The number of short-term and highly localized phenomena, such as thunderstorms, hailstorms, wind gusts or tornadoes, is expected to grow further in the coming years, with important repercussions in air traffic management activities (ATM). One of the main 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 ATM.

To this purpose the output of the numerical simulations, carried out with the Weather Research and Forecasting (WRF) model using a cycling 3D-VAR assimilation technique, and the output of a nowcasting algorithm, will be integrated into the Arrival Manager 4D-CARMA (4-Dimensional Cooperative Arrival Manager), an adaptive air traffic sequencing and management system for controllers. 4D-CARMA upgrades 4D trajectories avoiding areas affected by adverse phenomena with the aims of increasing flight safety and reducing controllers' and pilots' workload.

The results prove that it is possible to improve the prediction of the above-mentioned events in line with expectations and ATM needs.