A global terrestrial precipitable water vapor (PWV) dataset has been developed using observations from the MicroWave Radiation Imager (MWRI) aboard the FY-3 satellite series (FY-3B, FY-3C and FY-3D) spanning 2012 to 2020. The dataset offers twice-daily PWV records at a spatial resolution of 0.25° × 0.25°, aligned with the ascending and descending orbits of the FY-3 satellites. The dataset was generated using an automated machine learning (ML) model that leverages MWRI-based features characterizing surface conditions and an enhanced Global Position System (GPS) PWV dataset as a reference.
Trained on over one million sampling points from more than ten thousand stations worldwide, the model ensures a robust representation of global PWV variations. Independent evaluations against SuomiNet GPS and Integrated Global Radiosonde Archive Version 2 (IGRA2) PWV products yielded root mean square error (RMSE) of 4.47 mm and 3.89 mm, respectively, with RMSE values ranging from 2.90 to 5.49 mm across various surface conditions. The dataset effectively captures both spatial and temporal PWV variations, allowing for precise examination of localized and abrupt changes in water vapor induced by extreme weather events. Representing a significant advancement in global terrestrial PWV monitoring, the MWRI PWV dataset provides an all-weather, high-precision data record that bridges gaps in global coverage of passive microwave-based terrestrial PWV observations. It is a valuable resource for atmospheric research, climate modeling, water cycle studies, and beyond.
Data sourced from Figshare website（ https://figshare.com/ ）.
The dataset is constructed using an automated machine learning (ML) model that integrates surface feature parameters based on MWRI and uses the Enhanced Global Positioning System (GPS) PWV dataset as a reference benchmark.
The model training set covers over 10000 sites and millions of sampling points worldwide, ensuring robust representation of global PWV changes.
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CSTR:11738.11.NCDC.FIGSHARE.DB6812.2025
10.6084/m9.figshare.26712709