Abstract
Abstract. Satellite rainfall estimates (SREs) are prone to bias as they are indirect derivatives of the visible, infrared, and/or microwave cloud properties, and hence SREs need correction. We evaluate the influence of elevation and distance from large-scale open water bodies on bias for Climate Prediction Center-MORPHing (CMORPH) rainfall estimates in the Zambezi basin. The effectiveness of five linear/non-linear and time–space-variant/-invariant bias-correction schemes was evaluated for daily rainfall estimates and climatic seasonality. The schemes used are spatio-temporal bias (STB), elevation zone bias (EZ), power transform (PT), distribution transformation (DT), and quantile mapping based on an empirical distribution (QME). We used daily time series (1998–2013) from 60 gauge stations and CMORPH SREs for the Zambezi basin. To evaluate the effectiveness of the bias-correction schemes spatial and temporal cross-validation was applied based on eight stations and on the 1998–1999 CMORPH time series, respectively. For correction, STB and EZ schemes proved to be more effective in removing bias. STB improved the correlation coefficient and Nash–Sutcliffe efficiency by 50 % and 53 %, respectively, and reduced the root mean squared difference and relative bias by 25 % and 33 %, respectively. Paired t tests showed that there is no significant difference (p < 0.05) in the daily means of CMORPH against gauge rainfall after bias correction. ANOVA post hoc tests revealed that the STB and EZ bias-correction schemes are preferable. Bias is highest for very light rainfall (< 2.5 mm d−1), for which most effective bias reduction is shown, in particular for the wet season. Similar findings are shown through quantile–quantile (q–q) plots. The spatial cross-validation approach revealed that most bias-correction schemes removed bias by > 28 %. The temporal cross-validation approach showed effectiveness of the bias-correction schemes. Taylor diagrams show that station elevation has an influence on CMORPH performance. Effects of distance > 10 km from large-scale open water bodies are minimal, whereas effects at shorter distances are indicated but are not conclusive for a lack of rain gauges. Findings of this study show the importance of applying bias correction to SREs.
Highlights
Correction schemes for rainfall estimates are developed for climate models (Maraun, 2016; Grillakis et al, 2017; Switanek et al, 2017), for radar approaches (Cecinati et al, 2017; Yoo et al, 2014), and for satellite-based, multi-sensor approaches (Najmaddin et al, 2017; Valdés-Pineda et al, 2016)
Bias-correction schemes evaluated in this study are the spatio-temporal bias (STB), elevation zone bias (EZ), power transform (PT), distribution transformation (DT), and quantile mapping based on an empirical distribution (QME), this by our aim to correct for bias while daily rainfall variability is preserved
We present methods to assess the performance of biascorrection schemes for CMORPH rainfall estimates in the Zambezi River basin
Summary
Correction schemes for rainfall estimates are developed for climate models (Maraun, 2016; Grillakis et al, 2017; Switanek et al, 2017), for radar approaches (Cecinati et al, 2017; Yoo et al, 2014), and for satellite-based, multi-sensor approaches (Najmaddin et al, 2017; Valdés-Pineda et al, 2016). In this study the focus is on satellite rainfall estimates (SREs) to improve reliability in spatio-temporal rainfall representation. Studies in satellite-based rainfall estimation show that estimates are prone to systematic and random errors (Gebregiorgis et al, 2012; Habib et al, 2014; Shrestha, 2011; Tesfagiorgis et al, 2011; Vernimmen et al, 2012; Woody et al, 2014). W. Gumindoga et al.: Performance of bias-correction schemes
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