SWING, The Score-Weighted Improved NowcastinG Algorithm: Description and Application

Because of the ongoing climate change, frequency extreme rainfall events at global scale is expected to increase, resulting in higher social and economic impacts. Thus, improving forecast accuracy risk communication a fundamental goal limit damages. Both Numerical Weather Prediction (NWP) radar-based nowcasting systems still have open issues, mainly terms precipitation correct time/space localization predictability rapid decay, respectively. Trying overcome these this work aims present system combining an NWP model (WRF), using 3 h update cycling 3DVAR assimilation radar reflectivity data, with PhaSt through blending technique. Moreover, innovative post-processing algorithm named SWING (Score-Weighted Improved NowcastinG) has been developed order take into account timely spatial uncertainty convective field simulation. The overarching pave way for easy automatic heavy warning derived by procedure. results obtained applying over case study 22 days fall 2019 season suggest that could improve predictive capability traditional deterministic system, keeping useful timing thus integrating current procedures. Eventually, main advantage also its very high versatility, since it be used any meteorological multi-model approach.