Webster Gumindoga

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.

Satellite rainfall estimates (SRE) are prone to bias because such estimates are indirectly derived from visible, infrared, and/or microwave based information of cloud properties. We tested the influence of elevation and distance from large scale water bodies on bias for Climate Prediction Center-MORPHing (CMORPH) rainfall estimates. Effectiveness of five linear/non-linear and time-space variant/invariant bias correction schemes is evaluated. Evaluation also covers for different magnitudes of daily rainfall and climatic seasonality. We used daily rain gauge time series (1998–2013) from 60 stations, and counterparts from CMORPH time series for the Zambezi Basin. Taylor diagrams show that station elevation and distance from water bodies do not influence the estimation error of uncorrected CMORPH rainfall. For correction, the Spatio-temporal bias (STB) and Elevation zone bias (EZ) schemes showed best results in removing CMORPH rainfall bias for the Lower, Middle and Upper Zambezi subbasins. STB improved the correlation coefficient by 53 % and reduced the root mean squared difference by 25 %. Assessment of mean estimates by using a Taylor Diagram with mean estimates of correlation coefficient, root mean square difference and standard deviation showed that the EZ, Power transform, Distribution transformation and STB correction schemes best removed errors related to rainfall depth. Corrected CMORPH rainfall revealed an overestimation of very light rainfall ( 20.0 mm/day) for all five correction schemes. Bias is best reduced for rainfall magnitudes of 0.0–2.5 and 5.0–10.0 mm/day. Bias removal proved to be more effective in the wet season than in the dry season.

Obtaining reliable records of rainfall from satellite rainfall estimates (SREs) is a challenge as SREs are an indirect rainfall estimate from visible, infrared (IR), and/or microwave (MW) based information of cloud properties. SREs also contain inherent biases which exaggerate or underestimate actual rainfall values hence the need to apply bias correction methods to improve accuracies. We evaluate the performance of five bias correction schemes for CMORPH satellite-based rainfall estimates. We use 54 raingauge stations in the Zambezi Basin for the period 1998–2013 for comparison and correction. Analysis shows that SREs better match to gauged estimates in the Upper Zambezi Basin than the Lower and Middle Zambezi basins but performance is not clearly related to elevation. Findings indicate that rainfall in the Upper Zambezi Basin is best estimated by an additive bias correction scheme (Distribution transformation). The linear based (Spatio-temporal) bias correction scheme successfully corrected the daily mean of CMORPH estimates for 70 % of the stations and also was most effective in reducing the rainfall bias. The nonlinear bias correction schemes (Power transform and the Quantile based empirical-statistical error correction method) proved most effective in reproducing the rainfall totals. Analyses through bias correction indicate that bias of CMORPH estimates has elevation and seasonality tendencies across the Zambezi river basin area of large scale.