Normalized Burn Ratio (NBR) Imagery in AWIPS…

Landscapes that have succumbed to wildfires, or burn scars, present especially difficult hydrologic forecasting challenges for National Weather Service (NWS) Offices since they can be conducive to the development of flash flooding and debris flows.  While the relationship between burn severity and this threat is rather complicated and dependent on a number of factors, determining the severity of the burned landscape can be important.  In order to assess this threat, professionals from a range of disciplines comprising Burned Area Emergency Response (BAER) Teams conduct intensive field surveys at the burn site.  BAER Teams conduct surveys as soon as team logistics and conditions allow, including containment levels of the wildfire (50% to 80% in many cases).  However, the threat for the development of debris flows and flash flooding can occur before these assessments can be made and as the wildfire is still actively burning.  Additionally, surveys are not conducted at all burn scars, especially in non federally-owned lands.  Traditionally, satellite imagery of burn scars has been used to help remedy this gap in knowledge about burn severity at any given location.  This imagery utilizes contrasting spectral properties between burned areas and healthy vegetation from a combination of Near-IR (~0.86 µm) and Shortwave IR (~2.25 µm) bands.  Imagery from Landsat and other high-resolution instruments has commonly been sought and used in associated analyses, but passes from high-resolution imagers can be infrequent, and cloud cover cover can obscure a single pass.  Thus, waiting periods for this type of imagery can be days to weeks depending on temporal availability of satellite passes and weather conditions.  To help with this issue, NASA SPoRT has developed the generation of NBR imagery in real-time in the Automated Weather Interactive Processing System (AWIPS) using data from the GOES-16 and GOES-17 satellites (Image 1).  Additionally, imagery from the VIIRS instrument aboard S-NPP has also been developed and transferred to AWIPS on an experimental basis.

Image 1. GOES-16 NBR imagery ((0.86 µm – 2.25 µm) / (0.86 µm + 2.25 µm)) overlaid with part transparent Visible (0.64 µm) imagery, 1751 UTC 8 Nov 2019

The compromise with GOES imagery is the lack of higher-resolution and thus detail observed in other imagery, yet analysis of a few fires so far this past fire season has indicated good agreement between GOES and VIIRS imagery.  A few examples are posted below.  Based on the color scale used, healthy/undisturbed vegetation is indicated by green colors, while burned areas appear in colors ranging from brighter yellows to oranges to reds.  The difference in resolution between the 0.86 and 2.25 µm bands in GOES-17 imagery causes “false” signatures along bodies of water.

Image 2. Woodbury Fire burn scar, GOES-17 NBR 1936 UTC 1 July 2019 (left), S-NPP NBR 1936 UTC 1 July 2019 (right), along with 2019 Fire Perimeters (black outlines)


Image 3. Kincade Fire burn scar, GOES-17 NBR 2101 UTC 7 Nov 2019 (left), S-NPP NBR 2057 UTC 7 Nov 2019 (right), along with 2019 Fire Perimeters (black outlines)


Image 4.  Recent So. California fire burn scars, GOES-17 NBR 2056 UTC 7 Nov 2019 (left), S-NPP NBR 2057 UTC 7 Nov 2019 (right), along with 2019 Fire Perimeters (black outlines)

While BAER Teams and Incident Meteorologists (IMETs) have also expressed a desire to have these types of imagery outside of AWIPS, in GIS-friendly formats, the advantage of making the imagery available in AWIPS is that forecasters can overlay it with other relevant hydrologic data sets that may help forecasters to better estimate the threat for flooding and debris flows.  Another advantage of having data generated from GOES is the high temporal resolution of the data, allowing near-continuous analysis of burn scar development as the fire is ongoing (provided clear sky conditions from clouds or smoke).

Image 5. Sample loop of the Woodbury Fire in AZ, GOES-17 NBR overlaid with partial transparent visible (0.64 µm) imagery, 2101-2251 UTC 17 June 2019.  Notice the burn scar that has already developed in western parts of the burn area (orange/yellow colors), while smoke can be seen emanating from the ongoing fire in NE parts of the fire complex (red colors).

While related development work is continuing, the SPoRT team will be discussing the potential use of this imagery with collaborative NWS offices, especially in the West CONUS,  for the next wildfire season.

-Kris W.



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.


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,

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.

Viewing an Eyewall Replacement Cycle and the Structural Evolution of Hurricane Dorian using NASA’s GPM Constellation

Viewing an Eyewall Replacement Cycle and the Structural Evolution of Hurricane Dorian using NASA’s GPM Constellation

Written by Erika Duran and Emily Berndt

In our previous blog post, we highlighted the value of using Red-Green-Blue (RGB) composite imagery from the NASA Global Precipitation Measurement (GPM) constellation to analyze the evolution of storm structure in Hurricane Dorian. Several structural changes were visible in the 37 GHz and 89 GHz passive microwave RGB imagery from September 1-3, 2019, as Dorian approached and made landfall in the Bahamas, and then remained nearly stationary over Grand Bahama Island for nearly two days.

Figure 1: Animation of the GPM Constellation 37 GHz Passive Microwave RGB from 06:48 UTC on September 1, 2019 to 06:33 UTC on September 3, 2019.

The above animation of the 37 GHz passive microwave RGB demonstrates an eyewall replacement cycle that occurred from September 1-2, as well as the erosion of the western eyewall from September 2-3. A secondary eyewall is visible at 20:46 UTC on September 1 (below, Fig 2c), followed by a wider, single eyewall and a much wider eye at 10:36 UTC on September 2 (Fig 2d), as compare to the previous day (Fig 2a). The erosion of the western eyewall is visible in Fig 2f, likely a result from the storm’s interaction with land.

Figure 2. Snapshots of the GPM Constellation 37 GHz Passive Microwave RGB on September 1, 2019 at a) 06:48 UTC, b) 16:36 UTC, and 20:46 UTC, on September 2, 2019 at d) 10:36 UTC and e) 17:18 UTC, and on September 3, 2019 at f) 06:33 UTC.

An animation of the 89 GHz Passive Microwave RGB (Fig 3) provides a slightly different perspective of these same events, highlighting changes in Dorian’s deep convection.

Figure 3. As in Fig 1, but for the GPM Constellation 89 GHz Passive Microwave RGB.

While the eyewall replacement cycle is much more clear in the 37 GHz RGB imagery, structural changes associated with this cycle are still evident using the 89 GHz RGB, showing the presence of a “moat,” or a region of low-echo reflectivity and subsidence between the primary and secondary eyewalls (e.g., Sitkowski et al. 2011, Kuo et al. 2009). The moat is seen as the darker red colors surrounding the eye in Fig 4c, which is located between the primary and secondary eyewalls indicated in the 37 GHz imagery. Additionally, the erosion of the western eyewall is much more clear using the 89 GHz RGB, showing the asymmetry in Dorian’s deep convection on the left side of the eye (Fig 4f).

Figure 4. As in Figure 2, but for the GPM Constellation 89 GHz Passive Microwave RGB.


Sitkowski, M., J.P. Kossin, and C.M. Rozoff, 2011: Intensity and Structure Changes during Hurricane Eyewall Replacement Cycles.Mon. Wea. Rev.,139, 3829–3847,

Kuo, H., C. Chang, Y. Yang, and H. Jiang, 2009: Western North Pacific Typhoons with Concentric Eyewalls.Mon. Wea. Rev.,137, 3758–3770,

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.


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.

Reconstructing a Rare Bolt from the Blue Event Using Multiple Lightning Datasets

Reconstructing a Rare Bolt from the Blue Event Using Multiple Lightning Datasets

Written by Chris Schultz

On August 20, 2019, much of the Midwest was impacted by several rounds of severe thunderstorms.  These electrically active thunderstorms produced wind damage across Iowa, Illinois, Indiana, Ohio, Kentucky, and Missouri. However, it wasn’t the large flash rates that got the attention of those of us in SPoRT, but a rare bolt from the blue event that occurred nearly 50 miles (76 km) outside any surface precipitation.

During the 40 minutes leading up to the lightning event, the closest thunderstorm activity was located approximately 50 miles south of Dittmer, MO, across parts of Phelps, Dent, Washington, St. Francois, and Ste. Genevieve Counties (Fig. 1A).   Between 400 pm and 440 pm CDT zero lightning flashes occurred in Franklin, Jefferson, Warren, or St. Charles Co., MO (Fig. 1B).

Figure 1 – A- Radar reflectivity at 0.4 degrees elevation at 2140 UTC from KLSX in Weldon Spring MO, and  B- NLDN lightning detections between 21:00:00 and 21:40:16 UTC (4:00:00-4:40:00 pm CDT).

Then at 4:40:15 pm CDT, a positive lightning flash was observed by Vaisala’s National Lightning Detection Network well outside of any precipitation (Fig. 2).  This flash was positive polarity, was approximately 136 kiloamps, and located in an area that had not observed any lightning in the previous 40 minutes. This +CG flash was accompanied by 5 additional incloud flash detections, and one negative cloud to ground flash detection by the NLDN.  All 7 detections occurred within 1 second of each other, indicating that they were part of the same lightning event.  However, the question remained, where did this flash originate? Radar and previous lightning data from the NLDN indicate that there are 2-3 areas of thunderstorm activity to the south of this location which could be a possible origination point. But there wasn’t a definitive prospect because the NLDN point locations are spatially separated by several miles. 

Figure 2 – Radar reflectivity at 0.4 degrees elevation at 2140 UTC from KLSX in Weldon Spring MO (A) and NLDN lightning detections at 21:40:15 UTC (4:40:15 pm CDT).

Bringing in Geostationary Lightning Mapper Flash Extent Density data product for the same point in time (Fig. 3), there is a better idea of which thunderstorm this flash originated from.  There is a distinct lightning path from the thunderstorms over Dent and Phelps Counties in up to the NLDN flash locations in Jefferson and Franklin Counties. This single flash travelled nearly 57 miles (~ 92 km) from its original start location to the ground location, and actually propagated further north into Warren and St. Charles Counties.  

Figure 3 – GOES GLM Flash Extent Density overlaid on 0.64 µm ABI data at 2141 UTC (441 pm CDT).

Taking a vertical slice of the radar data between the parent thunderstorm and the location where the flash came to ground, there is a distinct path of precipitation aloft between 20,000 and 30,000 ft (Fig. 4).  Thus the lightning traveled through an anvil region before coming to ground approximately 41 miles (76 km) outside of the main precipitation near the surface.  Large bolt from the blue events have been reported in the literature previously (e.g., Kuhlman et al. 2009, Weiss et al. 2012, Lang et al. 2016). This flash was also a unique event because any lightning safety protocols would not have been in place for the location due to the absence of lightning within 6 miles during the previous 40 minutes.

Figure 4 – A vertical cross section of reflectivity from KLSX at 2140 UTC (440 pm CDT)

When GLM data are combined with ground based lightning networks like the NLDN or Earth Networks Total Lightning Network, the GLM Flash Extent Density can be used to connect point locations and determine where additional electrification may be present aloft that is not readily apparent at the surface.

Transition of Research to Operations – Gridded NUCAPS

Transition of Research to Operations – Gridded NUCAPS

By Emily Berndt

SPoRT has been part of a collaborative effort within the Joint Polar Satellite System (JPSS) Proving Ground Sounding Initiative* to develop the capability for 2D display of satellite soundings in the NOAA NWS decision support system (AWIPS).  CrIS/ATMS (Cross-track Infrared Sounder/Advanced Technology Microwave Sounder) temperature and moisture soundings are processed through the NOAA Unique Combined Atmospheric Processing System (NUCAPS) and are good quality in clear to partly cloudy regions but soundings are poor quality where cloud cover is over 85% and when precipitating conditions exist.  Currently, NWS offices receive NOAA-20 CrIS/ATMS NUCAPS Soundings through the Satellite Broadcast Network for display as vertical soundings and Gridded NUCAPS is the capability to process and view these data horizontally and vertically (Fig. 1).  Up until now, Gridded NUCAPS has been pre-processed at SPoRT and provided experimentally to Alaska Region NWS offices and the Hazardous Weather Testbed.  The team worked with NOAA/CIRA/MDL to create an AWIPS plug-in to grid the soundings upon arrival and ingest in AWIPS.  Gridded NUCAPS has been a successful multi-organizational collaborative R2O/O2R project with a transition to operations in sight. With the official 19.2.1 AWIPS release coming soon SPoRT is finalizing development of training material and an NWS VLab page to highlight the Gridded NUCAPS capability, products, and helpful hints….more information will be forthcoming  as these items are completed!  NWS offices that are beta testers for new AWIPS releases, such as the Huntsville forecast office will be able to display Gridded NUCAPS with AWIPS 19.2.1-29 prior to the official release.

*including NOAA NWS, Science and Technology Corporation, the Cooperative Institute for Research of the Atmosphere, Geographic Information Network of Alaska, Space Science Engineering Center/Cooperative Institute for Meteorological Satellite Studies, and NOAA/NWS/MDL.


Figure 1. Left: NOAA-20 CrIS/ATMS NUCAPS Sounding Availability in AWIPS and Right: Gridded NUCAPS plan view display of 700 mb Lapse Rates. Demontrates the NUCAPS Soundings are Gridded for plan view and cross section display.  Image courtesy of Kevin Fuell (UAH/NASA SPoRT).

Gridded NUCAPS was originally developed to diagnose Cold Air Aloft (CAA; Weaver et al. 2019) and NWS Anchorage Center Weather Service Unit aviation forecasters have benefited from this capability to issue public products regarding CAA. Additionally Gridded NUCAPS has been extensively evaluated at the Hazardous Weather Testbed for assessing the pre-convective environment (Berndt et al. 2017). As part of the JPSS Sounding Initiative, the team of collaborators is exploring new applications for Gridded NUCAPS (e.g., fire weather, turbulence, and icing) and exploring the benefits of the microwave-only NUCAPS Soundings for applications in cloudy regions.  A few new capabilities of Gridded NUCAPS include display of fields such as precipitable water to diagnose moist/dry layers in the atmosphere, the Haines Index for fire weather potential (Fig.2), and SPoRT-developed ozone products (e.g., Total Ozone, Ozone Anomaly, and Tropopause Level) to diagnose the potential for tropopause folding and cyclogenesis.

Look for Gridded NUCAPS posters and presentations at the National Weather Association Annual Meeting and the AMS Joint Satellite Conference – both in September!


Figure 2. Top: Example of Haines Index image and icons plotted with Gridded NUCAPS compared to Bottom: GFS Haines Index and Icons for a fire that began on 23 July 2018 near Northway, AK.