The Role of Physical Parameterizations on the Numerical Weather Prediction: Impact of Different Cumulus Schemes on Weather Forecasting on Complex Orographic Areas

Abstract

Numerical weather predictions (NWP) play a fundamental role in air quality management. The transport and deposition of all the pollutants (natural and/or anthropogenic) present in the atmosphere are strongly influenced by meteorological conditions such as, for example, precipitation and winds. Furthermore, the presence of particulate matter in the atmosphere favors the physical processes of nucleation of the hydrometeors, thus increasing the risk of even extreme weather events. In this framework of reference, the present work aimed to improve the quality of weather forecasts related to extreme events through the optimization of the weather research and forecasting (WRF) model. For this purpose, the simulation results obtained using the WRF model, where physical parametrizations of the cumulus scheme can be optimized, are reported. As a case study, we considered the extreme meteorological event recorded on 25 November 2016, which affected the whole territory of Sicily and, in particular, the area of Sciacca (Agrigento). In order, to evaluate the performance of the proposed approach, we compared the WRF model outputs with data obtained by a network of radar and weather stations. The comparison was performed through statistical methods on the basis of a “contingency table”, which allowed for ascertaining the best suited physical parametrizations able to reproduce this event.