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\begin{document}

\title{Retrieving precipitable water vapor over land from satellite passive
microwave radiometer measurements using automated machine learning}



\author[1]{Xinran Xia}%
\author[2]{Disong Fu}%
\author[3]{Wei Shao}%
\author[4]{Rubin Jiang}%
\author[5]{Shengli Wu}%
\author[5]{Peng Zhang}%
\author[6]{Dazhi Yang}%
\author[7]{Xiangao Xia}%
\affil[1]{Nanjing University of Information Science and Technology}%
\affil[2]{LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences}%
\affil[3]{Nanjing University of Information Science \& Technology}%
\affil[4]{Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, P. R. China}%
\affil[5]{National Satellite Meteorological Center}%
\affil[6]{Harbin Institute of Technology}%
\affil[7]{Institute of Atmospheric Physics, Chinese Academy of Sciences}%


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  \date{June 07, 2024}


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\begin{abstract}
Accurately retrieving precipitable water vapor (PWV) over wide-area land
surface remains challenging. Unlike passive infrared remote sensing,
passive microwave (PMW) remote sensing provides almost all-weather PWV
retrievals. This study developed a PMW-based land PWV retrieval
algorithm using the automated machine learning (AutoML). Data from the
Advanced Microwave Scanning Radiometer 2 (AMSR-2) serves as the main
predictor variables and high-quality Global Positioning System (GPS) PWV
data as the target variable. Unprecedentedly large GPS training samples
(over 50 million) from more than 12,000 stations worldwide are used to
train the AutoML model. New predictors with clear physical mechanisms
enable PWV retrieval over almost any land surface type, including snow
cover and near open water. Validation shows good agreement between PWV
retrievals and ground observations, with a root mean square error of 3.1
mm. This encouraging outcome suggests that the algorithm's potential for
application with other PMW radiometers with similar wavelengths.%
\end{abstract}%



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\textbf{Hosted file}

\verb`967113_0_art_file_11129030_rx322m.docx`  available at \url{https://authorea.com/users/636214/articles/653200-retrieving-precipitable-water-vapor-over-land-from-satellite-passive-microwave-radiometer-measurements-using-automated-machine-learning}

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