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Fusion of BeiDou and MODIS Precipitable Water Vapor Using the Random Forest Algorithm: A Case Study of Multi-Source Data Synergy in Hunan Province, China
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Fusion of BeiDou and MODIS Precipitable Water Vapor Using the Random Forest Algorithm: A Case Study of Multi-Source Data Synergy in Hunan Province, China
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Journal Article
Fusion of BeiDou and MODIS Precipitable Water Vapor Using the Random Forest Algorithm: A Case Study of Multi-Source Data Synergy in Hunan Province, China
2025
Overview
The accurate monitoring of water vapor is essential for understanding the hydrological cycle and improving weather forecasting. Although the Moderate-resolution Imaging Spectroradiometer (MODIS) provides spatially continuous precipitable water vapor (PWV), validation in Hunan Province reveals a systematic underestimation, with correlations to radiosonde (RS-PWV) around 0.40 and average RMSE and MAE reaching 23.80 and 18.04 mm. To address this issue, high-accuracy PWV derived from the BeiDou Navigation Satellite System (BDS-PWV), which show high consistency with RS-PWV, were incorporated. A random forest daily-scale water vapor fusion model was developed based on the differential characteristics of dry and wet season residuals. By employing day of year (DOY), latitude, longitude, and elevation as auxiliary factors, the model establishes a seasonal fusion framework that dynamically transitions between dry and wet seasons. Validation shows that the fusion PWV aligns closely with RS-PWV, reducing average RMSE and MAE to 4.71 and 3.81 mm, corresponding to improvements of 80.21% and 78.88% over MODIS, with accuracy increases exceeding 75% at all stations. The fusion model effectively mitigates MODIS’s underestimation and weather sensitivity, producing high-accuracy, spatially continuous daily PWV fields and offering strong potential for improving precipitation and weather forecasting in complex regions such as Hunan Province.
Publisher
MDPI AG
