Accurate forecasting of wind speed holds significant importance for the economic and social development of humanity. However, existing numerical weather predictions have certain inaccuracies due to various reasons. Therefore, it is highly necessary to perform statistical post-processing on forecasted results. However, traditional linear statistical post-processing methods possess inherent limitations. Hence, in this study, we employed two deep learning methods, namely the convolutional neural network (CNN) and the U-Net neural network, to calibrate the forecast of the Global Ensemble Forecast System (GEFS) in predicting 10-m surface wind speed in Southwest China with a forecast lead time of one to seven days. Two traditional linear statistical post-processing methods, the decaying average method (DAM) and unary linear regression (ULR), are conducted in parallel for comparison. Results show that original GEFS forecasts yield poorer wind speed forecasting performance in the western and eastern Sichuan provinces, the eastern Yunnan province, and within the Guizhou province. All four methods provided certain correction effects on the GEFS wind speed forecasts in the study area, with U-Net demonstrating the best correction performance. After correction using the U-Net, for a 1-day forecast lead time, the proportion of the 10-m U-component of wind with errors less than 0.5 m/s has increased by 46% compared to GEFS. Similarly, for the 10-m V-component of wind, the proportion of errors less than 0.5 m/s has increased by 50% compared to GEFS. Furthermore, we employed the mean square error-based error decomposition method to further diagnose the sources of forecast errors for different prediction models and reveal their calibration capabilities for different error sources. The results indicate that DAM and ULR perform best in correcting the Bias2, while the correction effects of all methods were variable for the distribution with the forecast lead time. U-Net demonstrated the best correction performance for the sequence.
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