Assimilation of radar reflectivity volumes in a pre‐operational framework

Abstract

AbstractThe assimilation of radar reflectivity data is of great interest for numerical weather prediction (NWP) models at the convective scale since these observations are very dense both in space and time and they are related to microphysical prognostic variables. So far, the assimilation of reflectivity in operational frameworks has been limited to derived products, like precipitation rates and humidity profiles. This approach is also adopted at ARPAE‐SIMC, the Hydro‐Meteo‐Climate Structure of the Regional Agency for Prevention, Environment and Energy of Emilia‐Romagna region in Italy, where latent heat nudging (LHN) is employed to assimilate precipitation rates in the COSMO‐2I model, the convection‐permitting version of the regional model developed by the COnsortium for Small‐scale MOdelling (COSMO) which is run operationally to provide high‐resolution forecasts over Italy. However, to fully exploit the information contained in these observations, the whole reflectivity volume should be directly assimilated. Nevertheless, despite several promising studies, its implementation in an operational framework has not yet been achieved. In this study, a set‐up designed to assimilate reflectivities operationally in the COSMO‐2I model through a local ensemble transform Kalman filter (LETKF) has been evaluated over 37 days in 2018 and performed 303 forecasts. The comparison with the current operational set‐up based on LHN reveals an average improvement in quantitative precipitation forecasts (QPFs) up to 7 hr, even if the impact on convective cases is much stronger than that observed in conditions of stratiform precipitation. Moreover, a small but positive impact is noticed for the RMSE of upper‐air variables, while the impact on bias is mixed. Mixed results are also obtained when considering surface variables. In the light of the results of this study, a pre‐operational parallel system in which COSMO‐2I analyses are generated replacing LHN by the direct assimilation of reflectivity volumes was implemented in April 2020.