NOAA makes improvements to the Global Ensemble Forecast System

The Finite-Volume Cubed Sphere (FV3) dynamical core is now being used by GEFS after being added to the Global Forecast System model in 2019.

The National Weather Service can provide numerical weather predictions up to four weeks in advance which allows more time when it comes to making decisions.

“This significant upgrade to GEFS - the first of its kind in five years - will improve probabilistic weather model guidance for public safety, quality of life and business decisions that drive U.S. economic growth,” said Neil Jacobs Ph.D., acting NOAA administrator. “These continued advancements underscore the Trump Administration’s commitment of improving our weather forecasting to protect life and property,” told by National Oceanic and Atmospheric Administration.

GEFS resolution has increased to 25km and individual forecast input increased to 31. This makes it possible for models to run at higher resolution or detail and provide more accuracy.

“This GEFS upgrade continues the ongoing revolution of numerical modeling that began with the introduction of ensemble modeling into operations over 25 years ago,” said Louis W. Uccellini, Ph.D., director of NOAA’s National Weather Service. “This new version will provide additional forecast skill and extended lead-times for the prediction of extreme weather and water events, and to address important forecast challenges in weeks three and four with a higher-resolution ensemble system.

During the GEFS testing, severe weather forecasts including hurricane track and intensity, precipitation, wave heights, and aerosol for regions across the world were improved.

“If our upgrade to the Global Forecast System last year (GFS) was like upgrading the engine in a car, this upgrade to GEFS is like replacing the engines in a fleet of cars,” said Ivanka Stajner, Ph.D., acting director of NOAA’s Environmental Modeling Center. “Each car takes advantage of the power of this new engine, and with slightly different features, they represent the forecast uncertainties more accurately, aiding the forecasters and decision-makers with information on a range of possibilities to be considered when issuing the forecasts.”