Severe Weather in the Mesosphere

So far (*knock on wood*), it’s been a pretty quiet year for severe weather. If you only count tornadoes, there have been 81 tornado reports from 1 January to 4 April this year. (11 of those have come just this week.) This is a lot fewer than the previous three year average of 192 tornadoes by the end of March. For that, you can thank the dreaded, terrifying “Polar Vortex” you’ve heard so much about over the winter. Tornadoes don’t like to come out when it’s cold everywhere. (Although, there was a notable exception on 31 March 2014, when a tornado hit a farm in Minnesota when the area was under a blizzard warning.)

I just said that there have been 11 tornado reports this week. Eight of those came in the past 24 hours. At the southern end of the line that brought the tornadoes to Illinois, Missouri and Texas, the severe weather included golf ball-size hail and this:

25 FEET BY 30 FEET SHED ANCHORED 3 FEET INTO
GROUND...TOTALLY RIPPED OUT AND IMPALED INTO A FENCE AND
A ROOF OF NEIGHBORING HOUSE

That report came from the National Weather Service in Corpus Christi, TX and it was caused by non-tornadic straight-line winds in Orange Grove. Winds capable of ripping a shed out of the ground, combined with golf ball-sized hail – that’s one recipe for broken windows. And it’s not a pleasant way to be awakened at 4:30 in the morning.

A couple of hours earlier, VIIRS caught this severe storm as it was rapidly growing. Here’s what the storm looked like in the high-resolution infrared channel (I-5, 11.45 µm):

VIIRS high-resolution IR image (channel I-5), taken at 08:13 UTC 4 April 2013.

VIIRS high-resolution IR image (channel I-5), taken at 08:13 UTC 4 April 2013.

Make sure you click on the image, then on the “2999×2985″ link below the banner to see the full resolution image, which, for some reason, is the only version where the colors display correctly.

The storm that hit Orange Grove is the southern-most storm, with what looks like a letter “C” imprinted on the top. (That kind of feature typically looks more like a “V” and makes this an “Enhanced-V” storm, which you can learn more about here. Enhanced-V storms are noted for their tendency to produce severe weather.) For those of you keeping score at home, the coldest pixel in this storm is 184.7 K (-88.5 °C).

Compare the image above with the Day/Night Band image below (from the same time):

VIIRS Day/Night Band image, taken at 18:13 UTC 4 April 2014

VIIRS Day/Night Band image, taken at 08:13 UTC 4 April 2014

There are a few interesting features in this image. For one, there’s a lot of lightning over Louisiana, Arkansas and Mississippi. (Look for the rectangular streaks.) There’s even some lighting visible where our “Enhanced-V” is. Two, it takes a lot of cloudiness to actually obscure city lights: only the thickest storm clouds appear to be capable of blocking out light from the surface. Three: there are a lot of boats out in the Gulf of Mexico at 3 o’clock in the morning (and a few oil rigs as well). And four: notice what appear to be concentric rings circling the location where our severe storm is with its enhanced-V.

In this image, there is no moonlight (we’re before first quarter, so the moon isn’t up when VIIRS passes over at night). The light we’re seeing in those ripples is caused by “airglow”, which we’ve seen before. And the ripples themselves may be similar to what is called a “mesospheric bore.” If you don’t want to get too technical, a mesospheric bore is when this happens in the mesosphere. They are related to – but not exactly analogous to – undular bores, which you can read more about here.

Unlike the situation described for the undular bore in that last link, the waves here are caused by our severe storm. To put it simply, we have convection that has formed in unstable air in the troposphere. This convection rises until it hits the tropopause, above which the air is stable. This puts a halt to the rising motion of the convection but, some of the air has enough momentum to make it in to the stratosphere. This is called the “overshooting top“, and is where our -88°C pixels are located. (Look for the pinkish pixels in the middle of the “C” in the full-resolution infrared image.) The force of this overshooting top creates waves in the stable layer of air above (the stratosphere) that propagate all the way up into the mesosphere. The mesosphere is where airglow takes place, and these waves impact the optical path length through the layer where light is emitted. This of course, impacts the amount of light we see. The end result: a group of concentric rings of airglow light surrounding our storm.

You could make the argument that the waves we see in the Day/Night Band image are not an example of a bore. Bores tend to be more linear and propagate in one direction. These waves are circular and appear to propagate in all directions out from a central point. It may be better to describe them as “internal buoyancy waves“, which are similar to what happens when you drop a pebble into a pond. Only, in this case the pebble is a parcel of air traveling upwards, and the surface of the water is a stable layer of air. Compare the pebble drop scenario with this video of a bore traveling upstream in a river to see the difference.

In fact, if you look closer at the Day/Night Band image, in the lower-right corner (over the Gulf of Mexico) there is another group of more linear waves and ripples in the airglow that may actually be from a bore. It’s hard to say for sure, though, without additional information such as temperature, local air density, pressure and wind speeds way up in that part of the mesosphere.

By the way, you can see mesospheric bores and other waves in the airglow if you have sensitive-enough camera, like the one that took this image:

Photograph of a mesospheric bore. Image courtesy T. Ashcraft and W. Lyons (WeatherVideoHD.TV)

Photograph of a mesospheric bore. Image courtesy T. Ashcraft and W. Lyons (WeatherVideoHD.TV)

And, if you’re interested, the Arecibo Observatory has a radar and optical equipment set up to look at these upper-atmosphere waves (scroll down to Panel 2 on this page). The effect of these waves on atmospheric energy transport is a hot topic of research.

Golf ball-sized hail at the Earth’s surface is related to energy transport 100 km up in the atmosphere!

 

NOTE: This post has been updated since it was first written to clarify that the circular waves are likely not evidence of a bore, as was originally implied. They are more likely internal buoyancy waves, which are also known as gravity waves. For more information, consult your local library.

Hell Froze Over (and the Great Lakes, too)

This has been some kind of winter. The media has focused a lot of attention on the super-scary “Polar Vortex” even though it isn’t that scary or that rare. (I wonder if Hollywood will make it the subject of the next big horror movie in time for Halloween.) Many parts of Alaska have been warmer than Georgia, with Lake Clark National Park tying the all-time Alaskan record high temperature for January (62 °F) on 27 January 2014. (Atlanta’s high on that date was only 58 °F.) Sacramento, California broke their all-time January record high temperature, reaching 79 °F three days earlier. In fact, many parts of California had record warmth in January, while everyone on the East Coast was much colder than average. Reading this article made me think of an old joke about statisticians: a statistician is someone who would say: if your feet are stuck in a freezer and your head is stuck in the oven, you are, on average, quite comfortable.

One consequence of the cold air in the eastern United States is that Hell froze over. No, not the Gates of Hell in Turkmenistan. This time I’m talking about Hell, Michigan. Hell is a nice, little town whose residents never get tired of people telling that joke.

It has been so cold in the region around Hell that the Great Lakes are approaching a record for highest percentage of surface area covered by ice. This article mentions some of the benefits of having ice-covered Lakes, including: less lake-effect snow, more sunshine and less evaporation from the Lakes, which would keep lake levels from dropping. Although, that is at the cost of getting ships stuck in the ice, and reducing the temperature-moderating effects of the Lakes, which allows for colder temperatures on their leeward side.

This article (and many other articles I found) uses MODIS “True Color” images to highlight the extent of the ice. Why don’t they show any VIIRS images? Well, I’m here to rectify that.

First off, I can copy all those MODIS images and show the “True Color” RGB composite from VIIRS:

VIIRS "True Color" RGB composite of channels M-3, M-4 and M-5, taken 17:27 UTC 11 February 2014

VIIRS "True Color" RGB composite of channels M-3, M-4 and M-5, taken 17:27 UTC 11 February 2014

While it was a rare, sunny winter day for most of the Great Lakes region on 11 February 2014, it’s hard to tell that from the True Color imagery. I mean, look at this True Color MODIS image shown on NPR’s website. Can you tell what is ice and what is clouds?

There are ways of distinguishing ice from clouds, which I have talked about before but, it doesn’t hurt to look at these methods again and see how well they do here. First, let’s look at my modification of the EUMETSAT “Snow” RGB composite:

VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 17:27 UTC 11 February 2014

VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 17:27 UTC 11 February 2014

This “Snow” RGB composite differs by using reflectances at 2.25 µm in the place of the 3.9 µm channel that EUMETSAT uses. (Their satellite doesn’t have a 2.25 µm channel.) It’s easy to see where the clouds are now. Of course, now the snow and ice appear hot pink, which you may not find aesthetically pleasing. And it certainly isn’t reminiscent of snow and ice.

If you don’t like the “Snow” RGB, you may like the “Natural Color” RGB composite:

VIIRS "Natural Color" RGB composite of channels I-01, I-02 and I-03, taken 17:27 UTC 11 February 2014

VIIRS "Natural Color" RGB composite of channels I-01, I-02 and I-03, taken 17:27 UTC 11 February 2014

This has the benefit of making snow appear a cool cyan color, and has the added benefit that you can use the high-resolution imagery bands (I-01, I-02 and I-03) to create it. There is twice the resolution in this image than in the Snow and True Color RGB images. Here’s another benefit you may not have noticed right away: the clouds, while still white, appear to be slightly more transparent in the Natural Color RGB. This makes it a bit easier to see the edge of the ice on the east side of Lake Michigan and the center of Lake Huron, for example.

If you’re curious as to how much ice is covering the lakes, here are the numbers put out by the Great Lakes Environmental Research Laboratory (which is about a 25 minute drive from Hell) from an article dated 13 February 2014:

Lake Erie: 96%; Lake Huron: 95%; Lake Michigan: 80%; Lake Ontario: 32% and Lake Superior: 95%. This gives an overall average of 88%, up from 80% the week before. The record is 95% set in 1979, although it should be said satellite measurements of ice on the Great Lakes only date back to 1973.

Why does Lake Ontario have such a low percentage? That last article states, “Lake Ontario has a smaller surface area compared to its depth, so it loses heat more slowly. It’s like putting coffee in a tall, narrow mug instead of a short, wide one. The taller cup keeps the coffee warmer.”  Doesn’t heat escape from the sides of a mug as well as the top? And isn’t Lake Superior deeper than Lake Ontario? Another theory is that “Lake Ontario’s depth and the churning caused by Niagara Falls means that it needs long stretches of exceptionally cold weather to freeze.”  Does Niagara Falls really have that much of an impact on the whole lake?

So, what is the correct explanation? I’m sorry, VIIRS can’t answer that. It can only answer “How Much?” It can’t answer “Why?”

 

BONUS UPDATE (17 February 2014):

It has come to my attention that the very next orbit provided better images of the Great Lakes, since they were no longer right at the edge of the swath. Here, then, are the True Color, Snow and Natural Color RGB composite images from 19:07 UTC, 11 February 2014:

VIIRS "True Color" composite of channels M-3, M-4 and M-5, taken 19:07 UTC 11 February 2014

VIIRS "True Color" composite of channels M-3, M-4 and M-5, taken 19:07 UTC 11 February 2014

 

VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 19:07 UTC 11 February 2014

VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 19:07 UTC 11 February 2014

 

VIIRS "Natural Color" composite of channels I-01, I-02, and I-03, taken 19:07 UTC 11 February 2014

VIIRS "Natural Color" composite of channels I-01, I-02, and I-03, taken 19:07 UTC 11 February 2014

 

UPDATE #2 (18 March 2014): The Great Lakes ice cover peaked at 92.2% on 6 March 2014, just short of the all-time record in the satellite era. March 6th also happened to be a clear day over the Great Lakes, and VIIRS captured these images:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 18:35 UTC 6 March 2014

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 18:35 UTC 6 March 2014

 

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 18:35 UTC 6 March 2014

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 18:35 UTC 6 March 2014

B-31 and the Pine Island Glacier

Nope. This post is not about a warplane, an alcoholic beverage or a “New Wave” band from the 1970s. (Those are all B-52s.) And I’m not talking about a county road in Michigan or a New York City bus line. B-31 is the rather bland name given to the massive iceberg that just broke off from the Pine Island Glacier in Antarctica. (Of course, if you tried to name every chunk of ice floating around Antarctica, how long would it take you to run out of names and just switch to random letters and numbers?)

This particular chunk of ice is special, however, as it has been described as the size of a city. Now, as a scientist, I have to say that the size of a city is a terrible unit of measurement. How big a city are we talking about? I suspect people who live in one of the ten largest cities in the world would laugh at what the people of Wyoming call a “city”. And are we talking the size of the greater metropolitan area or just what is within the city limits?

The article that describes B-31 as the size of city mentioned that it was roughly the size of Singapore, or twice the size of Atlanta. Those seem like odd choices for comparison. How many of you have a good idea of what the land area is of Singapore? And twice the size of Atlanta? They could have used New York City, which has just over twice the land area of Atlanta and people are probably more familiar with New York City. In any case, all of these size estimates have errors.

The original estimate came from this NASA MODIS image and associated caption, which put the size of B-31 as 35 km x 20 km. Now, that’s 700 km2 assuming the iceberg is a perfect rectangle, which you can see in the image that it isn’t. Singapore has a land area of 714 km2, while New York City is 768 km2 and Atlanta is 341 km2 (these are “within the city limits” numbers, not the size of the greater metropolitan area). Since the iceberg is actually smaller than the 35 km x 20 km rectangle based on the widest and longest dimensions of the iceberg, maybe “twice the size of Atlanta” is the most accurate estimate.

Anyway, MODIS is not the only satellite instrument out there capable of viewing B-31. Landsat-8 saw it in much higher resolution in another post from NASA. And, of course this entire blog is about what VIIRS can see. Now, VIIRS doesn’t have the resolution of Landsat or the highest-resolution channels on MODIS, but VIIRS has the Day/Night Band, allowing us to see the iceberg both day and night (at visible wavelengths).

To show why that is important, take a look at the infrared image (M-15, 10.7 µm) below. Images in the “infrared window” (the N-band window, according to this site) used to be the only way to detect surface features and clouds at night. At these wavelengths, the amount of radiation detected by the satellite is a function of the temperature of the objects the instrument is looking at. As always, to see the high resolution version of the image, click on it, then on the “1660×1706″ link below the banner.

VIIRS IR image (M-15) taken 23:34 UTC 7 November 2013

VIIRS IR image (M-15) taken 23:34 UTC 7 November 2013

See that slightly darker gray area near the center of the image? That’s open water in Pine Island Bay, which is only slightly warmer than the ice and low clouds surrounding it. Otherwise, there isn’t much detail in this picture. What really stands out are the cold, high clouds that are highlighted by the color scale. Contrast this with a visible wavelength image from the same time (M-5, 0.67 µm):

VIIRS visible (M-5) image, taken 23:34 UTC 7 November 2013

VIIRS visible (M-5) image, taken 23:34 UTC 7 November 2013

The open water in Pine Island Bay shows up clear as day because, well, it is daytime and the ice and snow reflect a lot more sunlight back to the satellite than the open water does. Icebergs can easily be distinguished from the low clouds now. You can even see through some of the low clouds to identify individual icebergs that are not visible in the infrared image. The difference in reflectivity between the ice and water at visible wavelengths is a lot greater than the difference in brightness temperature in the 10-12 µm infrared wavelengths, and that contrast is what makes things more easily visible.

Now, it is summer down there and at these latitudes, the sun is up for most of the day (actually, all day for everywhere in this scene on the Summer Solstice, which occurred on 21 December 2013), so you could say that using the VIIRS Day/Night Band to look at this stuff is unnecessary. But, since VIIRS is on a polar-orbiting satellite, it views the poles a lot more frequently than where you or I live: every 101 minutes on average, instead of every 12 hours in the low and mid-latitudes. That means it may occasionally capture a nighttime image here or there during the short nights and will frequently capture images where the day/night terminator crosses through the scene and we still want to be able to see what’s going on then. And you need the Day/Night Band to do that.

For the first time on this blog, however, we’re not going to show the Day/Night Band data exactly. We’re going to show the Near Constant Contrast imagery product, which is produced from the Day/Night Band. You can read up more on the Near Constant Contrast product and how it’s related to the Day/Night Band here. At this point, we’ll refer to NCC and DNB rather than having to type out Near Constant Contrast and Day/Night Band all the time.

Here’s a NCC image from 7 November 2013 at 20:15 UTC where the Pine Island Glacier has been identified. B-31 is still attached to the glacier – it’s sticking out into the bay and, if you look at the high resolution version of the image, you may be able to see the crack where it has started to calve.

VIIRS Near Constant Contrast image from 20:15 UTC 7 November 2013

VIIRS Near Constant Contrast image from 20:15 UTC 7 November 2013. The Pine Island Glacier is identified.

Keep your eye on that spot as you watch this zoomed-in animation of NCC images starting from the above image to 03:06 UTC 18 November:

Animation of VIIRS NCC images of the Pine Island Glacier from 7-18 November 2013

Animation of VIIRS NCC images of the Pine Island Glacier from 7-18 November 2013

I should say that the above animation does not include images from every orbit. I’ve subjectively removed images that were too cloudy to see anything as well as images where the VIIRS swath didn’t cover enough of the scene. This left 25 images over the 11 day period. Even so, VIIRS captured the moment of B-31 breaking free quite well.

Imagine the sound that this 600+ km2 chunk of ice made as it broke free. I bet it sounded something like this glacier calving event in Greenland:

 

One of the articles linked to above mentioned the importance of tracking such a large iceberg, because it could impact ships in the area. (Just this week a ship got stranded in ice off the coast of Antarctica.) So, I decided to see if VIIRS could track it. The results are in the MP4 video clip linked to below. You may need an appropriate browser plug-in or add-on (or whatever your browser calls it) to be able to view the video.

Animation of VIIRS NCC images from 7 November – 26 December 2013 (.mp4 file)

That’s 50 days of relatively cloud-free VIIRS NCC images (7 November – 26 December 2013), compressed down to 29 seconds. Go ahead, watch the video more than once. Each viewing uncovers additional details. Notice how B-31 doesn’t move much after 10 December. Notice how ice blocks the entrance to Pine Island Bay at the beginning of the loop, then clears out by the end of the loop. Notice all the icebergs near the shore that are pushed or pulled or blown out to sea from about 20 December through the end of the loop. Notice that B-31 isn’t even the biggest chunk of ice out there. Notice the large ice sheet on the west side of Pine Island Bay that breaks up right at the end of the loop. In fact, here’s another zoomed-in animated GIF to make sure you notice it:

Animation of VIIRS NCC images from 20-26 December 2013

Animation of VIIRS NCC images from 20-26 December 2013

That area of ice is much larger than B-31! (Dare I say, as large as the state of Rhode Island? Probably not, because then you’ll just think of how Rhode Island is the smallest US state, so it can’t be very impressive. It’s also not very accurate since that estimate is based on eye-balling it and thinking it looks like it could be four times the size of B-31.)

Of course, we are heading towards the middle of summer in the Antarctic when the ice typically reaches its minimum extent. So the ice breaking up isn’t unusual. Plus, large calving events occur on the Pine Island Glacier every few years. But, the B-31 event is noteworthy because Pine Island Glacier holds about 5% of the total freshwater contained on Antarctica.  It’s also the site of an ongoing field experiment where researchers are investigating glacier-ocean interactions. You can read up on what it’s like to install instruments on a glacier while living in a tent on the coldest continent 1000 miles from any other human settlement in this article. (That article doesn’t say if any instruments are still stuck in B-31 and floating out to sea, though.) And, if you’re curious, Pine Island Glacier has its own Twitter account. So far, the conclusions are that Pine Island Glacier is thinning, receding and speeding up. Large calving events are just one piece of the puzzle, but an important piece to understand since they contribute to sea level rise.

The calving process of B-31 was first noticed by NASA researchers noticing a crack forming in Pine Island Glacier while flying over the area in October 2011 – before VIIRS was even launched. But, VIIRS was there to capture the end result of that crack two years later!

 

UPDATE (22 April 2014): B-31 has continued to drift towards the open ocean. Researchers at NASA have been monitoring the movement of the massive iceberg since it first calved, and have put together their own video here, which tracks B-31 from the time of my video above into mid-March 2014.

Rare Super Typhoon in the Pacific Ocean

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 yearsAnemometers, 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

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

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 2013VIIRS 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

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

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

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

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

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

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.

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.