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.

中文翻译：

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物理参数化在数值天气预报中的作用：不同积云方案对复杂地形区天气预报的影响

数值天气预报（NWP）在空气质量管理中起着基本作用。大气中存在的所有污染物（天然的和/或人为的）的运输和沉积都受到气象条件（例如降水和风）的强烈影响。此外，大气中颗粒物的存在有利于水凝物成核的物理过程，因此甚至会增加极端天气事件的风险。在这种参考框架下，本工作旨在通过优化天气研究和预报（WRF）模型来提高与极端事件有关的天气预报的质量。为此，报告了使用WRF模型获得的仿真结果，其中可以优化积云方案的物理参数。作为案例研究 我们考虑了2016年11月25日记录的极端气象事件，该事件影响了西西里岛的整个领土，尤其是夏卡（Agrigento）地区。为了评估所提出方法的性能，我们将WRF模型的输出结果与通过雷达和气象站网络获得的数据进行了比较。比较是根据统计方法在“列联表”的基础上进行的，“列联表”可确定能够重现此事件的最合适的物理参数。我们将WRF模型的输出结果与通过雷达和气象站网络获得的数据进行了比较。比较是根据统计方法在“列联表”的基础上进行的，“列联表”可确定能够重现此事件的最合适的物理参数。我们将WRF模型的输出结果与雷达和气象站网络获得的数据进行了比较。比较是在“意外事件表”的基础上通过统计方法进行的，从而可以确定能够重现此事件的最合适的物理参数。