Correcting Storm Displacement Errors in Ensembles Using the Feature Alignment Technique (FAT)

Abstract

Abstract A goal of Warn-on-Forecast (WoF) is to develop forecasting systems that produce accurate analyses and forecasts of severe weather to be utilized in operational warning settings. Recent WoF-related studies have indicated the need to alleviate storm displacement errors in both analyses and forecasts. A potential solution to reduce these errors is the feature alignment technique (FAT), which mitigates displacement errors between observations and model fields while satisfying constraints. This study merges the FAT with a local ensemble transform Kalman filter (LETKF) and uses observing system simulation experiments (OSSEs) to vet the FAT as a potential alleviator of forecast errors arising from storm displacement errors. An idealized truth run of a supercell on a 250-m grid is used to generate pseudoradar observations, which are assimilated onto a 2-km grid using a 50-member ensemble to produce analyses and forecasts of the supercell. The FAT uses composite reflectivity to generate a 2D field of displacement vectors that is used to align the model variables with the observations prior to each analysis cycle. The FAT is tested by displacing the initial model background fields from the observations or modifying the environmental wind profile to create a storm motion bias in the forecast cycles. The FAT–LETKF performance is evaluated and compared to that of the LETKF alone. The FAT substantially reduces errors in storm intensity, location, and structure during data assimilation and subsequent forecasts. These supercell OSSEs provide the foundation for future experiments with real data and more complex events.