Validation of a High-Resolution WRF Forecast Ensemble for ESCAPE

The Experiment of Sea breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE) field campaign took place around the Houston area in June 2022. During the campaign, 24-h real-time 1-km ensemble forecasting simulations were run daily at 06Z and 12Z with the Weather Research and Forecasting (WRF) model. The WRF simulations were driven by real-time operational forecasts with varying microphysics, planetary boundary layer (PBL) schemes, and aerosol loadings, creating an ensemble of 16 simulations per initialization during the campaign. After ESCAPE, additional WRF simulations targeted at the convections observed during ESCAPE were conducted with 2 more different large-scale forcings, resulting in more than 1600 simulations total spanning the campaign period. In this study, model simulations were validated against several available data products including NCEP/EMC Stage IV surface precipitation, ASOS near-surface observations, and interpolated sondes collected around Houston.

Overall, the accuracy of 24-hour precipitation occurrence ranges from approximately 30% to 50% in the ensemble, with around two thirds of WRF simulations underestimating the precipitation frequency. The WRF simulations struggle with predicting precipitation intensity and timing on shorter timescales. While near-surface temperatures and vertical profiles are generally well represented, deviations in low-level moisture are quite large. Moreover, the WRF simulations show substantial sensitivity to the different initial/boundary conditions and PBL scheme, with the sensitivity to the microphysics scheme and aerosol loading playing a secondary role. Results also suggest that the 1-km ensemble mean generally demonstrates superior performance than the original coarser-resolution operational forecasts in terms of 24-h accumulated precipitation, near-surface thermodynamics, and atmospheric vertical profiles. However, identifying the underlying physical processes responsible for the model–observation discrepancies is difficult given the range of environmental conditions spanning the field campaign period. Therefore, it is of importance to conduct further individual case studies to understand the impact of physical processes and parameterizations on simulated and forecasted convective clouds and precipitation.