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.

基于深度学习技术，提出了一种新的适应性框架(DSAF)来解决区域天气预报(NWP)下缩尺度和偏差校正问题，DSAF在设计中独特地融入了自适应元素，以确保对不断变化的天气条件作出灵活的响应。实验证明，DSAF在现有最先进模型的基础上提供了卓越的性能，并且在现有模型中运用我们提出的模块时显著改善了结果。

DSAF：一种用于数值天气预报降尺度的双阶段自适应框架