Post-processing activities in Destination Earth On-demand Extremes

In phase 1 of Destination Earth On-demand Extremes, the capabilities of an on-demand digital twin designed for extreme weather phenomena is currently implemented. The DT workflow consists of an automatic event detection module which triggers a hyperresolution NWP model run on a flexible domain and resolution (depending on the phenomena), followed by error-correction and uncertainty estimation with post-processing techniques and impact models for different applications (renewables, air quality, hydrology). The post-processing framework, as part of the production chain, exploits existing event detection methods by searching for precursors of expected extremes, e.g. applying EFI and SOT index or storm tracking methods and investigates their applicability for triggering the on-demand workflow. Downstream the on-demand hyperresolution NWP model run, post-processing procedures are tailored to specific applications, event type, region and data availability, and complement the hyperresolution model simulations by error correction and probability estimation. These method are flexibly implemented, depending on the availability of NWP data and observational data for training purposes and will include baseline algorithms like neighborhood approach for areas with sparse observations as well as deep learning methods for regions with large samples of observation data. The presentation gives an overview of the current implementation status and its methods and techniques. First demonstrators will be discussed on behalf of selected events and use cases, including technical and scientific challenges. The usability of the hyperresolution NWP model and downstream applications for key users in Civil Protection, Marine Safety and Agriculture will be highlighted and perspectives for future achievements will be outlined.