Excessive Downward Shortwave Radiation in the HRRR and RAP Weather Models and Testing Strategies for Improvements

Abstract A set of Surface Radiation Budget Network (SURFRAD) measurements across the lower 48 United States has allowed a closer inspection of weather model representations of downward shortwave radiation in the last several years. In this study, it is found that downward shortwave radiation (SW↓) is excessive for the NOAA 3-km HRRR model at each of the 14 SURFRAD stations distributed across the lower United States when averaged over 2-month periods. Possible causes for this station-consistent SW↓ bias error were hypothesized. Three were eliminated by this study and two were then evaluated in this study. We found that this error was not from clear-sky errors but from insufficient attenuation by clouds. It was also found that this cloud deficiency was partly caused by a dry bias in atmospheric water vapor initial conditions. New experiments using the hourly cycled HRRR model–assimilation system were designed and carried out for three seasons with modified data assimilation addressing the dry bias problem and reduction of effective radius for cloud water droplets for both explicit and subgrid-scale clouds. The assimilation and cloud optical parameter changes contributed similarly toward a combined reduced SW↓ radiation bias by 80% in the fall season and 84% in the winter season but by only 35% in the summer season. Even with the improved data assimilation, a dry bias contributing to deficient clouds continues, which is a topic to be explored in a following study. Significance Statement Weather forecasts of all durations are dependent on accurate forecasts of clouds. Even the well-known 3-km NOAA HRRR model was found to have errors in clouds, resulting in forecasts of too-warm near-surface temperatures and too little precipitation. In our study including model experiments in three different seasons, we found two key ideas that can improve future storm forecasts: better use of observations to start the weather models to avoid initial dryness and to brighten model cloud forecasts by assuming that cloud droplets are slightly smaller than previously prescribed. These changes can improve NOAA forecasts for aviation, energy, and severe weather in successors to the current HRRR weather model.