GML’s surface radiation measurements improve NOAA’s operational weather forecast models
2025-12-02
In a new study published in the AMS journal Monthly Weather Review, GML’s surface radiation measurements were key to helping researchers identify the source of a surface temperature bias in NOAA’s operational weather forecast models.
Weather forecasts of all durations are dependent on accurate forecasts of clouds. NOAA’s 3-km High-Resolution Rapid Refresh (HRRR) and the Rapid Refresh (RAP) numerical weather prediction models, run operationally by the NOAA National Centers for Environmental Prediction (NCEP), were found to have errors in model cloud parameters resulting in forecasts of too warm temperatures and too little precipitation.
Continuous observations from GML’s U.S. Surface Radiation Budget (SURFRAD) network allowed researchers to quantify errors in model radiation that formed the basis of this study and improve the forecast models. Their research showed that a too-dry initial data assimilation and model clouds were in large part responsible for the excessive model surface temperatures.
The researchers, from NOAA’s Global Monitoring and Global Systems Laboratories and the Cooperative Institute for Research in Environmental Sciences, recommended two ways to improve future forecasts: better use of observations to start the weather models to avoid initial dryness and assuming that cloud droplets are slightly smaller than previously prescribed to brighten model cloud forecasts.
These changes can improve NOAA forecasts for aviation, energy, and severe weather in successors to the current HRRR weather model.