JPSS (SNPP and NOAA-20)

Rare Super Typhoon in the Pacific Ocean

Posted on November 14, 2013 by Curtis Seaman

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 from 7 November 2013

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:

[beforeafter][/beforeafter]

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.

Posted in Uncategorized | Tagged day/night band, Haiyan, I-5, MTSAT, Philippines, resolution, RGB composite, tropical storm, Yolanda | Comments Off

Rare Super Cyclone in the Indian Ocean

Posted on October 14, 2013 by Curtis Seaman

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.

Posted in Uncategorized | Tagged airglow, day/night band, I-5, India, IR window, Meteosat, Phailin, tropical storm, VIIRS | Comments Off

A Year in a Week – VIIRS Captures Colorado Flooding

Posted on October 2, 2013 by Curtis Seaman

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 over Northern Colorado, 9-16 September 2013

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?

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Record Russian Rain Runoff Responsible for Rapid River Rise

Posted on August 23, 2013 by Curtis Seaman

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!

Posted in Uncategorized | Tagged China, day/night band, false color, fire, flood, I-1, I-2, I-3, M-3, M-4, M-5, Natural Color, RGB composite, Russia, Siberia, smoke, true color | Comments Off

Abafado Bruma Seca

Posted on August 2, 2013 by Curtis Seaman

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.)

Posted in Uncategorized | Tagged Atlantic, Cape Verde, dust, M-3, M-4, M-5, RGB composite, Saharan Air Layer, true color | Comments Off

Wild Week of Wildfires, Part III

Posted on June 14, 2013 by Curtis Seaman

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.

Posted in Uncategorized | Tagged false color, fire, Fire Temperature RGB, M-10, M-11, M-12, M-13, M-3, M-4, M-5, M-7, Natural Fire Color RGB, RGB composite, true color | 1 Comment

Record Russian Spring Snowmelt

Posted on May 7, 2013 by Curtis Seaman

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.

Posted in Uncategorized | Tagged false color, flood, I-1, I-2, I-3, ice and snow, RGB composite, Russia | Comments Off

Land of Lincoln Underwater

Posted on April 23, 2013 by Curtis Seaman

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 and 21 April 2013

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 and 21 April 2013

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.

Posted in Uncategorized | Tagged false color, flood, I-1, I-2, I-3, ice and snow, Illinois, michigan, resolution, RGB composite | Comments Off

Drought in the Land of the Long, White Cloud

Posted on March 22, 2013 by Curtis Seaman

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

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 and 21 March 2013

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 and 21 March 2013

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.

Posted in Uncategorized | Tagged false color, M-10, M-3, M-4, M-5, M-7, NDVI, New Zealand, RGB composite, true color, vegetation | Comments Off

Chinese Super-Smog

Posted on February 15, 2013 by Curtis Seaman

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!

Posted in Uncategorized | Tagged China, day/night band, false color, ice and snow, M-10, M-11, M-12, M-14, M-15, M-16, M-3, M-4, M-5, M-7, RGB composite, smog, true color | 2 Comments