Evaluation of MODIS, ERA5, and MERRA2 Derived Integrated Precipitable Water Vapor of India Using Ground-Based GPS Data

Seema Rani and Jyotsna Singh

Annals of the American Association of Geographers, 2025, vol. 115, issue 7, 1506-1531

Abstract: This study assesses the effectiveness of remote sensing (MODIS), reanalysis (ERA5 and MERRA2), and their ensemble data sets in monitoring integrated precipitable water vapor (PWV) across India. The study aims to assess the performance of these data sets by comparing them with daily Global Positioning System (GPS) PWV data from 1 March 2013 to 28 February 2014, using statistical metrics such as mean bias (b), root mean square error (RMSE), correlation coefficient (R), and Nash–Sutcliffe efficiency (NSE). Additionally, the study examines and compares the seasonal and annual trends in integrated PWV across different climatic regions of India from 2003 to 2022 using the given data sets. The findings reveal that ERA5, among all the data sets, exhibits better agreement (R ≥ 0.97, NSE = 0.88–0.99) with GPS data in India. The trend analysis shows an overall increase in integrated PWV during the period from 2003 to 2022, with seasonal trends ranging from 0.08 mm/year to 0.18 mm/year. The postmonsoon season records the highest rising trend (0.18 mm/year), followed by the monsoon (0.11 mm/year), winter (0.10 mm/year), and premonsoon seasons (0.08 mm/year). Notably, MODIS NIR, ERA5, and MERRA2 exhibit a rising trend in PWV at both the seasonal and annual scales, whereas MODIS IR shows a positive trend only during the postmonsoon season. Annually, MODIS NIR shows the highest increasing trend of 0.17 mm/year, whereas MODIS IR shows a declining trend of −0.06 mm/year. Interregional variations in PWV trends across India reveal that the West Central region exhibits the highest annual PWV trends, followed by the Peninsular region, whereas the Northwest records the lowest annual trends. The significance of the study lies in improving the accuracy and reliability of PWV estimates, which enhances the precision of meteorological models, improves early warning systems for extreme weather events, and supports sustainable water resource management. This is particularly crucial in India, where diverse climatic conditions and seasonal variability significantly influence societal and economic activities.

Date: 2025
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DOI: 10.1080/24694452.2025.2493823

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