DSAF: A Dual-Stage Adaptive Framework for Numerical Weather Prediction Downscaling

• URL: http://arxiv.org/abs/2312.12476v1
• Date: Tue, 19 Dec 2023 13:13:17 GMT
• Title: DSAF: A Dual-Stage Adaptive Framework for Numerical Weather Prediction Downscaling
• Authors: Pengwei Liu, Wenwei Wang, Bingqing Peng, Binqing Wu and Liang Sun
• Abstract summary: We propose a novel framework to address regional NWP downscaling and bias correction tasks. Dual-Stage Adaptive Framework (DSAF) incorporates adaptive elements in its design to ensure a flexible response to evolving weather conditions.
• Score: 6.990912650604992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While widely recognized as one of the most substantial weather forecasting methodologies, Numerical Weather Prediction (NWP) usually suffers from relatively coarse resolution and inevitable bias due to tempo-spatial discretization, physical parametrization process, and computation limitation. With the roaring growth of deep learning-based techniques, we propose the Dual-Stage Adaptive Framework (DSAF), a novel framework to address regional NWP downscaling and bias correction tasks. DSAF uniquely incorporates adaptive elements in its design to ensure a flexible response to evolving weather conditions. Specifically, NWP downscaling and correction are well-decoupled in the framework and can be applied independently, which strategically guides the optimization trajectory of the model. Utilizing a multi-task learning mechanism and an uncertainty-weighted loss function, DSAF facilitates balanced training across various weather factors. Additionally, our specifically designed attention-centric learnable module effectively integrates geographic information, proficiently managing complex interrelationships. Experimental validation on the ECMWF operational forecast (HRES) and reanalysis (ERA5) archive demonstrates DSAF's superior performance over existing state-of-the-art models and shows substantial improvements when existing models are augmented using our proposed modules. Code is publicly available at https://github.com/pengwei07/DSAF.

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