A ML approach for Post-processing of NWP model on the European Weather Cloud

In the present study, the research activities aiming at investigating the use of modern Machine Learning algorithms for the early detection of convective systems are shown.
The study conceived and carried out at the Italian Air-Force Meteorological Centre, co-funded in the framework of the EUMETNET-SRNWP-EPS Project, focuses on the post-processing of NWP model output to provide to the forecasters an improved Decision Support System specifically designed for aviation hazards and severe weather phenomena, such as thunderstorms. In detail, the NWP fields generated from operational Limited Area Models (COSMO-IT, 2.2 km horizontal resolution) running at CNMCA (Centro Nazionale Meteorologia e Climatologia Aerospaziale, Italian AirForce Met Service) and ECMWF, were processed and fed, as input features, to a properly designed decision tree. Different targets were defined to train the ML model, starting from independent observation datasets, which include radar, lightning and satellite-derived observations. The products mentioned above and the input features were co-located in space and time to assess the correlation between the signal (the information content of NWP products) and the weather phenomenon (e.g., convective instability). 
The ML tool operates in near real-time mode for the prediction/test phase and offline mode for the training/validation phase. It runs on the European Weather Cloud (EWC), the cloud-based collaboration platform for European meteorological application development. In this context, the customizable computational capabilities of the EWC allowed us to manage the extensive dataset and reduce the model training time. Then the trained models were validated by considering external observations (i.e., the METAR dataset, which provides airports' messages concerning the main meteorological phenomena and parameters observed by weather stations, such as thunderstorms. Moreover, some case studies will be shown and, as a benchmark of the potential improvement, a comparison with classical (multi-variate, static-threshold decision-tree) post-processing applications, routinely used by the Meteorological Watch Office of the Centre, is included in the evaluation study.