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.

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.

Geostationary Lightning Mapper (GLM) Data Used to Aid in Warning Decision…

The NWS office in Huntsville, AL (HUN) has had a long history with the use of total lightning data in operations, which stretches back to the office’s inception (after NWS modernization) in 2003.  Back then, and until its removal to South America for GOES validation testing, the HUN office largely used data from the North Alabama Lightning Mapping Array (NALMA). Lightning data sources from NLDN and ENTLN have also been used to varying degrees, but the advent of the GLM aboard GOES-16 brought a new era of lightning observations.  Because of the office’s participation in early operational testing of the GLM, its use and familiarity have gradually increased over the past year.  This was probably made easier due to our familiarity with total lightning data from the NALMA network.  Generally, GLM data have been used in much the same way as those from the NALMA network, especially with regards to situational awareness purposes (i.e., airport weather warnings, real-time weather watches for EM partners, initial cell electrification, etc.).  The use of the data to aid in severe weather warning decisions has been a bit slower to evolve, as might have been expected.  After all, there are differences in the way the NALMA observes lightning as compared to the GLM.  Values from the GLM have typically been “muted” compared to those from NALMA, so forecasters have had to make internal adjustments and recalibrate, if you will, what is considered significant.  However, the physical mechanisms that generate increases in total lightning, that is, increases in mixed-phase updraft volume, are essentially observed either way.  Thus, GLM data can still be useful to relate important information about storm/cell evolution, and can help to “tip the scales” in the balance of evidence about whether or not a warning may be needed.

This particular application of the GLM data occurred this morning with operational meteorologists at the HUN office.  The short image loop below shows thunderstorms moving across northwestern portions of Alabama between 1226 and 1300 UTC.  The top panel of the image contains data from the KGWX radar (0.5 degree reflectivity), while the bottom panel contains GLM 1-minute Flash Extent Density (FED) data.  Notice that lightning activity is relatively limited initially as the storm moves across western Franklin County, AL (near center of image), with 1-min FED values ranging between 2 and 6 flashes per minute.  Then, at the 12:37 UTC time mark, flashes begin an increase that manifests in a statistical lightning “jump” (GLM sigma > 2).  The warning meteorologist at the time was watching this cell for potential severe weather, and observed the sudden increase in FED values.  This, together with other radar and satellite observations (not shown here), suggested that a severe weather warning was necessary as wind signatures aloft gradually increased.  A warning was subsequently issued at 1245 UTC.  Incidentally, this thunderstorm did end up producing some wind damage, with trees reported down in south-central portions of Franklin County.  Notice also that a number of strong cell signals were detected by radar as indicated by the higher dBZ values across the domain.  Another use of the GLM is allowing meteorologists to focus on the cells with the strongest updrafts, making the overall radar interrogation and warning process more efficient.  This case can help to demonstrate that the GLM can be used as an important indicator of storm evolution and as a useful operational tool for the evaluation of severe weather potential.

-Kris W.

2PanelLoop_GWX0.5Refl_andGLM1Min_17July2019

[Top] KGWX 0.5 deg Refl with NWS Severe Thunderstorm Warning (yellow box), [Bottom] GLM 1-min Flash Extent Density, 1226-1300 UTC 17 July 2019.

 

SPoRT LIS Shows Dry Soils During High Plains Blowing Dust Event…

Yesterday while working on some Dust RGB related training materials, I was looking at the RGB in AWIPS and noticed a dust event unfolding in real-time in the central High Plains.  The loop below shows Dust RGB imagery, generated by GOES-East, yesterday, 28 Jan 2019 during the late morning and early afternoon hours.  The loop is centered over NE Colorado and SW Nebraska where you’ll see the blowing dust develop and spread southeastward.  In case you’re not too familiar with this type of imagery, the dust is represented by the magenta colors.  It’s also possible to observe some of the individual dust streaks or plumes within the larger blowing dust event, which help to show their locations of origin.  (By the way, sorry about the loss of image fidelity when saving from AWIPS to an animated GIF).

Image 1.  GOES-East Dust RGB imagery, approx. 1737-2002 UTC, 28 Jan 2019. The blowing dust is defined by the magenta colors, near the center of the imagery.

Research has shown that it takes the right combination of factors to loft dust particles sufficiently to generate these larger scale blowing dust events, partly based on soil moisture and winds.  The SPoRT LIS 0-10 cm volumetric soil moisture (VSM) analysis at 18 UTC indicated very low values in the blowing dust source region, with VSM percentages generally around 12-16% (Image 2).  The METAR observations also indicate sustained winds were 35-40 knots with stronger gusts over 40 knots at one locations in the area.

Image 2. SPoRT LIS volumetric soil moisture (background colors) overlaid with surface METAR plots (yellow figures), valid at 18 UTC, 28 Jan 2019.

This last image is a snapshot of the Dust RGB taken at 1902 UTC, overlaid with surface visibility and ceiling observations.  Notice that at station KHEQ in far northeastern Colorado, a ceiling of 100 ft and visibility of 7 SM was reported, which was likely due to the blowing dust.

Image 3. GOES-East Dust RGB and ceiling and visibility observations from ground observation stations at approximately 19 UTC, 28 Jan 2019.

Some SPoRT collaborative NWS offices in the West CONUS have utilized LIS VSM values to locate areas where the probability of blowing dust events is heightened under the proper conditions.  However, SPoRT is looking into opportunities to better predict where these events will occur.

Passive Microwave Views of Hurricane Florence…

As Hurricane Florence has developed and flourished in the warm waters of the central and western North Atlantic, the NHC has been using data from microwave sensors aboard polar-orbiting satellites to obtain information about important physical characteristics of the hurricane not otherwise observed by conventional imagery from geostationary satellites.  Not only does the microwave data provide important information about the location, intensity and extent of precipitation bands and deep convection within the hurricane, but can also provide better fixes for the storm center location.  The first image below (Image 1) shows a GOES-16 visible image (~0.64 µm) at approximately 1812 UTC 12 Sep 2018.

Image 1.  GOES-16 Visible Image (~0.64 µm), 1812 UTC 12 Sep 2018

The visible image can be used to ascertain information about some physical characteristics of the hurricane, but the broad canopy of cirrus over much of the hurricane can obscure important, relevant features about banding structures, in particular.  Image 2 shows microwave data (~89 GHz) derived from the AMSR2 sensor at about the same time as the visible image (in Image 1).  Notice that much of the intense banding observed in the microwave data was concentrated along the W to N portions of the hurricane at this time, which might not have been immediately obvious based on the visible imagery alone.  In fact, notice the fairly thin band of convection along the SE side of the eyewall at 1812 UTC.

Image 2. 89 GHz (Horizontal) image from AMSR2, 1812 UTC 12 Sep 2018

Even an inspection of color-enhanced LW IR data/imagery (~10.34 µm) might have suggested a fairly even distribution of deep convection around the eyewall at this time (Image 3).

Image 3.  GOES-16 LW IR image (~10.34 µm), 1812 UTC 12 Sep 2018

However, the 10.34 um will observe cold cirrus cloud tops where present, which may have resulted from earlier convection, and ice crystals that have since been distributed more evenly around the upper-level outflow and not necessarily from recent convection.

Lastly, I thought I’d finish quickly with a loop of the available polar-orbiting passive microwave imagery over Hurricane Florence since early yesterday.  The background color that appears mostly static through the loop is the sea surface temperature data derived from the VIIRS instrument, which is produced by NASA SPoRT and sent to collaborative NWS offices through AWIPS.  Notice the abundance of orange/red colors in the basin through which the hurricane is moving, which is indicative of water temperatures in the mid 80s F (scale not shown).

Image 4. Available polar microwave imagery/data passes over Hurricane Florence since early Sep 11th, background data is sea-surface temperatures derived from the VIIRS instrument