Total Precipitable Water Retrieval from FY-3D MWHS-II Data

Yi-Fan Zhang, Geng‐Ming Jiang, Gao-Yuan Wei

Remote Sensing · 26 May 2025 · Fudan University (FuXi)

In one sentence. A BP neural network retrieves total precipitable water from FY-3D MWHS-II over sea and land, beating seven baseline methods with RMSE of 3.5-4.0 mm against radiosondes.

Abstract

The Total Precipitable Water (TPW) is a key variable of atmospheres, and its spatiotemporal distribution is of great importance in global climate change. This paper addresses the TPW retrieval over both sea and land surfaces from the data acquired by the Microwave Humidity Sounder II (MWHS-II) on Fengyun 3D (FY-3D) satellite. First, the Back Propagation Neural Network (BPNN) algorithms are developed with the spatiotemporal matching samples of the MWHS-II data with the fifth-generation European Centre for Medium-Range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. Then, the TPWs at spatial resolutions of 0.25° in longitude and latitude between 65°S and 65°N over both sea and land surfaces are retrieved from the pixel-aggregated FY-3D MWHS-II data in 2022. Finally, the TPWs retrieved in this work are validated with the radiosonde TPWs over both sea and land surfaces, and they are also compared to the F18 Special Sensor Microwave Imager Sounder (SSMIS) TPWs over sea surfaces. The results indicate that the BPNN algorithms developed in this work are valid and superior to the D-matrix method, the Ridge method, the Lasso method, the physical method, the random forest (RF) method, the support vector machine (SVM) method, and the eXtreme Gradient Boosting (XGBoost) method. Against the radiosonde TPWs, the mean error (ME), the root mean square error (RMSE), and mean absolute error (MAE) of the TPWs retrieved in this work are −1.17 mm, 3.46 mm, and 2.63 mm over sea surfaces, respectively, and they are −0.80 mm, 4.04 mm, and 3.13 mm over land surfaces, respectively. The TPWs retrieved in this work are much more accurate than the F18 SSMIS TPWs.

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