STAS: Adaptive Selecting Spatio-Temporal Deep Features for Improving Bias Correction on Precipitation

• URL: http://arxiv.org/abs/2004.05793v1
• Date: Mon, 13 Apr 2020 07:00:55 GMT
• Title: STAS: Adaptive Selecting Spatio-Temporal Deep Features for Improving Bias Correction on Precipitation
• Authors: Yiqun Liu, Shouzhen Chen, Lei Chen, Hai Chu, Xiaoyang Xu, Junping Zhang, Leiming Ma
• Abstract summary: We propose an end-to-end deep-learning BCoP model named Spatio-Temporal feature Auto-Selective (STAS) model to select optimal ST regularity from EC. Experiments on an EC public dataset indicate that STAS shows state-of-the-art performance on several criteria of BCoP, named threat scores (TS)
• Score: 27.780513053310223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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