The east coast of Australia is on fire!

There’s an ongoing serious situation in Australia: the bush in New South Wales and Queensland is on fire.

Here’s a look at what the Advanced Himawari Imager (AHI) on Himawari-8 saw on 8 November 2019: click here.

What you see in that loop is the “Natural Fire Color RGB” (known to American forecasters as the “Day Land Cloud Fire RGB”) on the left (link to PDF description here), and the “Fire Temperature RGB” on the right (link to PDF description here). These are precisely the products we debuted on this blog seven years ago when we first looked at fires in Australia. Except, now there is a difference: the “Natural Fire Color RGB” is now made with the 3.7 µm band as the red component (replacing the 2.25 µm band I used originally), since the 3.7 µm channel is even better at detecting fires. This also means that we can produce the VIIRS version using “I-band” resolution (375 m). AHI, used in the loop I linked to above, has 2 km resolution* for the mid- and shortwave infrared (IR) bands.

Along the coast, near the northern edge of the images is Brisbane, the third largest city in Australia. Near the southern edge of those images is Sydney, the largest city in Australia. As you can see from Himawari-8, much of the area between the two is on fire. And, this is not the “Outback” where very few people live. This region contains some of the highest population density in Australia, and it’s also prime habitat for koalas, which don’t live anywhere outside of eastern Australia (except in zoos).

It’s no secret that resolution plays in big role in fire detection from satellites. We’ve covered this many times before. But, to hammer the point home (bit of American slang), here’s the resolution difference between VIIRS and AHI in full view from 3:50 UTC on 7 November 2019:

Himawari-8 AHI Day Land Cloud Fire RGB composite of bands 2, 4, and 7 (03:50 UTC, 7 November 2019)

Himawari-8 AHI Day Land Cloud Fire RGB composite of bands 2, 4, and 7 (03:50 UTC, 7 November 2019)

S-NPP VIIRS Day Land Cloud Fire RGB composite of bands I-1, I-2 and I-4 (03:49 UTC, 7 November 2019)

S-NPP VIIRS Day Land Cloud Fire RGB composite of bands I-1, I-2 and I-4 (03:49 UTC, 7 November 2019)

As always, click on each image to bring up the full resolution version. If you just look at the elephant-thumbnail-sized images above without clicking on them, you might get the impression that fires are easier to spot with AHI than with VIIRS. That’s because AHI makes it appear that the entire 2km-wide pixel* is full of fire, when a fire typically only fills a very small percentage of the total area of the pixel. With 375 m resolution**, VIIRS more accurately pinpoints the locations of fire activity. Although, it should be noted that even this is still a larger scale than most fire fronts. To be really accurate, you need something with the resolution of Landsat’s OLI, or a similar radiometer attached to an aircraft – except these high-resolution instruments don’t provide full global coverage multiple times daily like VIIRS, or hemispheric coverage every 10 minutes like AHI. (*On AHI [and ABI and AMI] pixels may be approximated as square-shaped solid angles that are projected onto the curved surface of the Earth from a point roughly 36,000 km above the Equator. 2 km is the width of an IR pixel at the sub-satellite point [on the Equator], where the resolutions are the highest. **VIIRS pixel resolutions vary across the swath by a factor of 2 between nadir and edge of scan, as we shall see. 375 m is the nadir value.)

For completeness, we can do the same comparison with the Fire Temperature RGB:

Himawari-8 AHI Fire Temperature RGB composite of bands 5, 6 and 7 (03:50 UTC, 7 November 2019)

Himawari-8 AHI Fire Temperature RGB composite of bands 5, 6 and 7 (03:50 UTC, 7 November 2019)

S-NPP VIIRS Fire Temperature RGB composite of bands M-10, M-11 and M-12 (03:46 UTC, 7 November 2019)

S-NPP VIIRS Fire Temperature RGB composite of bands M-10, M-11 and M-12 (03:46 UTC, 7 November 2019)

This time, we’re comparing 2 km resolution (AHI) against 750 m resolution (VIIRS), so the differences aren’t as stark. But, this is a good opportunity to remind everyone that the Fire Temperature RGB provides information on fire intensity, while the Natural Fire Color (Day Land Cloud Fire) RGB provides information on fire detections (plus smoke and burn scars), and should be used more as a “fire mask”.

There’s another resolution difference that is easy to see from these fires, and it can be quite significant. I first noticed it when looking at this animation I made of the VIIRS Fire Temperature RGB from 1-11 November 2019:

Animated GIF of VIIRS Fire Temperature RGB images (1-11 November 2019)

Animated GIF of VIIRS Fire Temperature RGB images (1-11 November 2019)

You have to click on the animation to get it to play.

Did you notice the same thing I did? You probably noticed the explosive growth of the fires from 7-9 November, but that’s not what I’m talking about. (Hint: Pay close attention to the nighttime images.) At night, without any sunlight present, you lose information on clouds and the background land surface, and only the fires are visible (unless they are obscured by clouds). That’s where today’s feature of interest resides. I’ll zoom in on some of the fires from 5 November 2019 to make it easier to see:

Animated GIF of VIIRS Fire Temperature RGB images (5 November 2019)

Animated GIF of VIIRS Fire Temperature RGB images (5 November 2019)

The image from 14:01 UTC comes from S-NPP, while the image from 14:52 comes from NOAA-20. Is NOAA-20 better than S-NPP at detecting the fires? Well, the reverse happened two nights later:

Animated GIF of VIIRS Fire Temperature RGB images (7 November 2019)

Animated GIF of VIIRS Fire Temperature RGB images (7 November 2019)

This time, the fires appear hotter (brighter) in the 15:03 UTC image, which came from S-NPP. The 14:12 UTC image came from NOAA-20. Here’s a sequence of three images from 10 November where the NOAA-20 image is sandwiched by two S-NPP images:

Animated GIF of VIIRS Fire Temperature RGB images (10 November 2019)

Animated GIF of VIIRS Fire Temperature RGB images (10 November 2019)

So, why do the fires appear brighter in some images and not others? It’s possible that the fires are becoming more active in the middle image (due to an increase in winds, for example), but it’s more likely that you are seeing the direct result of resolution differences between the various overpasses. “But, I thought both VIIRS instruments had the same resolution,” you might say as though it were a question. And that statement would suggest that you forgot about the “bowtie-effect”. (Not the effect that has anything to do with diamonds, but the effect I wrote a whole chapter about here [PDF].) If you read the **above you would already know that the resolution of VIIRS degrades by a factor of two between nadir and the edge of scan. And, if you didn’t already know, NOAA-20 and S-NPP are positioned in space a half-orbit apart. This means that, in the time it takes between a NOAA-20 overpass and a S-NPP overpass, the Earth has rotated by half the width of the swath (approximately). So, when one VIIRS instrument views something at nadir, it will be close to the edge of scan on the other satellite (and have more coarse resolution as a result).

So, in the last animation, the first image (14:05 UTC) is S-NPP viewing the fires from the east near the edge of scan, the middle image (14:56 UTC) is NOAA-20 viewing the fires near nadir, and the third image is S-NPP viewing the fires from the west – even closer to the edge of scan. (Plus, the terrain is sloping away from S-NPP in the last image as well.)

Those factors contribute to the changing appearance of the fires. They also highlight the value of having two VIIRS instruments in space: if one satellite doesn’t get a good look at a fire, the other one likely will.

By the way, these fires have been producing a lot of smoke. Here is a loop of VIIRS True Color images from 6-11 November:

 

And the view from the ground is even more apocalyptic:

Tropical Cyclone Idai: Before, During and After

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

NOAA-20 VIIRS Flood Detection Product using a 3-day cloud-free composite (19-22 March 2019)

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

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

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

Ice, Ice, Baby

A winter storm moved through the Northeast U.S. over the weekend of 19-20 January 2019. This Nor’easter was a tricky one to forecast. Temperatures near the coast were expected to be near (or above) freezing. Temperatures inland were expected to be much colder. Liquid-equivalent precipitation, at least according to the GFS, was predicted to be in the 1-3 inch (25-75 mm) range the day before. This could easily convert to 1-2 feet (30-60 cm) of snow. The question on everyone’s mind: who gets the rain, who gets the snow, and who gets the “wintry mix”? The fates of ~40 million people hang in the balance. This is one of the situations that meteorologists live for!

The difference between 71°F and 74°F is virtually meaningless. The difference between 31°F and 34°F (with heavy precipitation, at least) is the difference between closing schools or staying open. It’s the difference between bringing out the plows or keeping them in the garage; paying overtime for power crews to keep the electricity flowing or just another work day; shutting down public transportation or life as usual.

Of course, the obvious follow-up question is: what is the “wintry mix” going to be? Rain mixed with snow? Sleet? Freezing rain? It doesn’t take much to change from one to the other, but there can be a big difference on the resulting impacts based on what ultimately falls from the sky.

So, what happened? Here’s an article that does a good job of explaining it. And, here are PDF files of the storm reports from National Weather Service Forecast Offices in Albany, Boston (actually in Norton, MA) and New York City (actually in Upton, NY). The synopsis: some places received ~1.5 inches (~38 mm) of rain, some places received ~11 inches (~30 cm) of snow and some places were coated in up to 0.6 inches (15 mm) of ice.

Of particular relevance here are the locations that received the ice. If you took the locations listed in the storm reports that had more than 0.1 inches (2.5 mm) of ice (at least the ones in Connecticut) and plotted them on a map, they match up quite well with this map of power outages that came from the article I linked to:

Map of power outages in Connecticut as a result of an ice storm (19-20 January 2019)

Map of power outages in Connecticut as a result of an ice storm (19-20 January 2019). Image courtesy Eversource/NBC Connecticut.

Now, compare that map with this VIIRS image from 22 January 2019 (after the clouds cleared out):

VIIRS channel I-3 image from NOAA-20, 17:09 UTC 22 January 2019

VIIRS channel I-3 image from NOAA-20, 17:09 UTC 22 January 2019

As always, you can click on the image to bring up the full resolution version. This is the high-resolution imagery band, I-3, centered at 1.6 µm from NOAA-20. Notice that very dark band stretching from northern New Jersey into northern Rhode Island? That’s where the greatest accumulation of ice was. Notice how well it matches up with the known power outages across Connecticut!

The ice-covered region appears dark at 1.6 µm because ice is very absorbing at this wavelength and, hence, it’s not very reflective. And, since it is cold, it doesn’t emit radiation at this wavelength either (at least, not in any significant amount). This is especially true for pure ice, as was observed here (particularly the second image), since there aren’t any impurities in the ice to reflect radiation back to the satellite. The absorbing nature of snow and ice compared with the reflective nature of liquid clouds is what earned this channel the nickname “Snow/Ice Band” (PDF).

At shorter wavelengths (less than ~ 1 µm), ice and snow are reflective. (Note how a coating of ice makes everything sparkle in the sunlight.) This makes it nearly impossible to tell where the ice accumulation was in True Color images:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 from NOAA-20, 17:09 UTC 22 January 2019

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 from NOAA-20, 17:09 UTC 22 January 2019

The Natural Color RGB (which the National Weather Service forecasters know as the Day Land Cloud RGB (PDF file)) includes the 1.6 µm band, which is what makes it useful for discriminating clouds from snow and ice. And, as expected, the region of ice accumulation does show up (although it is tempered by the highly reflective nature of snow and ice in the visible and “veggie” bands that make up the other components of the RGB):

VIIRS Natural Color RGB composite of channels, I-1, I-2 and I-3 from NOAA-20 (17:09 UTC, 22 January 2019)

VIIRS Natural Color RGB composite of channels, I-1, I-2 and I-3 from NOAA-20 (17:09 UTC, 22 January 2019)

Another RGB composite popular with forecasters is the Day Snow/Fog RGB (PDF file), where blue is related to the brightness temperature difference between 10.7 µm and 3.9 µm, green is the 1.6 µm reflectance, and red is the reflectance at 0.86 µm (the “veggie” band). This shows the region of ice even more clearly than the Natural Color RGB:

VIIRS Day Snow/Fog RGB composite of channels (I-5 - I-4), I-3 and I-2 from NOAA-20 (17:09 UTC, 22 January 2019)

VIIRS Day Snow/Fog RGB composite of channels (I-5 minus I-4), I-3 and I-2 from NOAA-20 (17:09 UTC, 22 January 2019)

Breaking things up into the individual components, we can see how the ice transitions from being reflective in the visible and near-infrared (near-IR) to absorbing in the shortwave-IR:

VIIRS high-resolution visible channel, I-1, from NOAA-20 (17:09 UTC, 22 January 2019)

VIIRS high-resolution visible channel, I-1 (0.64 µm), from NOAA-20 (17:09 UTC, 22 January 2019)

VIIRS high-resolution "veggie" channel, I-2, from NOAA-20 (17:09 UTC, 22 January 2019)

VIIRS high-resolution “veggie” channel, I-2 (0.86 µm), from NOAA-20 (17:09 UTC, 22 January 2019)

VIIRS channel M-8 from NOAA-20 (17:09 UTC, 22 January 2019)

VIIRS channel M-8 (1.24 µm) from NOAA-20 (17:09 UTC, 22 January 2019)

VIIRS channel M-11  from NOAA-20 (17:09 UTC, 22 January 2019)

VIIRS channel M-11 (2.25 µm) from NOAA-20 (17:09 UTC, 22 January 2019)

Of course, the 1.6 µm image was already shown, so I didn’t bother to repeat it. If you squint, you can even see a hint of the ice signature at 1.38 µm, the “Cirrus Band“, where most of the surface signal is blocked by water vapor absorption in the atmosphere:

VIIRS "cirrus" channel, M-9, from NOAA-20 (17:09 UTC 22 January 2019)

VIIRS “cirrus” channel, M-9 (1.38 µm), from NOAA-20 (17:09 UTC 22 January 2019)

If the ice had accumulated in southern New Jersey or Pennsylvania, though, it would not have shown up in this channel, since the air was too moist at this time to see all the way down to the surface. But, you can compare this image with the previous images to see why they call it the “cirrus band”, since the cirrus does show up much more clearly here.

So, mark this down as another use for VIIRS: detecting areas impacted by ice storms. And remember, even though ice storms may have a certain beauty, they are also dangerous. And, not just for the obvious reasons. This storm in particular came complete with ice missiles. So, for the love of everyone else on the road, scrape your car clean of ice before risking your life out there!

Rivers of Ice

Oh, Yakutsk! It has been a long time – 2012, to be exact – since we last spoke about you. It was a different time back then, with me still referring to the Natural Color RGB as “pseudo-true color”. (Now, most National Weather Service forecasters know it as the “Day Land Cloud RGB”). VIIRS was a only a baby with less than one year on the job. Back then, the area surrounding the “Coldest City on Earth” was on fire. This time, we return to talk about ice.

You see, rivers near the Coldest City on Earth freeze during the winter, as do most rivers at high latitudes. Places like the Northwest Territories, the Yukon, Alaska and Siberia use this to their advantage. Rivers that are frozen solid can make good roads, a fact that has often been overly dramatized for TV. Transporting heavy equipment may be better done on solid ice in the winter than on squishy, swampy tundra in the summer. But, that comes with a cost: ice roads only work during the winter.

In remote places like these, with few roads, rivers are the lifeblood of transportation – acting as roads during the winter and waterways for boats during the summer. But, what about the transition period that happens each spring and fall? Every year there is a period of time where it is too icy for boats and not icy enough for trucks. Monitoring for the autumn ice-up is an important task. And, perhaps it is more important to monitor for the spring break-up of the ice, since the break up period is often associated with ice jams and flooding.

We’ve covered the autumn ice up before (on our sister blog), but VIIRS recently captured a great view of the spring break up near Yakutsk, that will be our focus today.

We will start with the astonishing video captured by VIIRS’ geostationary cousin, the Advanced Himawari Imager (AHI) on Himawari-8 from 18 May 2018:

The big river flowing south to north in the center of the frame is the Lena River. (Yakutsk is on that river just south of the easternmost bend.) The second big river along the right side of the frame is the Aldan River, which turns to the west and flows into the Lena in the center of the frame.

Now that you are oriented, take a look at that video again in full screen mode. If you look closely, you will see a snake-like section of ice flowing from the Aldan into the Lena. This is exactly the kind of thing river forecasters are supposed to be watching for during the spring!

Of course, this is a geostationary satellite, which provides good temporal resolution, but not as good spatial resolution. The video is made from 1-km resolution imagery, but we are looking at high latitudes on an oblique angle, so the resolution is more like 3-4 km here. So, how does this look from the vantage point of VIIRS, which provides similar imagery at 375 m resolution? See for yourself:

(You will have to click on the image to get the animation to play.)

Animation of VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (18 May 2018)

Animation of VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (18 May 2018)

This animation includes both Suomi NPP and NOAA-20 VIIRS. That gives us ~50 min. temporal resolution to go with the sub-kilometer spatial resolution. Eagle-eyed viewers can see how the resolution changes over the course of the animation, as the rivers start out near the left edge of the VIIRS swath (~750 m resolution), then on subsequent orbits, the rivers are near nadir (~375 m resolution) and then on the right edge of the swath (~750 m resolution again). In any case, this is better spatial resolution than AHI can provide at this latitude.

One thing you can do with this animation is calculate how fast the ice was moving. I estimated the leading edge of the big “ice snake” moved about 59 pixels (22.3 km at 375 m resolution) during the 3 hour, 21 minute duration of the animation. That works out to an average speed of 6.7 km/hr (3.6 knots), which doesn’t seem unreasonable. Counting up pixels also indicates our big “ice snake” is at least 65 km long, and the Aldan River is nearly 3 km wide in its lower reaches when it meets the Lena River. That is in the neighborhood of 200 km2 of ice!

That much ice moving at 3 knots can do a lot of damage. Just look at what the ice on this much smaller river did to this bridge:

(Make sure you watch it all the way to the end!)

Don’t Eat Orange Snow

Roughly one month ago, social media (and, later, more conventional media) outlets were inundated with numerous reports of orange snow in eastern Europe and western Asia – reports like this one, this one and this one. Of course, it wouldn’t really be a hit with the media unless someone could claim it was “apocalyptic”. And of course, the apocalypse didn’t happen. It was simply Saharan dust picked up by high winds from an intense mid-latitude cyclone and deposited far away. We’ve seen this before with VIIRS.

These reports focused on Sochi, Russia, home of the 2014 Winter Olympics. Unfortunately, every time I looked for it in VIIRS imagery, it was cloudy in Sochi. But, the plume of Saharan dust that caused this event was clearly visible over the Mediterranean:

NOAA-20 VIIRS true color composite of channels M-3, M-4 and M-5 (10:03 UTC 25 March 2018)

NOAA-20 VIIRS true color composite of channels M-3, M-4 and M-5 (10:03 UTC 25 March 2018)

This image came from our new NOAA-20 VIIRS, which, at this point, is not operational and undergoing additional testing. If you look closer, you might also notice smoke or smog over Poland in the image above (upper left corner). If you really zoom in (click on the image to get to the full resolution version), you may notice a brownish tint to the snow along the north shore of the Black Sea – where the BBC report I linked to listed additional sightings of orange snow. But, the dust-covered snow shows up more clearly in this “before and after” image courtesy of S-NPP VIIRS and the @NOAASatellites twitter account:

"Before" and "After" S-NPP VIIRS true color images from 22 March 2018 (left) and 25 March 2018 (right) showing dust on snow in eastern Europe.

“Before” and “After” S-NPP VIIRS true color images from 22 March 2018 (left) and 25 March 2018 (right).

(As an aside: differences in technique used to produce these true color images are likely larger than the differences between S-NPP VIIRS and NOAA-20 VIIRS, so don’t read too much into the fact that the dust-on-snow appears more clearly in the @NOAASatellites image than in my own.)

But, dust-on-snow is not limited to areas within a few thousand kilometers of the Sahara Desert. (It is limited to areas within 40,000 km of the Sahara [in the horizontal dimension, at least], since that is roughly the circumference of the Earth – and assuming you ignore dust storms on Mars.) Dust on snow can happen anywhere you have snow within striking distance of a source of dust. Another example was captured by a new Landsat-like micro-satellite, Venµs, and its non-microsat predecessor, Sentinel-2B, Landsat’s European cousin. A more dramatic example happened last week right here in Colorado. Here is a VIIRS true color image of Colorado from S-NPP VIIRS, taken on 14 April 2018:

S-NPP VIIRS true color composite of channels M-3, M-4 and M-5 (19:45 UTC 14 April 2018)

S-NPP VIIRS true color composite of channels M-3, M-4 and M-5 (19:45 UTC 14 April 2018)

Here are similar images from NOAA-20 and S-NPP from 18 April 2018:

NOAA-20 VIIRS true color composite of channels M-3, M-4 and M-5 (19:20 UTC 18 April 2018)

NOAA-20 VIIRS true color composite of channels M-3, M-4 and M-5 (19:20 UTC 18 April 2018)

S-NPP VIIRS true color composite of channels M-3, M-4 and M-5 (20:11 UTC 18 April 2018)

S-NPP VIIRS true color composite of channels M-3, M-4 and M-5 (20:11 UTC 18 April 2018)

The trick is to compare these two images with the image from 14 April. The other trick is to know where you’re supposed to be looking. (Hint: we’re looking at the Sangre de Cristo mountains in southern Colorado.) Here’s a “before” and “after” image overlay trick I’ve used before. (You may have to refresh the page before it will work.) Both of these images are the S-NPP VIIRS ones, for simplicity:

If you slide the bar left to right, you should notice the snow is more brown in the mountains just right of center in the 18 April image. There are other areas where the snow melted between the two images, plus a couple of small clouds that add to the differences. Of course, this is only 750 m resolution. We get a better view with the 375m-resolution visible channel, I-1:

We lose the color information, of course, since we are looking at a single channel, but it is obvious the snow became less reflective in the 18 April image. And, we can prove that this was a result of dust. Here are the visible, true color, Dust RGB, “Blue Light Dust” and DEBRA Dust images from S-NPP on 17 April 2018, courtesy Steve M.:

S-NPP VIIRS true color composite of channels M-3, M-4 and M-5 (20:26 UTC 17 April 2018)

S-NPP VIIRS true color composite of channels M-3, M-4 and M-5 (20:26 UTC 17 April 2018)

S-NPP VIIRS Dust RGB image (20:26 UTC 17 April 2018)

S-NPP VIIRS Dust RGB image (20:26 UTC 17 April 2018)

S-NPP VIIRS Blue Light Dust image (20:26 UTC 17 April 2018)

S-NPP VIIRS Blue Light Dust image (20:26 UTC 17 April 2018)

S-NPP VIIRS DEBRA Dust image (20:26 UTC 17 April 2018)

S-NPP VIIRS DEBRA Dust image (20:26 UTC 17 April 2018)

If you are unfamiliar with them, we’ve looked at the Dust RGB, Blue Light Dust and DEBRA before, here and here. As seen in the above images, this was not a difficult to detect dust case. Even Landsat-8 captured this event, which is surprising given the narrow swath and 16-day orbit repeat cycle. (Sure, it’s higher resolution than VIIRS, but will it be overhead when you need it?)

So now we get to why dust-on-snow is important. There is a growing body of research (e.g. this paper) that shows dust-on-snow has a big impact on water resources in places like the Rocky Mountains. You see, dirty snow is less reflective than clean snow. That means it absorbs more solar radiation. This, in turn, means it heats up and melts faster, leading to earlier spring run-off. The end result is less water later in the season, which opens the door to wildfires and more severe droughts. This article that, coincidentally, was published as I was writing this, sums things up nicely. It is so important, the Center for Snow and Avalanche Studies has formed CODOS: the Colorado Dust on Snow Program, whose purpose is to monitor dust on snow and provide weekly updates.

As for why you shouldn’t eat orange snow, that should be obvious. You shouldn’t eat any snow that isn’t pure white (and even that might be risky). But, feel free to eat colorful ice, as long as you know where it came from.