation from satellite. The most widely used method is the Normalized Difference Vegetation Index (NDVI for short). The NDVI has been calculated for more than 40 years with Landsat and AVHRR. We can do the same calculation with VIIRS. That’s what is shown below.
VIIRS NDVI images of New Zealand from 28 January (left) and 21 March 2013 (right)
On this color scale, red and yellow colors indicate high values of NDVI (or very green vegetation). Green and blue colors indicate low values of NDVI (sparse, dead or brown vegetation). Notice how most of the North Island has gone from yellow or red in January (on the left) to blue or green in March (on the right). NDVI values have decreased by 20-30% over this period.
I guess if there is one benefit of the drought, it’s that it has been clear enough over New Zealand for satellites to see it. In fact, January and February have broken records for the amount of sunshine in many parts of the country. The land of the long, white cloud hasn’t been living up to its name.
No, not a Super-Smörg, super smog. Smog that is so thick, you can taste it. The smog in many parts of eastern China has been so bad this winter, it is literally “off-the-charts“. Based on our Environmental Protection Agency‘s not-very-intuitive Air Quality Index (see pages 13-16, in particular) any value above 300 is hazardous to everyone’s health. The scale doesn’t even go above 500 because the expectation is that the air could never get that polluted. Applying this scale to the air in Beijing, the local U.S. Embassy reported an Air Quality Index value of 755 on 13 January 2013. Visibility has been reduced to 100 m at times. This video (from 31 January 2013) gives a vivid description of the problems of the smog:
If that wasn’t bad enough, here’s video from NBC News where Brian Williams reveals a factory was on fire for three hours before anyone noticed because the smog was so thick!
Did you happen to notice in the beginning of the NBC video that the “air pollution is so bad that the thick smog can now be seen from space”? Of course, the satellite image shown in that clip came from MODIS. (It must have friends in high places. That, or people get the MODIS images out on their blogs less than two weeks after the event occurred, unlike this blog.) Needless to say, VIIRS has seen the smog, too, and it is terrible.
For comparison purposes, here’s what a clean air day looks like over eastern China:
VIIRS "true color" RGB composite of channels M-03, M-04 and M-05, taken 05:21 UTC 28 September 2012
This is a “true color” composite taken 05:21 UTC 28 September 2012. (As always, click on the image, then on the “2040×1552” link below the banner to see the full resolution image.) There appears to be some air pollution in that image (look near 33° N latitude between 112° and 116° E longitude), but it’s not that noticeable.
Here’s what it looks like when Beijing is reporting record levels of air pollution (04:56 UTC 14 January 2013):
VIIRS true color RGB composite of channels M-03, M-04 and M-05, taken 04:56 UTC 14 January 2013
You may have heard of a “brown cloud of pollution“. Here the clouds actually appear brown thanks to all that pollution. Notice the area around Shijiazhuang – the most polluted city in China – and how brown those clouds are in comparison to the clouds on the left and right edges of the image. Then look south from Shijiazhuang to where everything south and west of the cloud bank has a dull gray color. That is all smog! It’s enough to make anyone with a respiratory condition want to cough up a lung just from seeing this.
Now, this is a complicated scene with clouds, snow, ice and smog. So, to clear things up (in a manner of speaking), here is the same image with everything labelled:
VIIRS true color RGB composite of channels M-03, M-04, and M-05, taken 04:56 UTC 14 January 2013
The gray smog can be seen around Beijing as well, but it pales in comparison to the rest of eastern China. Think about that! Replay the videos above and consider that might not have even been the worst smog in China at the time!
Too bad there are a lot of clouds over the area. What does it look like on a “clearer” day? (“Clearer”, of course, refers to the amount of clouds, not air pollution.) It looks worse! The image below was taken at 04:32 UTC on 26 January 2013:
VIIRS true color RGB composite of VIIRS channels M-03, M-04, and M-05, taken 04:32 UTC 26 January 2013
The area covered by smog rivals the area of South Korea, which is visible on the right side of the image. (One of the reports I linked to above put the figure at 1/7th of the land area of China covered by smog around this time, which is actually a lot bigger than South Korea!) I’m just counting the smog in the image that is thick enough to completely obscure the surface. There is likely smog that isn’t as obvious (and isn’t labelled) in that image. The snow between Shijiazhuang, Tianjin and Beijing is covered by smog that isn’t quite thick enough to totally obscure it. And the large area of snow south of Tianjin is likely covered with smog. (It sure is a lot dirtier in appearance than the snow near the top of the image.)
If you don’t believe my labels, the “pseudo-true color” or “natural color” RGB composite clearly identifies the low clouds (which usually appear a dirty, off-white color even without smog), ice clouds (pale cyan) and snow (vivid cyan):
VIIRS false color RGB composite of channels M-05, M-07 and M-10 (a.k.a. "natural color"), taken 04:32 UTC 26 January 2013
Notice the smog in this image. It is an unholy grayish-greenish color with a value near 70-105-93 in R-G-B color space. The “natural color” composite is made from channels M-05 (0.67 µm, blue), M-07 (0.87 µm, green) and M-10 (1.61 µm, red), which are longer wavelengths than their “true color” counterparts. Longer wavelengths mean reduced scattering by atmospheric aerosols, so the higher green value may be due to the strong surface vegetation signal in M-07 being able to penetrate through the smog. (Either that or the smog is composed of some chemical compound that has a higher reflectivity value in M-07 than in the other two channels.)
I’ve looked at the EUMETSAT Dust, Daytime Microphysics and Nighttime Microphysics/Fog RGBs, which you might think would show super-thick smog and they don’t. At least, it’s not obvious.
The EUMESAT Dust RGB applied to VIIRS, valid 04:32 UTC 26 January 2013
The Dust RGB above uses M-14 (8.55 µm), M-15 (10.7 µm) and M-16 (12.0 µm) and requires there to be a large temperature contrast between the dust (cool) and the background surface (hot). Smog almost always occurs when there is a temperature inversion (the air at the ground is colder than the air above) so the necessary temperature contrast won’t exist.
The Daytime Microphysics RGB shows the smoggy areas are a slightly different color than other cloud-free surfaces, but that color can be confused with other non-smoggy surfaces. The clouds really stand out, though:
The EUMETSAT Daytime Microphysics RGB applied to VIIRS, valid 04:32 UTC 26 January 2013
Perhaps, with a different scaling, the smog might stand out more.
The Nighttime Microphysics RGB from the night before (18:50 UTC 25 January 2013) is interesting. Notice the cloud identified by the letter “B” and the non-cloud next to it, “A”:
The EUMETSAT Nighttime Microphysics/Fog RGB applied to VIIRS, valid 18:50 UTC 25 January 2013
Now compare this with the Day/Night Band image from the same time:
VIIRS Day/Night Band image of eastern China, taken 18:50 UTC 25 January 2013
This was a day before full moon. Thanks to the moon, clouds, snow and smog are visible in addition to the city lights. Points “A” and “B” have nearly identical brightness in the Day/Night Band, but only “B” shows up as a cloud in the Nighttime Microphysics RGB. These lighter areas around “A” and “B” are partially obscuring city lights, indicating “B” is a cloud, while “A” is smog. (If either was snow, you’d be able to see the city lights more clearly. See the lighter area northwest of Beijing, which is snow.)
Nothing sees super-smog like the true color composite, but the Day/Night Band will see it as long as there is enough moonlight. Smog as optically thick as a cloud… *hacking cough* … Yuck!
Do rocks float? The answer to that is “Depends on which rocks you’re talking about.”
We just looked at what happens in the atmosphere when a volcano like Copahue erupts. We also looked at the impact the 1912 eruption of Novarupta still has today. And, before VIIRS was launched into space, there was Eyjafjallajökull – the Icelandic volcano that nobody could pronounce. (Think “Eye-a-Fiat-la-yo-could” [click here to hear audio of some guy saying it properly].) These are examples of what geologists would refer to as an “explosive eruption”. Not all volcanoes blow ash into the atmosphere. Think of Kilauea in Hawaii – this is an example of an “effusive eruption” where lava oozes or bubbles up out of the ground in a rather non-violent manner. These are the most common volcanic eruptions on land that everyone should already be familiar with.
But, what happens when the volcano is underwater? You get what a group of New Zealand geologists are calling “Tangaroan” (named after the Maori god of the sea, Tangaroa). This article explains it in more detail, but the short version is this: at the bottom of the ocean, there is immense pressure from the weight of the water above the volcano that prevents an eruption from being truly “explosive”, yet the eruptions are often more violent than an effusive eruption. The magma, filled with gas, erupts into the ocean where the outer edges are instantly cooled and solidified. (The water is cold at the bottom of the ocean.) This traps all the gas inside and you get a rock that’s filled with millions of tiny air bubbles, which is called pumice. This new rock can be so light, it floats to the surface.
What does this have to do with VIIRS or a blog about imagery from weather satellites? Large underwater volcanic eruptions can create large quantities of pumice that float to the surface of the ocean and create what are called pumice rafts. VIIRS has seen these pumice rafts.
Here is a “natural color” or “pseudo-true color” RGB composite of VIIRS channels I-01 (0.64 µm, blue), I-02 (0.865 µm, green) and I-03 (1.61 µm, red), taken at 01:40 UTC 27 August 2012. Notice anything unusual in the water?
False color RGB composite of VIIRS channels I-01, I-02 and I-03, taken 01:40 UTC 27 August 2012
As always, click on the image, then on the “2798×2840” link below the banner to see the full resolution image. All those pale blue-gray swirls in the ocean surrounding Raoul Island and Macauley Island are the pumice rafts. They almost look like someone sprayed “Silly String” in the ocean.
To get a sense of the scale of these rafts, the latitude lines plotted on the image are ~111 km apart. Some of these rafts are 1-2 km wide in places. In this image you can see pumice rafts stretching from about 27.5 °S to 31.5 °S latitude and from about 175 °W to 178 °E longitude. That is a lot of floating rocks!
Here is a zoomed version of the previous image:
False color RGB composite of VIIRS channels I-01, I-02 and I-03, taken 01:40 UTC 27 August 2012
The main concentration of floating pumice is in the box the covers the area from 29 °S to 30 °S latitude and from about 176 °W to 178 °E longitude, although there is plenty of pumice south of that box – it’s just a little harder to see.
As an aside, Raoul and Macauley islands are part of the Kermadec Islands of New Zealand. If you’re interested, the New Zealand government is always looking for volunteers to spend six months on Raoul Island pulling weeds and keeping invasive species off the island. (There, that saves me from doing a Remote Island post to cover this.)
These pumice rafts have been traced back to the eruption of the Havre Seamount (an underwater volcano) on 18 July 2012. This new eruption is part of the “Ring of Fire” in the southwestern part of the Pacific Ocean, roughly 1,000 kilometers northeast of New Zealand. If you believe the Wikipedia article linked to first in this paragraph, the eruption was unknown until an aircraft passenger took pictures of the pumice raft from her plane on 31 July 2012. I have been able to track this pumice back to 26 July 2012. Before that, it is too cloudy, making it difficult to see anything. (Apparently, MODIS saw it on 19 July 2012.)
False color RGB composite of VIIRS channels I-01, I-02 and I-03, taken 01:39 UTC 26 July 2012
The red arrow points to the pumice raft. There’s a nice looking cyclone southwest of the pumice, but I’m not sure if it was given a name. If you zoom in, you can see Cheeseman Island and Curtis Island off to the east of the raft. These islands were obscured by clouds on the 27 August 2012 overpass. Cheeseman Island is only 7.6 ha (19 acres) and Curtis Island is 40 ha (99 acres), yet VIIRS has the resolution to see them!
In an effort to highlight these pumice rafts, a PCI analysis was performed on the five VIIRS high-resolution imagery (I-band) channels. PCI analysis uses principal components to identify the major modes of variability within the data. Analysis of the 5 VIIRS I-bands resulted in 5 PCIs or component images. Of those components, PCI-2, 3, and 5 appeared to show the pumice rafts. A particular RGB combination of those three components (red = PCI-5, green = PCI-2 and blue = PCI-3) resulted in the pumice appearing red on a green-blue ocean. Clouds are white, then cyan and then red for colder cloud-top temperatures. (Certain pepper-like black pixels are out of range in the PCI analysis.) The three principal components that highlight the pumice rafts are shown in the figure below, along with the resulting RGB composite. Unfortunately, these images were made using McIDAS-X, which has a habit of plotting VIIRS data upside-down. Therefore, north in each image is at the bottom.
PCI Analysis of the 5 VIIRS I-band channels from 01:40 UTC 27 August 2012. Panels A, B, and C are the second, third and fifth principal component images from this analysis (PCI-2, PCI-3 and PCI-5). Panel D is an RGB composite of these three images with PCI-5 as red, PCI-2 as green and PCI-3 as blue. Images courtesy Don Hillger.
This in an image you’ll want to zoom in on to see the details as you consider the information in the previous paragraph. There are two main results of this PCI analysis: it can be used to highlight pumice rafts (although they have the same color as cold cloud tops) and the temperature information from channel I-5 (11.5 µm), which shows up in PCI-5, indicates that the pumice has a tendency to collect along gradients in sea surface temperature.
Being able to track the pumice rafts is important for geology, biology and oceanography. They can act as a tracer for following ocean currents. Some of them crack and fill with water, causing them to sink to the bottom, depositing the newly formed rock in other parts of the sea floor. The nature of the pumice gives clues about what happens in underwater volcanoes, a process that is not well known at this point. And, as these floating pieces of pumice are carried around, organisms like algae, coral, and barnacles will attach to them and grow, eventually settling in far away places. Studying these rafts may shed new light on how life can spread across the oceans.
So, yes – rocks can float. And they can be seen by a weather satellite with 375 m resolution.
On the border between Chile and Argentina sits the volcano Copahue. (If you say it out loud, it is pronounced “CO-pa-hway”.) In the local Mapuche language, copahue means “sulfur water”. This name was given to the volcano as the most active crater contains a highly acidic lake full of sulfur. An eruption in 1992 filled the area with “a strong sulfur smell.” Later eruptions have involved “pyroclastic sulfur” (molten hot sulfur ash) and highly acidic mudflows. That doesn’t sound very pleasant.
Right before Christmas, Copahue was at it again. It erupted on 22 December 2012, sending a cloud of sulfur ash into the atmosphere, and MODIS got there first. VIIRS got there 4 hours later and took this image:
VIIRS "true color" RGB composite of channels M-03, M-04 and M-05, taken 18:38 UTC 22 December 2012
This is a “true color” image just like the MODIS one in the link. Make sure you click on the image, then on the “3200×2304” link below the banner to see it in full resolution. Then see if you can spot the volcanic ash cloud from Copahue. I’ll give you a hint: it’s the only cloud that appears brownish-gray.
If you still can’t see it, here’s a zoomed-in image with a yellow arrow to help you out:
VIIRS "true color" RGB composite of the Copahue volcano, taken 18:38 UTC 22 December 2012
Now compare the ash cloud in the VIIRS image with the ash cloud in the MODIS image from 4 hours earlier. (This is easier to do if you can locate in the VIIRS image the lakes marked as “Embalse los Barreales” in the MODIS image.) There’s a lot less ash in the VIIRS image, right?
Not so fast. As the ash dispersed, the plume thinned out, making it harder to see against the brown background surface. But, that doesn’t mean that it’s not there. Here’s the “split window difference” image from VIIRS at the same time:
VIIRS "split window difference" image (M-15 - M-16) taken 18:38 UTC 22 December 2012
That whole black plume is volcanic ash detected by the split window difference. The yellow arrow points to Copahue and the ash plume that is visible in the true color image. The red arrow points to the ash plume that is not visible in the true color image, yet is detected by this simple channel difference (M-15 minus M-16). A victory for the split window technique!
It was also a victory for the EUMETSAT Dust RGB, which didn’t work for the 100-year-old ash cloud over Alaska. Here’s what that RGB composite looks like when applied to VIIRS:
EUMETSAT's Dust RGB composite applied to VIIRS from 18:38 UTC 22 December 2012
It is interesting that the ash plume right over Copahue is tough to detect in this RGB composite because it is red, just like a lot of the other clouds. As the plume thins out away from the volcano, its color changes to a variety of pastels of pink and blue, and even appears to extend out over the Atlantic Ocean. Where clouds and ash coexist near the coast of Argentina, pixels show up orange and yellow and green (click to the high-resolution image to see that).
Why does the plume appear to extend into the Atlantic Ocean in the EUMETSAT Dust RGB, and not in the split window difference? It is due to the fact that the Dust RGB uses channel M-14 (8.55 µm), which is sensitive to absorption by sulfur dioxide (SO2) gas. The split window difference is better at detecting sulfuric ash particles, which may have mostly settled out of the atmosphere before reaching the Atlantic coast. There are likely still some ash particles in the plume, though – just not enough to show up easily in the split window difference. Detection of SO2 gas plumes has been used to infer the presence of volcanic ash.
Being able to see the location of the volcanic ash very important to pilots. Aircraft engines don’t work that well when they are sucking in particles of liquified sulfur and other abrasive and corrosive materials spit out by stinky volcanoes like Copahue.
Much of the United States has had a below-average amount of snow this fall (and below-average precipitation for the whole year). Look at how little snow cover there was in the month of November. Parts of Europe, however, have seen snow. It’s nice to know that it’s falling somewhere. But, can you tell where?
Here is a visible image (0.6 µm) from Meteosat-9, taken 12 December 2012 (at 12:00 UTC):
Meteosat-9 visible image of central Europe, taken 12:00 UTC 12 December 2012. Image courtesy EUMETSAT.
And here’s the infrared image (10.8 µm) from the same time:
Meteosat-9 IR-window image of central Europe, taken 12:00 UTC 12 December 2012. Image courtesy EUMETSAT.
These are images provided by EUMETSAT. Can you tell where the snow is? Or what is snow and what is cloud?
Here’s a much higher resolution image from VIIRS (zoomed in the Alps), taken only 3 minutes later:
VIIRS visible image (channel I-01) of central Europe, taken 12:03 UTC 12 December 2012
Now is it easy to differentiate clouds from snow? Just changing the resolution doesn’t help that much.
This has long been a problem for satellites operating in visible to infrared wavelengths. Visible-wavelength channels detect clouds based on the fact that they are highly reflective (just like snow). Infrared (IR) channels are sensitive to the temperature of the objects they’re looking at, and detect clouds because they are usually cold (just like snow). So, it can be difficult to distinguish between the two. If you had a time lapse loop of images, you’d most likely see the clouds move, while the snow stays put (or disappears because it is melting). But, what if you only had one image? What if the clouds were anchored to the terrain and didn’t move? How would you detect snow in these cases?
EUMETSAT has developed several RGB composites to help identify snow. The Daytime Microphysics RGB (link goes to PowerPoint file) looks like this:
Meteosat-9 "Daytime Microphysics" RGB composite of central Europe, taken 12:00 UTC 12 December 2012. Image courtesy EUMETSAT.
Snow is hot pink (magenta), which shows up pretty well. Clouds are a multitude of colors based on type, particle size, optical thickness, and phase. That whole PowerPoint file linked above is designed to help you understand all the different colors.
The Daytime Microphysics RGB uses a reflectivity calculation for the 3.9 µm channel (the green channel of the RGB). Without bothering to do that calculation, I’ve replaced the reflectivity at 3.9 µm with the reflectivity at 2.25 µm (M-11) when applying this RGB product to VIIRS, and produced a similar result:
VIIRS "Daytime Microphysics" RGB composite of the Alps, taken 12:03 UTC 12 December 2012
Except for the wavelength difference of the green channel (and minor differences between the VIIRS channels and Meteosat channels), everything else is kept the same as the official product definition. Once again, the snow is pink, in sharp contrast to the clouds and the snow-free surfaces. We won’t bother to show the Nighttime Microphysics/Fog RGB (link goes to PowerPoint file) since this is a daytime scene.
EUMETSAT has also developed a Snow RGB (link goes to PowerPoint file):
Meteosat-9 "Snow" RGB composite of central Europe, taken 12:00 UTC 12 December 2012. Image courtesy EUMETSAT.
This also uses the reflectivity calculated for the 3.9 µm channel. Plus, it uses a gamma correction for the blue and green channels. Is it just me, or does snow show up better in the Daytime Microphysics RGB?
If you switch out the 3.9 µm for the 2.25 µm channel again and skip the gamma correction when creating this RGB composite for VIIRS, the snow stands out a lot more:
VIIRS "Snow" RGB (with modifications as explained in the text), taken 12:03 UTC 12 December 2012
Now you have snow ranging from pink to red with gray land areas, black water and pale blue to light pink clouds. This combination of channels makes snow identification easier than the official “Snow RGB”, I think.
All of this is well and good but, for my money, nothing beats what EUMETSAT calls the “natural color” RGB. I have referred to it as the “pseudo-true color“. Here’s the low-resolution EUMETSAT image:
Meteosat-9 "Natural Color" RGB of central Europe, taken 12:00 UTC 12 December 2012. Image courtesy EUMETSAT.
And the higher resolution VIIRS image:
VIIRS "Natural Color" RGB composite of channels M-5, M-7 and M-10, taken 12:03 UTC 12 December 2012
The VIIRS image above uses the moderate resolution channels M-5, M-7 and M-10, although this RGB composite can be made with the high-resolution imagery channels I-01, I-02 and I-03, which basically have the same wavelengths and twice the horizontal resolution. Below is the highest resolution offered by VIIRS (cropped down slightly to reduce memory usage when plotting the data):
VIIRS "Natural Color" RGB composite of channels I-01, I-02 and I-03, taken 12:03 UTC 12 December 2012
Make sure to click on the image and then on the “2594×1955” link below the banner to see the image in full resolution.
This RGB composite is easier on the eyes and easier to understand. Snow has high reflectivity in M-5 (I-01) and M-7 (I-02) but low reflectivity in M-10 (I-03) so, when combined in the RGB image, it shows up as cyan. Liquid clouds have high reflectivity in all three channels so it shows up as white (or dirty, off-white). The only source of contention is that ice clouds, if they’re thick enough, will also show up as cyan.
Except for the cyan snow and ice, the “natural color” RGB is otherwise similar to a “true color” image. Vegetation shows up green, unlike the other RGB composites where it has been gray or purple or a very yellowish green. That makes it more intuitive for the average viewer. You don’t need to read an entire guide book to understand all the colors that you’re seeing.
Compare all of these RGB composites against the single channel images at the top of the page. They all make it easier to distinguish clouds from snow, although some work better than others. Now compare the VIIRS images with the Meteosat images. Which ones look better?
(To be fair, it’s not all Meteosat’s fault. The images provided by EUMETSAT are low-resolution JPG files [which is a lossy-compression format]. The VIIRS images shown here are loss-less PNG files, which are much larger files to have to store and they require more bandwidth to display.)
As a bonus (consider it your Christmas bonus), here are a few more high-resolution “natural color” images of snow and low clouds over the Alps. These are kept at a 4:3 width-to-height ratio and a 16:9 ratio, so they make ideal desktop wallpapers.
VIIRS "natural color" composite of channels I-01, I-02 and I-03, taken 12:29 UTC 14 November 2012. This is an ideal desktop wallpaper for 4:3 ratio monitors.
That was the 4:3 ratio image. Here’s the 16:9 ratio image:
VIIRS "natural color" composite of channels I-01, I-02 and I-03, taken 12:29 UTC 14 November 2012. This is an ideal desktop wallpaper for 16:9 ratio monitors.
Enjoy the snow (or be glad you don’t have to drive in it)!
Lost in all the commotion caused by Hurricane Sandy, a curious event occurred on the other side of the country on 30 October 2012. A cloud of ash obscured the skies of Kodiak Island, Alaska, diverting flights in the region and forcing the people of Kodiak to stay inside or wear masks. Alaska has quite a few volcanoes, so this may not be a big thing to them except, this was no ordinary volcanic eruption: it was the leftovers of a volcanic eruption from 100 years ago!
The volcano that came to be known as Novarupta erupted on 6 June 1912. It was one of the largest volcanic eruptions of recorded history. It was 10 times more powerful than Mt. St. Helens with 100 times more ash. The explosion was heard more than 1100 km (700 miles) away in Juneau. The force of the eruption caused nearby Mt. Katmai to collapse on itself (10 km away). It formed the Valley of Ten Thousand Smokes and, most importantly for us, covered the surrounding land with 150 m (500 ft) of ash.
This pile of ash – still there today – can be lifted by a stiff breeze (or, more appropriately, “strong breeze” or higher on the Beaufort wind scale), and blown pretty high off the ground (4000 ft according to the news report). This isn’t the first time this has happened. MODIS observed the same thing back in 2003.
So, what did VIIRS see? Here’s the “true color” image, the RGB composite of channels M-03 (0.488 µm, blue), M-04 (0.555 µm, green) and M-05 (0.672 µm, red):
VIIRS "true color" RGB composite of channels M-03, M-04 and M-05, taken 22:23 UTC 30 October 2012
Be sure (as with all the images) to click on the image, then on the link below the banner to see it at full resolution. (The link contains the dimensions of the full size image.)
The ash cloud (blowing right over the center of Kodiak Island) is not as obvious in this image as it was in the MODIS image in the link above, although it is visible. To be fair, the plume was much more optically thick in 2003, and there were fewer clouds and less snow to confuse it with.
Here is the false color (“pseudo-true color” or “natural color”) image, the RGB composite of channels M-05 (0.672 µm, blue), M-07 (0.865 µm, green) and M-10 (1.61 µm, red):
VIIRS false color RGB composite of channels M05, M-07 and M-10, taken 22:23 UTC 30 October 2012
Hmmm. Once again, the ash plume is visible but not particularly noticeable. Is there a way to highlight the ash plume to make it easier to see?
EUMETSAT (the European Organisation for the Exploitation of Meteorological Satellites) has defined an RGB composite for detecting dust. Their product, which was developed primarily to detect dust storms over the Saharan desert, uses channels that are present (or similar to ones that are present) on VIIRS. This means we can apply the dust product for VIIRS as the difference between M-16 and M-15 (red), the difference between M-15 and M-14 (green) and M-15 by itself (blue), all in units of brightness temperature. If you do that, and use the same color scaling they use, you get this image:
The EUMETSAT Dust RGB composite applied to VIIRS for 22:23 UTC 30 October 2012
The arrow points to the source region of the ash plume. In this RGB composite, dust shows up as hot pink (magenta), but it’s barely visible here. The reason is that this dust product is primarily useful where there is a large temperature contrast between the dust plume and the background surface, which we don’t have here.
A more common way to detect volcanic ash is to use the “split-window difference”. The “split-window difference” is the difference in brightness temperature between a 10.7-11.0 µm channel and a 12.0 µm channel. This difference is useful because volcanic ash has a difference of opposite sign to most everything else. Here’s what the split window difference (M-15 – M-16) looks like for this case:
VIIRS "Split-window difference" image from 22:23 UTC 30 October 2012
This image has been scaled so that the colors range from -1 K (black) to +7 K (white). The ash plume stands out a bit more here by being much darker than the background. The only problem is, it isn’t perfect. Large amounts of water vapor, optically thick clouds, desert surfaces and boundary layer temperature inversions can all produce a negative difference (just like volcanic ash does).
These problems can be overcome to a certain extent by combining the “split-window difference” with a Principal Component Image (PCI) analysis technique. (This technique is too complicated to describe here but, if you have access to AMS journals, check out these journal papers.) Now, the ash plume is the only thing that’s black:
VIIRS PCI split window analysis image from 22:23 UTC 30 October 2012. Image courtesy Don Hillger. Upside-down text courtesy McIDAS-X.
Notice the smaller plume identified by the orange arrow. This plume is not easy to identify in any of the previous images. The PCI technique works well. But, we’re not going to stop there.
Remember the dust plumes off the Cape Verde islands? They produced a strong signal in the difference between M-12 (3.7 µm) and M-15 (10.7 µm) due to solar reflection. Does a 100-year-old ash plume produce a similarly strong signal? See for yourself:
VIIRS channel difference image between M-12 and M-15 from 22:23 UTC 30 October 2012
It does produce a signal, but it’s not as bright as the surrounding clouds. The color scale here ranges from -2 K (black) to +90 K (white).
M-06 (0.746 µm) is highly sensitive to anything that reflects solar radiation in the atmosphere or on the surface, which we learned from Hurricane Isaac. Here’s what the M-06 image looks like:
VIIRS channel M-06 image, taken 22:23 UTC 30 October 2012
“Big deal,” you say. “None of those are better than the PCI analysis.” That may be true, but watch what happens when we combine M-06, the M-12 – M-15 image and the split-window difference image in a single RGB composite:
VIIRS RGB composite of M06 (blue), M12 - M15 (green) and M15 - M16 (red), taken 22:23 UTC 30 October 2012
In this composite, blue values represent the M-06 reflectance scaled from 0 to 1.6, green values represent the brightness temperature difference between M-12 and M-15 scaled from -2 K to +90 K, and red values represent the brightness temperature difference between M-15 and M-16 scaled from -1 K to +7 K.
From a theoretical perspective, this RGB composite does exactly what you want: make the thing you’re trying to detect the only thing that is a certain color. For example, the ash plumes are the only things in this image that are green. From a practical perspective, however, this RGB composite doesn’t work so well. It only works because the ash plume is over water (otherwise M-06 wouldn’t be very useful). It only works during the day, where M-06 is available and the difference between M-12 and M-15 is significant (no solar component to M-12 at night).
Plus, the rainbow of colors is difficult to make sense of: green ash; clouds ranging from light blue to purple to orange (a function of optical thickness, particle size, and phase); bright purple snow; dark purple vegetation; maroon water. It’s not exactly pleasing to the eye. In contrast, the PCI analysis technique that uses the split-window difference works day and night, over ocean and over land. And it isn’t confusing to look at. Maybe we should have stopped when we got to the PCI technique. But then, we wouldn’t have learned anything new.
“At 10 o’clock the Captain was walking on deck and saw what he supposed to be an immense iceberg. … the atmosphere was hazy, and then a heavy snow squall came up which shut it out entirely from our view. Not long after the sun shone again, and I went up again and with the glass, tried to get an outline of it to sketch its form. The sun seemed so dazzling on the water, and the tops of the apparent icebergs covered with snow; the outline was very indistinct. We were all the time nearing the object and on looking again the Captain pronounced it to be land. The Island is not laid down on the chart, neither is it in the Epitome, so we are perhaps the discoverers, … I think it must be a twin to Desolation Island, it is certainly a frigid looking place.”
VIIRS false color composite of channels I-01, I-02 and I-03, taken 09:16 UTC 27 October 2012
The text above was the journal entry of Isabel Heard, wife of the American Captain John Heard, on 25 November 1853. The couple was en route from Boston, Massachusetts to Melbourne, Australia (a long time to spend in a boat) and the land they spotted became known as Heard Island. It should be noted that “Desolation Island” refers to Îles Kerguelen, which has its own unique story of discovery.
Kerguelen Island was discovered in 1772 by Yves-Joseph de Kerguelen de Trémarec, a French navigator commissioned by King Louis XV to discover the unknown continent in the Southern Hemisphere that he believed to be necessary to balance the globe. (Look at a globe or map of the world and notice that most of the land area is in the Northern Hemisphere.) Kerguelen himself never set foot on the island, but he told his king the island was inhabited and full of forests, fruits and untold riches. He called it “La France Australe” (Southern France). Captain Cook actually did land on the island a few years later and named it Desolation Island because it had none of that stuff, and King Louis XV imprisoned Kerguelen after his lie was discovered. Oops.
Îles Kerguelen, made up of the main island (Kerguelen to us, La Grande Terre to the French) and the many small surrounding islands are part of the French Southern and Antarctic Lands (Terres Australes et Antarctiques Françaises or TAAF). Heard Island is part of the Australian territory of Heard Island and McDonald Islands (HIMI).
These islands are in the “Roaring Forties” and “Furious Fifties”, the region of the Southern Ocean (southern Indian Ocean in this case) between 40 °S and 60 °S latitude. Get out your globe or world map once again and notice that there is very little land in this latitude range. This region is where strong, persistent westerly winds circle the globe. With no land in the way, there isn’t much to disturb this flow. The high winds almost always from the same direction create huge waves of 10 m (33 ft) or more. (Now imagine being John or Isabel Heard. Well, actually, if you suffer from sea-sickness you probably shouldn’t imagine it.) The cold winds flow over the relatively warmer waters of the ocean, forming persistent cloudiness. If you zoom in on the image above (click on the image, then on the “1893×1452” link below the banner for full resolution) you can see quite a bit of structure in the resulting “cloud streets“.
The persistent cloudiness makes Kerguelen and Heard Island a rare sight from any satellite. We can see them here because the flow is stable and the islands are producing the equivalent of a “rain shadow” on the clouds. (It’s tempting to call it a “cloud shadow” but, since clouds actually do cast shadows, it would just confuse people.) If we zoom in on Kerguelen, this shows up more clearly:
VIIRS false-color RGB composite of channels I-01, I-02 and I-03 taken 09:16 UTC 27 October 2012
Notice how all the clouds are piling up on the west (windward) side of Kerguelen, where the highest mountains, are located. (These mountains are covered with snow and glaciers, as the cyan color indicates.) Could that be the equivalent of a bow shock near 68 °E longitude where there is an apparent crack in the clouds? On the leeward side of the island, downwind of the mountains, the air is descending, which prevents clouds from forming. Kerguelen created a hole in the clouds by disrupting the flow.
Now, let’s zoom in on Heard Island:
VIIRS false-color RGB composite of channels I-01, I-02 and I-03 taken 09:16 UTC 27 October 2012
In addition to creating a hole in the clouds, Heard Island is creating all sorts of waves in the atmosphere. The ones you probably noticed first look like the wake created by a boat (and have the same basic cause). But, why do they start well out ahead of the island where the yellow arrow is pointing? Because those first waves are actually caused by the McDonald Islands (discovered by Capt. William McDonald in 1854). Even though the highest point on McDonald Island is only 186 m above mean sea level (610 ft), it’s enough to disrupt the flow.
The highest point on Heard Island is Mawson Peak at 2745 m (9006 ft), which is actually the highest elevation in Australia. It is part of Big Ben, an active volcano that last erupted in 2008. This peak is creating a series of lenticular clouds in the above image. A patch of cirrus clouds also exists downwind of Heard Island (the more cyan colored clouds), although it is not clear if these clouds were formed by the waves caused by Heard Island.
If you’re interested in visiting either of these islands, here are some other interesting facts: Kerguelen has a year-round population of ~100, almost all scientists. It has a permanent weather station and office maintained by Météo-France (France’s version of the National Weather Service), and the French version of NASA (CNES) has a station for launching rockets and monitoring satellites. Heard Island has no permanent residents. Every few years a scientific expedition sets out for the island to study the geology, biology, weather and climate of the island. The next one is planned for 2014 and is being called an “open source expedition”. There may still be time to join in if you’re looking for an adventure!
Last time we visited Greenland, it was because VIIRS saw evidence of the rapid ice melt event in July 2012. We return to Greenland because of this visible image VIIRS captured on 18 October 2012:
VIIRS channel I-01 image taken 12:43 UTC 18 October 2012
This image was taken by the high-resolution visible channel, I-01 (0.64 µm), and was cropped down to reduce the file size. Greenland is in the upper-left corner of the image. The northwest corner of Iceland is visible in the lower-left corner of the image.
So, what’s with all the swirls off the coast of Greenland? Are they clouds swirled around by winds? Or some kind of sea serpent – perhaps a leviathan or a kraken? (Based on the descriptions, they would be big enough for VIIRS to see them.)
Sadly, for all you science fiction and fantasy fanatics, those swirls are just icebergs breaking up as they enter warmer water, the chunks of ice caught up in eddies in the East Greenland Current. This is easier to see when you look at the “true color” image below:
VIIRS "true color" RGB composite of channels M-3, M-4 and M-5, taken 12:43 UTC 18 October 2012
Make sure to click on the image, then on the “3200×1536” link below the banner to see the image at full resolution. Since the true color RGB composite is made from moderate resolution channels M-03 (0.488 µm, blue), M-04 (0.555 µm, green) and M-05 (0.672 µm, red), we can include more of the swath before we get into file size issues. That allows us to see the extent of the ice break-up along the Greenland coast.
There is a lot to notice in the true color image. The large icebergs at the top of the image breakup into smaller and smaller icebergs as they float down the east coast of Greenland, until they finally melt. These visible “swirls” (or “eddies” in oceanography terms) extend from 75 °N latitude down to 68 °N latitude where the ice disappears (melts).
The upper-right corner with missing data is on the night side of the “terminator” (the line separating night from day), where we lose the amount of visible radiation needed for these channels to detect stuff. (The Day/Night Band would still collect data, however, as it is much more sensitive to the low levels of visible radiation observed at night.) See how the ice and the high clouds appear to get a bit more pink as you move from west (left) to east (right)? It’s the same reason cirrus clouds often look pink at sunset. The sun is setting on the North Atlantic and more of the blue radiation from the sun is scattered by the atmosphere than red radiation. The red radiation that’s left is then reflected off the clouds (and ice and snow) toward the satellite.
Just to prove that the swirls are indeed ice and not clouds, here’s the “pseudo-true color” (a.k.a. “natural color”) RGB composite made from channels M-05 (0.672 µm, blue), M-07 (0.865 µm, green) and M-10 (1.61 µm, red):
VIIRS natural color image of channels M-05, M-07 and M-10, taken 12:43 UTC 18 October 2012
The deep blue color of the swirls in this RGB composite is indicative of ice, not clouds. These channels are not impacted by atmospheric scattering at any sun angle, though, so there is no change in the color of the clouds as you approach the terminator.
You may have also noticed the cloud streets downwind of the icebergs off the coast of Greenland. These clouds are formed in the same way as lake-effect clouds are in the Great Lakes. Cold, arctic air flowing south over the icebergs meets the relatively warm water of the open ocean. The moisture evaporating from the warmer waters condenses in the cold air and forms clouds.
How much warmer is that water? Here’s the high-resolution infrared (IR) image (I-05, 11.45 µm):
VIIRS channel I-05 image, taken 12:43 UTC 18 October 2012
At ~375 m resolution at nadir, this is the highest resolution available in the IR on a non-classified satellite today. Look at all the structure in the cloud-free areas of the ocean! Lots of little eddies show up in the IR that are invisible in the visible and near-IR channels shown previously. The only eddies visible in the true color and natural color images are the ones that had ice floating in them. Here we see they extend much further south than the ice.
The ice-free water that is not obscured by clouds is 10-15 K warmer than where the icebergs are found. The eddies are caused by the clash between the southward flowing, cold Eastern Greenland Current and the northbound, warm North Atlantic Drift (the tail end of the Gulf Stream), which are important in the global transport of energy. They are not ship-sinking whirlpools caused by any krakens in the area – at least VIIRS didn’t observe any.
UPDATE (February 2013): Below is another image of the eddies and swirls off the eastern coast of Greenland. This “natural color” image was taken 13:34 UTC 15 February 2013:
VIIRS false color RGB composite of channels M-05, M-07 and M-10, taken 13:34 UTC 15 February 2013. Image courtesy Don Hillger.
Since it is winter, the ice extends further south along the coast before it melts. Once again, there is a lot of structure visible in the edge of the ice, where the East Greenland Current and North Atlantic Drift interact. Another thing to notice is the shadows. At the top of the image just right of center is Scoresby Sound, which is completely frozen over. Given that the sun is pretty low in the sky over Greenland in the winter (if it rises at all, since most of Greenland is north of the Arctic Circle), the mountains south of the Sound cast some pretty long shadows on the ice. It’s possible to use the length of the shadows with the solar zenith angle to estimate the height of those mountains (although there are more accurate ways to determine a mountain’s elevation from satellite). VIIRS provides impressive detail, even from the moderate resolution bands.
How fast does an aurora move? I “googled” it, and got answers ranging from “fast” to “very fast”. Not very scientific. It also doesn’t help that the majority of aurora videos on the Internet are time-lapse footage, and there’s no way to know how fast the footage has been sped up. Although, I did find this video that claims to be real-time footage:
When the camera is still, you could try to calculate the speed of some of the aurora elements if you knew where the cameraman was, what stars were in the view (and how far apart they are), and how high up (or how far away) the aurora was at that time. All information that I don’t have.
What if I said we could estimate the speed of the aurora by examining VIIRS Day/Night Band (DNB) images?
Here’s a DNB image of the aurora australis (a.k.a. Southern Lights) over Antarctica, taken on 1 October 2012:
VIIRS DNB image of the aurora australis, taken 00:22 UTC 1 October 2012
Compare this image with the images of the aurora borealis shown back in March 2012. Something doesn’t look right. Far from looking like smooth curtains of light, the aurora (particularly the brightest one) has a jagged appearance, like a set of steps. (This is easier to notice if you click on the image to see it in higher resolution.) This is because the aurora wouldn’t stay still, and we can use this information to estimate the speed it was moving.
The stripes that you see in the image are a caused by the 16 detectors that comprise the DNB which, for various reasons, don’t have exactly the same sensitivity to light. (This condition is given a super-scientific name: “striping”.) The DNB senses light from the Earth by having a constantly rotating mirror reflect light onto these detectors. One rotation of the mirror (particularly the part that occurs within the field of view of the sensor) comprises one scan. Each detector comprises one row of pixels in each scan, each with 742 m x 742 m resolution at nadir. There are 48 scans in one “granule” (the amount of data transmitted in one data file), and it takes ~84 seconds to collect the data that make up one granule. That means it takes ~1.75 seconds per scan.
If you watch that video again, you’ll notice that the aurora can move quite a bit in 2 seconds. Now, let’s zoom in much more closely on one of the aurora elements:
Zoomed-in VIIRS DNB image of an aurora, taken 00:22 UTC 1 October 2012
This image has been rotated relative to the original image, in case you were wondering why it doesn’t seem to match up with the first image. The brightest pixels are where the brightest aurora elements were located. The “steps” (or “shifts” as they are typically called) occur every 16 pixels, which mark out the end of one scan and the beginning of the next. If you count the number of pixels that the brightest aurora elements shifted from one scan to the next, it varies from about 6 to 10 pixels. Assuming a constant resolution of 742 m per pixel along the scan (which isn’t exactly true, the resolution degrades a little bit as you get closer to the edge of the scan but not by much), that means this particular aurora element moved somewhere between ~4.5 and ~7.5 km in ~1.75 seconds from one scan to the next. Doing the math (don’t forget to carry the 1), that comes out to somewhere between 9000 and 15,000 km h-1 (rounded to account for possible sources of error), which I guess counts as “very fast”. But, it’s not as fast as the coronal mass ejections that create auroras. They have an average speed of 489 km s-1 (1,760,000 km h-1)!
So, what looks like an oddity in the VIIRS image, actually contains some interesting scientific information about the speed of an “active aurora“.
But, we’re not done yet. Let’s get back to the striping. Along with “stray light”, it’s one of the few remaining issues in VIIRS imagery. Stray light, which you can see evidence of in the lower right corner of first aurora image, is a particular problem in the DNB. It occurs when sunlight is reflected onto the detectors when the satellite is on the nighttime side of the Earth, but close to the edge of the day/night “terminator“. Our colleagues at Northrup Grumman have been working on a correction to stray light that also reduces the striping. This correction allows for much better viewing of auroras, which have a tendency to occur right where stray light is an issue.
Here is an image of another aurora over Antarctica, taken on 15 September 2012, corrected for stray light and striping:
VIIRS DNB image of the aurora australis over Antarctica, taken 18:56 UTC 15 September 2012. The data used in this image was corrected for stray light and striping by Stephanie Weiss (Northrup Grumman).
This was the night of a new moon, so the only light in the scene (once the stray light is taken out) is the aurora. (OK, there may be some “air glow” and starlight. But, it doesn’t show up on this brightness scale.)
This aurora was a lot less “active” so it looks more like smooth curtains of light. Although, when you zoom in on the brightest swirl in the upper right corner, you can see it did move 3-5 pixels between scans:
VIIRS DNB image of the aurora australis over Antarctica, taken 18:56 UTC 15 September 2012. This image has been zoomed in and rotated relative to the previous image of the same aurora. The data used in this image was corrected for stray light and striping by Stephanie Weiss (Northrup Grumman).
This translates to 4000 to 8000 km h-1, which still counts as “fast” even if it doesn’t count as “very fast”. See, Google was right! Auroras do move anywhere from “fast” to “very fast”. But, now we at least have an estimate to quantify that speed.
And, in case you were wondering, these estimates of the speed of auroras are consistent with earlier observations. According to the book Aurora and Airglow by B. McCormac (1967), the typical speed of auroras is between 0 and 3 km s-1 (up to 10,800 km h-1). So, it appears that VIIRS does give a reasonable estimate about the speed of an aurora. We just happened to catch one “typical” aurora and one “faster than typical” aurora.
I’m not talking about a Subaru. I’m talking about the vast expanse of sparsely-populated Australia. We’ve already seen fires in the United States, Russia and the Canary Islands. Well, they have been happening down under, too. (Is there any part of this planet not currently experiencing a drought?)
Despite the risk of getting fire fatigue (“Another post about fires?” *yawn*), we’re going to look at these fires for two reasons. First, it gives me a chance to show off the “fire tornado” video clip that has been making the rounds on the Internet:
Second, VIIRS saw the fire that produced the “fire tornado” (and a whole bunch of other fires) and it gives me a chance to show off the newly christened “Fire Temperature RGB”.
First, let’s look at the boring (yet still valuable) way of detecting fires: identifying hot spots in a 3.9 µm image. Here’s what VIIRS channel M-13 (4.0 µm) saw over Australia on 19 September 2012:
VIIRS channel M-13 image of central Australia, taken 04:34 UTC 19 September 2012
Pixels hotter than 350 K show up as black in this image. Given this information, how many fires can you see? (Hint: click on the image, then on the “3200×1536” link below the banner to see the image at full resolution. And, no, wise guy – you don’t count all the black pixels outside the boundaries of the data.)
Here’s the “pseudo-true color” RGB composite (this time made of M-05 [0.67 µm, blue], M-07 [0.87 µm, green], and M-10 [1.61 µm, red]):
False-color RGB composite of VIIRS channels M-05, M-07 and M-10, taken 04:34 UTC 19 September 2012
With this RGB composite, really hot fires show up as bright red pixels. More hot spots are visible in the M-13 image than the “pseudo-true color” image because M-13 is much more sensitive to the heat from fires than M-05, M-07 and M-10 are. M-10 only picks up the signal from the hottest (or biggest) fires. M-05 and M-07 don’t pick up the heat signal at all, because the radiation from the sun, reflected off the Earth’s surface, drowns it out (which is precisely why the hot spots look red). M-13 is also better at detecting fires because it works at night, unlike these three channels.
You can make the hot spots from the smaller/less hot (lower brightness temperature) fires more visible by replacing M-10 with M-11 (2.25 µm) as the red channel in the RGB composite. M-11 is more sensitive to hot spots than M-10. If you do that, you get this image:
False-color RGB composite of VIIRS channels M-05, M-07 and M-11, taken 04:34 UTC 19 September 2012
Since the previous RGB composite is often referred to as “natural color”, maybe this one should be called the “natural fire color” RGB composite. Now, most of the hot spots (not just the hottest ones) show up as red.
It should be noted that the fire complex in the grid box bounded by the 24 °S and 26 °S latitude and 128 °E and 132 °E longitude lines is where the video of the fire tornado came from. That fire is currently burning close to Uluru (a.k.a. Ayers Rock), the site where the creator beings live, according to local legend. According to an Uluru-Kata Tjuta National Park newsletter from back in July, prescribed burns were taking place in and around the park, although it’s not clear if the fires seen by VIIRS now (in September) are part of the prescribed burns.
EUMETSAT recently held a workshop on RGB satellite products, where a new RGB composite was proposed for VIIRS: the “Fire Temperature RGB”, made from M-10 (1.61 µm, blue), M-11 (2.25 µm, green) and M-12 (3.70 µm, red). Here’s what that looks like:
False-color RGB composite of VIIRS channels M-10, M-11 and M-12, taken 04:34 UTC 19 September 2012
In this composite, hot spots from fires show up as yellow, orange, bright red or white, depending on how hot they are. Liquid clouds show up as light blue. Ice clouds, which are missing from this scene, typically show up as dark green. The background surface shows up as a shade of purple. Burn scars, which show up as dark brown in the “natural color” and “natural fire color” composites, show up as more of a maroon color in the “fire temperature” composite. Coincidently, maroon is the “official color” of Queensland, although it looks like most of the maroon burn scars show up in the Northern Territory.
To easily compare the different views of the fires (and make it obvious to everyone what the fires look like), here’s an animation, zoomed in on the lower left corner of each of the images above:
Animated loop of images of the fires in Australia as seen by VIIRS, 04:34 UTC 19 September 2012
The yellow highlighted areas are where the active fires are.
Now that you’ve seen several different ways of displaying fire hot spots with VIIRS, which one do you like best?
VIIRS has seen Hell and, luckily, it did not get scared. No, I’m not talking about Hell, Michigan, which is actually a nice place (and not as scary as their website would indicate). I’m talking about the Gates of Hell (or Door to Hell, depending on who you talk to) in Turkmenistan. You can see a single video of it here and, if that isn’t enough to get a sense of it, someone compiled a list of 296 videos of the Gates of Hell near Derweze/Darvaza, Turkmenistan.
Turkmenistan doesn’t have much – 80% of it is the Karakum Desert – but it does have a lot of oil and natural gas deposits. Back in 1971, the Soviet Union wanted to take advantage of these deposits, so they began drilling a gas well near the town of Derweze. Unfortunately, the drilling opened up a sinkhole that ate the drilling rig and caused the natural gas to leak out in large quantities. Oh, no! What to do now? Light it on fire!
The team of geologists thought that the best way to prevent the town from being suffocated by the toxic fumes was to ignite the gas, let it burn itself out in a few days, and return to see what the damage was. Guess what? That fire is still burning today – 41 years later!
This constantly burning crater is only 230 ft (70 m) across. So it may come as a surprise (to some people, at least) that VIIRS has no trouble seeing it. The highest-resolution channels on VIIRS have a spatial resolution of ~375 m at nadir. The fiery pit is so visible, the Day/Night Band (DNB), with ~740 m resolution, makes the Gates of Hell look like the biggest town in central Turkmenistan:
VIIRS Day/Night Band image of Turkmenistan, taken 22:26 UTC 13 September 2012
The red arrow points out the light source that is the Gates of Hell. One other thing to note from this image is all the lights in the Caspian Sea. Those are oil rigs, with the largest light source (the one closest to the center of the Caspian Sea) being the floating/sinking city of Neft Daşları (a.k.a Oily Rocks), which sounds like a pretty interesting/sad/weird place to work.
In case you think the lights are coming from the town of Derweze and not the actual Gates of Hell, here’s a zoomed in image from the DNB along with the M-12 (3.7 µm) brightness temperatures:
VIIRS Day/Night Band image of the Derweze "Gates of Hell", Turkmenistan, taken 22:26 UTC 13 September 2012
VIIRS channel M-12 image of the Derweze "Gates of Hell", Turkmenistan, taken 22:26 UTC 13 September 2012. The color scale ranges from 210 K (white) to 300 K (black).
The Gates of Hell is the only light source that also shows up as a 345 K hot spot in channel M-12. Since this is a nighttime image, the signal in M-12 comes only from emission from the Earth (and clouds, etc.) without any contribution from solar reflection (as there would be during the day). What you see in the M-12 image is the temperature of the objects in the scene, just like a typical infrared (IR) satellite image, except with higher sensitivity to sub-pixel heat sources. The clouds show up as cold (bright, in this color table) above the warmer (darker) land surface. Sarygamysh Lake (and a few other smaller lakes) show up as really warm (dark) because the desert floor at night cools off much more than the water does.
The moon here was only ~10% full, so there wasn’t enough light reflecting off the few clouds in the scene for the DNB to detect them. In fact, with so little moonlight, everything is dark in the DNB. Everything, that is, except for the towns, villages and flaming craters of burning methane.
While Hurricane Isaac (then a tropical storm) did not destroy Tampa, Florida as many people feared, it certainly left its mark on the Gulf Coast. With many locations from Florida to Louisiana receiving more than 12″ of rain, and levees unable to keep out the storm surge, flooding was (and still is) a major problem. Look at these aerial photos of Isaac’s aftermath in Louisiana. The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi NPP saw that flooding, also.
But first, let’s look at the high resolution infrared (IR) window channel (I-05, 11.45 µm) which, at ~375 m resolution, is the highest-resolution IR window channel on a public weather satellite in space today. This image was taken when Isaac was still a tropical storm in the middle of the Gulf of Mexico:
VIIRS I-05 image of Tropical Storm Isaac, taken 18:50 UTC 27 August 2012
This image uses a new (to this blog, anyway) color scale, developed by our colleagues at CIMSS, that really highlights the structure of the clouds at the top of Isaac. The color scale is included in the image. For comparison, here’s the GOES Imager IR window channel (channel 4, 10.7 µm) image from roughly the same time:
GOES-13 Imager channel 4 image of Tropical Storm Isaac, taken 18:45 UTC 27 August 2012
GOES has ~4 km resolution in its IR channels. VIIRS provides amazing details of the structure of tropical cyclones that you just can’t get with current geostationary satellites.
The real story from Isaac, however, is the flooding. It’s hard to capture flooding from a visible and infrared imaging instrument, since flooding usually occurs when it’s cloudy. Clouds block the view of the surface when looking at visible and infrared wavelengths. But, large quantities of water that fail to evaporate or drain into the local rivers after a period of several days can be seen after the skies clear. That’s what happened with Isaac.
Here are before-Isaac and after-Isaac images of the southern tip of the Florida Peninsula. These are false color (“pseudo-true color”) composites of VIIRS channels I-01, I-02 and I-03. These images were taken on the afternoon overpasses of 23 August and 29 August 2012. Many cities on the east coast of Florida got 10-16 inches of rain (250-400 mm for those of you outside the U.S.). See if you can pick out the flooding.
False color RGB composite of VIIRS channels I-01, I-02 and I-03 taken before and after Tropical Storm Isaac (2012)
If you have been following this blog, you know that, in the “pseudo-true color” RGB composite, water shows up very dark – in most cases, almost black. That’s not always true, of course. You can see sun glint (particularly in the “before” image) that makes water a lighter color and shallow water (where visible radiation [i.e. channel I-01] is able to penetrate to the bottom) shows up as a vivid blue.
Now, notice the Everglades. Many areas of the Everglades, particularly on the east side, appear darker in the “after” image, because those swampy areas have a lot more water in them. Water has a lower reflectivity than vegetation or bare ground at these wavelengths.
The effect of water on the land surface shows up even better in the moderate resolution channel M-06 (0.75 µm). M-06 is a channel not shown before because it is perhaps the worst channel for producing interesting images. M-06 was designed to aid in ocean color retrievals and/or other uses that require atmospheric correction. The M-06 detectors saturate at a low radiance, so any radiation at 0.75 µm that reflects off of clouds, aerosols or the land surface easily show up. About the only things that have low reflectivity in M-06 are atmospheric gases and water surfaces without sun glint. Ocean color retrievals need a very clean atmosphere with no aerosols or clouds and no sun glint to work correctly. You also need to be able to identify what is or is not water, which is what makes M-06 useful for identifying flooding.
Here are the similar before-Isaac and after-Isaac images of Florida from M-06:
VIIRS channel M-06 images of southern Florida taken before and after Tropical Storm Isaac (2012)
Both the land and optically thick clouds saturate M-06, so this channel is useless at identifying clouds over land (except you can see some cloud shadows). Sun glint is saturating the pixels over the Gulf of Mexico in the “before” image, while it is mostly to the east of Florida in the Atlantic Ocean in the “after” image. In the “after image”, reflective cirrus clouds over the Gulf of Mexico show up that are not as easily visible in the RGB composite. Of primary importance here, however, is the dark appearance of the Everglades in the “after” image. All that flood water reduced the reflectivity of the land surface, making it appear darker. That means, if you know where the clouds (and, hence, the cloud shadows) are, it may be possible to use M-06 to identify large flooded areas.
Louisiana and the coast of Mississippi were the hardest hit by Isaac, and the flooding is easily visible here, too. In fact, the massive flooding is easier to see in the RGB composite in this region. Compare the “before” and “after” images, taken on 26 August 2012 and 1 September 2012:
False color RGB composites of VIIRS channels I-01, I-02 and I-03 of southeast Louisiana
To make it easier to see, here’s a quick animation of the before and after images. Watch the highlighted areas.
Animated GIF of false color RGB composites taken from VIIRS before and after Hurricane Isaac
After the passage of Hurricane Isaac, Lake Maurepas and Lake Pontchartrain almost appear to merge into one big lake! Other flooding is visible near Slidell, Bay St. Louis, Pascagoula Bay, and the heavily hit parishes of Plaquemines, St. Bernard, Lafourche and Terrebonne.
Thin cirrus clouds are visible in the “after” image, which limit the ability of M-06 to detect some of the flooding, but M-06 is still able to see the large areas of flooding highlighted in the animation above. M-06 also detects reflection off of the Twin Spans as well as the Lake Pontchartrain Causeway. And this is at ~750 m resolution!
VIIRS channel M-06 images of southeastern Louisiana taken before and after Hurricane Isaac (2012)
So, don’t try to do ocean color retrievals in pixels obscured by big bridges.
Sometimes, it seems like the whole world is on fire. Siberia. The western United States (which has been burning for some time). And now, the Canary Islands. The Spanish islands have been under a drought, as has much of Spain. (As an indication of how dry it has been, one fire in mainland Spain was started by someone flicking a cigarette butt out of their car window in a traffic jam – a fire that ultimately led to two deaths.) Back in July, fires got started on Tenerife – a major resort destination – and earlier this month, fires began on La Palma and La Gomera. At least two firefighters have already died battling these fires.
For your reference, here is a VIIRS “true color” image (M-3 [0.488 µm], M-4 [0.555 µm], M-5 [0.672 µm]) of the Canary Islands, with the major islands labelled:
VIIRS true color RGB composite of channels M-3, M-4 and M-5, taken 14:01 UTC 5 August 2012
If you look closely at this image, from 5 August 2012, you can see smoke plumes coming off of La Palma and La Gomera. You can also see what looks like a von Kármán vortex street downwind of La Palma. That’s the west coast of Africa in the lower-right corner of the image.
As discussed previously, the true color RGB composite is better for viewing the smoke plume, but you can’t actually see the fire directly. So, here’s the M-5 (0.672 µm), M-7 (1.61 µm) and M-11 (2.25 µm) composite from the same time:
VIIRS RGB composite of channels M-5, M-7 and M-11, taken 14:01 UTC 5 August 2012
It’s easy to see where the fires are actively burning with this composite. Let’s zoom in to make it even more obvious:
VIIRS false color RGB composite of channels M-5, M-7 and M-11, taken 14:01 UTC 5 August 2012
All the bright red pixels indicate where the fire is actively burning. You can also see the burn scar on Tenerife (not as easily as in Siberia) where the M-5, M-7, M-11 RGB composite shows the fire was back in July:
VIIRS false color RGB composite of channels M-5, M-7 and M-11, taken 14:38 UTC 18 July 2012
La Gomera has been the hardest hit island, where thousands of people had to be evacuated, and approximately 10% of Garajonay National Park has burned. Garajonay National Park is home to one of the last remaining laurisilva forests, which has been around for 11 million years. That lush vegetation burned hot, and channel I-04 (3.7 µm) reached saturation as that area went up in flames:
VIIRS channel I-04 image of fires in the Canary Islands, taken 14:01 UTC 5 August 2012
The two white pixels on La Gomera are where I-04 reached saturation and “fold-over” due to the heat from the fire. M-13 (4.0 µm), which is a dual-gain band designed to not saturate, reached a brightness temperature of 451 K over La Gomera, compared with a saturation brightness temperature of 367 K for channel I-04.
The fires also showed up in the Day/Night Band that night:
VIIRS Day/Night Band image of the Canary Islands, taken 02:25 UTC 6 August 2012
The red arrows point out the fires on La Palma and La Gomera. The fire on La Gomera covers a significant percentage of the island. The yellow arrow points to Lanzarote, which, for some reason, is not part of IDL’s map. On the night this image was taken, the moon was approximately 84% full, so you can see a number of clouds as well the city lights from the major resort areas of the Canary Islands. The biggest visible city in Africa is El Aaiún, the disputed capital of Western Sahara.
Finally, here’s the “pseudo-true color” composite of VIIRS channels I-01 (0.64 µm), I-02 (0.87 µm) and I-03 (1.61 µm) from 13:42 UTC 6 August 2012. This is a full granule at the native resolution of the Imagery bands with no re-mapping, showing the rich detail of VIIRS high-resolution imagery, including more interesting cloud vortices:
VIIRS false color RGB composite of channels I-01, I-02 and I-03, taken 13:42 UTC 6 August 2012
Make sure to click on the image, then on the “6400×1536” link to see it in its full glory.
Raise your hand if you’ve only ever heard of Yakutsk because of the board game “Risk”. (If you raised your hand, you might want to look around and make sure that no-one saw you raise your hand for no reason.) Yakutsk is actually the capital city of the Sakha Republic (a.k.a. Yakutia), which, according to Wikipedia, is the largest sub-national governing body in the world (only slightly smaller than India in terms of land area). Over 260,000 people live in Yakutsk, which has been called the “Coldest City on Earth” (with 950,000 total in Yakutia) even though, according to this article, it doesn’t sound very pleasant in the winter (or summer, for that matter). In January, the average temperature is -42 °C (-45 °F), and it isn’t very far from Oymyakon, where the lowest temperature ever recorded in a permanently inhabited location was observed (-71.2 °C or -96.2 °F). In the summer, it can make it up to +35 °C (95 °F) and legends tell of reindeer dying from choking on all the insects that cloud the air.
This summer, large areas of Siberia (including Yakutia) have been on fire. Some pictures from MODIS have already been circulating around the internet (e.g. here and here). And someone beat me to posting VIIRS images already. To make it easier to judge the size of the fires that are visible in the VIIRS Day/Night Band (DNB) image in the last link, here is a close-up with latitude and longitude lines added:
VIIRS DNB image of fires in Siberia, taken 16:25 UTC 4 August 2012
At this latitude, longitude lines are ~55 km apart. The latitude lines are ~111 km apart. So, you can see that these fires cover quite a large area. Unfortunately, you can’t see Yakutsk, which is underneath the clouds (and possibly smoke) at about 62° N, 130° E.
For comparison, here is the M-13 (4.05 µm) image from the same time. The primary purpose of M-13 is to detect wildfires. Notice how all of the hot spots (black spots) line up with all of the light sources that the DNB saw:
VIIRS channel M-13 brightness temperature image taken 16:25 UTC 4 August 2012
The visible image from earlier that day showed just how much smoke was produced by all of these fires:
Visible image of fires in Siberia from VIIRS channel M-5, taken 02:38 UTC 4 August 2012
Except for a few clouds near the edges of the scene, that is pretty much all smoke.
A few days later, the burn areas were easily visible with many fires still active, although not producing nearly as much smoke. RGB composites can really highlight what is going on with these fires, so let’s look at a few.
You should already be familiar with the “true color” image (M-3, 0.488 µm [blue], M-4, 0.555 µm [green] and M-5, 0.672 µm [red]):
True color image from VIIRS channels M3, M4 and M5 of fires in Siberia, taken 03:22 UTC 7 August 2012
And the “pseudo-true color” image made by combining the first three I-bands (I-01, 0.64 µm [blue], I-02, 0.865 µm [green] and I-03, 1.61 µm [red]):
False color (or "pseudo-true color") image of fires in Siberia from VIIRS channels I-01, I-02 and I03, taken 03:22 UTC 7 August 2012
The “pseudo-true color” image may be referred to as “natural color” depending on who you talk to. It should be noted that these last two images were kept at the native resolution of VIIRS with no re-mapping or re-sizing the image. There is only cropping to keep the file sizes manageable.
As discussed before, the pseudo-true color composite has the advantage of easily distinguishing ice and snow from liquid clouds, and it is really sensitive to vegetation. Plus, scattering by molecules in the atmosphere is greatly reduced, so you don’t have to do any atmospheric correction to produce a nice image. There is also the advantage that it uses I-bands, which have twice the resolution of the M-bands. But, that advantage was almost always neutralized by the fact that the images would have to be compressed to create a reasonable file size so that it would fit on this blog. If you click on the images above, then on the full-resolution link below the banner, you can easily compare the true resolution between the M-band image and the I-band image.
You can see here that the burn scars (all the dark brown areas) show up really well in the pseudo-true color image. (Some of the lighter or reddish brown areas are mountain ranges.) You might also notice that the active fires are still producing smoke, which shows up a lot better in the true color image. Some of the burn scars cover an area close to 60 km across.
As luck would have it (or, more accurately, the planning ahead by the scientists and engineers who designed VIIRS), channels M-5 (0.672 µm), M-7 (0.865 µm) and M-10 (1.61 µm) are very similar to the first three I-bands, so we can easily produce an M-band “pseudo-true color” image:
"Pseudo-true color" composite of VIIRS channels M-5, M-7 and M-10 of fires in Siberia, taken 03:22 UTC 7 August 2012
For reference, the location of Yakutsk has been identified. Also, if you’re curious, the big river that curves from the left-middle of the image to the top-center is the Lena River. It is up to 10 km wide in parts, particularly north of Yakutsk. Its second largest tributary, the Aldan River, is also easily visible as it meanders through a lot of the burn areas.
If you replace M-10 with M-11 (2.25 µm) as the red channel, you get this image:
False color RGB composite of VIIRS channels M-5, M-7 and M-11, taken 03:22 UTC 7 August 2012
Here, the green is darker due to the lower reflectivity of the surface in M-11 compared with M-10. The advantage of this RGB composite it that, if you zoom in, you can actually see where the fires are still active, as those pixels show up bright red. (If the fire is hot enough, you’ll get red pixels in the “pseudo-true color” composite also, but M-11 is more responsive to heat from fires than M-10, so you can see lower temperature fires this way.) You can also see the faint bluish smoke plumes originating from the areas that are actively burning.
If you go in the other direction and use only the shortest wavelengths, the surface becomes difficult to see, but the smoke stands out more. Here is the RGB composite of M-1 (0.412 µm [blue]), M-2 (0.445 µm [green]) and M-3 (0.488 µm [red]):
False color RGB composite of VIIRS channels M-1, M-2 and M-3, taken 03:22 UTC 7 August 2012
Here, the wavelengths of these channels range from the violet to the blue portion of the visible spectrum. At these shorter wavelengths, scattering in the atmosphere becomes much more important and the solar radiation has a tough time making it all the way to the surface. All the smoke and haze increases the scattering, so it is difficult to pick out features on the surface. That same scattering, though, really highlights the smoke plumes, which are difficult to see in the other false color composites. Since the scattering by the stuff in this image doesn’t vary much between these three channels, you get an image without much color to it.
With much of Colorado and, really, much of the western U.S. having burned already this year, it’s easy to know what the people of Siberia are going through. Fortunately, none of the fires have really threatened any towns. And, another plus: I bet those clouds of mosquitoes don’t like the dry weather that has caused all of these fires.
First, a preface: The purpose of this blog (and this blog post) is not to ignite some debate about global warming. This is about what one new satellite instrument has observed and the information it is providing to the scientific community.
With that out of the way, we can begin.
You may have heard on the news a story about the rapid ice melt that occurred in Greenland a couple weeks ago. Over a period of four days, the percentage of the surface of Greenland’s ice sheet that showed evidence that the ice was melting went from 40% to 97%. NASA’s Thomas Wagner does a good job explaining it in this interview. You’ll notice in the first link (from the Earth Times) that the rapid melt was first noticed by someone analyzing data from Oceansat-2. The ice melt was detected by its microwave scatterometer and was later confirmed by MODIS. Well, if MODIS can see this ice melt, surely VIIRS can see it, too. Let’s see.
First, let’s look at the false color RGB composite made from channels I-01 (0.64 µm, blue), I-02 (0.865 µm, green) and I-03 (1.61 µm, red). These images are comprised of 5 VIIRS granules stitched together and cropped slightly to get them in under the 15 MB limit for attachments to this blog. You really need to see them zoomed in to full resolution to see the kind of detail that the VIIRS bands provide. This isn’t even the full resolution of the satellite – these two images have been shrunk by a factor of 2 to get in under the file size limit, so it’s actually more like the resolution of the M-bands. (Click on the image, then click on the “2350 x 3372” link below the banner to see the full resolution image.)
Here’s what VIIRS saw on 8 July 2012, at 14:35 UTC:
False color RGB composite of VIIRS channels I-01, I-02 and I-03, taken 14:35 UTC 8 July 2012
And here’s what VIIRS saw five days later (14:42 UTC, 13 July 2012):
False color RGB composite of VIIRS channels I-01, I-02 and I-03, taken 14:42 UTC, 13 July 2012
First thing to notice is that the low liquid clouds over Greenland really stand out in this composite above the ice sheet. As discussed before, this is one of the advantages of this kind of RGB composite. The second thing to notice, which is easier to see in the 13 July image, is that Iceland is the island that’s green, and Greenland is the island that is almost entirely ice. (Those silly Vikings and their misnomers!)
What is relevant here, though, is more subtle. The ice sheet appears to be a significantly darker blue over much of Greenland on 13 July than it does on 8 July. Notice also in these composites that large bodies of liquid water appear black. Now, there’s a lot going on here.
Small, liquid droplets (which are nearly spherical) that make up many of the clouds in the scene are very good at reflecting the solar radiation at all three wavelengths (0.64 µm, 0.865 µm, and 1.61 µm). When you combine high (and nearly equal) levels of red, green and blue on a computer monitor, you get something close to white. This is why the liquid clouds appear whitish.
The small ice particles (found in some of the clouds in these two images) are very good at reflecting radiation at 0.64 µm and 0.865 µm, but not as good at reflecting radiation at 1.61 µm. That means, for this RGB composite, we have high levels of blue and green, but low levels of red. This gives the pale bluish color known as cyan. Snow and ice on the ground are even worse at reflecting radiation at 1.61 µm (they absorb it), so you have a more pure color of cyan. (Although, snow and ice do reflect more than water at this wavelength.)
Liquid water (not in tiny spherical droplets) is not a good reflector at any of these wavelengths. Therefore, the low (and nearly equal) levels of red, green and blue give you black. As snow and ice melt, the reflectivity changes at each of these wavelengths (as the ice becomes more water-like), so the cyan color becomes darker.
It should be said that the primary purpose of the 1.61 µm channel is to aid in snow and ice detection. VIIRS actually has two of these channels: I-03 and M-10. In fact, you can see the effect of the melting ice a bit easier when looking at this channel alone. Here are the M-10 images of Greenland from 8 July and 13 July 2012:
VIIRS channel M-10 reflectance image of Greenland, taken 14:35 UTC 8 July 2012
VIIRS M-10 reflectance image of Greenland, taken 14:42 UTC 13 July 2012
In the first image from 8 July 2012, you can see that the clouds stand out as being bright (highly reflective) and the area of still-frozen ice is visible (a medium to dark gray, meaning somewhat reflective) over the most of the center of Greenland. On 13 July 2012, Greenland shows up as black – just like the surrounding ocean – except for small patches of land along the coast that are not underneath the massive ice sheet (and the clouds, of course). It is particularly noticeable in south-central Greenland. This decrease in reflectivity at 1.61 µm over this period of time is due to the snow and ice becoming more water-like as it is melting. So VIIRS can say a thing or two about the ice melt event.
It’s been a while since we last looked at some tropical cyclones with VIIRS. If you don’t keep up to date on tropical activity, you might not know there that have been a few. Granted, since Debby dumped a bunch of rain on Florida three weeks ago, the Atlantic basin has been pretty quiet. The East Pacific basin, however, has had one storm after another. The national media has largely ignored them since they have posed no threat to any landmasses. See this article from the L.A. times. Boring! Unless you can capture video of Jim Cantore struggling to stand upright, it isn’t a hurricane, right?
Wrong! First of all, eastern Pacific hurricanes affect some major shipping lanes. Second, and this is true of all hurricanes: they transport energy and moisture and help moderate the temperature imbalance between the tropics and mid-latitudes. They are important components of global energy transport.
In this post, we are going to compare the view of hurricanes provided by VIIRS against the view provided by GOES (specifically GOES-15). On 9 July 2012, there were two storms in the East Pacific: Daniel and Emilia.
Here is the GOES-15 view of Daniel followed by the VIIRS view of Daniel in their respective visible channels:
GOES-15 visible image (channel 1) of Hurricane Daniel, taken 22:45 UTC 9 July 2012. Image courtesy John Knaff.
VIIRS visible image (channel I-01) of Hurricane Daniel, taken 22:29 UTC 9 July 2012
Both images have the same latitude and longitude lines printed on them for reference and they both use the same color scales. If you zoom in, you’ll notice that the VIIRS image, with ~375 m resolution at nadir shows a bit more detail than the 1 km (1000 m) resolution GOES image. The additional detail provided by VIIRS really stands out in the infrared (IR) window channels, where GOES has 4 km resolution and VIIRS still has ~375 m resolution:
GOES-15 IR image (channel 4) of Hurricane Daniel, taken 22:30 UTC 9 July 2012
VIIRS IR image (channel I-05) of Hurricane Daniel, taken 22:29 UTC 9 July 2012
Now, it is worth noting that the high resolution IR image of VIIRS shown above comes from channel I-05, which is centered at 11.45 µm. The GOES image was produced from Imager channel 4, which is centered at 10.7 µm, so the two channels don’t exactly have the same spectral properties. VIIRS has a 10.7 µm IR channel as one of its moderate resolution bands (M-15). Here’s what that image looks like:
VIIRS IR image (channel M-15) of Hurricane Daniel, taken 22:29 UTC 9 July 2012
There isn’t a big difference between the two VIIRS channels, although you can see a bit more detail in the higher resolution (I-05) image.
On the previous orbit, VIIRS caught images of Hurricane Emilia, which was also in the view of GOES-15. Here’s how the images compare:
GOES-15 visible image (channel 1) of Hurricane Emilia, taken 21:00 UTC 9 July 2012. Image courtesy John Knaff.
VIIRS visible image (channel I-01) of Hurricane Emilia, taken 20:48 UTC 9 July 2012
GOES-15 IR image (channel 4) of Hurricane Emilia, taken 20:48 UTC 9 July 2012
VIIRS IR image (channel I-05) of Hurricane Emilia, taken 20:48 UTC 9 July 2012
In addition to the resolution differences, there is also a time difference of ~15 minutes between the VIIRS images and the GOES images. If you were to overlap these images, you would see that Emilia rotated a bit during that time. Emilia was not willing to hold the same pose for that long when having her picture taken. Once again, the M-15 image from VIIRS looks pretty similar to the I-05 image, so there’s no pressing need to show it.
Finally, let’s compare GOES-15 with VIIRS on Hurricane Fabio, which formed about a week after Daniel and Emilia were hurricanes.
GOES-15 visible image (channel 1) of Hurricane Fabio, taken 20:30 UTC 15 July 2012. Image courtesy John Knaff.
VIIRS visible image (channel I-01) of Hurricane Fabio, taken 20:36 UTC 15 July 2012
GOES-15 IR image (channel 4) of Hurricane Fabio, taken 20:30 UTC 15 July 2012
VIIRS IR image (channel I-05) of Hurricane Fabio, taken 20:36 UTC 15 July 2012
The GOES and VIIRS images of Fabio were taken only 6 minutes apart, so there is less movement to impede the comparison.
In all three hurricanes, you can see a lot more structure to the VIIRS images in the both the visible and IR channels. It’s as if GOES represents a standard definition TV camera, and VIIRS represents a hi-def TV camera. All those wrinkles GOES is smoothing over are showing up in VIIRS. Daniel, Emilia and Fabio are going to need more makeup. (Or, they would if they weren’t already dead.)
Are you tired of 100 °F heat? We sure are in Colorado. Denver tied an all-time record of five consecutive days of 100+ °F high temperatures this week (two of which had the all-time highest recorded temperature of 105 °F). Much of the country experienced record-breaking heat as well. What better place to escape the heat than to visit the Islands of Refreshment?
The islands were given the name by a group of four Americans who sailed there in 1810, intending to make it their own kingdom. Unfortunately, 75% of them died in a boating accident less than two years after they arrived. I suppose, if the fourth one died we never would have heard this story. To the rest of the world, the islands were and are known as Tristan da Cunha, named after Tristão da Cunha – the Portuguese explorer who first found them in 1506.
It’s hard to get more remote than Tristan da Cunha. The four main islands, Tristan da Cunha, Inaccessible Island, Nightingale Island and Gough Island are part of the British Overseas Territory of Saint Helena, Ascension and Tristan da Cunha. The only way to visit them is by boat from South Africa – which takes a week – and boats only come around once or twice a month. You also need to write a proposal to the Secretary of the Administrator outlining what you plan to do there in order to gain permission to visit. The permanent population of the islands is less than 300, and they’ve even developed their own version of English. Another interesting fact: they only acquired television in the last 10 years (according to Wikipedia).
So where is Tristan da Cunha? The small island territory is 2,816 km from the nearest continent (Africa) and 2,430 km from their administrative capital (St. Helena). Let’s see if you can find it in high-resolution visible (I-01, 0.64 µm) imagery from VIIRS:
Visible image (I-01) of Tristan da Cunha from VIIRS, taken 14:49 UTC 25 June 2012
Give up? I’ll make it easier and show the false color RGB composite (I-01, I-02 and I-03):
False color RGB composite of VIIRS channels I-01, I-02 and I-03 taken 14:49 UTC, 25 June 2012
Three of the islands are easy to pick out now, particularly if you click to get the full size image. (Click on the image, then click on the 1512×1226 link below the banner.) The fourth island is difficult to see as it is covered by clouds and ice and snow, which look like clouds.
Here they are, labelled:
False color RGB composite of VIIRS channels I-01, I-02 and I-03 taken 14:49 UTC, 25 June 2012
Nightingale Island, at 3.2 km2, is only about 5×4 pixels in size! The volcano that makes up the main island, Queen Mary’s Peak, rises 6,765 ft. above sea level and is casting a “cloud shadow” (i.e. no clouds are seen immediately downwind, or northeast, of the island). There may even be a von Kármán vortex behind it. Gough Island is also casting a “cloud shadow”, although it is much smaller.
If you really zoom in, you can almost convince yourself that VIIRS can identify two much smaller islands off the northern tip of Nightingale Island, Middle Island and Stoltenhoff Island:
False color RGB composite of VIIRS channels I-01, I-02 and I-03 taken 14:49 UTC, 25 June 2012
Look for the two greenish pixels above Nightingale Island. These islands are both about 25 acres in size (0.1 km2).
While the only town, Edinburgh of the Seven Seas, is on Tristan da Cunha, there is also a year-round research facility on Gough Island. There are three meteorologist positions on the island, as it is an important weather station for South Africa and the United Kingdom. As a bonus, the record high temperature has never come close to 100 °F. So, if you’re really looking to get away from the heat (and everything else), Gough Island might be the place for you!
Last time on “Wild Week of Wildfires“, we looked at the Little Bear Fire and High Park Fire, two lightning-ignited fires burning out west that were so hot they caused saturation in the two 3.7 µm channels on VIIRS (I-04 and M-12). There was mention of the Duck Lake Fire, a lightning-ignited fire in northern Michigan, which VIIRS also saw, and I couldn’t resist showing some more images.
On 9 June 2012, the same day the High Park Fire exploded (figuratively speaking), the Duck Lake Fire finally reached 100% containment after burning over 21,000 acres. The next day (10 June 2012), Suomi NPP passed over the Upper Peninsula of Michigan, and it was actually a clear day. (This joke comes courtesy of 20+ years experience of living in Michigan.) Even with 100% containment, the hot spot of the fire was still clearly visible in VIIRS channel I-04 (3.7 µm) that afternoon:
Channel I-04 image of the Duck Lake Fire from VIIRS, taken 18:18 UTC 10 June 2012
The highest brightness temperature in the burn area in this channel at this time was ~331 K. As we saw before with the Lower North Fork Fire, the high resolution false color composite of channels I-01, I-02 and I-03 is useful in highlighting the burn area:
False color RGB composite of VIIRS channels I-01 (blue), I-02 (green) and I-03 (red), taken 18:18 UTC 10 June 2012
Notice the large, brown area that coincides with the hot spot in the I-04 image. The combination of wavelengths used in this composite (0.64 µm [blue], 0.865 µm [green] and 1.61 µm [red]) is quite sensitive to the amount (and health) of the vegetation.
You might have also noticed several other interesting features in the image that show up better when you zoom in:
False color composite of VIIRS channels I-01, I-02, and I-03 from 18:18 UTC 10 June 2012
The Upper Peninsula of Michigan was based on mining for most of its history, and several large mines and quarries still exist, which VIIRS can easily see.
If you didn’t know any better, you might confuse the iron mine southwest of Marquette, Michigan with a frozen lake, or miraculously un-melted snow leftover from winter, since that is just what snow and ice look like in this kind of RGB composite. Compare that with the true color view of the same area:
True color RGB composite of VIIRS channels M-3, M-4 and M-5, taken 18:18 UTC 10 June 2012
In this case, the iron mine stands out as a bright red. Why?
The true color composite uses wavelengths at 0.48 µm (blue), 0.55 µm (green) and 0.67 µm (red). The red channel in the true color composite is actually in the red portion of the visible spectrum. The blue channel in the false color composite (0.64 µm) is also in the red portion of the visible spectrum.
This example shows that the iron oxide (rust) produced at the iron mine is highly reflective in the red portion of the visible spectrum. That’s what gives it the characteristic rust color. Iron oxide is not nearly as reflective at shorter or longer wavelengths, so it shows up blue when red wavelengths are used as the blue channel (as in the false color composite) and red when they are used as the red channel (as in the true color composite).
Let this be a lesson to anyone who uses the false color composite as part of a snow and ice detection algorithm. Snow and ice are not the only things to show up that color. You may be looking at a really large iron mine.
The last few weeks have been filled with lightning-ignited wildfires across the United States. The County Line Fire, along the Florida-Georgia border was caused by lightning on 5 April 2012 and burned ~35,000 acres. The Whitewater-Baldy Complex (began 16 May 2012) – the largest wildfire in New Mexico history – started as two different fires (both caused by lightning) that merged together. It’s over 280,000 acres (that’s not a typo) and continues to burn (as of 13 June 2012). The Duck Lake Fire (began 24 May 2012) burned 21,000 acres of Michigan’s Upper Peninsula and was caused by lightning. The Little Bear Fire (began 4 June 2012), also in New Mexico, was caused by lightning and has burned ~37,000 acres. Much closer to home, the High Park Fire (began 9 June 2012) is already the largest wildfire in Larimer County history and the third largest fire in Colorado history. It has burned ~46,000 acres and I bet you can guess what caused it.
It’s not clear who is to blame here – there is a long list of suspects – but I bet it was Thor. Even though the U.S. is generally the domain of the Thunderbird, Thor has a mountain-crushing hammer called Mjöllnir, which makes him as good a suspect as any. He may have been in cahoots with Indra or Marduk who are the bringers of rain, and have been holding back on us. Look at how dry it has been across the majority of the country.
With all of these fires, it’s hard to know where to begin. We’re going to ignore the County Line Fire as it was put out over a month ago. We’re also going to ignore the Whitewater-Baldy Complex, as it is so big, it can be seen by GOES. (Kidding! We kid because we love.) Plus, it’s been done before. The VIIRS view of the High Park Fire has also been looked at by CIMSS, with an interesting comparison between VIIRS and MODIS.
What we are going to do is show off interesting features of some of these fires that haven’t been shown or discussed before (as far as we know). We begin with “saturation”. Both the High Park Fire and Little Bear Fire saturated the VIIRS 3.7 µm channels (I-04 and M-12):
Channel I-04 (3.7 µm) image of the Little Bear Fire from VIIRS taken 20:16 UTC 9 June 2012
Channel M-12 (3.7 µm) image of the Little Bear Fire from VIIRS taken 20:16 UTC 9 June 2012
Channel I-04 (3.7 µm) image of the High Park Fire from VIIRS taken 19:59 UTC 10 June 2012
Channel M-12 (3.7 µm) image of the High Park Fire from VIIRS taken 19:59 UTC 10 June 2012
The top two images are of the Little Bear Fire, which formed near the border of Lincoln and Otero counties in New Mexico. The bottom two images are of the High Park Fire in Larimer County, Colorado. For each fire, the high resolution 3.7 µm channel (I-04) is compared with the moderate resolution 3.7 µm channel (M-12). The colors range from white (cold) to black (hot). But, wait a minute! If white is cold, why are there white pixels mixed in with the black ones that indicate the hot spots? That’s because these channels are saturating and experiencing “fold-over”. The peak brightness temperatures these channels can measure is ~ 367 – 368 K. Anything warmer than that won’t be detected, so the channel is said to be saturated. When it really gets above that limit you can have “fold-over”, where not only are you not observing the higher, correct temperature, the detectors actually report a lower temperature or radiance. In these fires, the fold-over is resulting in brightness temperatures down to 203 K for M-12 and 208 K for I-04, which is about 90-100 K colder than even the area surrounding the fires!
Luckily, VIIRS has a 4.0 µm channel (M-13) that was designed to not saturate at the temperature of typical wildfires. Compare the hottest pixels in the M-13 images below with the fold-over pixels from M-12 and I-04 above:
Channel M-13 (4.0 µm) image of the Little Bear Fire from VIIRS taken 20:16 UTC 9 June 2012
Channel M-13 (4.0 µm) image of the High Park Fire from VIIRS taken 19:59 UTC 10 June 2012
The hottest pixel in M-13 reached a temperature of 588 K for the Little Bear Fire and 570 K for the High Park Fire – over 200 K warmer than the saturation points of M-12 and I-04!
These fires were so hot, they appeared in channels that don’t usually show a fire signal. Limiting our attention to the High Park Fire (which was almost literally in our back yard), here’s the I-05 (11.5 µm) image from 10 June 2012:
Channel I-05 (11.5 µm) image of the High Park Fire from VIIRS taken 19:59 UTC 10 June 2012
The highest temperature observed in I-05 was 380 K. Longer wavelength channels, such as in I-05 are less sensitive to sub-pixel hot spots than channels in the 3.7 – 4.0 µm range, so fires don’t often show up. For pixels to have a 380 K brightness temperature in I-05, it means that the average temperature over the entire pixel had to be above +100 °C – hot enough to boil water!
Fires don’t often show up at shorter wavelengths, either, because the amount of solar radiation usually dwarfs any signal from the Earth’s surface. But, the High Park Fire did reach saturation at 2.25 µm (M-11):
Channel M-11 (2.25 µm) image of the High Park Fire from VIIRS taken 19:59 UTC 10 June 2012
The color scale has been reversed so that it is more inline with visible imagery. The white pixels represent saturation in M-11 at a radiance of 38 W m-2 µm-1 sr-1. The reflectance of these pixels saturated at a value of 1.6, which means that the amount of radiation detected in this channel was more than 1.6 times the amount you would expect to see if the surface was a perfect mirror reflecting all the solar radiation back to the satellite. Thus, the fire’s contribution to the total radiance was significant in this channel.
The contribution from the surface (i.e., the fire) was also visible in the 1.6 µm channel (M-10), but it isn’t exciting enough to show. One channel shorter down on VIIRS (M-9, 1.38 µm) and the signal disappears against the high reflectivity of the smoke plume.
It’s impossible to leave out the Day/Night Band, which shows just how large and how close the High Park Fire got to Fort Collins:
Day/Night Band image of the High Park Fire from VIIRS taken 09:58 UTC 11 June 2012. Image courtesy Dan Lindsey.
The smoke plume, while not exactly visible, is affecting the view of the east side of the fire and Fort Collins, making them appear more blurry than they would if the sky were completely clear. You can also see that, overnight on 11 June 2012, the fire covered an area larger than any of the cities visible in the image (except for Denver, which is mostly cropped off the bottom of the image).
Hopefully, Marduk will start doing his job and bring us some rain and these will be the last fires for a while.
Cape Verde is an island nation off the west coast of Africa, located in the North Atlantic. The islands are a popular initiation point for tropical storms. The original capital of the 10-island archipelago was sacked twice by Sir Francis Drake, the same one who, in his later years, would fail to sack the villages along Lake Maracaibo in Venezuela due to Catatumbo lightning. That guy really got around, and I mean that literally: he circumnavigated the globe between 1577 and 1580, sacking nearly every village and boat he came across. But, this isn’t about Francis Drake – it’s about the Cape Verde islands and the amazing view of them captured by VIIRS.
False color RGB composite of VIIRS channels I-1, I-2 and I-3 taken 14:41 UTC 5 June 2012
Can you see the 10 major islands? One of them (Santa Luzia) is almost obscured by clouds. If you click on the image, you’ll see each of the major islands identified. Go ahead and click on it. It will help for later.
The image above was made from the RGB composite of VIIRS high-resolution imagery channels I-01, I-02 and I-03. While it technically is a false color image (uses reflectance at 0.64 µm [blue], 0.865 µm [green] and 1.61 µm [red]), it looks realistic in many situations, so that we refer to it as “pseudo-true color”. Snow and ice show up as an unrealistic blue, however, which is the main difference between it and a “true color” image. You might also notice a few more differences between the “pseudo-true color” image above and the “true color” image below.
True color RGB composite of VIIRS channels M-3, M-4 and M-5 taken 14:41 UTC 5 June 2012
The true color image uses moderate resolution channels M-3 (0.48 µm, blue), M-4 (0.55 µm, green) and M-5 (0.67 µm, red), which actually observe radiation in the blue, green and red portions of the visible spectrum. Apart from differences in resolution, the vegetation on the islands shows up a bit better in the “pseudo-true color” image. The islands just look brown in the true color image.
What is particularly interesting about these images are the visible effect that the islands have on the local atmosphere. Downwind (southwest, or to the lower left) of Sal, Boa Vista, and Maio, you can see singular cloud streets, much like the flow of water around a rock. In the photograph in that link, you can see how the water dips downward on both sides of the center line downstream of the rock, and upward in the middle (along the center line). The islands are acting like rocks in the atmosphere, causing upward motion behind them, and this lift was enough to form cloud streets. On either side of these cloud streets there is downward motion and, as a result, clear skies.
Downwind of São Nicolau, São Vicente and Santo Antão, the cloud streets highlight von Kármán vortices and vortex shedding, which you can see in more-controlled lab conditions here and here.
Many of the islands appear to be producing their own aerosol plumes (i.e. dust), and if you zoom in on the area between Boa Vista and Santiago, you can see gravity waves present in some of the plumes (highlighted by the arrows in the image below).
False color RGB composite of VIIRS channels I-1, I-2 and I-3 taken 14:41 UTC 5 June 2012
A common way to detect dust is the “split-window difference”: the difference in brightness temperature between the 11 µm channel and the 12 µm channel. On VIIRS, this means subtracting M-16 from M-15 which, when you do that, gives you this image:
Split-window difference from VIIRS (M15 minus M16) from 14:41 UTC 5 June 2012
The color scale goes from -0.16 K (black) to +4.0 K (white). For some reason, the dust or aerosol plumes don’t produce a strong signal here. It may be that the dust is too low in the atmosphere and the lack of temperature contrast with the surface prevents a strong signal. Maybe water vapor absorption effects in M16 are washing out the signal. Or, there could be some other explanation waiting to be discovered.
The plumes are highly reflective in the 3.7 µm channel (M-12), as are the clouds, which show up as warm spots in the image below (not as warm as the islands, however):
Moderate resolution 3.7 µm image (M-12) from VIIRS, taken 14:41 UTC 5 June 2012
Here, just to throw you off, the color scale has been reversed so that dark colors mean higher values. The scale ranges from 295 K (white) to 330 K (black). When you take the difference of this image and the 10.6 µm brightness temperature (M-15), the clouds and aerosol plumes really show up, along with the gravity waves and vortices:
Brightness temperature difference between VIIRS channels M-12 and M-15 from 14:41 UTC 5 June 2012
In this case, the M-12 brightness temperatures are always greater than the M-15 brightness temperatures (due to the combination of Earth’s emission and solar reflection in M-12 as opposed to just surface emission in M-15), so the scale varies from +5 K (black) to +30 K (white). Higher (brighter) values on this scale show off where the most solar reflection occurs at 3.7 µm – the liquid clouds and aerosol plumes.
There are much more sophisticated ways of identifying dust and aerosol plumes. To find out more, check out this article written by one of our resident experts, Steve Miller, who is currently working on applying dust detection algorithms to VIIRS.
If you are more interested in the von Kármán vortices, NASA has put together a great page that you can visit here. If you take the original image in this post, zoom out and rotate it a little bit, you can get a sense of just how far the vortices extend from their parent islands:
False color RGB composite of VIIRS channels I-1, I-2 and I-3 taken 14:41 UTC 5 June 2012. This image has been rotated from the previous images to highlight the length of the vortex streets.
Coincidentally, this image has been cropped to a size that makes it suitable for use as a desktop wallpaper, should you happen to have a 16:9-ratio monitor and a desire to stare at this image all day. (You have to click on the image, then click on the “1920 x 1080” link below the header to get the full resolution image.)
You may have noticed that many of the recent posts have featured imagery from the VIIRS Day/Night Band (DNB). That’s because the nighttime imagery produced by the DNB is so awesome! The DNB has seen clouds at night, auroras, forest fires, oil and gas flares, and even volcanic eruptions. Many of the previous images shown have included high resolution views of city (and even small town) lights. This post shows another interesting facet of DNB imagery: lightning. More specifically, Catatumbo lightning.
For those of you who don’t know (and didn’t click on that last link), Catatumbo lightning is one of the world’s most frequent lightning displays, with thunderstorms forming over the Catatumbo River in Venezuela an average of 160 nights per year. The lightning displays last up to 9 hours, beginning shortly after dusk. The lightning is nearly continuous and so vivid and reliable that it has been called the “Lighthouse of Maracaibo” or the “Catatumbo Lighthouse”, as fisherman and sailors have historically used it as a navigation aid. It is said that the locals were saved from an invasion by Sir Francis Drake in 1595, as the invading navy could not covertly enter Lake Maracaibo at night due to all the bright lightning. There is even a campaign to make Catatumbo lightning a UNESCO world heritage site. The lightning is so prominent, the state of Zulia in Venezuela has included it in their flag and coat of arms. Two years ago, the storms suddenly stopped for several months, causing mass panic in the streets- I mean, on the river- I mean… um, actually the villagers in this video don’t seem to care all that much.
Earlier this month, when the moon was about 80% full, Suomi NPP passed over Lake Maracaibo at night and, sure enough, a thunderstorm was present right over the mouth of the Catatumbo River.
VIIRS I-05 image of thunderstorms near Lake Maracaibo, Venezuela taken 06:44 UTC 10 May 2012
This image, taken from the high resolution imagery IR-window channel (I-05, 11.45 µm) on 10 May 2012, shows the deep convection over Venezuela and Colombia. The largest thunderstorm near the center of the image formed along the shore of Lake Maracaibo, near the mouth of the Catatumbo River. Here’s what the DNB saw at the same time:
VIIRS Day/Night Band image of thunderstorms near Lake Maracaibo, Venezuela taken 06:44 UTC 10 May 2012
The bright, almost rectangular streaks in the image are lightning strikes. The red arrow points out a lightning strike from the Catatumbo storm – a “Catatumbo lightning” strike, if you will.
The blocky appearance of lightning is due to the fact that VIIRS is a scanning radiometer. As the instrument scans the swath of the Earth that it sees, a bright, transient flash (such as from lightning) will show up in the along-scan direction as an individual streak of light in each sensor. The DNB has 16 different sensors that scan the swath simultaneously, and since lightning typically stretches over a large enough area to be detected by all of them, you get 16 different streaks all lined up next to each other. By the time the sensors have rotated back around for the next scan, the lightning flash has ended, producing abrupt edges in the direction along the satellite track. Compare this with the DMSP Operational Linescan System, which produces much more “streaky” lightning.
In addition to the “Catatumbo lightning”, you can see several other lightning flashes in the two deepest thunderstorms over Colombia. These are far enough away from Lake Maracaibo that they probably don’t count as Catatumbo lightning.
Other interesting features can be seen in these images as well. The moon was bright enough to cast shadows in the DNB image, allowing for the detection of the overshooting tops. These match-up with the coldest brightness temperatures in the I-05 image (which show up as dark blue to pure white in this color scale). A few pixels in the largest storm over Colombia (the one with two visible lightning flashes) have managed to make it to pure white on the color scale, indicating temperatures below 190 K (-83 °C). The dark blue pixels indicate brightness temperatures between 196 and 190 K (-77 to -83 °C). Brrr.
Overshooting tops exist when the convection is so vigorous, it peaks out above the anvil of the storm and penetrates the stable layer above (which is usually the stratosphere in storms this deep). In addition to acting as an indicator for severe weather, overshooting tops are important for energy and chemical transport between the troposphere and stratosphere.
It’s also interesting to see what looks like thin cirrus over the Caribbean Sea near Panama (left center of the image) that show up in the infrared (I-05) image, but not in the DNB. Plus, a number of cold clouds over Venezuela would appear to be optically thick due to their low brightness temperatures in the infrared image (yellow starts at 245 K down to green at 214 K), but they are optically thin enough to see city lights below in the DNB image. Awesome!
According to reports, a man camping along the Hewlett Gulch trail in Roosevelt National Forest on 14 May 2012 had his camping stove knocked over in a gust of wind. One week (and $2.9 million) later, the Hewlett Fire scorched more than 7600 acres before fire crews could gain the upper hand. At one point 80 homes were evacuated but, thankfully, none of them were damaged. The smoke plume could be seen as far away as Laramie, Wyoming. Less than 20 miles away from the Cooperative Institute for Research in the Atmosphere, our home, it certainly caught our attention.
VIIRS aboard Suomi NPP monitored the fire day and night. About an hour after the fire was first reported, VIIRS captured the hot spot in channel I-04 (3.7 µm):
Image of the Hewlett Fire from VIIRS channel I-04, 20:05 UTC 14 May 2012
In the above image, the warmest (darkest) pixel had a brightness temperature of 350 K. A simple RGB composite of channels I-01 (0.64 µm), I-02 (0.87 µm) and I-03 (1.61 µm), with no other manipulation, from the same time as the I-04 image above, produces a red spot right over the I-04 hot spot:
False color RGB composite of VIIRS channels I-01, I-02 and I-03, 20:05 UTC 14 May 2012
Perhaps more amazing (but less useful from a firefighting perspective) is that, if you look closely (and you know the geography of the area), you can make out the locations of the following highways: I-25, I-76 and I-80, plus the main Union Pacific railroad tracks that more-or-less parallel I-80 in southern Wyoming. The high resolution imagery bands on VIIRS have enough resolution to identify interstate highways!
Suomi NPP passed over the area that night (15 May 2012) and the Day/Night Band (DNB) captured the fire burning brightly:
Day/Night Band image of the Hewlett Fire, 08:25 UTC 15 May 2012. Image courtesy Dan Lindsey.
By the time of the 17 May 2012 nighttime overpass – two days later – the fire had grown significantly. With no clouds around, the DNB easily saw the Hewlett Fire, as it was the brightest thing in the area. The image below has been enhanced to make the nearby city lights easier to see relative to the fire.
Day/Night Band image of the Hewlett Fire, 09:26 UTC 17 May 2012
In the above image, lights from various cities have been identified. The red arrow indicates the Hewlett Fire, which was bright enough and large enough to be confused for a city. The yellow arrow indicates what might be oil and/or gas flares burning in rural Weld County, which you can also see in the 15 May 2012 DNB image. Weld County is home to a third of all the oil and gas wells in Colorado.
In this zoomed-in image, you can see that the light from the fire covered an area approximately one third the size of Fort Collins:
Zoomed Day/Night Band image of the Hewlett Fire, 09:26 UTC 17 May 2012. Image courtesy Dan Lindsey.
This image was taken before the burn area even reached its maximum size. At the same time, channel I-04 also saw this ring of fire (not to be confused with the “ring of fire” caused by the recent annular eclipse):
VIIRS channel I-04 image of the Hewlett Fire, 09:26 UTC 17 May 2012
Once again, darker colors indicate higher brightness temperatures. The peak temperature in channel I-04 at this time was 356 K.
Even though it caused no damage to homes or structures, it was a little too close for comfort for many people.
As a final note, our partners up the hill in the Department of Atmospheric Science have taken an interest in the Hewlett Fire. If you are interested in the non-satellite side of the research into this fire, research groups led by Professors Rutledge, Kreidenweis and Collett have collected radar observations and in situ aerosol samples of the smoke plume. Contact them for more information.
According to legend, Popocatépetl was a great warrior whose girlfriend, Iztaccíhuatl, died because her father was a jerk who lied. (An alternate story is that it was a rival warrior who was a jerk who lied.) Either way, Iztaccíhuatl was erroneously told that Popocatépetl died in battle, which caused her to die of grief. When Popoca, as he was known to his buddies, returned to find out that she was dead, he was very sad. Reports on what followed differ, but Popoca either died of grief himself, or committed suicide at the thought of living without Iztaccíhuatl. To commemorate these events, the gods turned them both into mountains. To this day, the mountain Popocatépetl spews out rock and ash and fire either because he’s still mad at what happened, or because it is his way of looking out for his girlfriend.
The name Iztaccíhuatl literally means “White Woman,” and is the name of the snow-covered mountain ~40 miles southeast of Mexico City. Popocatépetl literally means “Smoking Mountain,” and is the name given to the volcano just to the south of Iztaccíhuatl. It is one of Mexico’s most active volcanoes. Ole’ Popoca has recently begun to remind us that he is mad (or eternally vigilant).
The alert level was raised in mid-April after the volcano was heard rumbling and once again began spewing ash over the region. If you clicked on that link, you might have noticed this sentence:
“The joint NOAA-NASA Suomi NPP satellite snapped a picture of the ash cloud coming from Popocatépetl on April 16.”
Although they forgot to include the picture in the article, VIIRS on board Suomi NPP did see the ash cloud. Here’s an image of the I-01 reflectance (white = 1, black = 0) taken by VIIRS on 16 April 2012 at 20:25 UTC:
Image of Popocatepetl's ash plume from VIIRS channel I-01, 20:25 UTC 16 April 2012
The ash plume is pushed to the east by the winds surrounding the cloud-covered volcano (where the arrow is pointing). On a clearer day, you can see Popocatépetl, Iztaccíhuatl, Matlacuéyatl, and the tallest volcano in Mexico, Pico de Orizaba:
False-color RGB composite (I-01, I-02 and I-03) from VIIRS taken at 19:53 UTC 23 April 2012
The above image is a false-color RGB composite of VIIRS channels I-01, I-02 and I-03 taken at 19:53 on 23 April 2012. The volcanoes and nearby urban centers have been identified and labelled. Pico de Orizaba, Popocatépetl, and Iztaccíhuatl are the first, second and third tallest mountains in Mexico, respectively, and are normally the only mountains in Mexico to be snow-covered year-round. The snow on top of Pico de Orizaba and Iztaccíhuatl is clearly visible in the image. Popocatépetl lost its snow during the 1990s when it became more active. But, you can see the cloud of ash and steam from the volcano in the image, which is not being blown around in the wind as much on this day. In fact, you can watch a time-lapse video of the steam and ash cloud from a Mexican government webcam from around the time of the Suomi-NPP overpass where you can see the clouds produced/influenced by The Smoking Mountain.
On 20 April 2012, a photographer captured this amazing image of Popocatépetl’s eruption of lava at night. Being near a new moon (which occurred on 21 April), the Day/Night Band (DNB) was able to see this lava eruption:
VIIRS Day/Night Band image of the Popocatepetl eruption from 07:58 UTC 20 April 2012
VIIRS I-01 image of Popocatepetl taken at 19:53 UTC 23 April 2012
In the above images, the red arrows are pointing to the same spot – the top of Popocatépetl. The upper image is from the DNB at 07:58 UTC on 20 April 2012, the lower image is from I-01 at 19:53 UTC on 23 April 2012 (the same time as the RGB composite). If you were to overlay the images on top of each other, you would see that the light source visible in the DNB image is right at the top of the volcano. Since there are no towns up there, and people surrounding the volcano have been evacuated, the light is coming from the erupting lava.
CIMSS provided these images of the volcano and ash plume at night (the same time as the DNB image above), which were visible in channels I-04 and I-05:
Image of Popocatépetl from VIIRS channel I-04, 07:58 UTC 20 April 2012 (courtesy William Straka, III / CIMSS)
Image of Popocatépetl from VIIRS channel I-05, 07:58 UTC 20 April 2012 (courtesy William Straka, III / CIMSS)
The upper image is the I-04 image. Channel I-04, at 3.74 µm, is very sensitive to hot spots such as wildfires or, in this case, volcanic eruptions. The dark (warm) spot identified is the heat signature of the molten rock that is erupting from the volcano. The cooler (brighter) ash cloud is visible in the I-04 image, but it shows up more clearly in the I-05 (11.45 µm) image underneath it.
Someone compiled a time-lapse series of images (14 April – 22 April) of Popocatépetl from a “NASA satellite” (presumably GOES-13) and posted the video to YouTube, which you can watch here.
Given its proximity to Mexico City, Popocatépetl is on the list of dangerous volcanoes to watch out for. The folks at WIRED are keeping their eye on it. Hopefully, Ole’ Popoca is just letting off a little steam, and not planning to get real violent. His girlfriend died a long time ago – it’s time to just let it go already.
With the I-bands having ~375 m resolution at nadir, VIIRS is a powerful instrument. We have already seen the detailed imagery it produces of severe thunderstorms and tropical cyclones. But, you might ask (particularly if you’re thinking you need a vacation), what remote islands is it able to see?
Well, it can see Easter Island. Yes, the one with all the big-headed statues (moai).
False color RGB composite (I1-I2-I3) image of Easter Island, 20:44 UTC 25 April 2012
At approximately 24.6 km x 12.3 km, VIIRS has no problem identifying the triangular island, as this false color (I1-I2-I3) RGB composite shows. In this image, taken at 20:44 UTC on 25 April 2012, the 163 km2 island appears to be dwarfed by a thunderstorm just to its north. If you zoom in, you can see several small cumulus clouds over the island along with their shadows. Unfortunately, it is not quite the resolution needed to see the individual moai.
As Easter Island is in the southern hemisphere, it is autumn there now. The average high temperature is down to 76 °F (from a summertime peak of 79 °F in February). April and May are listed as the wettest months, so an image of Easter Island not obscured by clouds this time of year may be a rare occurrence.
The second major tornado outbreak of the year took place on 14 April 2012 (after the 2 March outbreak that slammed Indiana and Kentucky). At last count, 115 tornadoes were reported from Oklahoma to Iowa. Credit must be given to the Storm Prediction Center, National Weather Service offices, and local TV and other media outlets for accurately predicting the severe weather event and keeping people informed as it happened, and the people of the area for paying attention to the weather. It must be counted as a success on many levels that 115 tornadoes over 4 states only resulted in 6 deaths (and those deaths occurred in the toughest situation to warn people – a rain-wrapped tornado in the middle of the night where the tornado sirens were disabled due to a lightning strike earlier in the day).
The last bout of severe weather occurred with a squall line that formed in the late evening (~02:30 UTC 15 April 2012) along the dry line in western Texas and quickly expanded into Oklahoma and Kansas. This line produced the deadly tornado in Woodward, OK, along with many reports of 1-2″ diameter hail. Suomi-NPP passed over this line of storms between 07:45 and 07:50 UTC (15 April). The high resolution infrared window band, I-5 (11.45 µm), shows the immense scale of this storm system stretching from Wisconsin and Minnesota to Texas, in great detail. Be sure to click on the image, then on the “1497×1953” link below the banner to see it in full resolution. (The full resolution image is ~2MB in size.)
View of the squall line over the Central Plains from VIIRS channel I-5, 7:45 UTC 15 April 2012
The color scale here is the same one used for the 2 March 2012 tornado outbreak image and the 25 January squall line over southeast Texas. The darkest blue pixels visible amongst the white overshooting tops (more easily visible on the southern end of the squall line) have a brightness temperature below -77 C, indicative of very strong convection.
On 5 April 2012, the Joint Typhoon Warning Center was watching an area of the Mozambique Channel for possible development of a tropical cyclone. This area was named Invest 97S. As 6 April 2012 was a full moon, this is a good case to test the capabilities of low-light visible imagery channels for detection of tropical cyclone development at night.
The Operational Linescan System (OLS) aboard the Defense Meteorological Satellite Program (DMSP) satellite F-18 has a low-light visible channel (that inspired the development of the Day-Night Band (DNB) for VIIRS). The image below is from this channel on F-18, taken at 17:22 UTC, 5 April 2012 (courtesy the Naval Research Laboratory).
DMSP OLS low-light visible image of Invest 97S, taken at 17:22 UTC, 5 April 2012. Image courtesy Naval Research Laboratory.
The landmass on the right of the image is Madagascar with Mozambique on the left side of the image. A low-level circulation is visible in the clouds just off the coast of Madagascar in the center of the image.
Suomi-NPP passed over the area at 23:02 UTC. The images below are taken from the VIIRS DNB, which is a low-light visible channel (centered at 0.7 µm) with higher radiometric resolution, a higher signal-to-noise ratio and higher spatial resolution. The second image is a zoomed-in version of the first.
VIIRS DNB image of Invest 97S taken at 23:02 UTC, 5 April 2012. Image courtesy Dan Lindsey and Steve Miller.
Zoomed-in image of Invest 97S from the VIIRS DNB taken at 23:02 UTC, 5 April 2012. Image courtesy Dan Lindsey and Steve Miller.
In the nearly six hours that elapsed between the DMSP OLS image and the VIIRS DNB image, you can see that the line of deeper convection to the southwest of the circulation center has moved further south away from the center of the circulation and outflow from these storms has cleared out the low level clouds from where the storms used to be.
Compare these images with the high-resolution infrared window channel (11.45 µm), I-5, from VIIRS, seen below.
VIIRS channel I-5 image of Invest 97S, taken at 23:02 UTC, 5 April 2012.
The low level circulation is difficult to distinguish, given that there is no significant temperature contrast between the low level clouds and the background (ocean) surface. The deeper convective clouds are easy to spot in I-5, however.
The information provided by the VIIRS DNB near full moon events would be a great help to tropical cyclone forecasting in cases such as this where, typically, only IR data is available at night. Assuming latency issues with VIIRS can be solved, of course.
In the end, Invest 97S failed to develop into a tropical cyclone, which spared Madagascar and Mozambique – both of which had been affected by the cyclones Giovanna and Funso earlier this year.
On 26 March 2012, strong winds, high temperatures and low humidities re-ignited embers from a controlled burn that took place the previous week near Conifer, CO. The Lower North Fork fire quickly spread in the high winds, eventually burning more than 4000 acres and damaging or destroying 27 homes. Three people were killed, presumably because they were unable to evacuate before their homes were engulfed in flame. One family’s daring escape from the fire was caught on a cell phone camera and made national news (CAUTION: strong language has not been edited out). Many interesting pictures of the fire may be found here, here, and here.
Channel I-4 of VIIRS (centered at 3.74 µm) captured the hot spot from the Lower North Fork fire on each of Suomi-NPP’s afternoon (ascending) overpasses last week. These images make up the loop shown below.
5-day loop of afternoon I-4 images of the Lower North Fork fire
In this image loop, the color scale represents observed brightness temperature such that warmer pixels appear darker and cooler pixels appear lighter. Pixels warmer than 330 K appear black, and pixels colder than 250 K appear white. The time between each image in the loop is approximately 24 hours.
The first image in the loop, taken at 20:24 UTC on the 26th, captured the hot spot shortly after the fire was first reported. The hot spot as seen by I-4 expanded significantly during the first 24 hours, before lighter winds and firefighting efforts greatly limited the growth of the fire. Over the last three frames, the hot spot can be seen to cool and shrink slightly.
Low (liquid) clouds can be seen as dark splotches on the images from the 28th and 29th of March, which should not be confused with fires. This is due to the fact that liquid clouds are highly reflective at 3.7 µm, and the reflection of solar radiation during the day increases the observed brightness temperature, so they appear darker. The persistently bright sideways “C” shape to the northeast of the fire is Chatfield Reservoir, which has a low brightness temperature due to the low water temperature in the reservoir and the relatively low emissivity of liquid water at this wavelength. Cherry Creek Reservoir (to the northeast of Chatfield Reservoir) and Marston Lake (to the north of Chatfield Reservoir) can also be seen.
With clear skies, the burn area shows up quite clearly in the I-band false color RGB composite of I-1, I-2 and I-3, taken at 20:06 UTC 27 March 2012 – the same time as the second frame of the loop above.
RGB composite of VIIRS channels I-1, I-2 and I-3 of the Lower North Fork fire, 20:06 UTC 27 March 2012
The burn area shows up as a sizeable dark brown spot in the forests (which show up as green) southwest of Denver.
After the driest and warmest March on record in Denver, hopefully this is not the start of a long, devastating fire season (link goes to PDF file).
The Heartstrong Fire in Yuma County, Colorado, 18 March 2012 (uncredited photo)
On 18 March 2012, very warm, very dry and very windy conditions existed throughout eastern Colorado. Surface observations showed temperatures in the 70s and 80s, dew points in the teens and 20s, and sustained winds at 20-30 knots (gusting over 40 knots). Wind gusts up to 60 knots (~70 mph) were reported.
Surface observations, 19:00 UTC 18 March 2012 (courtesy UCAR)
A red flag warning was issued for nearly all of eastern Colorado. And with good reason! A grass fire started in Yuma County, CO (which borders Nebraska and Kansas) in the early afternoon, and quickly grew out of control. The media dubbed it the Heartstrong Fire. An area 14 x 16 miles had to be evacuated, although only 2400 acres actually burned. The smoke plume was easily visible from the Goodland, KS, National Weather Service radar. Two homes were destroyed, and three firefighters were injured battling the blaze.
Radar image of smoke from the Heartstrong Fire seen by the Goodland, KS, NWS radar, 21:17 UTC 18 March 2012 (courtesy UCAR)
"True Color" image (RGB composite of VIIRS channels M3, M4 and M5) of the Heartstrong Fire, 19:34 UTC 18 March 2012
Even though cirrus clouds covered the area (as seen in the true color image above), VIIRS observed the fire in its two 3.7 µm channels. The VIIRS images shown here, from 19:34 UTC, were taken roughly 20 minutes after the fire was first reported. The moderate resolution band M-12 (centered at 3.7 µm) identifies a hot spot (which shows up as black in the image below) that is approximately 6 pixels by 3 pixels. With ~750 m resolution at nadir in this band, that corresponds to a total area of 10.2 km² of pixels that contain a fire signal.
Image of the Heartstrong Fire from VIIRS channel M-12, 19:34 UTC 18 March 2012
The high resolution imagery band I-4 (centered at 3.74 µm) also identifies the hot spot. In this case it is approximately 11 pixels by 5 pixels in size. At ~375 m resolution at nadir, this corresponds to an area of 7.7 km² of pixels that contain a fire signal.
Image of the Heartstrong Fire (indicated by the red arrow) from VIIRS channel I-4, 19:34 UTC 18 March 2012
Thus, the difference in resolution between these two channels leads to a difference in the apparent size of the hot spot as seen by satellites. However, it should be noted that this apparent size is only an estimate of the size of the hot spot visible in the satellite image, not the actual size of the fire. Fires move in narrow flame fronts that cover only a small percentage of the pixel area. From a firefighting perspective, detecting which pixels actually contain fire and where the actual burning occurs within those pixels are two different things.
Of additional interest is the difference in observed brightness temperatures between these two channels. The warmest pixel in M-12 was 327 K, while the warmest pixel in I-4 was 342 K. As the observed brightness temperature is related to the fraction of each pixel covered by fire, the higher resolution images produce higher brightness temperatures in the hot spot.
This means that, to a human observer, the hot spot appears larger in the M-band image, while, from an automated algorithm point-of-view, the I-band image has a larger number of pixels within the hot spot, and higher brightness temperatures. The difference in the appearance of the hot spot between these channels is more clearly seen in the figure below. Be sure to click on the image, and then look for the “1700×702” link above the image title and click on that to see the comparison in its highest quality.
Comparison between the I-4 and M-12 views of the Heartstrong Fire. The previous I-4 and M-12 images (taken at 19:34 UTC, 18 March 2012) have been zoomed in for additional clarity.
As an additional note, band M-13 (centered at 4.05 µm) is the primary band used in active fire detection. This band was designed specifically to measure the radiative signal of hot spots without sensor saturation. The M-13 image of the fire is shown below.
Image of the Heartstrong Fire from VIIRS channel M-13, 19:34 UTC 18 March 2012
There is a dedicated team of researchers actively exploring fire detection from VIIRS. You can learn more about fire detection and the status of their current fire detection products by visiting viirsfire.geog.umd.edu.
On 6 March 2012, a massive solar flare erupted and an associated coronal mass ejection was launched toward Earth. Video of the solar flare from NASA’s Solar Dynamics Observatory can be viewed here. NOAA’s Space Weather Center forecast the coronal mass ejection to reach Earth on 8 March 2012, which you can view here. In another video, Joe Kunchas of the Space Weather Prediction Center talks about the solar flare, coronal mass ejection and possible impacts of the these events – including re-routing of aircraft, the effect on electric power grids and the best conditions to view the aurora. ABC News made it sound like the world was going to end.
Fortunately, the world did not come to an end and Suomi NPP sufferend no significant ill effects from the solar activity. In fact, the Day/Night band on VIIRS caught the aurora borealis, even in the presence of a full moon.
VIIRS Day/Night band image of the aurora over Saskatchewan and Manitoba, 9 March 2012
The Day/Night band (DNB) is a visible-wavelength band, centered at 0.7 µm, that is highly sensitive to low levels of light, so that it behaves like a visible channel even at night when the moon is out. As seen in the image, the DNB clearly shows the location of towns and cities at night. Since 8 March 2012 was a full moon, clouds, snow and ice (particularly over Lake Winnipeg) are also visible. The brightest swirl, extending from north of Saskatoon, over Reindeer Lake and into northwestern Manitoba is the aurora borealis.
On its previous orbit, the DNB captured the aurora over Ontario and Quebec, although it is more difficult to distinguish from the underlying clouds.
A VIIRS Day/Night band image taken at 7:35 UTC, 9 March 2012
In this image, the bright swirls extending from north-central Ontario, over James Bay and into northern Quebec are elements of the aurora. It is expected that auroras would be more visible in the DNB during new moon events, where the aurora would be the only light source (apart from cities, towns and other point-source human activity).
Many amateur and professional photographers got a good look at the auroras, including this video taken from the shores of Lake Superior and this one taken near Wasilla, Alaska. Imagine if we had two more DNB channels at shorter wavelengths, so that we could capture the amazing colors of the aurora that these videos show.
NPP/VIIRS passed over Southern Indiana on March 2 about thirty minutes before the most devastating tornadoes struck the towns of New Pekin and Henryville (among others). At 1935 UTC, a pair of rotating thunderstorms, also known as supercells, were advancing eastward across Indiana. The easternmost storm spawned the most damaging tornadoes. Below is a VIIRS true color image from the NPP pass at 1935 UTC.
VIIRS True Color image of the severe storms on 2 March 2012 at 1935 UTC.
A zoomed-in visible view of the storms is below.
VIIRS I-band 1 (375-m resolution) from 2 March 2012 at 1935 UTC
The infrared (I-band 5) image is below, along with some annotations pointing out the two active supercells discussed above. Note that the brightness temperatures associated with the overshooting top (OST) of the westernmost storm are colder than the easternmost storm, although both storms were quite strong at the time and the eastern storm ended up producing the deadlier tornadoes. OSTs are transitory, so it’s possible that a new cold OST formed with the eastern storm shortly after the NPP pass. These very high resolution infrared views of tornadic storms are among the first documented, given the recent launch of NPP.
To illustrate the effect of high resolution in the IR, below is a GOES-13 10.7 micrometer IR image from 1932 UTC, which has 4-km resolution at nadir. The coldest brightness temperature in the westernmost storm in southern Indiana from GOES is 206.6 K, but with VIIRS it’s 195 K.
GOES-13 4-km IR Image from 1932 UTC on 2 March. Compare this image to the 375-m VIIRS image above to see the improvement provided by VIIRS over GOES.
The day after the tornadoes, relatively cloud-free skies in eastern Kentucky allowed VIIRS to see some of the tornado tracks. In the image below, the faint white lines circled in red in Kentucky and West Virginia denote the new tornado damage paths. When green vegetation is disrupted/destroyed, the result is typically a brighter scene at visible wavelengths.
VIIRS I-band 1 from 3 March 2012 over eastern KY and western WV. The tornado tracks are circled and show up as faint white lines
It’s not every day the official National Weather Service forecast from Honolulu, HI calls for freezing rain and snow in parts of Hawaii, but that’s what happened 19 February 2012 for the Big Island. Mauna Loa and Mauna Kea both received snow over the last 24 hours, and VIIRS shows both volcanoes are covered with the white stuff.
True color image of the island of Hawaii from VIIRS, taken at 23:16 UTC, 19 Febraury 2012
False color (or "pseudo-true color") image of the island of Hawaii from VIIRS at 23:16 UTC, 19 February 2012
The top image is an RGB composite of moderate resolution channels M3 (blue), M4 (green) and M5 (red). As these channels observe radiances in the blue (0.488 μm), green (0.555 μm) and red (0.865 μm) portions of the visible spectrum, respectively, the image represents what the human eye sees, and is thus a “true color” representation.
The bottom image is an RGB composite of high resolution channels I-1 (blue), I-2 (green) and I-3 (red). These channels observe radiances centered on 0.64, 0.86 and 1.61 μm, respectively. As a result, differences in the optical properties of liquid droplets and ice particles at these wavelengths allow liquid and ice clouds (and snow) to be distinguished more easily. Liquid clouds appear white (or a dirty, brownish-white), while ice clouds appear blue. The snow on top of Mauna Loa and Mauna Kea stands out as a deeper blue. Vegetation still shows up as green, and barren ground as brown, producing an image that may be called “pseudo-true color”.
Both peaks, which are more than 13,600 ft above sea level, receive snow during most winters. The Mauna Loa observatory, at 11,100 ft above sea level, averages 3.7 inches of snowfall each year. While there are many observatories on Mauna Kea with weather stations, none of them seem to collect routine snowfall information. You can find the latest webcam imagery of Mauna Kea by clicking here to find out how much snow there is in the tropical paradise now. Aloha!
Back in January, Madagascar was brushed by tropical cyclone Funso, which caused periods of heavy rain, but was a bigger deal for neighboring Mozambique. This time around, Madagascar took a direct hit from tropical cyclone Giovanna, which reached “Super Cyclone” status as category 4 storm just prior to making landfall.
VIIRS got a great look at Giovanna while it was a category 4 storm.
A visible image of Super Cyclone Giovanna from VIIRS channel I-1 taken at 09:47 UTC, 13 February 2012 (Thanks to Dan Lindsey)
IR-window image of Super Cyclone Giovanna taken by VIIRS channel I-5 at 09:47 UTC, 13 February 2012 (Thanks to Dan Lindsey)
Fortunately, Giovanna weakened rapidly upon making landfall. Moving east to west across the center of the island and over the mountains of central Madagascar, Giovanna could not maintain its Super Cyclone status. At the time of this post, Giovanna was located over the west coast of Madagascar and down to sustained winds of 35 knots, making it a weak tropical storm. Early reports suggest between 2 and 5 people were killed, and some towns on the east coast had 60% or more of their buildings damaged or destroyed.
All things considered, the situation could have been much worse, considering residents were only given a half a day’s warning, and the primary method for disseminating weather warnings is through the use of town criers.
A severe squall line formed over eastern Texas on 25 January 2012. There were 19 tornado reports and 48 reports of wind damage, including “a house destroyed by a possible downburst”, according to the Storm Prediction Center. The high resolution imager on VIIRS captured this squall line as it was rapidly intensifying. Shown below are images collected from channel I-5, the high-resolution infrared window channel (11.45 μm). (Click on images for full resolution.)
A squall line over eastern Texas observed by VIIRS channel I05 (11.45 um) at 19:24 UTC on 25 January 2012.
This squall line had several overshooting tops over the Gulf of Mexico that reached a temperature of -77 C. A zoomed-in view of these tops are shown below.
A squall line over eastern Texas observed by VIIRS channel I05 (11.45 um) at 19:24 UTC on 25 January 2012.
The dark blue pixels near the center of the image indicate an overshooting top approximately 5 km in diameter where temperatures were less than -77 C. Several pixels in a storm top at the bottom center of the image and in a storm top at the top center of the image (near Galveston, TX) also reached that temperature.
A sounding was taken at 18:00 UTC at the Lake Charles, LA, National Weather Service (NWS) office, which observed a minimum temperature of -74 C at 17.9 km above sea level, indicating that these are some tall thunderstorms. Image courtesy the University of Wyoming.
NWS sounding taken at 18:00 UTC from the Lake Charles, LA office.
The VIIRS imagery was collected right as the squall line was intensifying. Shown below is the radar loop from the Houston/Galveston radar between 18:00 UTC and 21:00 UTC. Note, at the beginning of the loop, the southern end of system consists of two rather disorganized lines of cells. These lines of cells merge at around 19:25 UTC (the time of the Suomi NPP overpass), and a much stronger and more organized squall line develops.
Radar loop from the Houston/Galveston NWS WSR-88D radar beginning at 18:00 UTC, 25 January 2012.
At roughly 375-m resolution at nadir, the I-5 channel on VIIRS is providing some of the highest resolution infrared imagery available to the atmospheric science community. We are just beginning to see the capabilities of this powerful instrument.
