GSL's modeling team wins the 2021 NOAA Administrator's Award

GSL’s modeling team has been honored with the 2021 NOAA Administrator’s Award for developing and delivering final upgrades to the first hourly-updated weather model that predicts high-impact weather on an urban scale to help save lives and property. The High-Resolution Rapid Refresh (HRRR) now includes the first ensemble-based convective scale data assimilation system and made breakthroughs in the latest model physics. The HRRR is the result of years of collaborative research-to-operations efforts culminating in a model that produces a much more accurate and comprehensive representation of the atmosphere.

The HRRR model is the first in a new generation of weather prediction models designed to better represent the atmosphere and dynamics that drive high-impact weather events. It has resulted in immediate improvements to localized severe weather forecasting for safety preparedness and economic efficiency. With HRRR, NWS Forecast Offices can deliver better and faster customer service to local emergency managers dealing with environmental crises to reduce weather-related fatalities and economic losses. HRRR supports the NOAA’s Weather-Ready Nation goal and all its objectives, including a reduction in air traffic delays that cost the economy billions of dollars annually, and significant productivity gains in renewable energy through better guidance. The ultimate outcome is that decision-makers have more time to prepare for high-impact weather.

GSL’s team recently led the technical transition of the experimental Rapid Refresh (RAP) version 5 and High-Resolution Rapid Refresh (HRRR) version 4 numerical weather prediction models into National Weather Service (NWS) operations on 2 December 2020. These pioneering models are considered “game-changers” by the NWS and provide forecasters and other stakeholders with improved prediction of high-impact weather such as wildfire smoke, thunderstorms, and winter storms.

The team’s technical leadership and expertise in both data assimilation/model system design and the use of high-performance computer system architectures were critical to this transition. They collaborated across line offices and provided transition support to both the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC) and NCEP Central Operations (NCO) to accelerate testing and evaluation of all model system components, a crucial step in operational transitions. The team leveraged unique and invaluable technical expertise to efficiently identify and provide resolutions to model system configuration and software performance limitations. They quickly adapted to upstream changes with innovative solutions that ensured the integrity of the scientific and technical evaluations. The team also endured more rigorous stability requirements than usual to get this project over the finish line.

GSL coordinated transition efforts between research staff at GSL and operational staff at EMC/NCO and effectively communicated and coordinated cross-validation efforts to ensure successful reproduction of the model system design on the operational supercomputer. The team was always willing and able to address urgent testing issues 24/7 to keep the transition moving forward.

This significant research to operations transition would not have been possible without GSL’s technical leadership and impassioned commitment and expertise. These models will help the NWS and other users protect life and property and enhance the national economy through reduced transportation delays, improved stability of the electric grid, and better wildfire management. This GSL team directly supports the NOAA mission to understand and predict changes in climate, weather, oceans, and coasts.

GSL research resulted in diverse capabilities demonstrated in the HRRR.

Aviation: HRRR provides rapidly updated model guidance on convective storms, winds, turbulence, icing, ceiling height, and visibility for air traffic management (in collaboration with and support from the FAA) and severe weather forecasting (collaboration with NWS and the Storm Prediction Center).

Wildfire smoke: Through GSL research the HRRR now uses satellite data to calculate fire size and biomass burning emissions to predict the height of smoke plumes and their subsequent spread. NWS forecasters relied heavily on the HRRR smoke field, a feature now in the operational version with HRRRv4.

Lake-effect snow: The HRRR now includes coupling with GLERL’s Finite Volume Community Ocean Model (FVCOM), which feeds the HRRR Great Lakes surface temperature and ice data, resulting in improved prediction of notoriously variable - and prodigious - lake-effect snows. HRRR provides wind-stress forecasts over the Great Lakes which informs FVCOM. The team also implemented an inland lake model to improve lake temperature forecasts.

Hydrology: HRRR is coupled to the National Water Model to provide more quantitative precipitation forecasts with HRRRv4 for short-term components of the NWM to make streamflow forecasts more accurate. HRRRv4 provides improved precipitation bands for heavy snow and rain events

Severe storms: The GSL team worked with the National Severe Storms Laboratory to improve the way HRRR predicts strong updrafts and signals of rotation in severe storms.

Snow: GSL researchers developed a better way to track the density of snow with every model run and apply a technique that better forecasts how the temperature affects the density of snow

Wind forecasts: The HRRR has improved low-level wind forecasts resulting from 18 months of data collected to better understand how terrain and weather interactions affect wind speed and turbulence at the height of wind turbines. With the support of the energy industry, researchers were able to extend the HRRR forecast period to 48 hours. The upgrades to the low-level wind model forecasts improved wind energy forecasts by 15-25 percent depending on weather conditions (as reported by wind energy firms), as well as daily wind forecasts for the entire country. HRRRv4 now provides much more accurate wind speed predictions over water including the Great Lakes and oceanic areas and better guidance for marine operations.

Solar forecasts: The team was able to improve overall cloud forecasts especially for shallow cold air masses and reduce incoming shortwave bias to improve solar forecast accuracy.

Curtis Alexander (Fed)

David Dowell (Fed)

Steve Weygandt (Fed)

Terra Ladwig (Fed)

Stan Benjamin (Fed)

John Brown (Fed)

Joe Olson (Fed)

Ming Hu (Fed)

Trevor Alcott (Fed)

Dave Turner (Fed)

Amanda Back (CIRA)

Jeff Duda (CIRES)

Guoqing Ge (CIRES)

Eric James (CIRES)

Haidao Lin (CIRA)

Hongli Wang (CIRES)

Ravan Ahmadov (CIRES)

Hanna Barnes (CIRES)

Siwei He (CIRES)

Jaymes Kenyon (now with NWS)

Tanya Smirnova (CIRES)

Michael Toy (CIRES)

Jason English (CIRES)

Venita Hagerty (CIRA)

Jeff Hamilton (CIRES)

Brian Jamison (CIRA)

Bill Moninger (CIRES)

Randy Pierce (CIRA)

Molly Smith (CIRES)

Bonny Strong (CIRA)

Ed Szoke (CIRA)

Samuel Trahan (CIRES)