由 张 云飞 Improving Fengyun-3D satellite atmospheric precipitable water vapor products through machine learning-based post-processing correction
基于机器学习的风云三号D星大气可降水量产品后处理校正方法研究
引文格式 | Citation:
Li M N, Yang L K, Ji W Q, et al. Improving Fengyun-3D satellite atmospheric precipitable water vapor products through machine learning-based post-processing correction[J]. Atmospheric Research, 2025, 322: 108133. DOI: 10.1016/j.atmosres.2025.108133.
Precipitable water vapor, Validation, Post-processing correction, MERSI-II, AERONET
Precipitable water vapor
Validation
Post-processing correction
MERSI-II
AERONET
Validation
Post-processing correction
MERSI-II
AERONET
Abstract | 摘要
Satellite remote sensing has become essential for observing precipitable water vapor (PWV). However, limitations in sensor performance, algorithmic assumptions, and estimation methods can result in errors in satellite retrievals of PWV, which limits the accuracy of these products. This study analyzes the bias and post-processing correction of the Medium Resolution Spectral Imager-II (MERSI-II) PWV operational products aboard China’s polar-orbiting meteorological satellite, Fengyun-3D. Initially, a global quality assessment is conducted using AERONET observations from May 2019 to May 2020. Afterward, variations in the product’s bias are analyzed using various influencing factors. Validation results show that the four PWV products of MERSI-II tend to underestimate values to varying degrees. Bias varies based on factors such as solar zenith angle, view zenith angle, solar azimuth angle, view azimuth angle, digital elevation model, and normalized difference vegetation index. Based on the bias analysis, factors are selected, and a post-processing correction is implemented on the PWV products using the Extreme Gradient Boosting model. Post-processing correction results show that the mean bias of the PWV products is nearly zero, with minimal impact from the selected parameters. The correlation coefficient of the three-channel weighted product is 0.975, and 76.0 % of the matchups fall within the expected error envelope of ±(0.03 + 0.1 × PWVAERONET). These research findings assist in minimizing bias and enhancing the quality of MERSI-II PWV products.
卫星遥感已成为观测大气可降水量（PWV）的重要手段。然而，受传感器性能、算法假设及反演方法的限制，卫星PWV产品存在一定误差，影响了其应用精度。本研究针对中国极轨气象卫星风云三号D（FY-3D）搭载的中分辨率光谱成像仪-II（MERSI-II）业务化PWV产品，系统分析了其偏差特征并开展了后处理校正。首先，利用2019年5月至2020年5月期间的AERONET观测数据对产品进行了全球质量评估；进而，从多角度分析了产品偏差随不同影响因子的变化规律。验证结果表明，MERSI-II的四类PWV产品均存在不同程度的低估现象，其偏差随太阳天顶角、观测天顶角、太阳方位角、观测方位角、数字高程及归一化植被指数等因素呈现规律性变化。基于偏差分析结果，选取关键影响因子，采用极限梯度提升（XGBoost）模型对PWV产品进行了后处理校正。校正后，各PWV产品的平均偏差接近于零，且所选参数对偏差的影响显著降低。其中三通道加权产品的相关系数提高至0.975，76.0%的匹配点对落在预期误差范围 ±(0.03 + 0.1 × PWVAERONET) 内。该研究有助于减小MERSI-II PWV产品的系统偏差，提升产品质量与应用可靠性。