Performance Evaluation of Google Earth Engine Based Precipitation Datasets Under Different Climatic Zones over India

Abstract

Satellite as well as reanalysis-based datasets are widely available and useful in detecting spatial and temporal variability of rainfall at a finer resolution. These products have been widely used in weather forecasting and hydrological and climate studies. However, the accuracy of satellite products varies spatially and across different datasets. In this study, the accuracy of five satellite-based precipitation products with different spatial resolutions, i.e., CHIRPS, ERA5, TRMM, GPM, and TerraClim available on Google Earth Engine (GEE) were compared with India Meteorological Department (IMD) gridded data in six climate zones in India. The statistics such as RMSE, R2, MSE, and PBIAS were computed. It was observed that the performance of each product varies in different climatic zones. The GPM was observed to have high accuracy in arid, semi-arid, and tropical wet zones. TRMM showed a good match in tropical wet and dry, tropical wet, and semi-arid zones. TerraClim and ERA5 showed high accuracy in humid subtropical and montane regions, respectively. It was also observed that CHRIPS was found to be least suitable in all the climate zones across India. The findings from the present studies will serve as a guiding document for the researcher to select appropriate datasets for different applications such as drought monitoring, precipitation anomaly, hydrological models, or other related studies in India.

Varun Tiwari

PhD Candidate

I am a Ph.D. student with the Center for Geospatial Analytics at North Carolina State University.

Mirela G. Tulbure

Associate Professor

I am an Associate Professor with the Center for Geospatial Analytics at North Carolina State University (NCSU).