UHF/VHF

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

Take a second to think about what would happen if Florida was hit by four hurricanes in one month.

Would the news media get talking heads from both sides to argue whether or not global warming is real by yelling at each other until they have to cut to a commercial? Would Jim Cantore lose his mind and say “I don’t need to keep standing out here in this stuff- I quit!”? Would we all lose our minds? Would our economy collapse? (1: yes. 2: every man has his breaking point. 3: maybe not “all”. 4: everybody panic! AHHH!)

It doesn’t have to just be Florida. It could be four tropical cyclones making landfall anywhere in the CONUS (and, maybe, Hawaii) in a 1-month period. The impact would be massive. But, what about Alaska?

Of course, Alaska doesn’t get “tropical cyclones” – it’s too far from the tropics. But, Alaska does get monster storms that are just as strong that may be the remnants of tropical cyclones that undergo “extratropical transition“. Or, they may be mid-latitude cyclones or “Polar lows” that undergo rapid cyclogenesis. When they are as strong as a hurricane, forecasters call them “hurricance force” (HF) lows. And guess what? Alaska has been hit by four HF lows in a 1-month period (12 December 2015 – 6 January 2016).

With very-many HF lows, some of which were ultra-strong, we might call them VHF or UHF lows. (Although, we must be careful not to confuse them with the old VHF and UHF TV channels, or the Weird Al movie.) In that case, let’s just refer to them as HF, shall we?

The first of these HF storms was a doozy – tying the record for lowest pressure ever in the North Pacific along with the remnants of Typhoon Nuri. Peak winds with system reached 122 mph (106 kt; 196 k hr^-1; 54 m s^-1) in Adak, which is equivalent to a Category 2 hurricane!

Since Alaska is far enough north, polar orbiting satellites like Suomi-NPP provide more than 2 overpasses per day. Here’s an animation from the VIIRS Day/Night Band on Suomi-NPP:

Animation of VIIRS Day/Night Band images of the Aleutian Islands (12-14 December 2015).

It’s almost like a geostationary satellite! (Not quite, as I’ll show later.) This is the view you get with just 4 images per day. (The further north you go, the more passes you get. The Interior of Alaska gets 6-8 passes, while the North Pole itself gets all 15.) Seeing the system wrap up into a symmetric circulation would be a thing of beauty, if it weren’t so destructive. Keep in mind that places like Adak are remote enough as it is. When a storm like this comes along, they are completely isolated from the rest of Alaska!

Here’s the same animation for the high-resolution longwave infrared (IR) band (I-5, 11.5 µm):

Animation of VIIRS I-5 images of the Aleutian Islands (12-14 December 2015).

You may have heard of Himawari and its primary instrument, the Advanced Himawari Imager (AHI). AHI can be thought of as a geostationary version of VIIRS, and it’s nearly identical to what GOES-R will provide. Well, Himawari’s field of view includes the Aleutian Islands, and it takes images of the full disk every 10 minutes. Would you like to see how this storm evolved with 10 minute temporal resolution? Of course you would.

Here is CIRA’s Himawari Geocolor product for this storm:

Here is a loop of the full disk RGB Airmass product applied to Himawari. Look for the storm moving northeast from Japan and then rapidly wrapping up near the edge of the Earth. This is an example of something you can’t do with VIIRS, because VIIRS does not have any detectors sensitive to the 6-7 µm water vapor absorption band, which is one of the components of the RGB Airmass product. The RGB Airmass and Geocolor products are very popular with forecasters, but they’re too complicated to go into here. You can read up on the RGB Airmass product here, or visit my collegue D. Bikos’ blog to find out more about this storm and these products.

You might be asking how we know what the central pressure was in this storm. After all, there aren’t many weather observation sites in this part of the world. The truth is that it was estimated (in the same way the remnants of Typhoon Nuri were estimated) using the methodology outlined in this paper. I’d recommend reading that paper, since it’s how places like the Ocean Prediction Center at the National Weather Service estimate mid-latitude storm intensity when there are no surface observations. I’ll be using their terminology for the rest of this discussion.

Less than 1 week after the first HF storm hit the Aleutians, a second one hit. Unfortunately, this storm underwent rapid intensification in the ~12 hour period where there were no VIIRS passes. Here’s what Storm #2 looked like in the longwave IR according to Himawari. And here’s what it looked like at full maturity according to VIIRS:

VIIRS DNB image (23:17 UTC 18 December 2015).

VIIRS I-5 image (23:17 UTC 18 December 2015).

Notice that this storm is much more elongated than the first one. Winds with this one were only in the 60-80 mph range, making it a weak Category 1 HF low.

Storm #3 hit southwest Alaska just before New Year’s, right at the same time the Midwest was flooding. This one brought 90 mph winds, making it a strong Category 1 HF low. This one is bit difficult to identify in the Day/Night Band. I mean, how many different swirls can you see in this image?

VIIRS DNB image (13:00 UTC 30 December 2015).

(NOTE: This was the only storm of the 4 to happen when there was moonlight available to the DNB, which is why the clouds appear so bright. The rest of the storms were illuminated by the sun during the short days and by airglow during the long nights.) The one to focus on is the one of the three big swirls closest to the center of the image (just above and right of center). It shows up a little better in the IR:

VIIRS I-5 image (13:00 UTC 30 December 2015).

The colder (brighter/colored) cloud tops are the clue that this is the strongest storm, since all three have similar brightness (reflectivity) in the Day/Night Band. If you look close, you’ll also notice that this storm was peaking in intensity (reaching mature stage) right as it was making landfall along the southwest coast of Alaska.

Storm #4 hit the Aleutians on 6-7 January 2016 (one week later), and was another symmetric/circular circulation. This storm brought winds of 94 mph (2 mph short of Category 2!) The Ocean Prediction Center made this animation of its development as seen by the Himawari RGB Airmass product. Or, if you prefer the Geocolor view, here’s Storm #4 reaching mature stage. But, this is a VIIRS blog. So, what did VIIRS see? The same storm at higher spatial resolution and lower temporal resolution:

Animation of VIIRS DNB images of the Aleutian Islands (6-7 January 2016).

Animation of VIIRS I-5 images of the Aleutian Islands (6-7 January 2016).

This storm elongated as it filled in and then retrograded to the west over Siberia. There aren’t many hurricanes that do that after heading northeast!

So, there you have it: 4 HF lows hitting Alaska in less than 1 month, with no reports of fatalities (that I could find) and only some structural damage. Think that would happen in Florida?

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

A Graduate Level Course on DNB and NCC

September 28, 2015Uncategorizedaurora, day/night, ice and snow, twilightCurtis_Seaman

Is there any post on this blog that doesn’t have to do with scaling the DNB or NCC?

I was going to title this post “Revisiting ‘Revisiting “Revisiting Scaling on the Solstice”‘”, but that would just be ridiculous. Besides the fact that we just passed an equinox (and are months away from a solstice), this post is more of a follow-up to our very first post.

If that was an introduction, this is a graduate level course. Well, maybe not. It won’t take a whole semester to read through this, unless you are a really slow reader. But, since Near Constant Contrast (NCC) imagery is coming to AWIPS in the very near future, now is a good time to prepare for what’s coming.

We start off with some good news: NCC imagery is coming to AWIPS! NCC imagery provides an alternative to ERF-Dynamic Scaling, CSPP Adaptive Scaling and whatever this is:

Example VIIRS DNB image displayed in AWIPS using the Histogram Equalization method. Courtesy Eric Stevens (UAF/GINA).

(I know that the above image uses the “Histogram Equalization” algorithm that was developed for CSPP originally. I was just being dramatic.) NCC imagery is an operational product, not some fly-by-night operation from CIRA or CIMSS (who both do great work, by the way).

Now the bad news: NCC imagery as viewed in AWIPS might not be the best thing since sliced bread. It may not solve all of our problems. To understand why, you have to know the inner workings of AWIPS (which I don’t) and the inner workings of the NCC EDR product (which I do).

Here’s a first look at the NCC product as displayed in AWIPS:

Example NCC image (12:39 UTC 19 August 2015) displayed in AWIPS II. Image courtesy John Paquette (NOAA).

Notice the bright area of clouds northwest of Alaska that suddenly become black. Also notice the background surface just looks black, except for the Greenland Ice Sheet. These are examples of two different (but related) issues: the first being that AWIPS is blacking out areas where the image is saturated (the maximum value on the scaling bounds is too low), the second being that, at the low end of the scale, the image detail is lost (either the minimum value on the scaling bounds is too high, or AWIPS uses too few shades of gray in the display, which means you lose sensitivity to small changes in value).

If you read the first post on this blog (that I linked to), or you read my previous posts about ERF-Dynamic Scaling, you know that the primary problem is this: the VIIRS Day/Night Band is sensitive to radiance values that span 8 orders-of-magnitude from full sunlight down to the levels of light observed during a new moon at night. Most image display software, of which AWIPS is an example, are capable of displaying only 256 colors (or 96 colors if you use N-AWIPS). How do you present an 8-order-of-magnitude range of values in 256 colors without losing information?

Near Constant Constrast imagery does this by modeling the sun and moon to convert the Day/Night Band radiance values into a “pseudo-albedo”. Albedo (aka reflectance) is simply the percentage of incoming solar (and lunar) radiation that is reflected back to the satellite, so you end up with a decimal number between 0 and 1. That’s easy enough to display, but we’re not done. What happens when there is no moonlight at night because the moon is below the horizon (or it’s a new moon)? What happens when there is a vivid aurora, or bright city lights, or gas flares or fires? These light sources can all be several orders of magnitude brighter than the moon, especially when there is no moonlight. There are lots of light sources at night that the DNB detects that aren’t reflecting light – they’re emitting it. That’s why the NCC doesn’t provide a true albedo value.

When VIIRS first launched into space, the NCC algorithm assumed that pseudo-albedo values over the range from 0 to 5 would be sufficient to cover all these light sources, but that turned out to be incorrect. If you weren’t within a few days of a full moon, images contained fill values (no valid data) because these myriad light sources fell outside the allowed range of 0 to 5. It took a lot of work by the VIIRS Imagery Team to fix this and get the NCC algorithm to where it stands now. And where it stands now is that pseudo-albedo values are allowed to vary over the range from -10 to 1000. (The “-10” accounts for the occasional negative radiance in the DNB data and the “1000” allows for light sources up to three orders of magnitude brighter than the moon.) Now, the images don’t saturate or get blanked out by fill values at night away from a full moon. But, the range from -10 to 1000 still presents a challenge for those who want to display images properly.

To show this, here are the same three VIIRS NCC images linearly scaled between the full range of values (-10 to 1000), the original range of values (0 to 5) and the ideal range of values (which was subjectively determined for this scene):

Example VIIRS NCC image (08:55 UTC 5 August 2015) linearly scaled between -10 and 1000.

Can you see the one pixel that shows up in the above scaling? (There is one pixel with a value over 900.)

Example VIIRS NCC image (08:55 UTC 5 August 2015) scaled between 0 and 5.

Now you can start to see some cloud features and the city lights, but this image still looks too dark.

Example VIIRS NCC image (08:55 UTC 5 August 2015) scaled between 0 and 1.5.

Now we’re talking!

The above images were taken when there was moonlight available. What happens when there is no moonlight?

Example VIIRS NCC image (12:57 UTC 26 July 2015) scaled from -10 to 1000.

Scaling over the full range of values means you only see the city lights of Honolulu and the islands drawn on the map.

Example VIIRS NCC image (12:57 UTC 26 July 2014) scaled from 0 to 1.

Scaling from 0 to 1 is better, but I would argue that it’s still too dark. Let’s stretch it further.

Example VIIRS NCC image (12:57 UTC 26 July 2015) scaled between 0 and 0.5.

This is about as good as you can do without the image becoming too noisy.

And, of course, the presence of the aurora gives yet another result:

Example VIIRS NCC image (11:32 UTC 22 January 2015) scaled from -10 to 1000.

Can you see the aurora over northern Alaska? Maybe just barely. Once again, scaling over the full range of values doesn’t work (just like it wouldn’t for the DNB radiance values). What about using the scale of 0 to 1.5? It worked before…

Example VIIRS NCC image (11:32 UTC 22 January 2015) scaled from 0 to 1.5.

GAHH! I’m blinded! Although, you can see the clouds over the Gulf of Alaska pretty easily as well as ice leads in the Arctic Ocean. But, the aurora is too bright and you can’t see any details over most of Alaska.

It turns out, in order to prevent the aurora from saturating this scene, the image needs to be scaled over a range of 0 to 21:

Example VIIRS NCC image (11:32 UTC 22 January 2015) scaled from 0 to 21.

But, notice that you lose the detail of the cloud field over the Gulf of Alaska and the ice over the Arctic Ocean. This is a difficult case to scale correctly. More on that later.

So, we’ve seen that the optimum scaling bounds vary from scene to scene. The 0 to 1.5 scale seems to work for daytime and full moon scenes. New moon scenes require a scale more like 0 to 0.5 (or thereabouts) to be able to detect clouds, snow and ice. And the occasional scene requires a totally different scale altogether. Wouldn’t it be great if there were some way to automate this, so we wouldn’t have to keep fussing with the scaling on every image?

I’m here to say, “there might be.” And, it’s called “Auto Contrast.” The idea is to do what Photoshop and other image editing software do when they “automatically” improve the contrast in the image. The idea is to take the NCC image data, scaled over a range from 0 to 2, for example, bump up the maximum value bound of the scaling with the same kind of adjustment the ERF-Dynamic Scaling uses to prevent saturation in auroras, then apply something similar to Photoshop’s Auto Contrast algorithm to create the ideal scene contrast. Here’s what Auto Contrast does for the three cases above:

Example VIIRS NCC image (08:55 UTC 5 August 2015) scaled with Auto Contrast.

Example VIIRS NCC image (12:57 UTC 26 July 2015) scaled with Auto Contrast.

Example VIIRS NCC image (11:32 UTC 22 January 2015) scaled with Auto Contrast.

For the first two cases, Auto Contrast is very similar to the subjectively determined “ideal scaling”. For the aurora case, we can see that Auto Contrast is a compromise between “not allowing the aurora to saturate” and “allowing the aurora to saturate half of the image.” The aurora does saturate a portion of the scene, but you can still see ice on the Arctic Ocean and clouds over the Gulf of Alaska when you look closely.

Of course, there are a few caveats:

1) Auto Contrast has not been fully tested. These results are promising enough that I wanted to share it right away, but it might not produce ideal results in all cases. We are continuing to investigate this.

2) Sometimes, the image has poor contrast that Auto Contrast can’t fix. For example, a new moon case over land where there are lots of city lights or a vivid aurora. Non-city areas will be more like the Hawaii case, where clouds have pseudo-albedo value between 0 and 0.5, and the city lights or aurora will have pseudo-albedo values well over 100. If you stretch the scaling enough to see the clouds, you’ll be blinded by the city lights. If you scale it to the city lights, you won’t see the clouds or snow or ice.

3) Individual users may not care that the aurora saturated half the image in the third example because they can see the clouds and ice just fine. Auto Contrast makes the clouds and ice darker and harder to see. This is example of how “ideal contrast” not only varies scene to scene, but also from one user application to another. Pretty pictures are not always the same thing as usable images.

4) Demonstrating the utility of “Auto Contrast” is not the same thing as getting the algorithm up and running within AWIPS. (Or, sending files to AWIPS that have optimized contrast.) The JPSS Imagery Team is working with the developers of the AWIPS NCC product to improve how it is displayed, but it will likely take some time.

While it’s not clear how the NCC images are currently scaled in AWIPS, they almost certainly use a fixed scale. However, the examples shown here make it clear that the scaling needs to adjust from scene to scene – even if Auto Contrast is not the ultimate solution. So while we work to figure this out, if the NCC imagery looks sub-optimal in your AWIPS system, you know why.

One final thought: the Auto Contrast algorithm is designed to work with any image, not just NCC images. It’s possible that DNB images created with ERF-Dynamic Scaling may be improved with Auto Contrast as well. But, that’s a topic for another blog post about image scaling for the future. I may yet title a post “Revisiting ‘Revisiting “Revisiting Scaling on the Solstice”‘”.

The Land of 10,000 Fires

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

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

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

Map of known wildfires (23 June 2015), courtesy Alaska Interagency Coordination Center

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The nice (and dedicated) people of N-ICE

April 3, 2015UncategorizedArctic, clouds, day/night, ice and snow, Norway, ship, Svalbard, twilightCurtis_Seaman

Imagine this scenario: you’re stuck on a boat in the Arctic Ocean in the middle of the night. The winds are howling, the air is frigid, and the boat you’re in is completely encased in ice. Step off the boat and your face is constantly sand-blasted by tiny ice particles. Blink at the wrong time and your eyes freeze shut. The ice may crack under your feet (or between you and the boat) – without notice – leaving icy water between you and the only warm place for hundreds of kilometers. Have to swim for it? Look out for jellyfish. Decide to stay on your crumbling patch of ice? I hear polar bears can get pretty hungry. Death awaits every misstep and every wrong turn. Cowering in the boat? Internet access is limited, there are no re-runs of Friends to keep you entertained, and the shuffleboard court is outside. (Actually, it’s worse than that: there is no shuffleboard court!)

Now imagine this: you actually wanted to be there!

Most people would say, “That’s crazy! I would never do that!” But, for the scientists and crew aboard the research vessel (RV) Lance, it is a unique opportunity to further our understanding of the Arctic and its role in the Earth’s climate system.

You see, we are nearing the mid-point of the N-ICE 2015 field experiment, which is taking place from 1 January to 1 August 2015. The idea behind the experiment is to take a boat, freeze it in the Arctic ice sheet, and constantly monitor the environment around the boat for about six months. A group of scientists work in six-week shifts where they monitor everything from the weather to local biology. Of course, the primary objective is to see what happens with the ice itself.

One of our very own researchers at CIRA (and one of the world’s leading experts on snow) was on board during the first leg of the experiment. So, what is a snow expert doing on a ship whose primary purpose is to study ice?

Here’s the lowdown. There are two types of ice that concern Arctic researchers: “young” and “multi-year”. As the name implies, multi-year ice is ice that survives the summer and lasts for more than one year. Young ice does not reach its first birthday – it melts over the summer. Arctic researchers have been finding out that, not only is the Arctic ice sheet shrinking, it’s lost most of its multi-year ice, which is being replaced by young ice.

Multi-year ice is thicker, more resilient and tends to be brighter (more reflective), while young ice is thinner, darker (less reflective of sunlight), and less resilient. The less sunlight that is reflected, the more sunlight is absorbed into the system and this leads to warming, which melts more ice (and is a positive feedback). The less ice there is, the more open ocean there is, and open water is a lot less reflective than ice, which leads to more absorption of sunlight, more warming, more melting, etc.

The thinner “young ice” breaks up more easily due to wind and waves. This creates more leads of open water. The water, being much warmer than the air above it, pumps heat and moisture into the atmosphere, creating more clouds and snow – just like lake-effect or sea-effect snow. And, while most people have a hard time believing it, snow is a good insulator. Snow on top of the ice will create a blanket that protects the ice from the really cold air above. This reduces the rate at which the ice thickens up, keeping the ice thinner, and we have another positive feedback.

That’s just one of the things being studied on the 2015 Norwegian Young Sea Ice Cruise. Of course, I wouldn’t be mentioning any of this unless VIIRS could provide information to help out with the mission.

Go back to the N-ICE 2015 website. Notice the sliding bar/calendar on the bottom of the map. You can use that to follow the progress of the ship. Or, you can use the VIIRS Day/Night Band.

At the time of this writing, the Lance is docked in Longyearbyen, the largest town on the island of Spitsbergen in Norway. (Spitsbergen is part of the Svalbard archipelago, which has a direct connection to VIIRS. Svalbard has a receiving station used by NOAA that collects and distributes data from nearly all of their polar-orbiting satellites.) Longyearbyen is where the RV Lance and Norwegian icebreaker KV Svalbard departed for the Arctic back in mid-January. KV Svalbard escorted the RV Lance into the ice sheet, then returned to Longyearbyen while the Lance froze itself into the ice. See if you can see that in this loop of VIIRS Day/Night Band images from 12 – 17 January 2015:

Animation of VIIRS Day/Night Band images from 12-17 January 2015. These images cover the area of the N-ICE field experiment, north of Svalbard.

Notice how the one bright light follows a lead in the ice until it stops. Then the light appears to split in two, with one light source heading back the way it came and the other stuck in the ice. That is the start of N-ICE 2015! The KV Svalbard did its duty. If you look closely, there are also some other boats hanging out in the open water near the edge of the ice sheet during this time.

If you suspect there are jumps in the images you’re right. VIIRS passes over this area every day 6-8 times between 00 and 12 UTC, with no overpasses for the next 12 hours.

Toward the end of January you can see how the RV Lance drifted to the west along with the ice:

Animation of VIIRS Day/Night Band images from 23-30 January 2015. These images cover the area of the N-ICE field experiment, north of Svalbard.

This was all according to plan. But, then, in February, the winds shifted and helped the ice spit the boat back out towards the open water:

Animation of VIIRS Day/Night Band images from 8-15 February 2015. These images show the area of the N-ICE field experiment, north of Svalbard.

After this, the RV Lance needed help from the KV Svalbard to be repositioned in the ice sheet near where it started a month earlier. Otherwise, all the instruments they placed in the ice would no longer be in the ice – they’d be at the bottom of the ocean as the ice sheet broke up all around them.

If you want to know why the ship seems to disappear and reappear every day, you can thank the sun. You see, the first few weeks of the experiment took place during the long polar night. But, by mid-February, twilight began to encroach on the domain during the afternoons. This was enough light to drown out the light from the ship. (Sunrise occurred in early March.)

Another thing to notice with these last two animations: the cloud streets that form over the open water near Svalbard. The direction these cloud streets move gives a pretty good indicator of where the ice is going to go, since both the clouds and icebergs are being pushed and pulled by the same wind.

It’s fascinating to watch the movement of the ice over the first 6 weeks of the field experiment. To save on file size and downloading time, the animation below only uses one image per day (between 10 and 11 UTC). Here’s 6 weeks of images in 5 seconds:

Animation of VIIRS Day/Night Band images from 11 January to 28 February 2015. These images show the area of the N-ICE field experiment, north of Svalbard.

And you probably thought of sea ice as being relatively static.

Once again, we lose sight of the RV Lance because of afternoon twilight in mid-February, so we can’t see it or the KV Svalbard after that. And note that there’s a lot less open water near Svalbard by the end of the period.

What if we didn’t have the Day/Night Band? You wouldn’t be able to see the ships at all, that’s for sure! Plus, this area was under darkness (no direct sunlight) for this six week period, so none of the other visible wavelength channels will work. That leaves us with the infrared (IR), which looks like this:

Animation of VIIRS IR (M-15) images from 11 January to 28 February 2015. These images cover the area of the N-ICE field experiment, north of Svalbard.

Note that clouds appear to have a greater impact on the detection of ice (and distinction between ice and clouds) in the IR. When it’s relatively cloud-free, there is enough of a temperature contrast between the open water and ice to see the icebergs but, pretty much any cloud will obscure the ice. So, why doesn’t the Day/Night Band have this problem?

That has to do with the optical properties of clouds at visible and IR wavelengths. Most of these clouds are optically thick in the IR and optically thin in the visible. The Day/Night Band can see through these clouds (most of them, anyway) while channels like M-15 (10.7 µm) shown here, can’t. We’ve seen more extreme examples of this before.

In the rapidly changing Arctic, it is nice to know that there are a few dedicated individuals who risk frostbite, hypothermia and polar bears to provide valuable information on how the ice impacts the environment both locally and globally. Me: I’ll just stick to analyzing satellite data from my nice, comfortable office, thank you.

By the way, the N-ICE field experiment has it’s own blog, and pictures and other snippets of information about the people and progress of the mission are regularly posted to Instagram, Facebook and Twitter.

Revisiting “Revisiting Scaling on the Solstice”

January 27, 2015Uncategorizedaurora, day/night, ice and snow, twilightCurtis_Seaman

Imagine that you are an operational forecaster. (Some of you reading this don’t need to imagine it, because you are operational forecasters.) You’ve been bouncing off the walls from excitement because of all the great information the VIIRS Day/Night Band (DNB) provides. “This is so great! Visible imagery at night! It helps in so many ways,” you say to yourself or to anyone within earshot. What’s more: you read this blog and, in particular, you’ve read this blog post and/or this paper. “All our problems have been solved! We can use the DNB for any combination of sunlight and moonlight! I am so happy!” Then you come across an image like this:

VIIRS DNB image created using “erf-dynamic scaling” (15:14 UTC 21 January 2015)

If you’re short tempered, you’re thinking, “@&*!@#&#!!!” If you have better control of your emotions, you’re thinking, “Me-oh-my! Whatever happened here?” Welcome to the third installment of the seemingly-never-ending series on how difficult it is to display the highly variable DNB radiance values in an automated way.

In the previous installment, which I will keep linking to until you click on it and re-read it, I outlined a great new way to scale the radiance values as a function of solar and lunar zenith angles that I call the “erf-dynamic scaling” (EDS) algorithm because it is based on the Gaussian error function (erf). This algorithm uses smooth, continuous functions to account for the 8 orders-of-magnitude variability in DNB data that occurs between day and night, and which was demonstrated to beat many previous attempts at image scaling. Unfortunately, that algorithm produced the image you see above.

So, is my algorithm a failure?

Well, if you’re going to jump right to “failure” based on this, you need to calm down and back off the hyperbole. Do you feel like a failure every time you make a mistake? Besides, mistakes are opportunities for learning.

My demonstration of the quality of the EDS method was based on images taken near the summer solstice. Now, we’re a month after winter solstice. And you know what happens in the winter that doesn’t happen in the summer? The aurora! (Actually, the aurora is present just as much in the summer, but you can’t see it because the sun is still shining.) Now that the nights are so long and dark, the aurora is easily visible.

My EDS method accounts for sunlight and moonlight. It doesn’t account for auroras and they can be several orders of magnitude brighter than the moonlight – especially near new moon when there is no moonlight. And guess when the image above was taken relative to the lunar cycle.

Now, I knew auroras would mess up my scaling algorithm (“Oh, sure you did!”), but I underestimated their occurrence. As a “Lower-48er,” I’ve seen the aurora once in my life. But, at high latitudes (*cough* Alaska *cough*) they happen almost every night in the winter. They’re not always visible due to clouds, but you can’t call them a “rare occurrence”.

From the perspective of DNB imagery, auroras can get in the way. Or, auroras can act as another illumination source to light up important surface features. Let’s look at the above image, with the data re-scaled by manually tweaking the settings in McIDAS-v:

VIIRS DNB image manually scaled (15:14 UTC 21 January 2015)

Of course, this image is rotated differently, but that’s not important. The important thing is that you can see now that it’s an aurora and you can see surface features underneath it. Cracks in the sea ice are visible! (And, remember, there is no moonlight here – just aurora and airglow.) Much better than the wall of white image, right? This proves that it’s a problem with my scaling and not with the DNB itself.

So, how do we get my scaling to work for this case? In theory, the answer is simple: bump up the max values until it’s no longer saturated. In practice, however, it’s not that simple. This was a broad, relatively diffuse aurora that was barely brighter than the max values. Some auroras are much more vivid (and much brighter than the max values), like this one:

VIIRS DNB image with modified “erf-dynamic scaling” (11:34 UTC 22 January 2015)

If you increase the max values until nothing is saturated, you’ll only be able to see the brightest pixels (which are usually city lights) and nothing else. And, don’t forget: we don’t want to increase the max values everywhere all the time, because the algorithm works as-is when the aurora isn’t present (or when the moonlight is brighter than the aurora).

Here’s the solution: calculate max and min values with the EDS method as before, but increase the max values by 10% at a time until only a certain percentage of the image is saturated. That’s what I’ve done in the last image above, where I’ve adjusted the max values until only 0.5% of the image is saturated. In case you’re wondering, here’s the same image without this additional correction:

VIIRS DNB image with unmodified “erf-dynamic scaling” (11:34 UTC 22 January 2015)

The correction makes it much better. What about for the first case I showed? Here’s the corrected version:

VIIRS DNB image with modified “erf-dynamic scaling” (15:14 UTC 21 January 2015)

Once again, much better than before. You can see the cracks in the sea ice now! (Maybe it’s not as good as the manual scaling but, because it’s automated, it takes less time to produce. )

Of course, this correction assumes that less than 0.5% of the image is city lights or wildfires or lightning. And, it might not work too good if the data spans all the way from bright sunlight to new-moon night beyond the aurora because it darkens the non-aurora parts of the scene (as can be seen in the images from 22 January 2015). But, the great thing is: if the scene is not saturated by the aurora (or some other large bright feature) no correction is applied, so you still get the same great EDS algorithm results you had before.

As a bonus to make up for the initial flaws in the EDS algorithm (and to get any short-tempered viewers to stop cursing), enjoy the images below of a week’s worth of auroras as seen by the DNB (with the newly modified scaling). Make sure you look for the “Full Resolution” link to the upper right of each image in the gallery to see the full resolution version: