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Evaluation of Satellite Retrieval Algorithm to Ground Rainfall Estimates Over Malaysia

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Abstract

Satellite precipitation data is an indirect measurement for land and water area over the globe. The information from this data is important especially to the area where direct rain gauge measurement is limited. However, the precipitation estimates require calibration and validation to assure the accuracy. Satellite rainfall estimations together with comprehensive ground validation and calibration have been found to offer good prospect for accurate and global rainfall database especially for remote areas and large bodies of water. This work presents performance evaluation of TRMM 3B43 V7 rainfall retrieval algorithms over Malaysia. Inter-comparison and validation of TRMM 3B43 V7 rainfall product with ground measurement is analysed statistically. The result of continuous statistical evaluation shows good agreement, in which, the best correlation for the algorithm 3B43 versus rain gauge is 0.9384. At lower percentage bias threshold, the 2 by 2 categorical statistic of rain or no rain occurrence for annual estimation reveals lower value of probability of detection and higher value of false alarm ratio. However, reverse results are shown at higher bias threshold. The accuracy of the algorithm for a threshold of 1–10 % which falls within International Telecommunication Union—Radio recommendation for radio propagation to discriminate between rain and no rain is 0.53, 0.49 and 0.48 for annual, monthly and wet season, respectively. From analysis, the categorical statistical approach has been able to reveal the level of accuracy of the algorithms as applicable to detection and estimation of rainfall.

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Acknowledgments

The authors would like to appreciate the support of National Aeronautics and Space Administration (NASA) USA and National Space Development Agency (NASDA) Japan for free access to their rainfall data archive. Many thanks go to Malaysia Meteorological Department (MMD) and Department of Irrigation and Drainage Malaysia (DIDM), for making their precipitation data available to us.

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Authors and Affiliations

  1. Department of Electronic and Electrical Engineering, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria

    F. A. Semire

  2. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Eng. Campus, 14300, Nibong Tebal, Pulau Pinang, Malaysia

    R. Mohd-Mokhtar

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Correspondence to F. A. Semire.

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Semire, F.A., Mohd-Mokhtar, R. Evaluation of Satellite Retrieval Algorithm to Ground Rainfall Estimates Over Malaysia. MAPAN 31, 177–187 (2016). https://doi.org/10.1007/s12647-016-0171-7

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