Multi-physics ensemble using different planetary boundary layer scheme in WRF model for PBL height prediction over Apulia region

The present work concerns the prediction of "Wind Days" (WD) affecting Taranto city (Apulia region, southeastern Italy), which is included in so-called areas at high risk of environmental crisis because of the presence of a heavy industrial district including the largest steel factory in Europe, ILVA. WD events are characterized by at least 3 consecutive hours of wind coming from Nord-West quadrant with an associated speed higher than 7 m/s. During these days, a Tamburi neighborhood, named Tamburi, located less than 1 km far from ILVA, is affected by a deterioration in air quality due to its extreme proximity to the industrial area. In order to improve the air quality in Tamburi, in 2012 the Apulia Government adopted the regional air quality plan, which forces the industrial plants in Taranto to reduce by 10% their industrial emissions in case of Wind Days. The plan requires that these events are predicted 72 hours in advance. The goal of our study is to use different statistical methods to exploit the information contained in a global ensemble forecasting system (GEFS) in order to maximize the accuracy of wind days prediction, also in comparison with deterministic runs (GFS and ECMWF). Together with the classical approach based on the percentage of ensemble members that predict the occurrence of the event, we propose two alternative techniques. One is based on the distribution quantiles, the other one uses the ensemble probability density function, where we hypothesize that the prediction error of a reference run (deterministic or ensemble control member) is provided by the ensemble distribution at the same time. Results show that the latter method improves the accuracy in the prediction of wind days compared both to deterministic runs and to other techniques to manage the probabilistic information. We plan to extend the application of similar techniques to rainfall prediction.