SSAS: Spatiotemporal Scale Adaptive Selection for Improving Bias Correction on Precipitation

By utilizing physical models of the atmosphere collected from the current weather conditions, the numerical weather prediction model developed by the European Centre for Medium-range Weather Forecasts (ECMWF) can provide the indicators of severe weather such as heavy precipitation for an early-warning system. However, the performance of precipitation forecasts from ECMWF often suffers from considerable prediction biases due to the high complexity and uncertainty for the formation of precipitation. The bias correcting on precipitation (BCoP) was thus utilized for correcting these biases via forecasting variables, including the historical observations and variables of precipitation, and these variables, as predictors, from ECMWF are highly relevant to precipitation. The existing BCoP methods, such as model output statistics and ordinal boosting autoencoder, do not take advantage of both spatiotemporal (ST) dependencies of precipitation and scales of related predictors that can change with different precipitation. We propose an end-to-end deep-learning BCoP model, called the ST scale adaptive selection (SSAS) model, to automatically select the ST scales of the predictors via ST Scale-Selection Modules (S3M/TS2M) for acquiring the optimal high-level ST representations. Qualitative and quantitative experiments carried out on two benchmark datasets indicate that SSAS can achieve state-of-the-art performance, compared with 11 published BCoP methods, especially on heavy precipitation.