Abstract: In response to the limitations of the widely utilized deep learning model U-Net, which is unable to adequately learn spatial features and suffers from the loss of image detail information, the S-CUnet 3+ model has been proposed. S-CUnet 3+ enhances U-Net in two ways: firstly, it integrates the original model with the Swin Transformer, enabling it to learn global features of images, and secondly, it introduces multi-scale connection operations. The model also adopts pre-training and fine-tuning strategies to correct multiple forecast lead times simultaneously. Experimental results of correcting daily average 2 m temperature forecasts across seven lead times show that the S-CUnet 3+ model has a significant correction effect for all lead times, with the best correction effect at the 24-hour lead time. The mean absolute error and root mean square error are reduced by 50.64% and 49.25%, respectively. Moreover, S-CUnet 3+ outperforms seven existing correction methods: anomaly numerical-correction with observations, quantile regression, ridge regression, U-Net, CU-Net, Dense-CUnet, and RA-UNet.

Key words: data correction, deep learning, Swin Transformer, pre-training, finetuning