Observing the First Major Thundersnow Outbreak of the 2019-2020 Winter Season

Written by Sebastian Harkema and Emily Berndt

The first major heavy-banded snowfall event of the 2019-2020 winter season occurred from Oct. 9-12 and produced over two feet of snowfall in North Dakota. Throughout the event, the NESDIS merged snowfall rate (mSFR; Meng et al. 2017) product tracked the heaviest snowfall rates, including bands with snowfall rates greater than 2 in/hr. With a temporal resolution of 10 minutes, this product can be used in real-time to forecast the location and evolution of snowbands producing heavy snowfall, and even anticipate cloud-seeding. SPoRT has collaborated closely with NESDIS to experimentally transition and assess the passive microwave and merged snowfall rate products with NWS forecast offices (Ralph et al. 2018).  Therefore, this product is available in AWIPS and forecasters can select different snow-to-liquid ratio values to best fit the situation.

Figure 1: NESDIS mSFR product and GOES-EAST ABI (Ch. 13) on October 9, 2019.

Figure 1 demonstrates the mSFR product overlapping GOES-East ABI (channel 13) for October 9th as the snowband traversed across Montana. While the mSFR product provides a unique way to monitor snowfall, the phenomenon known as thundersnow captivated the attention of some operational forecasters as well as the general public, in part by the availability of Geostationary Lightning Mapper (GLM) observations. Recent work from NASA SPoRT has shown that the overlap of GLM and mSFR data can be used to objectively identify and characterize electrified snowfall (i.e., thundersnow; Harkema et al. 2019a). In fact, Harkema et al. 2019a demonstrated that thundersnow flashes identified by GLM contain on average more total optical energy per flash area than other flashes in the GLM field-of-view. Harkema et al. 2019a also demonstrate that thundersnow flashes observed by GLM are spatially larger compared to non-thundersnow flashes and is likely a result of weaker mesoscale updrafts and slower charging rates compared to severe summertime convection.

Figure 2: NESDIS mSFR product, GOES-EAST ABI (Ch. 13), and GLM flash extent density observations on October 10, 2019.

Figure 2 demonstrates the objective identification of thundersnow based on the overlap of mSFR and GLM flash extent density observations on October 10th around the Colorado/Nebraska/Wyoming border region. From the loop, this region experiences an enhancement of snowfall rates approximately 30-40 minutes after the first occurrence of thundersnow. Even though it appears as though thundersnow can be used as a precursor for enhancement of snowfall rates in the near future, thundersnow has a spatial offset of 131±65 km from the heaviest snowfall rates (Harkema et al. 2019b, In Review). This spatial offset is evident when examining the thundersnow that occurred along the Minnesota/Manitoba border between 12-15 UTC on October 11th (Fig. 3).

Figure 3: NESDIS mSFR product, GOES-EAST ABI (Ch. 13), and GLM flash extent density observations on October 11, 2019.

The thundersnow observed by GLM occurs on the northern extent of the heaviest snowfall rates (purples/whites). The separation of thundersnow and the heaviest snowfall rates is likely caused by hydrometeor lofting of the snowfall as it descends to the surface because of the low terminal fall speed of the ice crystals.

Winter is fast approaching and the NESDIS mSFR product and GLM can be used in tangent with each other to improve situation awareness. NASA SPoRT is at the forefront of understanding the operational implications of electrified snowfall and continues to investigate the thermodynamic and microphysical properties that are associated with it. See the official JPSS Quick Guide and a past JPSS Science Seminar for more product information.

References

Harkema, S. S., C. J. Schultz, E. B. Berndt, and P. M. Bitzer, 2019a: Geostationary Lightning Mapper Flash Characteristics of Electrified Snowfall Events. Wea. Forecasting, 43(5), 1571–1585, https://doi.org/10.1175/WAF-D-19-0082.1.

Harkema, S. S., E. B. Berndt, and C. J. Schultz, 2019b: Characterization of Snowfall Rates, Totals, and Snow-to-Liquid Ratios in Electrified Snowfall Events from a Geostationary Lightning Mapper Perspective. Wea. Forecasting. In Review.

Meng, H., Dong, J., Ferraro, R., Yan, B., Zhao, L., Kongoli, C., Wang, N.‐Y., and Zavodsky, B. ( 2017), A 1DVAR‐based snowfall rate retrieval algorithm for passive microwave radiometers, J. Geophys. Res. Atmos., 122, 6520– 6540, doi:10.1002/2016JD026325.

NASA SPoRT’s SST Composite Maps Capture Upwelling in the Wakes of Hurricanes Dorian and Humberto

NASA SPoRT’s SST Composite Maps Capture Upwelling in the Wakes of Hurricanes Dorian and Humberto

Written by Patrick Duran, Frank LaFontaine, and Erika Duran

Category 5 Hurricane Dorian passed over the Bahamas between September 1 and 3 2019, producing catastrophic destruction and causing at least 60 direct fatalities in the island nation. In addition to the impacts on human life, strong, slow-moving hurricanes like Dorian can leave lasting effects on the ocean over which they travel. Through a process known as upwelling, hurricanes bring colder water from below the surface up to the top layer of the ocean.  As a result, a trail of cooler sea surface temperatures (SSTs), also referred to as a “cold wake,” is often visible behind a passing storm. Meteorologists and oceanographers can monitor changes in SST and identify a cold wake following tropical cyclones using satellite data.

NASA SPoRT produces composite maps of SST twice daily using data from the VIIRS-NPP, MODIS-Aqua, and MODIS-Terra instruments, along with OSTIA-UKMO data obtained from the GHRSST archive at NASA’s Jet Propulsion Laboratory and the NESDIS GOES-POES SST product. The input data are weighted by latency and resolution to produce the composite, which is available at 2 km resolution.

Figure 1 shows a loop of the SPoRT SST composite from August 31 – September 23, 2019 over a region that includes the Bahamas and the Southeast United States. Two rounds of SST cooling are observed as Hurricanes Dorian and Humberto move through the region.

Figure 1: Animation of NASA SPoRT SST Composite Maps from August 21 through September 23, 2019. Daily images are displayed at 1800 UTC.

On August 31, very warm SSTs of around 29-30 deg Celsius (84-86 deg Fahrenheit) overspread the waters surrounding the Bahamas (Fig. 2).

Figure 2: SST Composite Map at 1800 UTC on August 31, 2019.

After Hurricane Dorian tracked through the region and made landfall in North Carolina on September 6, the waters north of the Bahamas were considerably cooler – in the 26–29 deg Celsius (79–84 deg Fahrenheit) range (Fig. 3).

Figure 3: As in Fig. 2, but for September 6, 2019.

Over the next week, the surface waters warmed a degree or two (Fig. 4), but did not fully recover to the same temperature observed on 31 August.

As in Figs. 2-3, but for September 13, 2019.

On September 13, Tropical Storm Humberto formed 210 km (130 miles) ESE of Great Abaco Island. As the storm tracked northeast past the Bahamas, it encountered the cold wake left by Hurricane Dorian the previous week. These cooler waters, combined with the influence of some dry air and vertical wind shear, inhibited the storm’s intensification as it passed by the Bahamas. On September 15, Humberto moved over the warmer waters of the Gulf Stream off the coast of North Florida (Fig. 5) intensified to hurricane strength.

Figure 5: As in Figs 2-4, but for September 15, 2019.

Humberto continued to strengthen, and attained a maximum sustained wind speed of 125 MPH as it passed by Bermuda on September 19. Its strong winds and associated waves overturned the same region of ocean that was previously affected by Hurricane Dorian, decreasing sea surface temperatures to as low as 25 deg Celsius (77 deg Fahrenheit) in some areas (Fig. 6).

Fig. 6: As in Figs. 2-5, but for September 19, 2019.

These images highlight the effect that tropical cyclones can have on SST, and how a hurricane can make it more difficult for any subsequent storms to intensify over the same region. Satellite analyses of SSTs (such as those produced by NASA SPoRT) allow forecasters to monitor SST across the globe, helping them to produce better forecasts of tropical cyclone intensity in all ocean basins.

The Evolution of Hurricane Dorian as Viewed from NASA’s GPM Constellation

The Evolution of Hurricane Dorian as Viewed from NASA’s GPM Constellation

Written by Erika Duran, Emily Berndt, and Patrick Duran

As of Friday morning on August 30, 2019, Hurricane Dorian is forecast to steadily intensify to a major hurricane as it moves northwestward toward the Bahamas over the Labor Day weekend. Many factors can act together to contribute to storm intensification, and satellite imagery offers a variety of perspectives to monitor the evolution of tropical cyclone (e.g., hurricane) structure as a storm undergoes intensity change. Multispectral Red-Green-Blue (RGB) composite imagery derived from the Global Precipitation Measurement Constellation of passive microwave sensors provides value in monitoring the evolution of convection within a tropical cyclone, and can reveal structures such as developing and concentric eyewalls, as well as spiral rainbands.

Figure 1 shows the evolution of Dorian from Wednesday, Aug 28, 2019 through Thursday, August 29, 2019 as viewed from the 37 GHz RGB, which is sensitive to warm precipitation (i.e., rain; Lee et al. 2002). Light blue colors demonstrate regions of lighter rain, indicative of mainly stratiform precipitation, and pink to red colors demonstrate areas of heavier rainfall, indicative of convective precipitation. As Dorian moves through the eastern Caribbean, it consistently demonstrates spiral rainband structure, as well as the presence of an eye as it moves north of Puerto Rico (Fig 1b,c). Such features suggest a maturing tropical cyclone, and indicate environmental conditions that are favorable for development. Notice that the wide eye present at 10:56 UTC on August 29, 2019 (Fig 1c) appears to erode on the southern edge by 16:06 UTC on August 29, 2019 (Fig 1d), and most of the precipitation is found north and east of the center of Dorian; this asymmetry in precipitation suggests a negative influence on storm intensification, such as the presence of wind shear, or dryer air being ingested into the storm from the south.

Figure 1: 37 GPM Constellation 37 GHz Passive Microwave RGB on Wednesday, August 28 at a) 05:33 UTC and b) 2106 UTC, and on Thursday, August 29 at c) 1056 UTC and d) 1606 UTC.

Fig 2 illustrates the same snapshots of Dorian on August 28th and 29th, but using the 89 GHZ RGB imagery, which is sensitive to frozen precipitation (i.e., ice; Lee et al. 2002). Red colors indicate regions of strong convection. Similar features such as rainbands and an eye/eyewall are also visible at this frequency, but this RGB demonstrates some structural differences; for example, the 89 GHz RGB indicates the presence of an eye and a symmetric eyewall at 21:06 UTC on August 28th (Fig 2b), while the 37 GHz RGB demonstrates an asymmetric eyewall (Fig 1b). Comparing features from these two RGBs can help to highlight differences in the storm structure at different levels of the atmosphere, since the 89 GHz RGB is more sensitive to cloud microphysical characteristics found at higher altitudes of the storm.

Figure 2: As in Figure 1, but for the GPM Constellation 89 GHz Passive Microwave RGB.

Figure 3 demonstrates the satellite-derived instantaneous rain rate (in/hr) for the same snapshots described for the RGBs above. These images provide another perspective on storm structure by demonstrating where precipitation is occurring. Similar spiral rainbands are visible in these images as well, and Fig 3c shows a well-defined eye and eyewall structure as Dorian moves northwest of Puerto Rico. As in Fig 1d and 2d, notice how at 16:06 UTC on Aug 29, most of the precipitation is occurring north and east of the center of Dorian (Fig 2d.)

Figure 3: As in Figures 1 and 2, but for the satellite-derived instantaneous rain rate (in/hr).

Comparing the RGBs and rain rate with GOES-East water vapor imagery can help diagnose the environment surrounding Dorian at this time. The black and orange colors in Fig 4a and 4b and the red to orange colors in Fig 4c and 4d illustrate the presence of dry air south of Dorian, which appears to have penetrated into the core of the storm. This drier air likely contributed to the degradation of the eye and eyewall structure visible in Figs 1d, 2d, and 3d, and helped to create the asymmetry in precipitation.

Figure 4: GOES East imagery of Hurricane Dorian on Thursday, Aug 29 for the mid-level water vapor infrared band (Channel 9) at a) 1050 UTC and b) 1610 UTC and the low-level water vapor infrared band (Channel 10) at c) 1050 UTC and d) 1610 UTC.

Fig. 5 shows an animation of the GPM Constellation 89GHz passive microwave RGB at 23:06 UTC on August 29 and 08:32 UTC and 11:17 UTC on August 30th. Notice how Dorian appears to organize as it moves northwestward, exhibiting more spiral rainband structures and an eye in the center of the storm, accompanied by deep convection (red colors).

Figure 5: An animation of the GPM Constellation 89GHz passive microwave RGB on (1) August 29, 2019 at 23:06 UTC, (2) 08:32 UTC and (3) 11:17 UTC on August 30, 2019.

As GPM Early Adopters since 2014, the NASA SPoRT center has a history of providing RGB imagery to national centers, including the National Hurricane Center (NHC) for use in operations. Today, the imagery is extensively used in hurricane analysis and forecasting, leveraging the ability to detect features of interest and to identify the hurricane center, structure, and intensity. Real-time products are also available on the SPoRT website. More information on GPM products and applications can be found in the NASA GPM Overview, which is a SPoRT contribution to the National Weather Service’s Satellite Foundational course for JPSS. These examples demonstrate how using a combination of satellite products can be helpful in diagnosing different structural features of tropical cyclones.

References

Lee, T. F., F. J. Turk, J. Hawkins, and K. Richardson, 2002: Interpretation of TRMM TMI images of tropical cyclones. Earth Interactions, 6, 1-17, doi:10.1175/1087-3562(2002)006<0001:IOTTIO>2.0.CO;2.

Using Synthetic Data to Prepare for the NASA TROPICS Mission

In 2021, we anticipate the launch of The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) satellite, which is a National Aeronautics and Space Administration (NASA) Earth Venture Mission designed for observing hurricanes (i.e. tropical cyclones, typhoons). TROPICS presents a unique opportunity in hurricane remote sensing though an unprecedented combination of horizontal and temporal resolution: this mission is expected to provide nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at a mean refresh rate of 30 minutes. These measurements will be available in both the inner-core and the external environment of hurricanes, allowing for the observation of dynamic processes and on mesoscale time scales which are often challenging to observe.

As an Early Adopter, NASA SPoRT is currently assessing the capabilities and applications of the upcoming TROPICS mission through the use of synthetic TROPICS data. Proxy data are designed to mimic the resolution, format, and accuracy of an anticipated project, and provide a way to evaluate the potential value of a mission prior to launch. Fig 1. shows an example of TROPICS proxy data, derived from a high-resolution numerical simulation of a hurricane lifecycle in the North Atlantic Ocean (proxy data are courtesy of Ralf Bennartz, Vanderbilt University, UW-Madison SSEC). The estimated channel frequency is 205 GHz, which sensitive to the distribution of ice suspended in the atmosphere.

Fig. 1: An example of TROPICS proxy data estimated at a channel frequency of 205 GHz. Image courtesy of Frank LaFontaine, Raytheon/NASA SPoRT.

Blue and green colors demonstrate deep convective clouds near the eye of the cyclone (located near 22 degrees N and 62 degrees W), and red and orange colors demonstrate drier air wrapping around the storm. These data can potentially help to accelerate our abilities to use real TROPICS data once calibrated and in orbit.

TROPICS will have 12 channel frequencies, most of which are used for temperature and humidity measurements. More information on the TROPICS mission and applications is available here.

Identifying Dust and Fog in Geostationary Imagery through Machine Learning

Identifying Dust and Fog in Geostationary Imagery through Machine Learning

written by Chris Jewett

NASA SPoRT has a long history of creating state-of-the-art multispectral (red, green, blue, or RGB) imagery derived from both polar-orbiting (MODIS, VIIRS, AVHRR) and geostationary imagers (AHI, ABI, and SEVIRI). RGB imagery is provided to forecasters to allow them to visualize different types of meteorological phenomena and hazards that are sometimes not readily apparent in single channel imagery.  Two of the multispectral composites analyzed by forecasters include dust and nighttime microphysics, the latter of which allows for the detection of low clouds and fog.

Both blowing dust and fog events can produce a substantial reduction in visibility, yielding a significant hazard to motorists. Forecasters currently investigate the RGB imagery for these hazards qualitatively to provide necessary information to the public.  As an extension to these products, SPoRT is utilizing machine learning methods to determine if these hazards can be quantitatively identified in remote sensing observations, giving forecasters a probability on whether each pixel contains a certain hazard. This probability can then be used along with the RGB to aid visual classification and interpretation.

The RGB composites are typically produced using 3-4 channels from the imagers. For example, the dust RGB composite combines two spectral differences, 12.3 – 10.3 µm (Red) and 11.2 – 8.4 µm (Green), along with the 10.3 µm channel (Blue). Using specific ranges of the spectral differences, as well as the surface temperature derived from 10.3 µm channel, allows for dust to appear as a pink/magenta color in the RGB imagery. To produce a more quantitative analysis, numerous machine learning methods (Random Forest, Logistic Regression, Naïve Bayes, Convolutional Neural Networks) were applied to both the data that makes up the RGB composites and other channels, such as the 7.3 µm band, which can help distinguish dry low-level environments typically associated with blowing dust events.

In the animated image below, an example of the results from Random Forest are shown for a dust event that occurred on April 10, 2019 as a strong extratropical cyclone traversed across the Central/Northern Plains. Note how the brighter pinks and magentas corresponding to more dust within the plumes are associated with the greatest probabilities of dust. It is exciting to see how machine learning can clearly pull out the dust signature from the satellite imagery. Moving forward, the SPoRT team is administering machine learning applications to the visibilities associated with the dust plumes, so not only would the forecaster have knowledge of the probability of the dust, but also information on the anticipated degraded visibility. Early indications also show hopeful results on applying similar methods to the nighttime microphysics composite for the detection of low clouds and fog.

Left: RGB image created from GOES-16 data. Right: Probability of dust, based on the Random Forest Machine Learning algorithm.

GLM “sees” apparent meteor flash in Western Cuba…

So, I was seeing some news reports on Twitter this afternoon about an apparent meteor that struck Western Cuba.  Pulling up data/imagery from the GLM in AWIPS, I was able to see some relatively high Flash Extent Density (FED) values from that area at the same time of the meteor report.  The first image below shows FED values (1818 UTC) overlaying GOES-16 Visible (0.64 µm) imagery at 1817 UTC.

Meteorite_WCuba_1818UTC01Feb2019

Image 1. GLM data shows an apparent meteor flash over western portions of Cuba at ~1818 UTC 1 Feb 2019. The GLM Flash Extent Density overlay GOES-16 visible (0.64 um) imagery from ~1817 UTC.

Also, notice the large amount of lightning observed by the GLM in central portions of the Gulf of Mexico.  Here’s a short 30-min image loop around this time period (the suspected meteor flash shows up about midway through the loop).  Importantly, before the GLM sensor, the amount and extent of lightning activity over open ocean areas, away from ground networks, was generally not known, especially at such high spacial/temporal resolution.

Meteorite_WCuba_30minLoop01Feb2019.png

Image 2. GLM (Flash Extent Density) and GOES-16 visible imagery (0.64 µm) loop from 1802-1830 UTC, 01 Feb 2019. An apparent meteor shows up in western Cuba at 1818 UTC in the loop. Also, notice the active deep convection and lightning over the Gulf of Mexico during the period.

 

 

3D GOES-16/17 Imagery at NWS Huntsville

Yes, you read the title correctly.  We have 3D visible imagery from the GOES-16/17 satellites in the Advanced Weather Interactive Processing System (AWIPS) at the Weather Forecast Office in Huntsville!  So, how did we do this?  I’ll explain.

Several days ago, Kevin McGrath at NASA SPoRT created Facebook and Twitter posts detailing the capability of generating 3D imagery when using both the GOES-16 and GOES-17 satellites in one image.  This is made possible by taking advantage of the slightly different viewing angles by the two satellites in their current GOES East and Center positions.  Yesterday, we explored the possibility of doing this in AWIPS here at the Huntsville WFO and were successful.  I’ll tell you how we did it (which is actually not that difficult), but first I’ll show some 3D imagery from around the Southeast U.S. region this morning.  By the way, to view the imagery in its full 3D glory, you’ll need some standard red/cyan 3D glasses.  Let us also add that the original imagery appears much better in AWIPS as there is always some loss of fidelity when generating images in .gifs and then transferring and viewing these from other platforms.  Anyway, hopefully you’ll get a good sense of the 3D aspects contained within the image loops, and I’ll add it’s better to view with your screen brightness turned up and under darker ambient conditions.

Vis3D_includingHurricaneFlorence_14Sep2018

Image 1. GOES-16/17 3D Visible image loop (0.64 µm), 1307-1442 UTC, 14 Sep 2018

Next, we’ll take a closer look at some of these cloud scenes.  First, here’s a look at Hurricane Florence as it churns along the N. Carolina coast.  You may notice (as we did) that it is much easier to observe the differential motion and distinguish among the various cloud layers in this type of imagery.  Unfortunately, some of the image fidelity is lost when saving as a .gif, as observed particularly in the cirrus cloud layer in the image loop.

Vis3D_zoomHurricaneFlorence_14Sep2018

Image 2. GOES-16/17 3D visible image loop (0.64 µm) of Hurricane Florence, 1317-1452 UTC, 14 Sep 2018

It is rather extraordinary to view developing convection in 3D.  This convective cloud scene in the NW Gulf of Mexico details this capability well (Image 3).

Vis3D_NWGulf_14Sep2018

Image 3. GOES-16/17 3D visible image loop (0.64 µm) centered over the NW Gulf of Mexico, 1317-1452 UTC, 14 Sep 2018

This next cloud scene is not as active, however, it is interesting how one can get a sense of the differences in cloud depth between the fog hugging some of the southern Appalachian valleys and the outer cirrus band extending far west of Hurricane Florence.

Vis3D_fogSouthernAppsAndOuterCirrusBandFlorence_14Sep2018

Image 4. GOES-16/17 3D visible image loop (0.64 µm) centered over the Southern Appalachian region, 1222-1357 UTC, 14 Sep 2018

Lastly, to demonstrate the advantage of this type of imagery, we thought we’d show a simple GOES-16 visible loop (Image 5) compared to a 3D visible loop (Image 6).

GOES16Vis_complexCloudSceneSouthernTX_14Sep2018

Image 5.  GOES-16 Visible image loop (0.64 µm) centered over south TX, 1447-1627 UTC 14 Sep 2018

Vis3D_complexCloudSceneSouthernTX_14Sep2018

Image 6.  GOES-16/17 3D visible image loop (0.64 µm) centered over south TX,

Now, you may notice a lack of “brightness” in the 3D imagery, which is due to the layering process.  But, perhaps you can get a better sense of the complex layered cloud scene over southern portions of TX in the 3D loop as we did.  Of course, as stated previously, there’s generally something lost in translation when moving and viewing graphics between various screens and viewing platforms.

So, now to answer the question…how did we do this?  Well, it was somewhat simple actually.  As you can see in the images, the GOES-17 image is layered on top and GOES-16 on the bottom.  Now, it doesn’t actually matter which satellite image is layered on top.  But, whichever one that is, it will need to be set to 50% transparency.  Then, we modified the color map in AWIPS, applying a pure black to red color curve for the GOES-17 reflectivity values, and black to cyan (or equal contributions of blue and green) for GOES-16.  When doing this initially, we used a simple linear stretch to the color map.  However, we realized a more appropriate methodology utilizes the default non-linear ABI VIS gray scale color map.  So, we simply modified that color map by changing all of the blue and green color values to 0.0, saving this as a new color map and applying this to GOES-17 imagery.  Taking the original color map again, we changed all of the red color values to 0.0 for the GOES-16 imagery.  Voila!  When viewing through the standard red (left eye), cyan (right eye) 3D colored glasses, the left and right eye will see the two GOES images from their respective viewing angles and the imagery appears in 3D.

The lingering question may be…so this is cool and all, but what is the application?  As suggested, this type of imagery does offer a more realistic depiction of the atmosphere and helps to differentiate different cloud layers.  Sure, there are some fantastic RGBs now that can aid in this too.  But, this is another tool in the forecaster toolbox, so to speak.  Additionally, I noticed yesterday and today that it is easier to get a sense of shear in tilted convective updrafts, and when speaking with forecasters at the WFO here, it helps provide them a more thorough and realistic conceptual model of the troposphere.  So, these are some things to consider.  We’ll be exploring more use of this imagery over the coming days/weeks.  The are some caveats to all of this.  First, people with significant red/green color deficiencies may not be able to view the 3D imagery as intended.  Second…we don’t know if this will still work once GOES-17 gets shifted to its eventual GOES-West position later this year.  There may be too great of a difference in the viewing angles.  A quick inspection of GOES-15/16 imagery using this same format seemed to indicate an issue there.  We’ll see.  Anyway, for now, this is a fascinating way to view the visible cloud scene.

-Kris White & Kevin McGrath