Testing the Feature Alignment Technique (FAT) with Multiple Storms

A goal of Warn-on-Forecast (WoF) is to develop forecasting systems that produce accurate analyses and forecasts of severe weather for forecasters to utilize in operational warning settings. Recent WoF-related studies have indicated the need to alleviate storm displacement errors in both storm-scale analyses and short-term forecasts. One promising method to reduce these errors is the feature alignment technique (FAT). The FAT mitigates displacement errors between observations and a model field while weakly satisfying user-defined constraints (e.g., smoothness) to form a 2-D field of displacement vectors, which are used to adjust the prior model fields to more closely match the observations. Previous work merged the FAT with the CM1-LETKF (local ensemble transform Kalman filter) system and used observation system simulation experiments (OSSEs) for an isolated supercell to vet the FAT as a potential alleviator of forecast errors arising from storm displacement errors. The results from that work revealed the beneficial impact the FAT-LETKF system may have in cases of storm motion bias resulting from mesoscale analysis or model errors.

For this study, to more fully test the capabilities of the FAT, additional OSSEs will be conducted using more complex convective situations with multiple storms. Similar to the previous work, idealized Truth runs of various storm configurations will be used to generate pseudo-radar observations (i.e., reflectivity and radial velocity). The pseudo-observations will be assimilated by the CM1-LETKF system using a 50-member ensemble to produce analyses and forecasts of storms on a 2-km grid. The FAT will use the composite reflectivity field to generate 2-D fields of displacement vectors and apply the vectors to the model state variables at all model levels at the start of each LETKF analysis cycle. Ensemble analyses and forecasts from no-data-assimilation, LETKF-only, and FAT-LETKF experiments will be evaluated against the Truth runs and compared among themselves. The results from these new experiments will be presented.