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Archive for the ‘RGBs’ Category

November 19th has been eagerly anticipated by the meteorological community as it is the launch of the next-generation GOES-R satellite.  The satellite will carry a suite of space weather instruments as well as two Earth observing sensors.  The Advanced Baseline Imager (ABI) will provide three times more channels to view the Earth, four times greater spatial resolution, and 5 times faster coverage.  The ABI will provide new means to monitor atmospheric phenomena.  Additionally, GOES-R will carry the first ever lightning observation sensor on a geostationary platform; the Geostationary Lightning Mapper (GLM).  Numerous organizations, including NASA SPoRT, have been supporting the GOES-R Proving Ground for many years to aid the operational community in preparing for the new capabilities of GOES-R.

Specifically, NASA SPoRT has been formally involved with the Proving Ground since 2009, although much of our work prior to this point has provided relevant information with respect to GOES-R.  SPoRT has been primarily involved in two activities.  The first has been the assessment of and training for multi-spectral imagery, often called red-green-blue (RGB) composites.  The RGB composites are used to combine multiple single channels into a single image in order to help emphasize phenomena that forecasters wish to monitor.  This can range from air mass microphysics to atmospheric dust.  This work has leveraged work by Europe’s EUMETSAT organization who first developed several of these RGB composites for their Meteosat Second Generation satellite.  SPoRT has worked with NASA’s MODIS instruments from Aqua and Terra as well as the JPSS VIIRS instrument to create the respective RGBs from polar orbiting instruments.  These snapshot demonstrations provided forecasters local examples of RGB composites to allow them to investigate these products prior to GOES-R’s launch.  SPoRT has also coordinated with other product developers to help transition their early development work to National Weather Service forecasters.  This included the University of Alabama in Huntsville’s GOES-R convective initiation product and the NESDIS quantitative precipitation product.

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MODIS Dust RGB demonstrating a future capability of the GOES-R ABI. Dust (magenta) can be seen approaching Las Vegas, Nevada.

In additional to the ABI work, SPoRT has been integral to supporting total lightning (intra-cloud and cloud-to-ground) observations in operational applications.  This dates back to 2003 with the first transition of experimental ground-based lightning mapping arrays that evolved into the pseudo-geostationary lightning mapper (PGLM) product in 2009 to provide operational training for the GLM.  Since then, SPoRT has developed the GLM plug-in for the National Weather Service’s AWIPS system, has personnel serving as the National Weather Service liaison for the GLM, and have developed foundational training that is being provided to every forecaster in the National Weather Service.

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Sample of the pseudo-geostationary lightning mapper demonstration product in AWIPS being used for training on the Geostationary Lightning Mapper.

SPoRT will continue to be actively engaged in GOES-R applications post launch.  This will take the form of developing an applications library, or short 3-5 focused case examples, for both the ABI RGBs and the GLM.  SPoRT will also participate in the formal applications training for RGBs and GLM that will be released to the National Weather Service.  Lastly, SPoRT will be leading an operational assessment of the GLM with National Weather Service forecasters and associated emergency managers.

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GOES-R launching on November 19, 2016!

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A potent winter storm system impacted portions of New Mexico on March 26, 2016, ending an extended stretch of very dry weather. Snowfall amounts of 3 to 9 inches were reported from the Sangre de Cristo Mountains eastward across the northeast plains. The MODIS and VIIRS satellite products proved useful for illustrating the extent of snow cover in both the daytime and nighttime scenes. The images below are graphical briefings posted to the NWS Albuquerque web page and shared via Twitter after this much needed snowfall event.

Graphical briefing showing the extent of snow cover during the nighttime and daytime periods on March 27, 2016.

Graphical briefing (part one) showing the extent of snow cover during the nighttime and daytime periods on March 27, 2016.

Graphical briefing showing the extent of snow cover through RGBs on March 27, 2016.

Graphical briefing (part two) showing the extent of snow cover through RGBs on March 27, 2016.

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MODIS Air Mass RGB (left) and 11 um image (right) from 08 February 2016 at 1427 UTC.

MODIS Air Mass RGB (left) and 11 um image (right) from 08 February 2016 at 1427 UTC.

An image captured this morning by the MODIS Terra instrument shows an impressive cyclone off the eastern coast of the US. The image on the left shows the cyclone in SPoRT’s Air Mass RGB and the image on the right shows the 11.0 µm from Terra (from 8 February 2016 at 1427 UTC). The deep red color on the RGB shows the intrusion of ozone-rich stratospheric air, which is an indication of deformation zones, jet streaks, and potential vorticity anomalies associated with rapid cyclogenesis, which itself indicates strong winds at the surface. This RGB is also limb-corrected for cooling at the edges of the swath, so we can assume the cyclone in this imagery is every bit as intense as it looks.

The new generation of geostationary satellites being deployed globally, such as Himawari, MTG, and GOES-R, will allow us to observe imagery like the Air Mass RGB several times an hour, enabling us to watch the cyclogenesis as it happens.

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Recently, I had the opportunity to travel to the Tucson NWS office and work with forecasters there concerning a number of experimental data sets transitioned by the SPoRT group.  Primarily, this involved the SPoRT LIS, GPM Constellation and IMERG, and NESDIS QPE data sets.  However, I also had the opportunity to see how other products were being utilized by forecasters.  While taking a look at the Nighttime Microphysics RGB image, I was initially perplexed by the apparent presence of fog and low clouds in parts of the desert southwest.  The first image below is a 4-panel image from AWIPS, showing the Longwave (LW) and Shortwave (SW) IR, the LW-SW IR channel difference, and the Nighttime Microphysics RGB from the VIIRS instrument on the morning of Sept 23rd.

Image 1. Suomi-NPP VIIRS imagery valid 0915 UTC 23 Sep 2015, Longwave IR (upper left), Shortwave IR (upper right), LW-SW IR channel difference (

Image 1. Suomi-NPP VIIRS imagery valid 0915 UTC 23 Sep 2015, Longwave IR (upper left), Shortwave IR (upper right), LW-SW IR channel difference (“fog product”, lower left), and the Nighttime Microphysics RGB (lower right).

The difference in brightness temperatures between the LW and SW IR channels in parts of SW Arizona, SE California and areas of NW Mexico around the Gulf of California, results in relatively large positive values.  Notice the yellow colors that appear in these areas in the channel difference imagery (image 1, lower right), and the corresponding appearance of white-aqua colors in the Nighttime Microphysics RGB (the 10.8-3.9 channel difference represents the green color component of the RGB recipe).  For a forecaster accustomed to looking at these imagery in other parts of the country (and those will less sandy surfaces), these channel difference values and colors in the RGB would suggest the presence of low stratus and/or fog.  However, no clouds or fog were present in those locations during the morning.  You can, however, see some low clouds in portions of central and eastern New Mexico, as indicated by the brighter white-aqua colors.

So, what is going on here?  Well, as eluded to above, it’s the presence of dry sand.  The image below (courtesy of COMET) shows the IR emissivity over several different surface features: tree leaves, red clay, dry sand, and water.

Image 2. IR emissivity vs. wavelength of several surface features, including tree leaves, red clay, dry sand, and water.

Image 2. IR emissivity vs. wavelength of several surface features, including tree leaves, red clay, dry sand, and water.  (image courtesy of COMET)

Notice that the emissivity over dry sand changes fairly substantially through portions of the SW and LW portion of the spectrum, and is lower at 3.9 µm than at 10.8 µm.  The channel difference between 10.8 and 3.9 µm will result in positive values (given clear sky conditions of course) over dry sandy areas, thus mimicking the presence of low clouds and/or fog, as would be the interpretation in other areas.  The next image below demonstrates the LW and SW IR brightness temperatures and differences, along with the Nighttime Microphysics RGB, as sampled over a clear, dry sandy area.

Image 3. Suomi-NPP VIIRS image from 0902 UTC 25 Sep 2015

Image 3. Suomi-NPP VIIRS image from 0902 UTC 25 Sep 2015, LW IR (upper left), SW IR (upper right), LW-SW IR channel difference (lower left), and the Nighttime Microphysics RGB (lower right).

Notice the substantial resulting green color contribution in the Nighttime Microphysics RGB (lower right in above image).  These colors are very similar to colors that would be indicative of fog and other low cloud features as they traditionally appear under similar temperature conditions in other areas outside of dry, sandy areas (image 4 below).

Image 4. Nighttime Microphysics image depicting fog and low clouds (white-aqua colors) in portions of the southern and central Appalachian region.

Image 4. Nighttime Microphysics image depicting fog and low clouds (white-aqua colors) in portions of the southern and central Appalachian region.

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Yet another bout of snow, sleet and ice recently affected much of the Tennessee and Ohio Valley regions.  Although clouds were clearing in western portions of this region, allowing for a broad scale satellite view of the newly laid snow/ice field, eastern portions remained cloud-covered until sunset.  While ground reports contain valuable information about the depth of snow and/or ice, they’re only point measurements, so assumptions often have to be made about the spatial extent of the snow, until satellite observations are available (unless clouds obscure).  So, those observations would have to wait until the next day, during visible sunlight hours…or would they?  Well, not exactly…which is the point of this blog post.

The image below (Image 1) is a Snow/Cloud RGB produced by SPoRT and disseminated to collaborative NWS field offices.  The green colors represent the background surface (grass, trees, cities, etc.), while the deeper reds represent snow/ice cover.  White colors depict clouds, while reddish-white represents very cold clouds containing ice crystal clouds.  Notice the swath of snow that is visible from NE Texas into the Midwest.  Meanwhile, clouds obscure any snow/ice in eastern areas.

Image 1.  MODIS  Snow/Cloud RGB 1631 UTC 5 March 2015

Image 1. MODIS Snow/Cloud RGB 1631 UTC 5 March 2015

Clouds had pushed eastward by sunset, but did still not move far enough to provide a clear indication of the eastward extent of the snow/ice field that had just fallen.  However, once the VIIRS Day-Night Band imagery became available later that night, the spatial extent of the snow and ice could be fairly easily observed.  Notice in the next image (Image 2) the snow and ice cover that was apparent over portions of the Tennessee and Ohio Valley region.

Image 2.  Suomi-NPP VIIRS Day-Night Band Radiance RGB, 0805 UTC 6 March 2015.  The solid white line indicates the extent of the snow/ice cover.  Clouds are also in the image over portions of the southern Appalachians and the Gulf into the Atlantic Coastal Plain.

Image 2. Suomi-NPP VIIRS Day-Night Band Radiance RGB, 0805 UTC 6 March 2015. The solid white line indicates the extent of the snow/ice cover. Clouds are also in the image over portions of the southern Appalachians and the Gulf into the Atlantic Coastal Plain.

This type of imagery can be helpful for operational forecasters when trying to assess the potential societal impacts of lingering snow and ice, and also the impacts on sensible parameters such as temperatures and relative humidity, which can help improve weather forecasts.

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