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Remote Sens. 2016, 8(10), 836; doi:10.3390/rs8100836

Evaluation of the Performance of Three Satellite Precipitation Products over Africa

1
Water Global Practice, The World Bank, Washington, DC 20009, USA
2
Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ 85721, USA
3
Montgomery and Associates Ltd., Santiago 7550120, Chile
4
Biosphere 2, The University of Arizona, Tucson, AZ 85721, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Zhongbo Su, Yijian Zeng, Zoltan Vekerdy, Magaly Koch, Richard Müller and Prasad S. Thenkabail
Received: 19 May 2016 / Revised: 31 August 2016 / Accepted: 22 September 2016 / Published: 13 October 2016
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Abstract

We present an evaluation of daily estimates from three near real-time quasi-global Satellite Precipitation Products—Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Center (CPC) Morphing Technique (CMORPH)—over the African continent, using the Global Precipitation Climatology Project one Degree Day (GPCP-1dd) as a reference dataset for years 2001 to 2013. Different types of errors are characterized for each season as a function of spatial classifications (latitudinal bands, climatic zones and topography) and in relationship with the main rain-producing mechanisms in the continent: the Intertropical Convergence Zone (ITCZ) and the East African Monsoon. A bias correction of the satellite estimates is applied using a probability density function (pdf) matching approach, with a bias analysis as a function of rain intensity, season and latitude. The effects of bias correction on different error terms are analyzed, showing an almost elimination of the mean and variance terms in most of the cases. While raw estimates of TMPA show higher efficiency, all products have similar efficiencies after bias correction. PERSIANN consistently shows the smallest median errors when it correctly detects precipitation events. The areas with smallest relative errors and other performance measures follow the position of the ITCZ oscillating seasonally over the equator, illustrating the close relationship between satellite estimates and rainfall regime. View Full-Text
Keywords: satellite precipitation products; Africa; ITCZ; error analysis; bias correction; PERSIANN; CMORPH; TMPA 3B42-RT; product averaging satellite precipitation products; Africa; ITCZ; error analysis; bias correction; PERSIANN; CMORPH; TMPA 3B42-RT; product averaging
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Serrat-Capdevila, A.; Merino, M.; Valdes, J.B.; Durcik, M. Evaluation of the Performance of Three Satellite Precipitation Products over Africa. Remote Sens. 2016, 8, 836.

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