Validation of Satellite-Based Gridded Rainfall Products with Station Data Over Major Cities in Punjab

Syeda Nimra Raza Geelani; Sawaid Abbas; Muhammad Umar; Muhammad Usman; Irum Yousfani

Authors

Syeda Nimra Raza Geelani Smart Sensing for Climate and Development, Center for Geographical Information System, University of the Punjab, Lahore, Pakistan
Sawaid Abbas Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR
Muhammad Umar Smart Sensing for Climate and Development, Center for Geographical Information System, University of the Punjab, Lahore, Pakistan
Muhammad Usman Interdisciplinary Research Center for Aviation and Space Exploration, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Irum Yousfani Department of Computer Science, Asian Institute of Technology, Thailand

Keywords:

Rainfall, CHIRPS, IMERG, ERA-5, APHRODITE, Punjab

Abstract

A Critical evaluation of newly developed gridded rainfall datasets is essential for their effective application. Over the past two decades, the availability of gridded rainfall measurements has increased; however, finding suitable proxies for traditional station-based measurements remains challenging. This study conducted a comparative assessment of rainfall estimates from IMERG, CHIRPS, ERA-5, and APHRODITE against meteorological station data from five cities in Pakistan: Lahore, Faisalabad, Multan, Islamabad, and Murree. The assessment covered multiple temporal scales (daily, monthly, and yearly) using daily data recorded from 2001 to 2022. Analytical metrics applied included Bias, Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (CC), and Coefficient of Determination (R²). The results revealed notable spatial and temporal patterns of agreement among the datasets. Correlations for daily data were generally weak across all gridded datasets, with APHRODITE performing the best. Monthly aggregates showed that IMERG had the highest association with ground data, followed by CHIRPS. Yearly accumulated rainfall records indicated that IMERG had the highest correlation, followed by CHIRPS. Overall, IMERG demonstrated higher consistency across stations at both monthly and yearly scales. CHIRPS exhibited lower errors (RMSE and bias) at most locations, especially Lahore, but showed higher errors in Murree at the monthly scale. The study concludes that a single satellite dataset alone may not provide sufficient accuracy over large areas; a combination of products may be required for better estimation.

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