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应用科学学报 >

2025 , Vol. 43 >Issue 1: 51 - 65

DOI: https://doi.org/10.3969/j.issn.0255-8297.2025.01.004

计算机应用专辑

基于U-Net改进的日平均2 m气温订正方法

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  • 1. 南京信息工程大学 计算机学院, 江苏 南京 210044;
    2. 南京气象科技创新研究院 中国气象局交通气象重点开放实验室, 江苏 南京 210041;
    3. 苏州大学 江苏省计算机信息处理技术重点实验室, 江苏 苏州 215000

收稿日期: 2024-07-17

  网络出版日期: 2025-01-24

基金资助

国家自然科学基金（No.42075007，No.42475149）；中国气象局流域强降水重点开放实验室开放研究基金（No.2023BHR-Y14）；中国气象局交通气象重点开放实验室开放研究基金项目（北极阁基金项目）（No.BJG202306）资助

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Improved Daily Average 2 m Temperature Correction Method Based on U-Net

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  • 1. School of Computer, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China;
    2. Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, Jiangsu, China;
    3. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215000, Jiangsu, China

Received date: 2024-07-17

  Online published: 2025-01-24

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摘要

针对数据订正常用的深度学习模型U-Net中不能充分学习空间特征以及图像细节信息丢失的问题，提出了S-CUnet 3+模型。S-CUnet 3+采取以下两个措施对U-Net进行改进：一是将原模型与能够学习图片全局特征的Swin Transformer有机结合起来；二是引入多尺度连接操作。模型还采用了预训练与微调的训练策略针对多个预报步长同时订正。7个预报步长的日平均2 m气温预报值订正的实验结果表明，S-CUnet 3+模型对所有预报步长的预报都有明显的订正效果，其中24 h预报步长的订正效果最好，平均绝对误差和均方根误差分别下降了50.64%和49.25%，且相比于基于历史资料的模式距平积分预报订正、分位数回归、岭回归、U-Net、CU-Net、Dense-CUnet和RA-UNet这7种订正方法，S-CUnet 3+取得了更好的订正效果。

关键词： 数据订正; 深度学习; Swin Transformer; 预训练; 微调

本文引用格式

王冰轮, 方巍 . 基于U-Net改进的日平均2 m气温订正方法[J]. 应用科学学报, 2025 , 43(1) : 51 -65 . DOI: 10.3969/j.issn.0255-8297.2025.01.004

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

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