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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/26814
Title: An assessment of global satellite-based precipitation datasets in capturing precipitation extremes: A comparison with observed precipitation dataset in India
Authors: Gupta V.
Jain, Manoj Kumar
Singh P.K.
Singh V.
Published in: International Journal of Climatology
Abstract: The availability of global satellite-based precipitation datasets provides an asset to accomplish precipitation dependent analysis where gauge based precipitation datasets are not available or limited. In this study, we have taken three most popular and globally accepted satellite-based daily gridded (0.25° × 0.25°) precipitation datasets such as Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Satellite Soil Moisture to Rain (SM2RAIN-ASCAT) and Tropical Rainfall Measuring Mission (TRMM now available as Global Precipitation Measurement [GPM]) for 10 years (2007–2016) time-series durations to test their reliability across India. The India Meteorological Department (IMD) observed daily gridded (0.25° × 0.25°) precipitation data have been taken as reference data to compare the other three satellite-based gridded precipitation datasets by developing standard extreme precipitation indices (SEPIs). The precipitation extremity has been tested in the wet season (June–July–August–September) and throughout the year. We have also analysed the extreme behaviour of precipitation (in both upper and lower tails) using quantile-quantile (Q–Q) regression analysis after selecting 33 random precipitation grids across India. The overall analysis results showed that all satellite-based datasets have significant spatial heterogeneity in estimating precipitation extremes accurately which varies across India. Among all satellite-precipitation datasets, TRMM found closer to IMD than SM2RAIN-ASCAT and CHIRPS. The frequency based SEPIs showed that CHIRPS, TRMM and SM2RAIN-ASCAT have similarities to IMD precipitations. The intensity-based SEPIs show that TRMM and CHIRPS have significant similarities with IMD precipitations. The wet season-based analysis results showed that TRMM and CHIRPS are closer to IMD precipitations than SM2RAIN-ASCAT satellite-precipitations. Overall TRMM and CHIRPS datasets performed well across most regions in India, while SM2RAIN-ASCAT dataset has performed poorly in India, especially for extreme precipitation cases. Q–Q plots show that each satellite-based precipitation dataset captured most of extreme cases in different quantile intervals with respect to IMD precipitation; however, SM2RAIN-ASCAT has slightly under-performed at many regions in India. © 2019 Royal Meteorological Society
Citation: International Journal of Climatology, 40(8): 3667-3688
URI: https://doi.org/10.1002/joc.6419
http://repository.iitr.ac.in/handle/123456789/26814
Issue Date: 2020
Publisher: John Wiley and Sons Ltd
Keywords: Climate Hazards Group InfraRed Precipitation with Station
extreme precipitation
Indian Meteorological Department precipitation
Soil Moisture to Rain
Tropical Rainfall Measuring Mission
ISSN: 8998418
Author Scopus IDs: 57208825220
57225721930
57208371734
57225865788
Author Affiliations: Gupta, V., Department of Hydrology, Indian Institute of Technology, Roorkee, India
Jain, M.K., Department of Hydrology, Indian Institute of Technology, Roorkee, India
Singh, P.K., Water Resources System Division, National Institute of Hydrology, Roorkee, India
Singh, V., Water Resources System Division, National Institute of Hydrology, Roorkee, India
Funding Details: The authors would like to thank National Institute of Hydrology and Indian Institute of Technology Roorkee, India, for providing facilities to carry out this research work. The authors are also thankful to Indian Meteorological Department Pune for providing the gridded precipitation dataset. The authors are thankful to CHIRP, TRMM data and SM2RAIN-ASCAT data generation teams for providing the meteorological data at free of cost. They are thankful to Python developers project team who made the software available free of cost. This research work has not got any funding from any source. The authors would like to thank National Institute of Hydrology and Indian Institute of Technology Roorkee, India, for providing facilities to carry out this research work. The authors are also thankful to Indian Meteorological Department Pune for providing the gridded precipitation dataset. The authors are thankful to CHIRP, TRMM data and SM2RAIN-ASCAT data generation teams for providing the meteorological data at free of cost. They are thankful to Python developers project team who made the software available free of cost. This research work has not got any funding from any source. Indian Institute of Technology Roorkee, IITR
Corresponding Author: Singh, V.; Water Resources System Division, India; email: shalu.ashu50@gmail.com
Appears in Collections:Journal Publications [HY]

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