Estimation of Precipitable Water Using Numerical Prediction Data

Abstract

Precipitable water (PW) is an important variable in the climate system. Interferometric synthetic aperture radar (InSAR) is a powerful remote sensing technique for measuring the topography and deformation of the Earth’s surface. However, variations in atmospheric water vapor content affect the accuracy of InSAR measurements. Therefore, it is important to understand the distribution of PW to mitigate atmospheric effects on remote sensing data. Herein, we estimated the PW distribution with high spatial resolution using numerical prediction data and digital elevation model (DEM) data from the Kanto region of Japan. We estimated the PW distribution at a resolution of 90 m from mesoscale model grid point value data while accounting for the difference in surface elevation within pixels using DEM data with a resolution of 90 m. The PW distribution at 90-m resolution could be estimated using the proposed method with good accuracy (root-mean-square difference within 4.0 mm) throughout the year. The proposed method provides high-resolution information on atmospheric water vapor content and its variation at 3-h intervals. This method is expected to be applicable in climate research and for the atmospheric correction of remote sensing data, which can improve the accuracy of remote sensing measurements.