Estimation of Precipitable Water Using Numerical Prediction Data

  • Shin Akatsuka Kochi University of Technology
  • Junichi Susaki Kyoto University
  • Masataka Takagi Kochi University of Technology


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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.

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Author Biographies
Shin Akatsuka

School of Systems Engineering, Kochi University of Technology, Kochi 782-8502, Japan

Junichi Susaki

Department of Civil and Earth Resources Engineering, Graduate School of Engineering, Kyoto University, Kyoto 615-8540, Japan

Masataka Takagi

School of Systems Engineering, Kochi University of Technology, Kochi 782-8502, Japan

Vol 22 No 3, Jun 28, 2018
How to Cite
S. Akatsuka, J. Susaki, and M. Takagi, “Estimation of Precipitable Water Using Numerical Prediction Data”, Eng. J., vol. 22, no. 3, pp. 257-268, Jun. 2018.

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