Articles | Volume 21, issue 12
https://doi.org/10.5194/hess-21-6117-2017
https://doi.org/10.5194/hess-21-6117-2017
Research article
 | 
01 Dec 2017
Research article |  | 01 Dec 2017

Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale

Harsh Beria, Trushnamayee Nanda, Deepak Singh Bisht, and Chandranath Chatterjee

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Cited articles

Akhtar, M. K., Corzo, G. A., van Andel, S. J., and Jonoski, A.: River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin, Hydrol. Earth Syst. Sci., 13, 1607–1618, https://doi.org/10.5194/hess-13-1607-2009, 2009.
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Bisht, D. S., Chatterjee, C., Raghuwanshi, N. S., and Sridhar, V.: Spatio-temporal trends of rainfall across Indian river basins, Theor. Appl. Climatol., 1–18, https://doi.org/10.1007/s00704-017-2095-8, online first, 2017.
Collischonn, B., Collischonn, W., and Tucci, C. E. M.: Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates, J. Hydrol., 360, 207–216, https://doi.org/10.1016/j.jhydrol.2008.07.032, 2008.
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Short summary
High-quality satellite precipitation forcings have provided a viable alternative to hydrologic modeling in data-scarce regions. Ageing TRMM sensors have recently been upgraded to GPM, promising enhanced spatio-temporal resolutions. Statistical and hydrologic evaluation of GPM measurements across 86 Indian river basins revealed improved low rainfall estimates with reduced effects of climatology and topography.