An AI-based approach for bias correction of temperature and precipitation forecasts to develop an early warning system

The project DAKI-FWS (Data and AI-supported Early Warning System to stabilize the German Economy), funded by the Federal Ministry of Economic Affairs and Climate Action (Germany), develops an innovative early warning system with a seasonal time horizon to protect and support lives, jobs, land and infrastructure. High-skilled, innovative time and space-dependent bias correction and high resolution downscaling artificial intelligence approaches, such as deep learning and reinforcement learning techniques, are designed and implemented on ensemble seasonal forecast data. A fundamental challenge in bias correction is to preserve climate trends and plausible representation of the physical properties (variables) of the climate data.Thus in this work, a trend preserving AI-based correction approach is implemented. The high quality bias-corrected data can be introduced into the various climate-related practical applications of the overall project, such as the detection of extreme events but also evolution of pandemics or subtropical/tropical diseases and hydrological models. State-of-the-art AI techniques are applied not only for preprocessing and preparation of the climate and sectoral data but also for the analysis and post-processing phases. Weather and climate extremes, such as heatwaves, storms and droughts, and concurrent extremes are identified from the large pool of meteorological and climatological reference datasets, seasonal forecasts as well as event lists.  Such a comprehensive early warning system with seasonal horizon that contributes to the estimation of the outbreak and development of climate and health crises and supports disaster management and risk reduction and mitigation, does not yet exist for Germany, illustrating the importance and potential of this work.