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Proceedings Paper

Evaluation of multi-satellite rainfall products over India during monsoon
Author(s): Ashis K. Mitra; Satya Prakash; D. S. Pai; A. K. Srivastava
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Paper Abstract

Simulation and prediction of Indian monsoon rainfall at scales from days-to-season is a challenging task for numerical modelling community worldwide. Gridded estimates of daily rainfall data are required for both land and oceanic regions for model validation, process studies and in turn for model development. Due to recent developments in satellite meteorology, it has become possible to produce realistic near real-time gridded rainfall datasets at operational basis by combining satellite estimates with rain gauge values and other available in-situ observations. Microwave and space based radar based estimates of rainfall has revolutionised the preparation of rainfall datasets especially for tropics. However, a variety of multi-satellite products are available over Indian monsoon region from a variety of sources. Popular products like TRMM TMPA3B42 (RT and V7), GsMaP, CPC/RFE, GPCP and GPM are available to end users in various space/time scales for applications and model validation. In this study, we show the representation and skill of monsoon rainfall from a variety of multi-satellite products over the Indian region. The bias and skill of multi-satellite rainfall are evaluated against gauge based observations. It was found that the TRMM based TMPA was one of the best dataset for Indian monsoon region. Attempt is made to compare the latest GPM based data with other products. The GPM based rainfall product is seen to be superior compared to TRMM.

Paper Details

Date Published: 5 May 2016
PDF: 8 pages
Proc. SPIE 9876, Remote Sensing of the Atmosphere, Clouds, and Precipitation VI, 987607 (5 May 2016); doi: 10.1117/12.2223311
Show Author Affiliations
Ashis K. Mitra, ESSO-NCMRWF (India)
Satya Prakash, New York City College of Technology (United States)
D. S. Pai, IMD Pune (India)
A. K. Srivastava, IMD Pune (India)


Published in SPIE Proceedings Vol. 9876:
Remote Sensing of the Atmosphere, Clouds, and Precipitation VI
Eastwood Im; Raj Kumar; Song Yang, Editor(s)

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