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Remote Sens. 2015, 7(9), 11639-11663; doi:10.3390/rs70911639

Performance of High Resolution Satellite Rainfall Products over Data Scarce Parts of Eastern Ethiopia

1
Department for Geology, Technische Universität Bergakademie Freiberg, Freiberg, 09599 Gustav-Zeuner-Str. 12, Germany
2
Water and Land Resource Centre, P.O.Box 3880, Addis Ababa, Ethiopia
3
Department of Earth and Environment, Florida International University, Miami, FL 33199, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra and Prasad S. Thenkabail
Received: 7 April 2015 / Revised: 24 August 2015 / Accepted: 7 September 2015 / Published: 11 September 2015
(This article belongs to the Special Issue Remote Sensing of Water Resources)
View Full-Text   |   Download PDF [835 KB, uploaded 11 September 2015]   |  

Abstract

Accurate estimation of rainfall in mountainous areas is necessary for various water resource-related applications. Though rain gauges accurately measure rainfall, they are rarely found in mountainous regions and satellite rainfall data can be used as an alternative source over these regions. This study evaluated the performance of three high-resolution satellite rainfall products, the Tropical Rainfall Measuring Mission (TRMM 3B42), the Global Satellite Mapping of Precipitation (GSMaP_MVK+), and the Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Networks (PERSIANN) at daily, monthly, and seasonal time scales against rain gauge records over data-scarce parts of Eastern Ethiopia. TRMM 3B42 rain products show relatively better performance at the three time scales, while PERSIANN did much better than GSMaP. At the daily time scale, TRMM correctly detected 88% of the rainfall from the rain gauge. The correlation at the monthly time scale also revealed that the TRMM has captured the observed rainfall better than the other two. For Belg (short rain) and Kiremt (long rain) seasons, the TRMM did better than the others by far. However, during Bega (dry) season, PERSIANN showed a relatively good estimate. At all-time scales, noticing the bias, TRMM tends to overestimate, while PERSIANN and GSMaP tend to underestimate the rainfall. The overall result suggests that monthly and seasonal TRMM rainfall performed better than daily rainfall. It has also been found that both GSMaP and PERSIANN performed better in relatively flat areas than mountainous areas. Before the practical use of TRMM, the RMSE value needs to be improved by considering the topography of the study area or adjusting the bias. View Full-Text
Keywords: satellite rainfall; TRMM 3B42; GSMaP_MVK+; PERSIANN; rain gauge satellite rainfall; TRMM 3B42; GSMaP_MVK+; PERSIANN; rain gauge
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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

Gebere, S.B.; Alamirew, T.; Merkel, B.J.; Melesse, A.M. Performance of High Resolution Satellite Rainfall Products over Data Scarce Parts of Eastern Ethiopia. Remote Sens. 2015, 7, 11639-11663.

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