Tag Archives: resolution

Fires near the hottest city in the Arctic

June 30, 2020UncategorizedArctic, Day Land Cloud Fire RGB, fire, Fire Temperature RGB, GeoColor, Himawari-8, resolution, Siberia, Snow RGB, True Color RGBCurtis Seaman

Something incredible happened in the Arctic a few days ago. Rather than type it out, I will let the World Meteorological Organization (WMO) explain it:

In case you’re wondering where Verkhoyansk is, you can look it up on Google Maps.

If that temperature is verified, it would be the hottest temperature ever recorded north of the Arctic Circle. To put that 38 °C into perspective, that was only 2 °C off from the high temperature in Phoenix, AZ on 20 June 2020 – a place where 40 °C is normal in the summer. It’s also worth reiterating how unusual it is for any location to average 8-10 °C (15-20 °F) above normal for an entire month. Russia as a whole – by far the largest country on Earth – has averaged 8 °C above normal for the entire first half of 2020!

If you clicked that last link, you saw an excerpt of the video above, plus more information on the unusual impacts of this heatwave. The clouds of mosquitoes. The collapsing buildings due to melting permafrost. (One of the largest oil spills ever in the Arctic happened in May, caused by melting permafrost.) And, an even more alarmist impact of the heat: “zombie fires“.

That’s right – if you didn’t have enough with the coronavirus or the murder hornets or the melting Arctic, you can now panic about zombie fires. In all seriousness, the silly name has been applied to the phenomenon of fires in peat bogs never really being fully extinguished, and continuing to smolder deep down below the ice and snow that covered it up all winter. Then, when a heatwave happens the next summer, the smoldering turns to re-ignition of the fire, and it once again appears on the surface.

Fires have been happening on a massive scale throughout Siberia this summer (and they’re probably not all zombie fires). But, we have a tool to observe them from satellite: Polar SLIDER.

I’m sure there may be a few of you who are already familiar with the website. For those who aren’t, here’s a brief synopsis: Polar SLIDER is designed to show the most recent VIIRS imagery available anywhere on Earth in as close to real-time as possible. Images from individual orbits from both Suomi-NPP and NOAA-20 are stitched together to create hemispheric composites that always feature the most recent imagery on top. The way the orbits work, when Suomi-NPP is crossing over into the Southern Hemisphere, NOAA-20 is crossing into the Northern Hemisphere (and vice versa). By combining imagery from both satellites, there is a ~50 minute refresh over the poles, giving a quasi-geostationary satellite view of each pole. Imagery is available at six different zoom levels, separated by factors of 2, so you can zoom in to see full resolution VIIRS imagery anywhere on Earth.

Here’s an example of Polar SLIDER, reduced in size to play well with this blog software, showing our GeoColor product (True Color imagery during the day, blended with the Day/Night Band and a low cloud detection algorithm at night) over Siberia on 23-24 June 2020:

VIIRS GeoColor animation (23-24 June 2020)

How much smoke can you see? Did you count the plumes? Did you see the swirl in the smoke at about 70°N, 140°E? (For reference, Verkhoyansk is near 67°N, 133°E.)

That loop covers approximately 30 hours in the Arctic and, since we’re so close to the summer solstice, you can estimate the location of the Arctic Circle, even though it isn’t plotted.

Even those of you who have heard of (or seen) Polar SLIDER before might not be aware of the recent upgrades made in May 2020. For the first 18 months of its existence, Polar SLIDER had all 22 VIIRS channels (DNB, 16 M-bands, 5 I-bands) plus GeoColor (as shown in the loop above) and, occasionally, the I-band Natural Color (aka Day Land Cloud RGB) product. Now, we have added 10 new products, including the most popular RGB composites (the ones that are available from VIIRS, anyway) and two new RGBs for snow monitoring that utilize the 1.24 µm band that are not available on any geostationary satellite. (More information on those is available here.) We’ve also fixed the issues with the Natural Color RGB, making it a permanent fixture, rather than an anomaly.

Among the new products available on Polar SLIDER is what we call Natural Fire Color (and the National Weather Service calls “Day Land Cloud Fire RGB“), made from VIIRS bands I-1 (0.64 µm, blue), I-2 (0.86 µm, green) and I-4 (3.7 µm, red). As it is made from VIIRS I-bands, it is available at 375 m resolution around the globe. Here’s what it shows from 22-23 June 2020 over this part of Siberia:

VIIRS Day Land Cloud Fire RGB animation (22-23 June 2020) – click to play

This animation is too large for WordPress. You have to click on it to get it to play. But, I couldn’t resist showing the full resolution imagery. Also, a note about the timestamps on Polar SLIDER: it takes ~50 min for each satellite to cover each hemisphere, and the image times displayed on Polar SLIDER represent the Equator-crossing time as the satellite leaves the hemisphere, which is most likely not the time the satellite was viewing the area you’re looking at.

The Natural Fire Color/Day Land Cloud Fire loop covers a ten hour period from ~ 8:00 AM to 6:00 PM local time (depending on where you are in the scene, it might be 7:00 AM to 5:00 PM), during which time there were 11 consecutive VIIRS overpasses over this region between the two satellites. This is a textbook example of how fires typically die down at night (or, at least, when the sun is hovering over the horizon) and intensify during the heating of the day.

Of course, you can get a better idea of the intensity of the fires by looking at the Fire Temperature RGB, which is also now on Polar SLIDER:

VIIRS Fire Temperature RGB animation (22-23 June 2020) – click to play

Once again, you have to click on the animation to get it to play.

The Fire Temperature RGB is made with VIIRS M-bands (750 m resolution), so the fires don’t look as crisp when viewed at the 375 m zoom level. But, since it uses more information from fire-sensitive bands in the shortwave IR, it provides a qualitative estimate of fire intensity, not just the locations of the active hot spots. (As fires become more intense, their color changes from red to orange to yellow to white in the Fire Temperature RGB.)

Other differences to note between the two loops are: the Natural Fire Color RGB shows the reddish-brown burn scars more clearly amongst a background of green vegetation; it shows the bluish smoke more clearly; and it shows ice in the Arctic Ocean, which appears nearly black in the Fire Temperature RGB. We’ve covered all of this before, both here and elsewhere. We’ve also covered the importance of VIIRS’ high resolution (compared to geostationary satellites) when it comes to fires before. But, it’s worth looking at again. Compare the loops above with the view from the Advanced Himawari Imager (AHI) on Himawari-8:

AHI Day Land Cloud Fire RGB animation (23 June 2020) – click to play

AHI Fire Temperature RGB animation (23 June 2020) – click to play

You can find a loop of the AHI GeoColor showing the smoke plumes here.

It’s difficult to identify any fires in the AHI Natural Fire Color/Day Land Cloud Fire RGB, given the resolution of the 3.9 µm channel is 2 km at the Equator (more like 3-6 km in this part of the world) – not 375 m like the VIIRS version. Hot spots show up better in the AHI Fire Temperature RGB this far north, because this combination of channels makes the background surface appear darker relative to the pixels with active fires in them, whereas the background is brighter in the Natural Fire Color RGB.

Lastly, because I mentioned new RGBs for snow on Polar SLIDER, one of them has an interesting artifact when it comes to fires. The Snow RGB originally developed by MétéoFrance utilizes the 2.25 µm band as the blue component, making hot spots appear blue:

VIIRS MeteoFrance Snow RGB composite of channels M-11, M-8 and M-7 (23 June 2020) — VIIRS MétéoFrance Snow RGB composite of channels M-11, M-8 and M-7 (23 June 2020)

Of course, if you’re looking for fires, don’t reach for the Snow RGB. But, someone, somewhere is going to be looking at the Snow RGB when they spot a couple of bright blue pixels and wonder, “What’s going on here?” And, I’m here to say, “Those are moderately intense fires.”

A four-birds-eye-view of fires in Alberta

May 29, 2019UncategorizedAlberta, Canada, fire, Fire Temperature RGB, GOES-16, GOES-17, resolution, smoke, True Color RGBCurtis Seaman

It has been three years since the devastating Fort McMurray Fire – the costliest natural disaster in Canada’s recorded history. The city is still rebuilding. And, I’m sure the people of Alberta don’t want to be reminded of it. I hate to be the bearer of bad news but, here we go again! This time, it is the town of High Level, Alberta that has been evacuated. This is one of four “out of control” fires currently burning in Alberta:

Map of known fires from Alberta Wildfire (24 May 2019)

The red flame icons represent “out of control” fires, while the green ones are “under control”. This map was produced by Alberta Wildfire on 24 May 2019. The northernmost red flame represents the fire near High Level. According to Alberta Wildfire, the southern two fires started on 18 May 2019, while the other two started on 11-12 May 2019. But, as you shall see, they became out of control on 18 May.

This being Alberta, satellites are often the first source for spotting fires in these remote areas. Plus, Alberta is in a unique position: it is far enough east to be within the view of GOES-16 and far enough west to be within view of GOES-17. It is also far enough north to get several overpasses from VIIRS on both Suomi-NPP and NOAA-20 each day. So, what do these satellites have to say about these fires? Let’s take a look.

We’ll begin with GOES-16, which is now operational as “GOES-East” and has been since December 2017. Since April 2019, the GOES-16 ABI has been running in Mode 6 operations. This means full disk images every 10 minutes. Here is the GeoColor loop (link goes to PDF file) from GOES-16, starting at 1700 UTC on 18 May 2019 and running until sunset (~0400 UTC 19 May 2019):

For the uninitiated, GeoColor is a blend of true color imagery during the day with a low cloud/fog detection product at night. True color is particularly useful for detecting smoke, as we have seen before. So, did you see it in the above loop?

I’ll admit, the clouds make it difficult to see, but there are two smoke plumes visible in that video. (It helps to view the video in full screen mode.) The smoke plume for the fire near High Level shows up first (around 1940 UTC), then the smoke from the pair of fires northeast of Lesser Slave Lake appears around 2150 UTC and become obvious around 0000 UTC on 19 May.

Here is the Fire Temperature RGB from GOES-16 ABI over the same time period:

This product is sensitive to hot spots – not smoke – but it still has an issue with clouds, which block the signal. Notice that the northernmost fire near High Level first appears in the Fire Temperature RGB around 1840 UTC – an hour before the smoke is really evident. The first fire northeast of Lesser Slave Lake is visible starting at 2200 UTC (10 min. after the smoke is visible), and the second one first appears at 2320 UTC (about 20 min. after its smoke plume appears). The fourth fire (the westernmost of all of them, and the one in the middle north-to-south) first appears at 2100 UTC, although it is in and out of the clouds, and its smoke plume is never very visible.

Compare that with GOES-17 ABI, which is now operational as “GOES-West” (since February 2019), and is also running Mode 6 operations:

These videos cover the same time period as the GOES-16 versions.

Moving clockwise starting at the fire near High Level, the first sighting of smoke according to GOES-17 is around 1900 UTC, 2200 UTC (first Lesser Slave Lake fire), 0000 UTC (second Lesser Slave Lake fire), and 2310 UTC (westernmost fire). This is 40 min. earlier, similar to, similar to, and not easily comparable to GOES-16. (Since GOES-16 never got a clear look at the smoke from the westernmost fire, it’s difficult to compare times against GOES-17, which was able to detect smoke from that fire. Also, I say a 10 minute difference is “similar to”, since it is a judgement call as to which image an analyst would confidently say they first saw the smoke or the hot spot.)

Comparing the hot spots in the same clockwise fashion, the time of first detection was 1820 UTC, 2140 UTC, 2320 UTC and 2050 UTC. This is 20 min. earlier, 20 min. earlier, similar to, and similar to GOES-16. If you don’t believe me, compare them frame by frame in this video:

GOES-16 is on the right and GOES-17 is on the left. When you can directly compare them like this, a few things jump out. 1) The fire near High Level appears to grow more in GOES-17 than it does in GOES-16. 2) The two fires near Lesser Slave Lake appear so close together in GOES-17 that they are difficult to distinguish as separate fires, although they are clearly separate fires according to GOES-16. 3) The westernmost fire is the most difficult to see due to the presence of clouds.

Here, it is important to note a few things. A) Alberta is generally closer to GOES-17 than it is to GOES-16. B) The position the clouds and the motion of the fire relative to the lines of sight of the satellites lead to significant differences between GOES-16 and GOES-17 in this case. C) The plural of “anecdote” is not “data”. The exact circumstances that apply here are not going to apply in the future but the concepts still will.

The fire near High Level moves perpendicular to the GOES-17 line of sight, and moves parallel to the GOES-16 line of sight (away from the satellite on a curved surface to boot!) with slightly more coarse resolution in GOES-16. That explains the differences in apparent motion between the two views. The differences in viewing angle also explain why the fires near Lesser Slave Lake appear separate (GOES-16) or together (GOES-17). And, clouds blocking the line of sight explain why the fire near High Level appears later in GOES-16, while GOES-17 had a clear view of the fire 20 minutes earlier. The differences in perspective offered by the two satellites also make it tough to tell which fire is furthest west (the one I’ve been calling “westernmost”).

Both geostationary satellites are viewing Alberta at a high angle. What about VIIRS on Suomi-NPP and NOAA-20, which flies more-or-less directly overhead? Since these satellites are polar-orbiting, they can’t provide 10 min. imagery of the fires. But, they do combine to provide ~50 min. imagery of the fires for a few orbits in the early afternoon and early morning hours, and they have much better spatial resolution.

The first VIIRS overpass was from NOAA-20 at around 1920 UTC, and here’s what it saw (according to the Fire Temperature RGB):

NOAA-20 VIIRS Fire Temperature RGB (1920 UTC, 18 May 2019)

This image is about an hour after GOES-17 first spotted the fire near High Level, which is the most obvious fire in the scene. In fact, the westernmost fire is hidden under the clouds and the fires near Lesser Slave Lake haven’t started yet. The closest time matched images with both GOES are provided below.

GOES-17 (left) and GOES-16 (right) Fire Temperature RGB (1920 UTC 18 May 2019)

Approximately 50 min. later, Suomi-NPP passed overhead and provided this view:

S-NPP VIIRS Fire Temperature RGB (2011 UTC, 18 May 2019) — S-NPP VIIRS Fire Temperature RGB (20:11 UTC, 18 May 2019)

And the GOES view from approximately the same time:

GOES-17 (left) and GOES-16 (right) Fire Temperature RGB (2010 UTC 18 May 2019)

Here the fire near High Level is quite a bit more intense, as you could see if you were to toggle back and forth between the two VIIRS images. This is a definite sign that the fire is a dangerous one! Still no sighting of the Lesser Slave Lake fires, which didn’t get going for another 110 minutes or so, according to both GOES. There was another NOAA-20 overpass at 2101 UTC, then a Suomi-NPP overpass that caught the western portion of the scene at 2151 UTC. Combine all four overpasses in an animation, and you get this:

Animation of VIIRS Fire Temperature RGB images (18 May 2019)

Remember to click on the image to get the animation to play. If you look closely, you can see both of the fires northeast of Lesser Slave Lake in the NOAA-20 image from 2101 UTC – a full 50 minutes earlier than either GOES was able to detect them. But, they are just single pixels at this point, making them likely too small for ABI to detect them (particularly at such a high viewing angle).

As for the smoke, it’s not any easier to detect with VIIRS given all the clouds overhead (click to play):

Animation of VIIRS True Color images (18 May 2019)

You might have noticed in the above animation that Lake Athabasca is still covered in ice, which is not apparent in the Fire Temperature RGB. That means fire season in Alberta started before all the ice had melted!

Over the next few days, clouds weren’t as much of a problem so the hot spots and smoke plumes were easily visible from all four satellites. Here are direct comparisons between GOES-16, GOES-17 and the two VIIRS for 19 May 2019:

NOAA-20 VIIRS Fire Temperature RGB (1901 UTC, 19 May 2019)

GOES-17 (left) and GOES-16 (right) Fire Temperature RGB (1900 UTC, 19 May 2019)

S-NPP VIIRS Fire Temperature RGB (1959 UTC, 19 May 2019)

GOES-17 (left) and GOES-16 (right) Fire Temperature RGB (2000 UTC, 19 May 2019)

We can even do better than that and really zoom in:

Comparison between GOES-17 ABI, NOAA-20 VIIRS, and GOES-16 ABI Fire Temperature RGB images (1900 UTC, 19 May 2019) zoomed in at 400%

Comparison between GOES-17 ABI, S-NPP VIIRS, and GOES-16 ABI Fire Temperature RGB images (2000 UTC, 19 May 2019) zoomed in at 400%

Here, the previous images have been cropped to the fire near High Level, and zoomed in 400%. The difference in resolution between VIIRS and ABI at this latitude is obvious.

Here is a four day loop of True Color RGB images from both VIIRS (click to play):

Animation of VIIRS True Color images (18-21 May 2019)

And, click on these links for similar loops of GeoColor from GOES-16, GOES-17 and a side-by-side comparison of the two. (The videos are too large to embed here.)

And, finally, here is a four day loop of Fire Temperature RGB images from both VIIRS (click to play):

Animation of VIIRS Fire Temperature RGB images (18-21 May 2019)

And links to MP4 video files from GOES-16, GOES-17 and a side-by-side comparison of the two.

Rivers of Ice

May 24, 2018UncategorizedAHI, ice and snow, Natural Color RGB, NOAA-20, resolution, RGB composite, river, S-NPP, SiberiaCurtis Seaman

Oh, Yakutsk! It has been a long time – 2012, to be exact – since we last spoke about you (on our sister blog). It was a different time back then, with me still referring to the EUMETSAT 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 this 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. (Note: the scene in the video above is approximately the same latitude as the Yukon River delta, so this acts as a good preview of what GOES-17 and its Advanced Baseline Imager [ABI] will offer.) So, how does this look from the vantage point of VIIRS, which provides similar imagery, but 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 (or ABI will 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 over 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!)

Watch for Falling Rock

July 11, 2016UncategorizedAlaska, glacier, I-1, I-2, I-3, landslide, M-3, M-4, M-5, Natural Color RGB, resolution, RGB composite, True Color RGB, visibleCurtis Seaman

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, take a look at this animated GIF made from those two images:

Animation of VIIRS True Color images highlighting the Lamplugh Glacier landslide

The arrow is pointing out the location of the 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!

UHF/VHF

January 28, 2016UncategorizedAlaska, Aleutian Islands, cyclogenesis, day/night, HF low, I-5, resolutionCurtis Seaman

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 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).

You may have heard of Himawari and its primary instrument, the Advanced Himawari Imager (AHI). AHI can be thought of as a geostationary version of VIIRS, and it’s nearly identical to what GOES-R will provide. Well, Himawari’s field of view includes the Aleutian Islands, and it takes images of the full disk every 10 minutes. 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?

PS: I know this is a VIIRS blog, but if you want to look at CIRA’s Himawari data products, we have both full disk and North Pacific (including the Aleutians) sectors available in near real-time on this website.

The Land of 10,000 Fires

July 8, 2015UncategorizedAlaska, fire, Fire Temperature RGB, Natural Color RGB, optical effects, resolution, RGB composite, smoke, True Color RGBCurtis Seaman

Minnesota calls itself the “Land of 10,000 Lakes” – they even put it on their license plates. To an Alaskan, it seems funny to brag about that since Alaska has over 3,000,000 lakes. That’s like a Ford Escort bragging to a Bugatti Veyron that it can achieve highway speeds!

Alaska has Minnesota beat in one other area, and it’s one they’re definitely not putting on their license plates: the number of wildfires. OK, so it may not be 10,000 fires as my title implies, but there sure are a lot:

AICC Fire Map (28 June 2015) — Map of known wildfires (23 June 2015), courtesy Alaska Interagency Coordination Center

That map shows the number of known wildfires in Alaska on 23 June 2015 and was produced by the Alaska Interagency Coordination Center (AICC). To say that 2015 has been an active fire season in Alaska is an understatement. That would be like saying a Bugatti Veyron is a vehicle capable of achieving highway speeds! (By the way, if anyone in the audience works at Bugatti, and would like to compensate me for this bit of free advertising by, say, giving me a free Veyron, it would be much appreciated.)

Imagine being the person responsible for keeping track of all these fires! (That’s what the good folks at AICC do on a daily basis. There’s also a graduate student at the University of Alaska-Fairbanks working on this very same problem, who has come up with this solution.) This is being called the worst fire season in Alaska since, well, the beginning of recorded history. 2004 was the worst on record, but 2015 is on pace to shatter that. By 26 June 2015, Alaska’s fires had burned over 1.5 Rhode Islands worth of land area (or, alternatively, 0.8 Delawares). By 2 July, the total burned acreage had achieved 3 Rhode Islands (1.6 Delawares; 2/3 of a Connecticut). On 7 July, the total hit 3,000,000 acres (1 Connecticut; 2.5 Delawares; 5 Rhode Islands). (2004 ended at 2 Connecticuts worth of land burned, so there is a chance the pattern could switch and Alaska will get enough rain to fall short of the record, but at this rate, that seems unlikely.)

It’s interesting to see how this came to be, given that there were only a couple of fires burning in the middle of June:

VIIRS Fire Temperature RGB composite of channels M-10, M-11 and M-12 (21:28 UTC 15 June 2015)

If you followed this blog last year, you should know about this RGB composite, called “Fire Temperature.” If not, read this and this. Click to the full size image and see if you can see the six obvious fires. (Two are in the Yukon Territory.) Now, count up the number of fires you see in this image from just one week later:

VIIRS Fire Temperature RGB composite of channels M-10, M-11 and M-12 (22:16 UTC 23 June 2015)

Why so many fires all of a sudden? Well, it has been an abnormally dry spring following a winter with much less snow than usual. Plus, there have been a number of dry thunderstorms that produced more lightning than rain. You can see them in the image above as the convective clouds, which appear dark green because they are topped with ice particles. (Ice clouds appear dark green in this composite. Liquid clouds appear more blue.) A number of thunderstorms filled with lightning formed on the 19^th of June, and a lot of fires got started shortly after. Here is an animation of Fire Temperature RGB images from 15-25 July (showing only the afternoon VIIRS overpasses):

Animation of VIIRS Fire Temperature RGB images (15-25 June 2015)

It’s difficult to see the storms that led to all these fires, because the storms don’t last long and they typically form and die in between images. Plus, some of the fires may have started from hot embers of other fires that were carried by the wind.

Of course, when there’s smoke there’s fire. I mean – when there’s fire there’s smoke. Lots of it, which you’d never be able to tell from the Fire Temperature RGB. The Fire Temperature RGB uses channels at long enough wavelengths that it sees through the smoke as if it weren’t even there. But, the True Color RGB is very sensitive to smoke. Here’s a similar animation of True Color images:

Animation of VIIRS True Color RGB images (16-25 June 2015)

Look at how quickly the sky fills with smoke from these fires. And also note that the area covered by smoke by the end of the loop (25 June 2015) is too large to be measured in Delawares – units of Californias might be more useful.

The last frame in each animation comes from the VIIRS overpass at 21:30 UTC on 25 June 2015. It’s nice to know that you can still detect fires in the Fire Temperature RGB even with all that smoke around.

Another popular RGB composite to look at is the so-called “Natural Color”. This is the primary RGB composite that can be created from the high-resolution imagery bands I-1, I-2 and I-3. The Natural Color RGB is sort-of in-between wavelengths compared to the Fire Temperature and True Color. The True Color uses visible wavelengths (0.48 µm, 0.55 µm and 0.64 µm), the Fire Temperature uses near- and shortwave infrared wavelengths (1.61 µm, 2.25 µm and 3.7 µm), and the Natural Color spans the two (0.64 µm, 0.87 µm and 1.61 µm). This means the Natural Color is not as sensitive to smoke and not as sensitive to fires – except in the case of very intense fires and very thick smoke plumes.

Well, guess what? These fires in Alaska have been intense and have been putting out a lot of smoke, so they do show up. Here’s a comparison between the True Color and Natural Color images from 19 June 2015:

Animation comparing the True Color and Natural Color RGB composites (21:50 UTC 19 June 2015)

Thin smoke is invisible to the Natural Color, but thick smoke appears blueish (because the blue component at 0.64 µm is the most sensitive to it). As you go from visible wavelengths to near-infrared wavelengths, the smoke’s influence on the radiation transitions from Rayleigh scattering to Mie scattering, and the light is scattered more in the forward direction. This makes the smoke much more visible in the Natural Color composite when the sun is near the horizon, as in this image from 13:47 UTC on 24 June:

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (13:47 UTC 24 June 2015)

Notice the red spot in the clouds at 154 °W, 65 °N? (Click on the image to zoom in.) Here, the smoke plume is optically thick at 0.64 µm (blue component) and 0.87 µm (green component), but transparent at 1.61 µm (red component). It’s like the smoke is casting a shadow in two of the three wavelengths, but is invisible in the other. It’s the combination of large smoke particles and large solar zenith angle creating a variety of Rayleigh and Mie scattering effects leading to this interesting result.

A more dramatic example of this can be seen from the 12:57 UTC overpass on 21 June:

VIIRS Natural Color RGB composite of channels I-1, I-2, and I-3 (12:57 UTC 21 June 2015)

Notice the reddish brown band of clouds just offshore along the coast of southeast Alaska.

But, that’s not all! Really intense fires may be visible at 1.6 µm, so it’s possible the Natural Color composite can see them. Here’s the Natural Color composite from 22:09 UTC on 4 July zoomed in on an area of intense fires:

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (22:09 UTC 4 July 2015)

Notice the salmon- and red-colored pixels at the edges of some of the smoke plumes? Those are very intense hot spots showing up at 1.6 µm (I-3). In fact, the fires were so intense that they saturated the sensor at 3.7 µm (I-4) and this lead to “fold-over“:

VIIRS shortwave IR (I-4) image (22:09 UTC 4 July 2015). The color scale highlights pixels with a brightness temperature above 340 K.

Fold-over is when the sensor detects so much radiation above its saturation point, the hardware is “tricked” into thinking the scene is much colder than it is. In the image above, colors indicate pixels with a brightness temperature above 340 K. The scale ranges from red at 340 K to orange to yellow at 390 K. Channel I-4 reaches its saturation point at 368 K. Notice the white and light gray pixels inside the hot spots: the reported brightness temperature in these pixels is ~ 210 K – much colder than everything else around – even the clouds! This is an example of “fold-over”.

The reddish pixels in the Natural Color image match up very closely with the saturated, “fold-over” pixels in I-4:

Animation comparing the Natural Color RGB and I-4 images (22:09 UTC 4 July 2015)

What to do if we have fires saturating our sensor? Use M-13 (4.0 µm), which has a sensor designed to not saturate in these conditions:

VIIRS shortwave IR (M-13) image (22:09 UTC 4 July 2015)

Here, we have reached color table saturation (yellow is as high as it goes), but M-13 did not saturate. In fact, the “fold-over” pixels in I-4 have a brightness temperature above 500 K in M-13. That’s 130-140 K above the saturation point of I-4 (110-120 K above the top of the color table)! The lack of saturation is also why the hot spots appear hotter in M-13, even though it has lower spatial resolution:

Animation comparing VIIRS shortwave IR bands I-4 and M-13 (22:09 UTC 2015)

The fact that intense fires show up at 1.6 µm is part of the design of the Fire Temperature RGB. Most fires show up at 3.7 µm (red component). Moderately intense fires are also visible at 2.25 µm (green component) and will appear orange to yellow. Really intense fires, like these, appear at 1.6 µm (blue component) and will appear white (or nearly white):

VIIRS Fire Temperature RGB composite of M-10, M-11, and M12 (22:09 UTC 4 July 2015) — VIIRS Fire Temperature RGB composite of M-10, M-11, and M-12 (22:09 UTC 4 July 2015)

And, if you’re curious as to how all four of these images compare, here you go:

Animation comparing VIIRS images of intense wildfires (22:09 UTC 4 July 2015)

Shortwave infrared wavelengths are good for detecting fires, visible wavelengths are good for detecting smoke and the Natural Color composite, which uses wavelengths in-between, might just detect both – especially when intense fires exist.

Seeing the Light

VIIRS in the Arctic