Evaluation of High-Resolution Gridded Precipitation Datasets Against a Dense Rain Gauge Network During the Indian Summer Monsoon
Abstract
Advancements in remote sensing have led to development of several satellite-derived precipitation products; however, their accuracy must be evaluated before use in scientific and operational studies.
This study comprehensively assesses six widely used datasets PERSIANN CCS, CHIRPS, MSWEP, IMERG, AgERA5, and GSMaP ISRO against a dense rain gauge network across Karnataka, a southern Indian state characterized by diverse climatic conditions and complex topography.
The analysis focuses on the Indian summer monsoon season for 2011 to 2022.
To complement traditional metrics, tools from complex network theory were applied to investigate spatial organization and connectivity patterns of rainfall.
A functional climate network approach was used to construct rainfall correlation networks, while event synchronization, a nonlinear measure, quantified the co occurrence of extreme events.
Most products reproduced large scale monsoon features, yet their ability to represent intensity categories and extremes varied.
GSMaP ISRO showed the highest correlation, lowest bias, and RMSE across subregions, whereas PERSIANN CCS exhibited systematic errors, particularly in Western Ghats, though correlations improved over interior plains.
Network-based analysis reaffirmed GSMaP ISROs skill in replicating spatial correlation structures, capturing high coherence in regions dominated by large-scale processes and lower coherence in areas influenced by localized dynamics.
The observed rainfall network revealed strong synchronization between the coastal region and central Karnataka, indicating broad spatial co occurrence of extremes, while the Malnad region showed weaker connectivity, suggesting localized events.
GSMaP ISRO closely reproduced this degree distribution, reflecting corrections using IMD gridded dataset.
Future work should improve sub-daily and localized rainfall estimates, especially in complex terrain.
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