DSAF: A Dual-Stage Adaptive Framework for Numerical Weather Prediction
  Downscaling
Pengwei Liu Wenwei Wang Bingqing Peng Binqing Wu Liang Sun
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.