Tropical Cyclone Idai: Before, During and After

Posted on March 28, 2019 by Curtis_Seaman

As of the time of this writing, there is currently a humanitarian crisis in Mozambique caused by what was Tropical Cyclone Idai. Here’s the situation as of 25 March 2019.

Wikipedia actually has a pretty detailed history of Idai. Long story short, one of the worst (“worst” meaning large negative impact on humans) tropical cyclones in recorded history for the Southern Hemisphere formed just off the coast of Mozambique on 4 March 2019. It quickly headed inland as a tropical storm, where it dropped heavy rains on northern Mozambique and Malawi. Then, it turned back into the Mozambique Channel, headed for Madagascar, stopped, turned around, rapidly intensified, and then hit Mozambique a second time as a Category 2 cyclone. After making it on land a second time, it stalled out and dissipated, dropping more heavy rain in the process on central Mozambique and eastern Zimbabwe. Here is a long loop from Meteosat-8 showing much of the life cycle of Cyclone Idai as it appeared in the longwave infrared (IR).

Here’s a visible (True Color) loop from VIIRS that covers most of the month of March:

Animation of VIIRS True Color images from both S-NPP and NOAA-20 (1-25 March 2019)

This loop has been reduced in resolution to half of its original size to save on file size. Even with only 2-3 images per day (since we combined both S-NPP and NOAA-20 images), you can still clearly see the cyclone over Mozambique early in the loop head out to sea and then turn around and hit Mozambique again, where it dumped heavy rain for several days.

But, I want to draw your attention to several of the images in that loop: the beginning, the middle, and the end. On 1 March 2019, NOAA-20 got a pretty clear view of central Mozambique:

NOAA-20 VIIRS True Color composite image (11:32 UTC, 1 March 2019)

We’ll call this the “Before” image – and this one is full resolution (750 m). (NOTE: You have to click on it show it at full resolution.) We can also look at the Natural Color RGB (also known as the Day Land Cloud RGB and about a dozen other names), which we can make with the high resolution imagery bands I-1, I-2 and I-3:

NOAA-20 VIIRS Natural Color RGB composite image (11:32 UTC, 1 March 2019)

This is also at full resolution (375 m). (Again, only if you click on it.)

The worst of the flooding occurred with Idai’s second landfall on 14 March 2019, and both VIIRS got great views of Idai prior to landfall:

NOAA-20 Natural Color RGB composite image (10:47 UTC, 14 March 2019)

S-NPP Natural Color RGB composite image (11:38 UTC, 14 March 2019)

These images were taken ~50 min. apart. And, if you couldn’t already tell, they’re the high resolution Natural Color images. This is for two reasons: 1) who doesn’t want to see tropical cyclones at the highest resolution possible? and 2) the Natural Color RGB brings out details in the cloud structure you can’t see in True Color. As we’ve discussed before, Natural Color highlights ice clouds in a cyan color, while liquid clouds are nearly white. But, if you look closely in the above images, you will see lighter and darker cyan regions in the clouds above (or at the top of) the eyewall. This is due to differences in particle size. Larger ice particles appear more cyan, while smaller ice particles appear more white. (Of course, there is also some shadowing going on, which accounts for the darkest regions.)

Another thing to note is the first image comes from NOAA-20, which was to the east of Idai. This provides a great view of the sloped structure of the west side of the eyewall. (And, not much information on the east side of the eyewall.) The second image comes from Suomi-NPP, which was to the west of Idai, looking at the east side of the eyewall. The two satellites in tandem provide an almost 3D view of the clouds in the eyewall (separated by 50 minutes, of course).

Also, see that peninsula that is just to the west of the eyewall in the last two images? (Hint: you won’t see it unless you bring up the full resolution versions.) That’s where the city of Beira is (or was). Beira was home to half a million people, and was one of the major ports in Mozambique. It took a direct hit from the eyewall of Idai, which destroyed approximately 90% of the buildings there. Beira was also ground zero for the resulting flooding, and the pictures coming out are not pretty.

This is a good segue to talk about the images from the end of the loop. NOAA-20 captured a relatively cloud-free view of Mozambique on 25 March 2019:

NOAA-20 VIIRS True Color composite image (10:42 UTC, 25 March 2019)

NOAA-20 VIIRS Natural Color RGB composite image (10:47 UTC, 25 March 2019)

These images were collected 10 days after landfall, and the flooding is still evident. Don’t believe me? Compare these “After” images with the “Before” images shown earlier (zoomed in on Beira):

Animation comparing NOAA-20 True Color RGB composite images from 1 March 2019 and 25 March 2019

Notice the fertile, green agricultural land surrounding Beira in the “before” image that is covered by brown floodwater in the “after” image. Just like what we saw in the pictures from Beira.

But, there’s a lot flooding that is not so easy to see in the True Color that shows up better in the Natural Color RGB:

Animation comparing NOAA-20 Natural Color RGB images from 1 March 2019 and 25 March 2019

Since this VIIRS Natural Color imagery has twice the resolution of True Color, this animation is too large for WordPress to play it automatically. You have to click on it to see the animation play.

We’ve talked before about differences between True Color and Natural Color when it comes to flooding, and this example shows it quite well. You see, True Color can miss flooding, because water is pretty transparent at visible wavelengths. If the water is clear, you can see through it and, from the perspective of VIIRS, you see the ground underneath the water (as long as the water is relatively shallow). If the water is muddy, like most of this flooding, it’s easier to see (since radiation reflects off the particles in the water), but it can look the same as the mud (or bare ground) that isn’t covered by water.

Natural Color uses longer wavelengths, where water is much more absorbing, so water appears nearly black. That’s why it is typically easier to see flooding against a background of non-flooded land in Natural Color than True Color. But, the flooding around Beira is so muddy, the high reflectivity in the visible channel (which is the blue component of the RGB) starts to win out, and the floodwater appears more blue than black.

We can prove it by looking at the individual bands that make up these RGB composites. Remember to click to play the animations for the I-bands:

Comparison of NOAA-20 channel I-1 images from 1 March and 25 March 2019

Comparison of NOAA-20 VIIRS channel I-1 (0.64 µm) images from 1 March and 25 March 2019

Comparison of NOAA-20 channel I-2 images from 1 March and 25 March 2019

Comparison of NOAA-20 VIIRS channel I-2 (0.87 µm) images from 1 March and 25 March 2019

Comparison of NOAA-20 channel I-3 images from 1 March and 25 March 2019

Comparison of NOAA-20 VIIRS channel I-3 (1.61 µm) images from 1 March and 25 March 2019

Note that the flooded areas look brighter in I-1 (thanks to the dirty water) and look darker in I-2 and I-3 (because they are less sensitive to the dirt in the water and more sensitive to the water itself).

The individual M-bands that comprise the True Color RGB, shown below, have been corrected for Rayleigh scattering and scaled the same as in the True Color images above:

Comparison of NOAA-20 channel M-3 images from 1 March and 25 March 2019

Comparison of NOAA-20 VIIRS channel M-3 (0.48 µm) images from 1 March and 25 March 2019

Comparison of NOAA-20 channel M-4 images from 1 March and 25 March 2019

Comparison of NOAA-20 VIIRS channel M-4 (0.55 µm) images from 1 March and 25 March 2019

Comparison of NOAA-20 channel M-5 images from 1 March and 25 March 2019

Comparison of NOAA-20 VIIRS channel M-5 (0.67 µm) images from 1 March and 25 March 2019

It is quite difficult to detect the flooding using the visible channels (M-3, M-4, M-5 and I-1) alone. But, the flooded areas are generally brighter in the “after” images. However, the water is easy to see in the shortwave IR channels (I-2, and I-3 along with M-7 and M-10, which were not shown).

Of course, this was a very long-winded way of looking at the flooding. We could have just used the JPSS Program’s official Flood Product made with VIIRS, created by researchers at George Mason University. Here is a three day composite image (composited to reduce the impact of clouds), covering 19-22 March 2019:

NOAA-20 VIIRS Flood Detection Product using a 3-day cloud-free composite (19-22 March 2019). Image courtesy S. Li (GMU).

Red and yellow areas show where flooding is detected. Gray areas are areas that were cloudy all three days. As an interesting side note, this product is validated against the Natural Color RGB. For more on this product, click here. If you want to know how much precipitation actually fell, here is a loop provided by NASA made with observations from GPM (Global Precipitation Measurement Mission):

You get bonus points if you can read the scale below the images. But, even without a magnifying glass, you can probably guess: it’s a lot of rain!

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:

VIIRS Day/Night Band image of the central Philippines, taken 16:50 UTC 31 October 2013

VIIRS Day/Night Band image of the central Philippines, taken 17:02 UTC 10 November 2013

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.

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.

Hurricane Isaac: Before, During and After

Posted on September 4, 2012 by Curtis_Seaman

While Hurricane Isaac (then a tropical storm) did not destroy Tampa, Florida as many people feared, it certainly left its mark on the Gulf Coast. With many locations from Florida to Louisiana receiving more than 12″ of rain, and levees unable to keep out the storm surge, flooding was (and still is) a major problem. Look at these aerial photos of Isaac’s aftermath in Louisiana. The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi NPP saw that flooding, also.

But first, let’s look at the high resolution infrared (IR) window channel (I-05, 11.45 µm) which, at ~375 m resolution, is the highest-resolution IR window channel on a public weather satellite in space today. This image was taken when Isaac was still a tropical storm in the middle of the Gulf of Mexico:

VIIRS I-05 image of Tropical Storm Isaac, taken 18:50 UTC 27 August 2012

This image uses a new (to this blog, anyway) color scale, developed by our colleagues at CIMSS, that really highlights the structure of the clouds at the top of Isaac. The color scale is included in the image. For comparison, here’s the GOES Imager IR window channel (channel 4, 10.7 µm) image from roughly the same time:

GOES-13 Imager channel 4 image of Tropical Storm Isaac, taken 18:45 UTC 27 August 2012

GOES has ~4 km resolution in its IR channels. VIIRS provides amazing details of the structure of tropical cyclones that you just can’t get with current geostationary satellites.

The real story from Isaac, however, is the flooding. It’s hard to capture flooding from a visible and infrared imaging instrument, since flooding usually occurs when it’s cloudy. Clouds block the view of the surface when looking at visible and infrared wavelengths. But, large quantities of water that fail to evaporate or drain into the local rivers after a period of several days can be seen after the skies clear. That’s what happened with Isaac.

Here are before-Isaac and after-Isaac images of the southern tip of the Florida Peninsula. These are false color (“pseudo-true color”) composites of VIIRS channels I-01, I-02 and I-03. These images were taken on the afternoon overpasses of 23 August and 29 August 2012. Many cities on the east coast of Florida got 10-16 inches of rain (250-400 mm for those of you outside the U.S.). See if you can pick out the flooding.

False color RGB composite of VIIRS channels I-01, I-02 and I-03 taken before and after Tropical Storm Isaac (2012)

If you have been following this blog, you know that, in the “pseudo-true color” RGB composite, water shows up very dark – in most cases, almost black. That’s not always true, of course. You can see sun glint (particularly in the “before” image) that makes water a lighter color and shallow water (where visible radiation [i.e. channel I-01] is able to penetrate to the bottom) shows up as a vivid blue.

Now, notice the Everglades. Many areas of the Everglades, particularly on the east side, appear darker in the “after” image, because those swampy areas have a lot more water in them. Water has a lower reflectivity than vegetation or bare ground at these wavelengths.

The effect of water on the land surface shows up even better in the moderate resolution channel M-06 (0.75 µm). M-06 is a channel not shown before because it is perhaps the worst channel for producing interesting images. M-06 was designed to aid in ocean color retrievals and/or other uses that require atmospheric correction. The M-06 detectors saturate at a low radiance, so any radiation at 0.75 µm that reflects off of clouds, aerosols or the land surface easily show up. About the only things that have low reflectivity in M-06 are atmospheric gases and water surfaces without sun glint. Ocean color retrievals need a very clean atmosphere with no aerosols or clouds and no sun glint to work correctly. You also need to be able to identify what is or is not water, which is what makes M-06 useful for identifying flooding.

Here are the similar before-Isaac and after-Isaac images of Florida from M-06:

VIIRS channel M-06 images of southern Florida taken before and after Tropical Storm Isaac (2012)

Both the land and optically thick clouds saturate M-06, so this channel is useless at identifying clouds over land (except you can see some cloud shadows). Sun glint is saturating the pixels over the Gulf of Mexico in the “before” image, while it is mostly to the east of Florida in the Atlantic Ocean in the “after” image. In the “after image”, reflective cirrus clouds over the Gulf of Mexico show up that are not as easily visible in the RGB composite. Of primary importance here, however, is the dark appearance of the Everglades in the “after” image. All that flood water reduced the reflectivity of the land surface, making it appear darker. That means, if you know where the clouds (and, hence, the cloud shadows) are, it may be possible to use M-06 to identify large flooded areas.

Louisiana and the coast of Mississippi were the hardest hit by Isaac, and the flooding is easily visible here, too. In fact, the massive flooding is easier to see in the RGB composite in this region. Compare the “before” and “after” images, taken on 26 August 2012 and 1 September 2012:

False color RGB composites of VIIRS channels I-01, I-02 and I-03 of southeast Louisiana

To make it easier to see, here’s a quick animation of the before and after images. Watch the highlighted areas.

Animated GIF of false color RGB composites taken from VIIRS before and after Hurricane Isaac

After the passage of Hurricane Isaac, Lake Maurepas and Lake Pontchartrain almost appear to merge into one big lake! Other flooding is visible near Slidell, Bay St. Louis, Pascagoula Bay, and the heavily hit parishes of Plaquemines, St. Bernard, Lafourche and Terrebonne.

Thin cirrus clouds are visible in the “after” image, which limit the ability of M-06 to detect some of the flooding, but M-06 is still able to see the large areas of flooding highlighted in the animation above. M-06 also detects reflection off of the Twin Spans as well as the Lake Pontchartrain Causeway. And this is at ~750 m resolution!

VIIRS channel M-06 images of southeastern Louisiana taken before and after Hurricane Isaac (2012)

So, don’t try to do ocean color retrievals in pixels obscured by big bridges.

Daniel, Emilia and Fabio, oh my!

Posted on July 18, 2012 by Curtis_Seaman

It’s been a while since we last looked at some tropical cyclones with VIIRS. If you don’t keep up to date on tropical activity, you might not know there that have been a few. Granted, since Debby dumped a bunch of rain on Florida three weeks ago, the Atlantic basin has been pretty quiet. The East Pacific basin, however, has had one storm after another. The national media has largely ignored them since they have posed no threat to any landmasses. See this article from the L.A. times. Boring! Unless you can capture video of Jim Cantore struggling to stand upright, it isn’t a hurricane, right?

Wrong! First of all, eastern Pacific hurricanes affect some major shipping lanes. Second, and this is true of all hurricanes: they transport energy and moisture and help moderate the temperature imbalance between the tropics and mid-latitudes. They are important components of global energy transport.

In this post, we are going to compare the view of hurricanes provided by VIIRS against the view provided by GOES (specifically GOES-15). On 9 July 2012, there were two storms in the East Pacific: Daniel and Emilia.

Here is the GOES-15 view of Daniel followed by the VIIRS view of Daniel in their respective visible channels:

GOES-15 visible image (channel 1) of Hurricane Daniel, taken 22:45 UTC 9 July 2012. Image courtesy John Knaff.

VIIRS visible image (channel I-01) of Hurricane Daniel, taken 22:29 UTC 9 July 2012

Both images have the same latitude and longitude lines printed on them for reference and they both use the same color scales. If you zoom in, you’ll notice that the VIIRS image, with ~375 m resolution at nadir shows a bit more detail than the 1 km (1000 m) resolution GOES image. The additional detail provided by VIIRS really stands out in the infrared (IR) window channels, where GOES has 4 km resolution and VIIRS still has ~375 m resolution:

GOES-15 IR image (channel 4) of Hurricane Daniel, taken 22:30 UTC 9 July 2012

VIIRS IR image (channel I-05) of Hurricane Daniel, taken 22:29 UTC 9 July 2012

Now, it is worth noting that the high resolution IR image of VIIRS shown above comes from channel I-05, which is centered at 11.45 µm. The GOES image was produced from Imager channel 4, which is centered at 10.7 µm, so the two channels don’t exactly have the same spectral properties. VIIRS has a 10.7 µm IR channel as one of its moderate resolution bands (M-15). Here’s what that image looks like:

VIIRS IR image (channel M-15) of Hurricane Daniel, taken 22:29 UTC 9 July 2012

There isn’t a big difference between the two VIIRS channels, although you can see a bit more detail in the higher resolution (I-05) image.

On the previous orbit, VIIRS caught images of Hurricane Emilia, which was also in the view of GOES-15. Here’s how the images compare:

GOES-15 visible image (channel 1) of Hurricane Emilia, taken 21:00 UTC 9 July 2012. Image courtesy John Knaff.

VIIRS visible image (channel I-01) of Hurricane Emilia, taken 20:48 UTC 9 July 2012

GOES-15 IR image (channel 4) of Hurricane Emilia, taken 20:48 UTC 9 July 2012

VIIRS IR image (channel I-05) of Hurricane Emilia, taken 20:48 UTC 9 July 2012

In addition to the resolution differences, there is also a time difference of ~15 minutes between the VIIRS images and the GOES images. If you were to overlap these images, you would see that Emilia rotated a bit during that time. Emilia was not willing to hold the same pose for that long when having her picture taken. Once again, the M-15 image from VIIRS looks pretty similar to the I-05 image, so there’s no pressing need to show it.

Finally, let’s compare GOES-15 with VIIRS on Hurricane Fabio, which formed about a week after Daniel and Emilia were hurricanes.

GOES visible image (channel 1) of Hurricane Fabio, taken 20:30 UTC 15 July 2012

GOES-15 visible image (channel 1) of Hurricane Fabio, taken 20:30 UTC 15 July 2012. Image courtesy John Knaff.

VIIRS visible image (channel I-01) of Hurricane Fabio, taken 20:36 UTC 15 July 2012

GOES-15 IR image (channel 4) of Hurricane Fabio, taken 20:30 UTC 15 July 2012

VIIRS IR image (channel I-05) of Hurricane Fabio, taken 20:36 UTC 15 July 2012

The GOES and VIIRS images of Fabio were taken only 6 minutes apart, so there is less movement to impede the comparison.

In all three hurricanes, you can see a lot more structure to the VIIRS images in the both the visible and IR channels. It’s as if GOES represents a standard definition TV camera, and VIIRS represents a hi-def TV camera. All those wrinkles GOES is smoothing over are showing up in VIIRS. Daniel, Emilia and Fabio are going to need more makeup. (Or, they would if they weren’t already dead.)

JPSS (SNPP and NOAA-20)

VIIRS Imagery and Visualization Team Blog

Tag Archives: tropical storm

Tropical Cyclone Idai: Before, During and After

Rare Super Typhoon in the Pacific Ocean

Rare Super Cyclone in the Indian Ocean

Hurricane Isaac: Before, During and After

Daniel, Emilia and Fabio, oh my!