How can numerical weather prediction support the ATM activity during severe weather events?

Kurzfassung

Climate change is intensifying the water cycle, bringing more intense precipitation and flooding in some regions, as well as longer and stronger droughts in others. The number of short-term and highly localized phenomena, such as thunderstorms, hailstorms, wind gusts or tornadoes, is expected to grow in the coming years, with important repercussions in air traffic management activities (ATM). One of the challenges for meteorologists is to improve the location and timing of such events that develop on small spatial and temporal scales. In this regard, the H2020 Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project aims to demonstrate that numerical weather forecasts with high spatial and temporal resolution, benefiting from the assimilation of radar data, in situ weather stations, GNSS and lightning data, could improve the prediction of severe weather events for the benefit of air traffic control (ATC) and air traffic management (ATM). As part of the project, three severe weather events were identified on the Italian territory which resulted in the closure of the airport with heavy delays on arrivals and departures as well as numerous diversions. The data of the numerical simulations, carried out with the Weather Research and Forecasting (WRF) model and the 3D-VAR assimilation technique, will be integrated into air traffic control and management systems (Arrival Manager) in order to generate and optimize 4D trajectories avoiding areas affected by adverse phenomena with the objectives of increasing flight safety and predictability and reducing controllers' workload. In addition to the numerical simulations, a nowcasting technique called PHAse- diffusion model for STochastic nowcasting (PhaSt) has been investigated to further improve ATC supporting systems during severe weather. This work presents the results of the WRF and PhaSt experiments, for the Milan Malpensa case study of 11 May 2019, demonstrating that it is possible to improve the prediction of such events in line with expectations and ATM needs.