摘要
偏差订正是为了优化传统数值天气预报模型的预测精度,在气象学中,温度是一个至关重要的指标,它对天气现象、大气循环、气候模式以及人类活动等方面有着深远的影响,与此有必要对传统数值天气预报产品进行温度偏差订正。本文设计了一种适用于温度偏差订正的新型深度学习网络模型SWGU-ConvLSTM,采用了unet和双向对抗网络架构。该模型使用ConvLSTM模块提取局部信息,使用SwinTransform模快提取全局信息,然后使用IAFF模块将ConvLSTM和SwinTransform模块的输出特征进行融合,并且对融合后的特征信息使用U形连接和跳跃连接,以更好的拼接浅层信息和深层信息,并提取不同尺度的信息,最后将上述融合模块作为生成器和辨别器进行双向对抗训练,以此来增强模型的学习和预测能力。本文使用ECMWF平台公开的TIGGE数值模式数据作为被订正数据,使用ERA5再分析资料作为标签数据,订正6小时预报气温,实验结果表明,提出的SWGU-ConvLSTM模型在MSE、MAE、SSIM等指标上明显优于其他对比模型,相比较于simvp模型,他的MSE和MAE误差分别下降了30%和27%,提高了温度订正的准确率。
Bias correction is crucial for optimizing the prediction accuracy of traditional numerical weather prediction models.In meteorology,temperature is a vital parameter that significantly influences weather phenomena,atmospheric circulation,climate patterns,and human activities.Therefore,it is essential to perform temperature bias correction on traditional numerical weather prediction products.This paper designs a novel deep learning network model,SWGU-ConvLSTM,for temperature bias correction,which adopts the U-Net and bidirectional adversarial network architectures.The model uses the ConvLSTM module to extract local information and the SwinTransform module to capture global features.The outputs of the ConvLSTM and SwinTransform modules are then fused using the IAFF module.Furthermore,the fused feature information is processed with U-shaped connections and skip connections to better combine shallow and deep features and capture multi-scale information.Finally,the fusion module is employed as both the generator and discriminator in a bidirectional adversarial training framework to enhance the model′s learning and prediction capabilities.In this study,the TIGGE numerical model data from the ECMWF platform is used as the input data to be corrected,and ERA5 reanalysis data is used as the target data.The model is applied to correct 6-hour temperature forecasts.Experimental results show that the proposed SWGU-ConvLSTM model outperforms other comparison models in terms of MSE,MAE,and SSIM.Compared to the simvp model,its MSE and MAE errors are reduced by 30%and 27%,respectively,improving the accuracy of temperature bias correction.
作者
周旺亮
秦华旺
Zhou Wangliang;Qin Huawang(Electronics and Information Engineering College,Nanjing University of Information Science and Technology,Nanjing 210044,China)
出处
《电子测量技术》
北大核心
2025年第8期144-153,共10页
Electronic Measurement Technology
关键词
深度学习
偏差订正
对抗网络
天气预报
deep learning
bias correction
adversarial networks
weather forecasting
作者简介
周旺亮,硕士研究生,主要研究方向为深度学习、偏差订正。E-mail:3149825698@qq.com;通信作者:秦华旺,博士,教授,博士生导师,主要研究方向为深度学习。E-mail:qin_h_w@163.com。
