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To form precipitation datasets that are accurate and, at the same time, havehigh spatial densities, data from satellites and gauges are often merged in theliterature. However, uncertainty estimates for the data acquired in this mannerare scarcely provided, although the importance of uncertainty quantification inpredictive modelling is widely recognized. Furthermore, the benefits thatmachine learning can bring to the task of providing such estimates have notbeen broadly realized and properly explored through benchmark experiments. Thepresent study aims at filling in this specific gap by conducting the firstbenchmark tests on the topic. On a large dataset that comprises 15-year-longmonthly data spanning across the contiguous United States, we extensivelycompared six learners that are, by their construction, appropriate forpredictive uncertainty quantification. These are the quantile regression (QR),quantile regression forests (QRF), generalized random forests (GRF), gradientboosting machines (GBM), light gradient boosting machines (LightGBM) andquantile regression neural networks (QRNN). The comparison referred to thecompetence of the learners in issuing predictive quantiles at nine levels thatfacilitate a good approximation of the entire predictive probabilitydistribution, and was primarily based on the quantile and continuous rankedprobability skill scores. Three types of predictor variables (i.e., satelliteprecipitation variables, distances between a point of interest and satellitegrid points, and elevation at a point of interest) were used in the comparisonand were additionally compared with each other. This additional comparison wasbased on the explainable machine learning concept of feature importance. Theresults suggest that the order from the best to the worst of the learners forthe task investigated is the following: LightGBM, QRF, GRF, GBM, QRNN and QR...

Machine learning for uncertainty estimation in fusing precipitation observations from satellites and ground-based gauges