Enhancing the performance of weather nowcasting by use of machine learning techniques applied on weather data

The number and intensity of severe weather events is increasing, leading to loss of goods, property, and human lives. In 2017, a storm caused 5 deaths in the Romanian city of Timișoara, triggering the creation of a nation-wide emergency alerting system. Improved weather forecasting, especially for severe weather events expected less than 6 hours in the future, also known as nowcasting, is expected to help mitigate the outcome of such events. Issuing accurate weather warnings is difficult for meteorologists, as they must consider changes in the speed and direction of wind at different heights, air temperature and pressure, cloud cover, as well as the effect of terrain features and climate - all changing from one hour to the next. Decisions must be made quickly and broadcast to people in the affected area in time to take necessary precautions.

The WeaMyL project is funded within the Norway Grants research programme and focuses on applying deep learning to analyse present and past meteorological data gathered by satellites, weather radars and ground stations. The major goal is to provide an efficient, seamless nowcasting platform which will be integrated with national warning systems from Romania and Norway, and which will employ multiple meteorological data sources to provide automated weather nowcasting. This will allow meteorologists to give people more precise and earlier warnings.