大气科学学报2025，Vol.48Issue(4)：663-673,11.DOI:10.13878/j.cnki.dqkxxb.20231130001
基于MHS微波数据的大气湿度廓线反演研究
Retrieval of atmospheric specific humidity profiles using MHS microwave data
摘要
Abstract
Atmospheric humidity is a fundamental parameter in weather forecasting and atmospheric science,pla-ying a critical role in weather analysis and numerical simulation.Providing specific humidity profiles with broad spatial coverage and high accuracy remains a key challenge for improving the performance of numerical weather prediction models.In this study,we propose a deep learning approach for retrieving atmospheric specific humidity profiles at multiple pressure levels across China,using microwave humidity sounder(MHS)data and a UNet3+neural network against ERA-5 reanalysis data from the European Center for Medium-Range Weather Forecasts(ECMWF).This method effectively mitigates the ill-posedness and uncertainty commonly encountered in tradi-tional quantitative remote sensing retrievals,enabling robust and accurate humidity profile estimations across long-term series and multiple atmospheric layers.Experiments were conducted at the 700 hPa level,with data from 2011-2019 used for training,2020 for validation,and 2021 for testing.Results show a slight underestimation of specific humidity compared with ERA-5,though seasonal differences in retrieval error were not statistically signif-icant.Spatially,larger retrieval errors were observed in southern China and over land surfaces,while lower errors occurred in northern regions and over oceans.The root mean square error(RMSE)remained below 1.3 g/kg in all seasons,with the lowest average RMSE of 1.15 g/kg in winter.Temporal correlation coefficients exceeded 0.9,with an annual mean of 0.92,indicating strong spatial and temporal consistency with ERA-5.Further analysis was conducted across pressure levels from 300 hPa to 1 000 hPa.The retrieved specific humidity showed good agreement with ERA-5 in spatial patterns,with RMSE values across all levels remaining below 1.53 g/kg and correlation coefficients above 0.9.The retrieval accuracy improved with decreasing altitude,showing better a-greement near the surface.Comparisons with radiosonde data confirmed these results,with an average annual RMSE of 0.91 g/kg from 300 hPa to 1 000 hPa.The inversion results were slightly lower than radiosonde obser-vations overall,particularly above 700,while at near-surface levels(e.g.,1 000 hPa)a notable RMSE reduction of approximately 0.6 g/kg was observed.These findings demonstrate the effectiveness and high accuracy of the proposed deep learning-based inversion method for retrieving atmospheric specific humidity profiles from satellite microwave data.关键词
微波湿度计/深度学习/UNet3+/湿度廓线/大气反演Key words
microwave humidity sounder/deep learning/UNet3+/specific humidity profile/atmospheric retrieval引用本文复制引用
郭玲,张希帆,王雪娇,崔嘉文,平方圆..基于MHS微波数据的大气湿度廓线反演研究[J].大气科学学报,2025,48(4):663-673,11.基金项目
环渤海区域科技协同创新基金项目(QYXM202206) （QYXM202206）
天津市海洋气象重点实验室开放基金项目(2022TKLOM06 （）
2023TKLOM06) （）
天津市气象局科研项目(202422ybxm1) （202422ybxm1）
