A Multimodel Approach for Forecasting of convective weather in Support of BULATSA Air Traffic management

A Multimodel Approach for Forecasting of convective weather in Support of BULATSA Air Traffic management Authors: Ilian Manafov PhD BULATSA – Bulgarian Air Traffic Services Authority Rosen Penchev BULATSA – Bulgarian Air Traffic Services AuthorityAbstractBackground:The impact of adverse weather condition on European ATM Network have increased significantly in recent years, which led to a significant increase in delays in summer 2024. In an attempt to mitigate the impact of weather, Bulgarian Air Navigation Service Provider BULATSA has introduced a special weather procedure that expected from weather forecasters to provide more accurate and spatially detailed predictions of severe weather areas within the Bulgarian airspace.Methods:This study introduces a multimodel forecasting approach aimed at improving predictions of hazardous convective phenomena. Operational weather forecasters at BULATSA utilize a variety of weather models. The proposed method combines special chosen NWP parameters from four numerical weather prediction models including ICON-EU, ECMWF deterministic model and two non-hydrostatical regional models: BULATSA-WRF and version of ALADIN integrated by Bulgarian National Institute of Meteorology and Hydrology.A new forecast product, CB-Conditions, is developed based on the intersection of forecasted parameters from two of the models. It identifies areas with a high likelihood of hazardous CB activity. The overall probability of occurrence is derived using a weighted average of probabilities from all four models.Validation and Tools:To assess the accuracy and determine optimal model weights, at least 20 cases of intense convection will be analyzed. The CB forecasts are drawn as a polygons and verified against archived data from BULATSA Weather Radar network and LINET network.Results and Conclusion:Preliminary results indicate promising performance of the CB-Conditions product. The multimodel approach demonstrates potential to significantly enhance the accuracy and operational value of convective weather forecasts, contributing to safer and more efficient air traffic management.