Remote Islands IV: Where’s Waldo (Pitcairn)? Edition

Posted on February 10, 2015 by Curtis_Seaman

Take a look at this VIIRS “Natural Color” image and see if you can find Pitcairn Island. It’s in there somewhere:

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3, taken 22:25 UTC 10 April 2014

You’re definitely going to want to click through to the full resolution version. (Click on the image, then click again.) You won’t be able to see it otherwise. Take your time. Note: this is actually pretty similar to searching for fires.

Did you see it?

If you answered “no”: Good! That’s just what the early settlers of Pitcairn Island wanted: an island that no one could find! If you answered “yes”: I think you’re mistaken. You probably saw Henderson Island, which is bigger and easier to see.

Pitcairn is only 3.6 km across. That’s just 7 pixels in this composite of high-resolution (375 m at nadir; I-band) channels. It’s total land area is 4.6 km². Henderson Island is 37.3 km². There’s even a third island visible in this picture, but you need the eyes of an eagle to see it – Oeno at 0.65 km². Look again and see if you see any green pixels.

If you give up, here’s the answer:

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3, taken at 22:25 UTC 10 April 2014. The visible islands are labelled.

Now, you may have just clicked to the full-resolution version and are now wondering if I’m right about Oeno Island. Is there really anything there? Yes. Just look at that part of the image zoomed in by 800%:

VIIRS Natural Color image (10 April 2014) zoomed in on Oeno Island

See those three green pixels (not counting the latitude line drawn on there) that are surrounded by lighter blue pixels? That’s Oeno. It is one of the smallest islands you can say that VIIRS “saw”. Here’s what it looks like from a really high-resolution satellite. The light blue pixels surrounding it are the surrounding reef and lagoon of the atoll.

So, why all the interest in a couple of tiny islands in a remote part of the Pacific Ocean? First of all, there are winter storms battering both coasts of the United States, so it’s nice to enjoy a little bit of escapism. Now you can fantasize about being on a tropical island instead of facing the reality of shoveling another 2 feet of snow. Second, it’s fun to look for little islands that can’t be seen with current geostationary satellites (although it will be interesting to see if the high-resolution [0.5 km] visible channel on Himawari will be able to see it; it might be too far east, though). Plus, it’s been over two years since I last looked at remote islands – there may a whole new generation of viewers interested in this stuff who never knew this was part of the blog. Third, I don’t have to write as much and you don’t have to read as much as I fill my blog post quota for the month.

However, to barely keep things on the topic of atmospheric science and satellite meteorology, I will note that, in the images above, you can see a string of clouds streaming to the northwest from both Pitcairn and Henderson Islands. This is the visible manifestation of fluid dynamics which we have discussed before.

If you’ve heard of Pitcairn Island prior to this, it’s probably because you heard of the Mutiny on the Bounty. A group of mutineers who didn’t want to be hanged for their crime settled on Pitcairn Island and burned their ships so they could never leave and, hopefully, never be found. That is the very definition of “getting away from it all”. (Pitcairn is also known to stamp collectors who seek the very rare stamps from the far corners of the world. Selling stamps to tourists is actually a significant part of their economy.)

Today, the island is home to ~50 people – all but two of which are direct descendents of the mutineers. Oeno and Henderson Islands are uninhabited. Henderson Island is a UNESCO World Heritage Site that has been largely untouched by mankind. Oeno Island is a favorite “get-away” spot for Pitcairn Islanders for whom an island of 50 people is just too crowded!

If you want to know more about Pitcairn or you have an hour of free time to use up, check out this documentary on the island, its history, and the people who make it their mission to visit one of the world’s most remote islands:

Sea-effect Snow

Posted on January 13, 2015 by Curtis_Seaman

Take a look at this image:

Photo credit: İskender Şengör via Severe Weather Europe on Facebook

Is this picture from A) the Keweenaw Peninsula of Michigan in 1978? B) Orchard Park, New York in November 2014 (aka “Snowvember”)? or C) İnebolu, Turkey from just last week?

If you pay attention to details, you will have noticed that I credited İskender Şengör with the picture and properly surmised that the answer is C. If you don’t pay attention to details, get off my blog! The details are where all the interesting stuff happens! You’d never be able to identify small fires or calculate the speed of an aurora or explain the unknown without paying attention to details.

If you follow the weather (or social media), you probably know about lake-effect snow. (Who can forget Snowvember?) But, have you heard of sea-effect snow?

Areas downwind of the Great Lakes get a lot more snow than areas upwind of the Lakes. I was going to explain why in great detail, but this guy saved me a lot of time and effort. (I have since been notified that much of the material in that last link was lifted from a VISIT Training Session put together by our very own Dan B. You can watch and listen to that training session here.) The physical processes that cause lake-effect snow are not limited to the Great Lakes, however. Anywhere you have a large body of relatively warm water (meaning it doesn’t freeze over) with episodes of very cold winds in the winter you get lake-effect or sea-effect snow.

When you think of the great snowbelts of the world, you probably don’t think of Turkey – but you should! Arctic air outbreaks associated with strong northerly winds blowing across the Black Sea can generate snow at the same rate as Snowvember or Snowpocalypse or Snowmageddon or any other silly name that the media can come up with that has “snow” in it (Snowbruary, Snowtergate aka Frozen-Watergate, Snowlloween, Martin Luther Snow Day, Snowco de Mayo, Snowth of July… Just remember, I coined all of these phrases if you hear them later). Plus, the Pontic Mountains provide a greater upslope enhancement than the Tug Hill Plateau in Upstate New York.

One such Arctic outbreak occurred from 7-9 January 2015, resulting in the picture above. Parts of Turkey received 2 meters (!) of snow (78 inches to Americans) in a 2-3 day period, as if you couldn’t tell from that picture or this one.

From satellites, sea-effect snow looks just like lake-effect snow. (Duh! It’s the same physical process!) Here’s a VIIRS “True Color” image of the lake-effect snow event that took place last week on the Great Lakes:

VIIRS “True Color” RGB composite, taken 19:24 UTC 7 January 2015.

Wait – that’s no good! We need to be able to distinguish the snow from the clouds. Let’s try that again with the “Natural Color” RGB composite:

VIIRS “Natural Color” RGB composite, taken 19:24 UTC 7 January 2015.

That’s better. Notice how the clouds are formed right over the lakes and how the clouds organize themselves into bands called “cloud streets“. The same features are visible in the sea-effect snow event over Turkey (from one day later):

VIIRS “Natural Color” RGB composite, taken 10:36 UTC 8 January 2015.

Look at how much of Turkey is covered by snow! (Most of that snow cover is from the low pressure system that passed over Turkey a couple days before the sea-effect snow machine kicked in.) And – *cough* attention to details *cough* – you can even see snow over Greece and more sea-effect snow on Crete. There’s also snow down in Syria, Lebanon and Israel (Israel is off the bottom of the image), which is bad news for Syrian refugees.The heavy snow has shut down thousands of roads, closed schools and businesses, and was even the source of a political scandal.

But, on the plus side, the Arctic outbreak in the Middle East brings a unique opportunity to see palm trees covered in snow. And, how often do you get to see the deserts of Saudi Arabia covered in snow? (EUMETSAT has provided more satellite images of this event at their Image Library.)

Take another look at that image over the Black Sea. See how the biggest snow band extends south (and curving to the southeast) from the southern tip of the Crimean Peninsula? That is an example of how topography impacts these snow events. Due to differences in friction, surface winds are slightly more backed over land than over water, therefore areas of enhanced surface convergence exist downwind of peninsulas. The snow bands are more intense in these regions of enhanced convergence. There are also bigger than normal snow bands downwind of the easternmost and westernmost tips of Crimea, and extending south from every major point along the west coast of the Black Sea. This is not a coincidence. Land-sea (or land-lake) interactions explain this. Go back and listen to the VISIT training session for more information.

Sea-effect snow affects other parts of the globe as well. It’s why the western half of Honshu (the big island of Japan) and Hokkaido are called “Snow Country“. Japan was also hit with a major sea-effect snowstorm last week and, of course, VIIRS caught it:

VIIRS “Natural Color” RGB composite, taken 03:48 UTC 8 January 2015.

See the clear skies over Korea and the cloud streets that formed over the Sea of Japan? Classic sea-effect clouds. You can even see snow all along the west coast of Honshu in between the breaks in the clouds. Topographic impacts are once again visible. Notice the intense snow band extending southeast from the southern tip of Hokkaido/northern tip of Honshu similar to the super-strength snow band off of Crimea. And there’s another one downwind of the straits between Kyushu and Shikoku. Another detail in this image you should have noticed is the impact that Jeju Island has on the winds and clouds. Those are classic von Kármán vortices which we have discussed before.

Fortunately, 8 January 2015 was near a full moon, so the Day/Night Band was able to capture a great image of these von Kármán vortices:

VIIRS Day/Night Band image, taken 18:09 UTC 7 January 2015.

So, to the people of the Great Lakes: Remember you’re not alone. There are people in Turkey and Japan who know what you go through every winter.

UPDATE #1: While I was aware (and now you are aware) that sea-effect snow can impact Cape Cod, it was brought to my attention that there is a sea-effect snow event going on there today (13 January 2015). Here’s what VIIRS saw:

VIIRS “Natural Color” RGB composite, taken 17:29 UTC 13 January 2015.

According to sources at the National Weather Service, some places have received 2-3 cm (~ 1 inch) of snow in a four-hour period. It’s not the same as shoveling off your roof in snow up to your neck, but it’s something!

When China Looks Like Canada

Posted on November 21, 2014 by Curtis_Seaman

OK, so there probably aren’t any “Canadatowns” in China like there are Chinatowns in Canada. (Now you’re probably wondering what a Canadatown in China would look like. Maybe stores and restaurants selling poutine and maple syrup? Hockey rinks and curling sheets everywhere? A Tim Hortons on every street corner?) But this isn’t about that!

Last time I made the comparison between Canada and China, it was because there were numerous fires, particularly in the Northwest Territories, that produced so much smoke that it choked the air, making it difficult to breathe. This smoke was visible all the way down to the Lower 48 United States. These huge smoke plumes looked a lot like Chinese super-smog. Today, we’re talking not about the smoke and smog… well, actually, smoke and smog will be mentioned… hmm. Uh, what I mean is we’re focusing on the zillions of fires that VIIRS saw over Manchuria – just like the zillions of fires in the Northwest Territories. Well, OK, not “just like” – those fires were caused by Mother Nature. These fires appear to be intentionally set by human beings and are much smaller.

A CIRA colleague was checking out a real-time loop of MTSAT 3.75 µm imagery over northeastern China and reported seeing bright spots (which are typically hot spots from fires) throughout the area for most of the last month. So what is going on there?

MTSAT has ~4 km spatial resolution, so it’s not the best for fire detection. (At the time of this writing, CIRA has access to MTSAT-2, aka Himawari-7, which has 4 km spatial resolution in the infrared channels. The Advanced Himawari Imager {AHI} was successfully launched on Himawari-8 on 7 October 2014 and, when the operational imagery becomes available, it will have 2 km resolution in this channel [and it will have many of the channels that VIIRS has]. CIRA has plans to acquire this data when it becomes available. Until then, you’ll have to deal with coarse spatial resolution.) To really see what is going on, you need the spatial resolution of VIIRS.

Of course, spatial resolution is not the only thing you need. Check out the VIIRS M-13 (4.0 µm) image below from 04:48 UTC 18 November 2014. How many hot spots can you see?

VIIRS M-13 image of northeastern China, taken 04:48 UTC 18 November 2014.

This image uses a color table specifically designed to highlight hot spots from fires. Any pixel above 317 K (44 °C or 111 °F) is colored. (As always, click on the image to see it in full resolution.) There aren’t that many colored pixels, even though we’re using a relatively low temperature threshold for fire detection. There are, however, a lot of nearly black pixels, which means they are warmer than the background, but not warm enough to be highlighted. (In case you’re not sure, I’m talking about the area between 45° and 48°N, 123° and 128°E.) If we used this temperature threshold in a summer scene, there would be a lot false alarm fire detections.

A situation like this is when the Fire Temperature RGB composite comes in handy. It can detect the small (or low temperature) fires with no problem, particularly since the background isn’t very warm. Try to count up all the red pixels in this image from the same time:

VIIRS Fire Temperature RGB composite of channels M-10, M-11 and M-12, taken 04:48 UTC 18 November 2014.

That’s a lot of fires! It’s probably because there are so many of them that they were visible in MTSAT. If you look closely at the full resolution image, there are two significant fires in North Korea, plus many more smaller fires/hot spots northwest and north of the Yellow Sea. Go back and compare the Fire Temperature RGB with the M-13 image. Admit it: fires in this scene are easier to see in the RGB composite.

If you don’t believe me, check out the M-13 and Fire Temperature RGB images that have been zoomed in on main concentration of fires. The Fire Temperature RGB has been lightened a little bit and the M-13 image has been darkened a little bit to highlight the hot spots better.

VIIRS M-13 image (as above) but zoomed in and slightly darkened.

VIIRS Fire Temperature RGB image (as above) but zoomed in and lightened slightly.

If you want to know why the Fire Temperature RGB composite works, go back and read this and this. Otherwise, stay put. If you’re familiar with the Fire Temperature RGB, because you are a loyal follower of this blog, you may be wondering why the overall image looks so dark.

All the previous cases where I’ve shown this RGB have been in the summer, typically under bright sunlight (since fires don’t tend to occur in winter). Here, it’s almost winter so there is less sunlight and the background surface is colder, which are going to make the image appear darker. Plus, there is some snow in the scene and snow appears black in this RGB composite. It’s not reflective at 1.61 µm (blue component) or 2.25 µm (green component) or at 3.74 µm (red component), plus it’s cold so it doesn’t emit much radiation at any of these wavelengths either.

The Natural Color RGB shows where the snow is. Look for the cyan signature of snow and ice here:

VIIRS Natural Color RGB composite of channels M-5, M-7, and M-10, taken 04:48 UTC 18 November 2014.

The Natural Color RGB shows that the fires are occurring in an area with a lot of lakes. Also, there isn’t a very strong green signature from vegetation in this area. So what is burning? Your guess is as good as mine. (Unless your guess is a bunch of Chinese children using magnifying glasses to burn ants. That’s not a very good guess – particularly because, as I said, there is less sunlight in the winter and it’s colder so the ants wouldn’t ignite easily. Also, that’s a cruel thing to suggest and my reasoned account of why that wouldn’t work should not be taken as an implicit admission that I ever did such a thing as a kid.)

A quick perusal of Google Maps reveals that it is an area full of agricultural fields. So my guess is that it’s some sort of end-of-year burning of agricultural waste. They are all small or low temperature fires and they’re not anything that made the news (I checked), so it’s doubtful that it’s a zillion uncontrolled fires.

How do we even know they’re fires? Besides the fact that they show up in the Fire Temperature RGB, we can also see the smoke. Check out this True Color RGB image and focus on the area where the majority of the fires are occurring:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken at 04:48 UTC 18 November 2014.

There are visible smoke plumes right where the greatest concentration of hot spots is located. There is also a long plume of gray along the base of the Changbai Mountains stretching southwest to the shores of the Yellow Sea, but it’s not clear if that is also smoke or simply smog. By the way, if you have respiratory ailments, don’t look at the southwest corner of the image (west of the Yellow Sea) because that’s definitely smog! The northern extent of that large area of smog is the Beijing metropolitan area.

What is most cough- and barf- inducing about that smog near Beijing is that it is thick enough to completely obscure the view of the surface. Last time we looked at that, it was record levels of smog that was receiving international attention. The thick, surface obscuring smog you see here isn’t record breaking or news-worthy – it’s simply a normal late fall day in eastern China!

If you can’t think of anything else to be thankful for on Thursday, you can be thankful that you don’t have to breathe air like that. Unless you live there. But, then, you wouldn’t be celebrating Thanksgiving anyway. And, if you live in Canada, you already had your Thanksgiving. You get to just sit back, relax, and watch Americans trample each other to death for discount electronics. Being able to avoid the Black Friday mob is something to be truly thankful for!

Beginning of Autumn in the Great Lakes

Posted on October 7, 2014 by Curtis_Seaman

Have you noticed it? The seasons are changing (for the mid- and high latitudes, at least). Days are getting shorter (or longer if you live in the upside-down hemisphere). This time of year, if you live in Alaska or Scandinavia or similar high latitude locations, you lose about 5-10 minutes of available daylight each day. (That’s between a half and one hour per week!) You may have noticed by the fact that your neighbor no longer mows the lawn at 11:00 PM because it’s still bright outside and hey, why not? I wasn’t going to sleep anyway.

Closer to home – in the mid-latitudes – loss of daylight is more like 1-3 minutes per day, which isn’t as noticeable. But, one day, you watch the sun set and look at the clock and realize that it’s only 6:30 PM and you think, didn’t it used to be light out later than this?

That’s not the only way to tell the seasons are changing. For one, there’s the arrival of snow. (Although parts of Montana, Wyoming and South Dakota received snow earlier this year while it was still technically summer.) And, for two, there’s what VIIRS observed on 27 September 2014:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 17:57 UTC 27 September 2014

In case it’s not obvious, here’s what VIIRS saw earlier in the month (8 September 2014):

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 17:13 UTC 8 September 2014

Notice anything different between the two images? (Remember to click on the images, then on the “1735 x 1611” links below the banner to see the images in full resolution.)

That’s right – the loss of daylight leads to one of the benefits of autumn: fall foliage. VIIRS True Color imagery shows, quite clearly, that the leaves of New England and eastern Canada have changed color. Forests that were green in early September have turned orange, red and brown by the end of the month.

Another thing you may have noticed comparing those two images: the change from green to beige in the area around Montreal, Quebec. This is another sign of autumn: the fall harvest. This is a productive agricultural region in eastern Canada, and what you are seeing is the green vegetation (crops) being harvested, leaving behind bare dirt.

True Color imagery is useful for observing the changing foliage and the harvest because it is designed to reproduce what we humans observe on the ground. The red, green and blue components of the RGB composite are channels in the red (M-5, 0.67 µm), green (M-4, 0.55 µm) and blue (M-3, 0.48 µm) portions of the electromagnetic spectrum. When leaves change from green to red, the True Color RGB detects that.

Now, you’ve probably known since elementary school (or at least middle school) that leaves change color because of chlorophyll. And, unless you became a botanist, that is probably the limit of your knowledge on the subject. But, there’s a lot of interesting chemistry that goes on inside a leaf (and the whole tree) that determines it’s color.

Of course, leaves are green because they contain chlorophyll. Chlorophyll is necessary for plants to convert sunlight into sugar. Chlorophyll, by necessity due to it’s job, is highly absorbing of visible-wavelength radiation, although it is slightly less absorbing of green wavelengths. Green light is therefore preferentially reflected out of the leaves and into your eye, and the leaves appear green.

When the sunlight goes away and the air becomes cold, deciduous trees go into hibernation. They break down the chlorophyll in their leaves, and send the remaining nutrients down into the trunk and roots. This exposes the carotinoids that were in the leaves and these carotinoids have a yellow or orange color – they preferentially reflect yellow and/or orange wavelengths. Red colors come from a pigment called anthocyanin, which was recently discovered to be a sort of “plant sunscreen”.

Now, utilizing sunscreen when you get all your energy from the sun may sound silly but, recent studies have shown that anthocyanin protects the leaves from sun damage once the chlorophyll is gone so that the tree has time to extract all the nutrients out of the leaves before they fall off. Trees in poor soil conditions are more likely to turn red in the fall as a natural defense mechanism – they need to store all the nutrients they can from their leaves, since they aren’t getting them from the soil.

Oak and other leaves turn brown in the fall because of a buildup of tannin (link to PDF file), which is a waste product. Brown leaves are full of plant poo! Think about that the next time you go on a fall color driving tour.

Now, back to the satellite science before the biologists come after me for grossly oversimplifying leaf chemistry. I’ve often talked about the Natural Color RGB composite as being similar to the True Color RGB in many instances (except for the detection of ice and snow). So, what does that look like here?

Here’s the VIIRS Natural Color RGB from 8 September 2014:

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 17:13 UTC 8 September 2014

And here’s the same RGB from 27 September 2014:

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 17:57 UTC 27 September 2014

Why does the vegetation still appear green when the leaves have changed color? Because we’ve made vegetation artificially appear green. The Natural Color RGB uses the red wavelength visible channel (M-5, 0.67 µm) as the blue component. The green component is a near-infrared channel (M-7, 0.87 µm), where plants are their most reflective – leaves and other plant tissues don’t absorb radiation at this wavelength. The red component is a longer wavelength channel (M-10, 1.61 µm) where the water inside the leaves starts to absorb radiation and the reflectance goes down. Cellulose and lignin also weakly absorb at 1.61 µm. The bottom line is, plants are highly reflective at 0.87 µm regardless of how healthy the plant is, or what color the leaves are – so they will always appear green in the Natural Color images.

You might also note the one difference (apart from clouds) that shows up between the two Natural Color images is the lack of green surrounding Montreal in the 27 September image. This is another sign of the fall harvest: the highly reflective plants have been removed and all that’s left is dirt, which is not as reflective. That’s why those areas appear more brown in the later image.

If we look a bit further west in the True Color imagery from 27 September 2014, the fall color really stands out:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 17:57 UTC 27 September 2014

Fall colors are visible from the Adirondacks of Upstate New York and Quebec to the Upper Peninsula of Michigan. The most vivid fall color is in Ontario – both in the area of Sault Ste. Marie and in the area of Algonquin Provincial Park, the oldest provincial park in Canada. Every autumn, the Friends of Algonquin Park post pictures of the fall colors, including this shot from 27 September 2014 showing just what VIIRS was seeing. Amazing colors!

We have sunny days, cool nights and plant survival techniques to thank for that.

BONUS:

Here’s a desktop wallpaper that’s zoomed in on the above image and cropped to the most popular screen resolution (1366×768):

VIIRS True Color RGB Composite Desktop Wallpaper (17:57 UTC 27 September 2014). This image fits monitors with a 16:9 ratio and is optimized for 1366×768 screen resolutions.

Make sure you click on the image, then on the “1366 x 768” link below the banner to get the full resolution image. Then you can right-click on the image and choose “Set as desktop background” to save it as your new desktop wallpaper.

Hell Froze Over (and the Great Lakes, too)

Posted on February 14, 2014 by Curtis_Seaman

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

BONUS UPDATE (17 February 2014):

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

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

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

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

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

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

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

JPSS (SNPP and NOAA-20)

VIIRS Imagery and Visualization Team Blog

Tag Archives: Natural Color

Remote Islands IV: Where’s Waldo (Pitcairn)? Edition

Sea-effect Snow

When China Looks Like Canada

Beginning of Autumn in the Great Lakes

Hell Froze Over (and the Great Lakes, too)