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This study evaluates a machine learning-based precipitation ensemble technique (MLPET) over three mountainous tropical regions. The technique, based on quantile regression forests, integrates global satellite precipitation datasets from CMORPH, PERSIANN, GSMaP (V6), and 3B42 (V7) and an atmospheric reanalysis precipitation product (EI_GPCC) with daily soil moisture, specific humidity, and terrain elevation datasets. The complex terrain study areas include the Peruvian and Colombian Andes in South America and the Blue Nile in East Africa. Evaluation is performed at a daily time scale and 0.258 spatial resolution based on 13 years (2000-12) of reference rainfall data derived from dense in situ rain gauge networks. The technique is evaluated using K-fold, separately in each region, and leave-one-region-out validation experiments. Comparison of MLPET with the individual satellite and reanalysis precipitation datasets used for the blending and the recent Multi-Source Weighted-Ensemble Precipitation (MSWEP) global precipitation product exhibited improved systematic and random error statistics for all regions. In addition, it is shown that observations are encapsulated well within the ensemble envelope generated by the blending technique. [ABSTRACT FROM AUTHOR]

The performance of six satellite-based and three newly released reanalysis rainfall estimates are evaluated at daily time scale and spatial grid size of 0.25 degrees during the period of 2000 to 2013 over the Upper Blue Nile Basin, Ethiopia, with the view of improving the reliability of precipitation estimates of the wet (June to September) and secondary rainy (March to May) seasons. The study evaluated both adjusted and unadjusted satellite-based products of TMPA, CMORPH, PERSIANN, and ECMWF ERA-Interim reanalysis as well as Multi-Source Weighted-Ensemble Precipitation (MSWEP) estimates. Among the six satellite-based rainfall products, adjusted CMORPH exhibits the best accuracy of the wet season rainfall estimate. In the secondary rainy season, unadjusted CMORPH and 3B42V7 are nearly equivalent in terms of bias, POD, and CSI error metrics. All error metric statistics show that MSWEP outperform both unadjusted and gauge adjusted ERA-Interim estimates. The magnitude of error metrics is linearly increasing with increasing percentile threshold values of gauge rainfall categories. Overall, all precipitation datasets need further improvement in terms of detection during the occurrence of high rainfall intensity. MSWEP detects higher percentiles values better than satellite estimate in the wet and poor in the secondary rainy seasons. [ABSTRACT FROM AUTHOR]