This dataset utilizes a machine learning algorithm to enhance PWV observations, indicating its value for climate modeling and hydrological studies.
Precipitable water vapor (PWV) is a key component of the Earth’s climate system, playing a vital role in weather, climate, and hydrological cycling. Passive microwave remote sensing offers a promising approach to measure all-sky PWV, though it remains challenging over land. Building on our previous development of a machine learning algorithm, we have created a global terrestrial PWV dataset using measurements from the MicroWave Radiation Imager (MWRI) aboard three FY-3 satellite series (FY-3B, FY-3C and FY-3D). The dataset spans from 2012 to 2020 at a spatial resolution of 0.25° × 0.25°. It was validated against SuomiNet GPS and IGRA2 (Integrated Global Radiosonde Archive Version 2) PWV products, achieving root-mean-square errors (RMSEs) of 4.47 and 3.89 mm, respectively, with RMSE values ranging from 2.90 to 5.49 mm across diverse surface conditions. As an all-weather PWV product with high-precision, the MWRI PWV dataset addresses gaps in global passive microwave-based terrestrial PWV observations, offering significant value for atmospheric research, climate modeling, hydrological studies, and beyond.
No takes yet. Share an insight, caveat, or question.
Xia et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: