STAS: Adaptive Selecting Spatio-Temporal Deep Features for Improving Bias Correction on Precipitation
This work addresses bias correction in weather prediction to improve forecast accuracy, particularly for heavy precipitation, representing an incremental advance in domain-specific deep learning applications.
The paper tackles bias correction in precipitation forecasts from the European Centre for medium-range weather forecasts by proposing the STAS model, which adaptively selects spatio-temporal features and achieves state-of-the-art performance on threat scores compared to eight existing methods.
Numerical Weather Prediction (NWP) can reduce human suffering by predicting disastrous precipitation in time. A commonly-used NWP in the world is the European Centre for medium-range weather forecasts (EC). However, it is necessary to correct EC forecast through Bias Correcting on Precipitation (BCoP) since we still have not fully understood the mechanism of precipitation, making EC often have some biases. The existing BCoPs suffers from limited prior data and the fixed Spatio-Temporal (ST) scale. We thus propose an end-to-end deep-learning BCoP model named Spatio-Temporal feature Auto-Selective (STAS) model to select optimal ST regularity from EC via the ST Feature-selective Mechanisms (SFM/TFM). Given different input features, these two mechanisms can automatically adjust the spatial and temporal scales for correcting. Experiments on an EC public dataset indicate that compared with 8 published BCoP methods, STAS shows state-of-the-art performance on several criteria of BCoP, named threat scores (TS). Further, ablation studies justify that the SFM/TFM indeed work well in boosting the performance of BCoP, especially on the heavy precipitation.