关键词: 气温预报, 误差校正, 深度学习, 空间注意力

Abstract: Error correction for temperature forecasting is significant for social development and personal life. The U-Net-based models have limitations in correcting meteorological data with geographical differences due to their inherent inductive bias. To address this, a Spatial Attention-based Grid Temperature Correction Network (SAGTCN) was proposed, which uses fully connected networks for grid-point-independent correction and adds spatial attention layers for dynamic regional weighting. On Inner Mongolia's meteorological data, SAGTCN outperforms U-Net and Attention U-Net-based models, with a 5.8% lower Mean Absolute Error (MAE), 14.96% and 10.98% lower Mean Square Error (MSE). In the test of forecasts with 13 - 48 hours lead time, SAGTCN is better in 31 out of 36 lead times, with only 3 close ones, and 2 slightly worse ones.

Key words: air temperature forecasting, error correction, deep learning, spatial attention