AI-aided Convection Forecast for Aviation Planning

In the context of aviation planning over Europe, a two-day forecast of the convective activity is required. Such a product, named the Cross-Border Convection Forecast (CBCF), is regularly produced during the convective season by several meteorological services across Europe and coordinated by EUMETNET. Using a collaborative editing platform, human forecasters generate forecast polygons to quantify the expected convective situation. In particular, the forecast includes the expected degree of organization of the convective cells and the expected likelihood of occurrence.

The goal of this work is to provide an automatic guidance product based on AI methods to assist the forecasters. Moreover, the guidance product should directly provide the final CBCF-forecast category, i.e. degree of organization and its likelihood of occurrence. We report on the development of such a guidance product, where the basic forecast data are taken from the ICON-EU ensemble forecasts that are operationally produced at the German Meteorological Service and cover the whole desired European forecast domain. Based on this ensemble data, several derived statistical quantities are computed and serve as input to the AI method.

One significant challenge in the development of the guidance product is the definition of the target for training of the AI model since direct observations are not available for the final forecast categories. As a result, another quantity needs to be used as target for the training. In our approach we employ the area fraction of lightning, being a derived product of lightning observations and defined as the area covered by lightning within a radius of 50km. We show preliminary results for the AI methods that use the area fraction of lightning as target. Moreover, we discuss possible modifications of this quantity to adapt it better to the desired final product.