Weather impacts on renewables and agriculture:explainable AI for hyperresolution downscaling for S2S

Description

Extreme weather events can wreak havoc on human health, infrastructure, and economy, accentuating the need for timely detection and accurate prediction. Current sub-seasonal and seasonal NWP forecast models, e.g. the ECMWF extended range and SEAS5 products, lack the spatial resolution necessary for applications operating at a smaller scale. Dynamic downscaling can improve spatial and temporal granularity, but is computationally expensive, whereas traditional statistical downscaling requires high-resolution target data. The application of machine learning has shown promise in weather forecast downscaling, but most methods are not transferable to new regions, disregard physical boundaries, and often oversimplify the downscaled fields, thereby diminishing the representation of extreme events.

Our proposed project idea for the Harry-Otten Prize aims at developing a novel, computationally inexpensive, transferable, and physics-aware post-processing methodology including a pre- and post-processing framework addressesing these challenges. We aim to develop a low-cost computational setup, incorporating physics-informativeness and tail-awareness, establish trustworthiness within the scientific community and the public, and assess transferability to untrained regions.

Period	4 Sept 2023
Event title	EMS Annual Meeting 2023: Europe and droughts: Hydrometeorological processes, forecasting and preparedness
Event type	Conference
Location	Bratislava, SlovakiaShow on map
Degree of Recognition	International