SPoRT Graduate Student Spotlight: Angela Burke

Written by Ben Houser

SPoRT is home to several graduate researchers, who are each working on completing the research required to earn their master’s degrees. Previously, we featured Sebastian Harkema’s trip to NASA’s Wallops Flight Facility as a forecaster for the IMPACTS campaign. This week, we are spotlighting the work of another graduate student: Angela Burke. Angela has completed a bachelor’s degree in Earth System Science with a concentration in Atmospheric Science at the University of Alabama in Huntsville, and is now working on exciting research with SPoRT.

Angela Burke, one of SPoRT’s graduate researchers.

Angela Burke is now a graduate researcher, but began working for SPoRT as an undergraduate in 2016. Her current research concerns issues with the algorithms used to create SPoRT imagery products from the GOES-R satellite series’ Advanced Baseline Imager; the ABI is the series’ primary instrument responsible for imaging Earth’s weather and climate. Sometimes, the ABI picks up signals from the surface that mimic the signal SPoRT’s algorithms associate with a low cloud, creating a “false alarm” in the data. Angela is hoping to understand and correct these false alarms, especially in SPoRT’s Nighttime Microphysics RGB product.

The NASA SPoRT Nighttime Microphysics RGB focused on the Southwestern U.S., highlighting the similarity between the “false alarm” regions and actual marine stratocumulus clouds.

Angela’s favorite part of working with SPoRT has been the unique opportunities it has provided. Since she began her undergraduate degree at UAH, Angela has quickly made a name for herself in the field of atmospheric science, and SPoRT has been there in support along the way. Angela began working at SPoRT in the fall of her sophomore year, and the following spring she was awarded the competitive NOAA Hollings Undergraduate Scholarship, which earned her a ten-week research position at NOAA’s Cooperative Institute for Research in the Atmosphere, in Fort Collins, Colorado. In the summer of 2017, Angela interned at the Department of Energy’s Oak Ridge National Laboratory, where she collaborated with SPoRT to analyze satellite imagery of power outages after natural disasters. In the summer of 2019, Angela worked at the Jet Propulsion Laboratory in California, where she researched Jupiter’s polar atmosphere. Angela’s time at SPoRT has provided her with research opportunities, support, and professional connections.

Angela seems to have a bright future ahead of her. She plans on finishing up her master’s degree with SPoRT, hopefully fixing the Nighttime Microphysics RGB imaging issue while she is at it. After completing her masters, Angela plans to pursue a PhD in Planetary Science.

SPoRT Graduate Researcher Returns From Wallops

SPoRT Graduate Researcher Returns From Wallops

Written by Ben Houser

One of SPoRT’s graduate researchers, Sebastian Harkema, recently returned from a two week stay at NASA’s Wallops Flight Facility in Virginia, where he worked as a forecaster for the Investigation of Microphysics Precipitation for Atlantic Coast-Threatening Snowstorms, or IMPACTS, a NASA field campaign to better understand the way weather systems form snowfall. The timing and location of snowfall is one of the most difficult things for weather forecasters to predict, but the IMPACTS campaign is hoping to gain a new understanding of snowstorms that will make dangerous winter weather easier to forecast.            

Scientists’ understanding of snow storms is limited, but they know that intense snowfall is generally produced by snow bands, which are long regions of clouds responsible for most of the snowfall the Northeast encounters. However, it is unclear what goes on within these snow bands themselves. Smaller systems within snow bands take on a life of their own, and it is impossible to predict which areas of a band will produce intense snowfall. IMPACTS aims to study the dynamic meteorological landscape within snowstorms, and, as a result of this new understanding, make intense snowfall over specific areas easier to predict. Researchers for IMPACTS are gathering crucial data by flying two airplanes through snowstorms; the first plane, the P-3 research aircraft, flies through the storm itself, and the second, the ER-2, flies above. These two aircraft collect data on the microphysics of the storm: the characteristics and movement of miniscule moisture particles, such as snow or drops of rain. The aircraft also measure the storm’s environmental characteristics, such as temperature. With this data, researchers hope to reconstruct the inner workings of snowstorms and enable forecasters to pinpoint where dangerous snowfall will occur.

The P-3 Aircraft preparing for a test flight. Photo by Sebastian Harkema.

Sebastian spent two weeks at Wallops and worked as the campaign’s lead forecaster for the second half of his stay. The job entailed showing up to work at 5:45 a.m., preparing the daily forecast presentation by 8:45 a.m., and then presenting at 9:00 a.m. Sebastian presented the forecast to the campaign’s mission scientists, instrumentation team, and anyone else who wanted to listen in. The forecast was used by the campaign’s scientists to prepare the team for storms, and to “plan a rough idea of where the aircraft would be flying.” Sebastian said that when skies were blue, the forecast was easy, and the take-away from one of his early presentations was simple: “it doesn’t look like anything is happening in the short term, but in five days it appears that something is going to occur. We can’t tell where, but all indicators are saying that something is going to happen.” Something did happen, and the storm in question arrived on January 25. It was the second storm that IMPACTS aircraft flew through during Sebastian’s stay. When the team was preparing for the flight, Sebastian and the other forecasters came together to brief the pilot and mission scientists on important information, such as the altitude at which the air temperature would drop below freezing.                  

IMPACTS conducted two flights during Sebastian’s stay. During the first flight, the ER-2 aircraft encountered a minor malfunction, and was forced to turn around for the pilot’s safety. During the second flight, the weather was less than ideal, as cold rain, rather than snow, was hitting the ground. However, the pair of aircraft were still able to collect useful data; researchers focused on snow particles suspended in the atmosphere. Throughout the flight, researchers collected data on the characteristics of millions of miniscule weather particles, tiny pieces of the storm which Sebastian had helped forecast.

Real-time data from the ER-2’s cloud radar system, collected on January 25. Photo by Sebastian Harkema.

Sebastian’s PhD research concerns thundersnow, or lightning caused by snowstorms, and so he was excited to spend time around other snow scientists. Sebastian said, “it was nice to collaborate with a lot of the researchers, because I’m going to be using this data in my own research.” Being surrounded by snow science experts gave Sebastian a better idea of what to focus on in his research and what to look for in weather models. Working in the field as a forecaster afforded Sebastian hands-on experience and a new perspective on his work. “It made me understand that there are still big gaps in what needs to be done in forecasting snow,” said Sebastian, and he hopes that his research can help to fill these gaps.

Preparing for the SWOT mission in Alaska

Preparing for the SWOT mission in Alaska

NASA SPoRT is an Early Adopter for the Surface Water Ocean Topography (SWOT) mission, which will launch in 2021 to provide the first global inventory of terrestrial surface water. SWOT features a radar interferometer that provides 120-km swath measurements of water surface elevation (WSE) for rivers wider than 50 m, reservoir height, and inundation. SPoRT is investigating potential SWOT applications for Alaska, since SWOT orbit characteristics will provide measurements with much higher spatial coverage than is currently available with in situ stream gauges (Figure 1).

Figure 1. SWOT observable rivers (legend) in southcentral Alaska, number of SWOT obs. per 21 day repeat cycle (background; grayscale colorbar), and current United States Geological Survey (USGS) stream gauge sites (green dots).

SPoRT has developed a Weather Research and Forecasting Hydrological extension package (WRF-Hydro; Gochis et al. 2018) configuration in Alaska that mimics the NOAA National Water Model (NWM) conterminous United States (CONUS) configuration. The WRF-Hydro domain contains the upper Tanana River basin (upstream of Nenana) in central Alaska, as well as the Susitna River basin in southcentral Alaska (Figure 1). Figure 2 shows WRF-Hydro streamflow output for May-June 2015 and provides insight into streamflow responses to snowmelt. The animation shows two strong signals in streamflow related to snowmelt due to the seasonal rise in temperature and stemming from diurnal heating. Rapid snowmelt regularly contributes to flooding in Alaska, and monitoring streamflow in tributaries using modeling systems such as WRF-Hydro provides essential information for improving forecasts and enhancing situational awareness.

Figure 2. May-June 2015 WRF-Hydro streamflow for the upper Tanana River basin.

Using proxy SWOT data generated from an Observing System Simulation Experiment (OSSE), virtual stream gauges are used to quantify SWOT impacts on hydrological modeling in Alaska prior to mission launch (Figure 3). SPoRT has demonstrated the utility of SWOT in improving WRF-Hydro streamflow prediction through data assimilation (Elmer et al. 2018) and in calibrating hydrological models such as WRF-Hydro in ungauged (lacking in situ stream gauges) river basins (Elmer et al. 2019). Ongoing and future work seeks to integrate these new capabilities into the future NWM Alaska domain to enable immediate use of SWOT observations within operational systems immediately after launch.

Figure 3. Proxy SWOT virtual gauge WSE along the Chena River in Alaska derived from WRF-Hydro using an OSSE.

Tracking Snow Squalls with our NESDIS mSFR product

Written by Sebastian Harkema

On December 18th/19th, the National Weather Service (NWS) produced over 40 snow squall warnings in the northeastern portion of the United States. Snow squalls are fast moving and are extremely hazardous, as snowfall rates can exceed 1 in/hr and drastically drop the visibility to near zero. One such squall created whiteout conditions in midtown Manhattan for a short period of time and another caused a major pileup on I-80 in central Pennsylvania.

The first snow squall warning was issued at 1815 UTC on 18 Dec. 2019 near the Finger Lakes region of New York. From this point forward the NWS had numerous snow squall warnings over the next 6+ hours from Pennsylvania to Maine. Two hours after the first warning was issued, the NWS issued the first snow squall warning for New York City which provided residences with at least 10 minutes of lead time before whiteout conditions would occur. The ability to identify these snow squalls highlights the hazards that these phenomena are associated with, and could be lifesaving now and in the future.

Fig 1: NESDIS mSFR product and NWS snow squall warnings (light blue polygons) from 1300 UTC on 18 Dec. 2019 through 0250 UTC on 19 Dec. 2019.

With a temporal resolution of 10 minutes, forecasters can use our NESDIS merged snowfall rate (mSFR; Meng et al. 2017) product to track snow squalls with the heaviest snowfall rates. SPoRT has collaborated with NESDIS to transition this experimental product to the NWS forecast offices. Figure 1 (above) highlights the mSFR product from 18 Dec. 2019 at 1300 UTC to 19 Dec. 2019 at 0250 UTC, with NWS snow squall warnings popping up in light blue. Throughout the event, there is good spatial and temporal correlation between the warnings and regions of heavier snowfall rates. At 2100 UTC, the mSFR product indicates snowfall rates at or above 1.5 in/hr around New York City (Fig. 2; below). Around this time, numerous videos were produced showing the visibility dropping drastically in a short period of time.

Fig. 2: NESDIS mSFR product and NWS snow squall warnings at 2100 UTC on 18 Dec. 2019 for the snow squall that caused whiteout conditions in midtown Manhattan.

Additionally, something interesting shows up in the mSFR product data over New Hampshire. A snow squall warning was issued at 2318 UTC for central New Hampshire with the squall associated with snowfall rates exceeding 2 in/hr (Fig. 3; below). Over the next 60 minutes, it appears as though the snow squall “bows out” and quickly dissipates just as the snow squall warning expired at 0015 UTC on 19 Dec. 2019. Again, this demonstrates the benefit of the mSFR product, which can be used in tandem with other methods and/or observations to identify mesoscale snow squalls.

Fig. 3: NESDIS mSFR product and NWS snow squall warnings at 2300 UTC on 18 Dec. 2019 for a snow squall that appears to be bowing out.

With meteorological winter officially starting on Dec. 1st, we are likely to see numerous snow squall events in the coming months. Be aware and stay safe, especially if you’re caught on the road, when one of these snow squalls moves through. The mSFR product enhances situational awareness for many winter weather scenarios including the ability to track these mesoscale snow squalls. See the JPSS Quick Guide and a past JPSS Science Seminar for more product information.

References

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.

Storm Tracking and Monitoring with the Geostationary Lightning Mapper (GLM)

Written by Chris Schultz

One of the unique capabilities of the Geostationary Lightning Mapper (GLM) is two-dimensional mapping of lightning from space.  This two-dimensional information allows for the tracking of storm features in space and time, while providing insight into the strength and magnitude of mixed phase vertical motion in thunderstorms.  During the severe weather event across the Southeast US on December 16-17, 2019, GLM information helped identify discrete mixed phase updrafts within a convective line as it moved across North Mississippi, Southern Tennessee, and North Alabama.  Often co-located with these discrete updraft locations was low-level rotation that produced wind damage and possible tornadoes, pending the outcome of storm surveys at the time of this post.

We begin the analysis at 2200 UTC, as the line is pushing through Northeast Mississippi and South Central Tennessee (Figure 1). Horizontal reflectivity (left) and radial velocity (center) at 0.5 degrees elevation shows a solid line of convection and three distinct areas of low-level rotation from the Columbus, MS NEXRAD radar, KGWX.  Co-located with each of these rotation areas were local maxima in GLM flash extent density (FED; Figure 1, right panel), with the highest values located with the strongest rotation in Prentiss and Alcon Counties in Mississippi.

Fig 1. Horizontal reflectivity (left), radial velocity (center) and GLM flash extent density (right) and 22:00 UTC on Dec. 16, 2019

Stepping forward 30 minutes to 2230 UTC, the three main areas of rotation at low-levels continue to be co-located with local maxima in GLM FED (Figure 2). The FED has increased near the circulation in Hardin and Wayne Counties in Tennessee, while the other two FED maxima have maintained or slightly weakened their intensity. The low-level circulation in Itawamba County Mississippi remains strong as it crosses the Alabama/Mississippi state line, while the southern circulation has weakened.

Fig 2. As in Fig 1, but for 22:30 UTC on Dec. 16, 2019

At 2300 UTC the storms have exited Mississippi and entered Northwest Alabama (Figure 3).  The strong rotation in southern Tennessee continues near Lawrenceburg, and FED continues to indicate strong lightning activity and a robust mixed phase updraft. Two areas of concentrated GLM activity have emerged in Lauderdale and Colbert Counties in Alabama, both co-located with low-level rotation signatures (this time from the NEXRAD at Hytop, Alabama, KHTX).  Shortly after this time, a tornado debris signature was observed on the southernmost area of interest as the storm entered Lawrence County Alabama. 

Fig 3. As in Figs 1-2, but for 23:00 UTC on Dec. 16, 2019

By 2330 UTC, the northern and southern-most areas of concentrated FED have weakened, but continue to be co-located with areas of rotation (Figure 4). The center area of interest that developed around 2300 UTC in Figure 3, has ramped up in intensity.  Strong low-level radial velocity signature now straddles the Tennessee/Alabama border.

Fig 4. As in Figs 1-3, but for 23:30 UTC on Dec. 16, 2019

At 0000 UTC, the area of interest along the Alabama/Tennessee state line continues to show strong lightning activity co-located with low-level rotation (Figure 5).  The southernmost circulation has taken on a backward C shape indicating strong low-level winds, and coincidentally, the GLM activity has ramped down at this location, indicating less robust mixed phase updrafts.  The northernmost area of interest in Bedford County, Tennessee has ramped back up, and a strong low-level signature continues. By 0030 UTC, the low-level rotation along the Tennessee/Alabama state line and in South Central Tennessee have weakened (Figure 6). GLM activity has ramped down, indicating that strong mixed phase updrafts are not as prevalent in these storms as they had been in the previous 2.5 hours.

Fig 5. As in Figs 1-4, but for 00:00 UTC on Dec. 17, 2019
Fig 6. As in Figs 1-5, but for 00:30 UTC on Dec. 17, 2019

Talking with WFO Huntsville this morning, their forecasters were utilizing the GLM measurements for critical situational awareness and as another metric for intensification within their warning decision framework. Thus, this case is one example of how radar and GLM can be complementary in a warning decision environment.

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.