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Assessing a machine learning-based downscaling framework for obtaining 1km daily precipitation from GPM data.

Tao Sun1, Nana Yan2, Weiwei Zhu2

  • 1College of Geomatics Science and Technology, Nanjing Tech University, Nanjing, 211816, China.

Heliyon
|September 17, 2024
PubMed
Summary

This study developed a high-resolution precipitation dataset for arid regions using machine learning. The Extreme Gradient Boosting (XGBoost) model significantly improved accuracy, offering a valuable tool for hydrological analysis.

Keywords:
Downscaling frameworkFeature factorMachine learningSpatiotemporal downscaling

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Area of Science:

  • Hydrology and Remote Sensing
  • Environmental Science
  • Geospatial Analysis

Background:

  • Satellite precipitation data in arid regions suffer from coarse spatial resolution, limiting detailed hydro-meteorological analysis.
  • Accurate precipitation monitoring is crucial for water resource management and disaster prevention in water-scarce areas.

Purpose of the Study:

  • To develop and evaluate machine learning techniques for downscaling satellite precipitation data to high spatio-temporal resolutions.
  • To create enhanced annual, monthly, and daily precipitation products for the Hai River Basin.

Main Methods:

  • Evaluated Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Back Propagation (BP) neural networks for precipitation downscaling.
  • Integrated environmental variables (LST, NDVI, DEM, PWV, albedo) to downscale Global Precipitation Measurement (GPM) data from 0.1° to 1 km resolution.
  • Applied residual correction and calibration with terrestrial rain gauge data, using Geographical Difference Analysis (GDA) and Kriging.

Main Results:

  • The XGBoost model, calibrated with GDA and Kriging, achieved the highest accuracy with a Mean Absolute Error (MAE) of 58.40 mm for annual precipitation (14% improvement).
  • Downscaled monthly and daily precipitation products maintained accuracy comparable to the original GPM data, with MAE values of 11.61 mm and 1.79 mm, respectively.
  • Key variables influencing precipitation prediction included longitude, latitude, DEM, LST_night, and PWV.

Conclusions:

  • Machine learning, particularly XGBoost, effectively downscales satellite precipitation data, enhancing spatial and temporal resolution for arid regions.
  • The developed high-resolution precipitation products provide a valuable reference for hydrological studies and water resource management.
  • Accurate precipitation estimation is critical for understanding and managing water resources in vulnerable environments.