For evaluation of the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product in the Zambezi Basin, daily time series (1998–2013) of 60 rain gauge stations are used. Evaluations for occurrence and rain rate are at sub-basin scale and at daily, weekly, and seasonal
timescale by means of probability of detection (POD), false alarm ratio (FAR), critical success index (CSI) and frequency bias (FBS). CMORPH predicts 60% of the rainfall occurrences. Rainfall detection is better for the wet season than for the dry season. Best detection is shown for rainfall
rates smaller than 2.5 mm/day. Findings on error decomposition revealed sources of Hit, Missed and False rainfall bias. CMORPH performance (detection of rainfall occurrences and estimations for rainfall depth) at sub-basin scale increases when daily estimates are accumulated to weekly estimates.
Findings suggest that for the Zambezi Basin, errors in CMORPH rainfall should be corrected before the product can serve applications such as in hydrological modelling that largely rely on reliable and accurate rainfall inputs.
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Document Type: Research Article
Faculty ITC, University of Twente, Enschede, The Netherlands
International Water Management Institute (IWMI), Addis Ababa, Ethiopia
Civil Engineering Department, University of Zimbabwe, Harare, Zimbabwe
Department of Civil Engineering, University of Siegen, Siegen, Germany
Publication date: October 18, 2019
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