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This study focuses on increasing the resolution and accuracy of the satellite-based rain data. The satellite-based rain data used in this study is the Tropical Rainfall Measuring Mission (TRMM). The investigation started by collecting data on the TRMM and rain gauge stations (RGSs) data. The downscaling process was performed using the Geographically Weighted Regression (GWR) method. Then, the downscaled TRMM was calibrated using six RGS training data, and the remaining was used in the validation. For calibration, the Geographical Differential Analysis (GDA) method was used. Statistical index tests were conducted in the validation, which are root mean square error (RMSE) and mean absolute error (MAE). The result shows the significant benefit of the increase of the spatial resolution, i.e., capturing the spatial pattern of the rainfall intensity that isn’t captured very well in the original TRMM data. The result also shows that the calibration and downscaling process increase accuracy significantly, i.e., reducing RMSE and MAE from 1645.279 and 1610.7 to 562.509 and 554.6 mm/year, respectively, or in daily resolution, the errors are around 1.6 mm/day. Thus, the calibration and downscaling process is auspicious to increase the resolution and accuracy of the TRMM data.

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Performance of the Geographically Weighted Regression and Geographical Differential Analysis to Capture the Detail of Spatial Patterns of Satellite-Based Rainfall Data

  • 会议论文

  • 发表日期：

    2025年

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  • Raden Harya Dananjaya,
  • Umar Abdul Karim,
  • Galuh Chrismaningwang,
  • Niken Silmi Surjandari,
  • Yusep Muslih,
  • Bambang Setiawan,
  • Siti Nurlita

摘要

This study focuses on increasing the resolution and accuracy of the satellite-based rain data. The satellite-based rain data used in this study is the Tropical Rainfall Measuring Mission (TRMM). The investigation started by collecting data on the TRMM and rain gauge stations (RGSs) data. The downscaling process was performed using the Geographically Weighted Regression (GWR) method. Then, the downscaled TRMM was calibrated using six RGS training data, and the remaining was used in the validation. For calibration, the Geographical Differential Analysis (GDA) method was used. Statistical index tests were conducted in the validation, which are root mean square error (RMSE) and mean absolute error (MAE). The result shows the significant benefit of the increase of the spatial resolution, i.e., capturing the spatial pattern of the rainfall intensity that isn’t captured very well in the original TRMM data. The result also shows that the calibration and downscaling process increase accuracy significantly, i.e., reducing RMSE and MAE from 1645.279 and 1610.7 to 562.509 and 554.6 mm/year, respectively, or in daily resolution, the errors are around 1.6 mm/day. Thus, the calibration and downscaling process is auspicious to increase the resolution and accuracy of the TRMM data.