Performance of the WRF Model for the Forecasting of the V-Shaped Storm Recorded on 11–12 November 2019 in the Eastern Sicily

• Published in: Atmosphere 2023-02, Vol.14 (2), p.390
• Main Authors: Castorina, Giuseppe, Semprebello, Agostino, Insinga, Vincenzo, Italiano, Francesco, Caccamo, Maria Teresa, Magazù, Salvatore, Morichetti, Mauro, Rizza, Umberto
• Format: Article
• Language: English
• Subjects: Climate change; Cyclone forecasting; Data assimilation; Data collection; False alarms; FDDA; Forecasting; Mathematical models; Methods; Modelling; numerical weather prediction; Observatories; Performance indices; Precipitation; precipitation forecast; Precipitation forecasting; Probability theory; Rain; Rain and rainfall; Rainfall; Root-mean-square errors; Severe weather; severe weather events; Simulation; Storms; Temperature; Vortices; Weather; Weather forecasting; Weather stations; WRF model
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos14020390

During the autumn season, Sicily is often affected by severe weather events, such as self-healing storms called V-shaped storms. These phenomena cause significant total rainfall quantities in short time intervals in localized spatial areas. In this framework, this study analyzes the meteorological event recorded on 11–12 November 2019 in Sicily (southern Italy), using the Weather Research and Forecasting (WRF) model with a horizontal spatial grid resolution of 3 km. It is important to note that, in this event, the most significant rainfall accumulations were recorded in eastern Sicily. In particular, the weather station of Linguaglossa North Etna (Catania) recorded a total rainfall of 293.6 mm. The precipitation forecasting provided by the WRF model simulation has been compared with the data recorded by the meteorological stations located in Sicily. In addition, a further simulation was carried out using the Four-Dimensional Data Assimilation (FDDA) technique to improve the model capability in the event reproduction. In this regard, in order to evaluate which approach provides the best performance (with or without FDDA), the Root Mean Square Error (RMSE) and dichotomous indexes (Accuracy, Threat Score, BIAS, Probability of Detection, and False Alarm Rate) were calculated.