Knowing the actual precipitation in space and time is critical in hydrological modelling applications, yet the spatial coverage with rain gauge stations is limited due to economic constraints. Gridded satellite precipitation datasets offer an alternative option for estimating the actual precipitation by covering uniformly large areas, albeit related estimates are not accurate. To improve precipitation estimates, machine learning is applied to merge rain gauge-based measurements and gridded satellite precipitation products. In this context, observed precipitation plays the role of the dependent variable, while satellite data play the role of predictor variables. Random forests is the dominant machine learning algorithm in relevant applications. In those spatial predictions settings, point predictions (mostly the mean or the median of the conditional distribution) of the dependent variable are issued. The aim of the manuscript is to solve the problem of probabilistic prediction of precipitation with an emphasis on extreme quantiles in spatial interpolation settings. Here we propose, issuing probabilistic spatial predictions of precipitation using Light Gradient Boosting Machine (LightGBM). LightGBM is a boosting algorithm, highlighted by prize-winning entries in prediction and forecasting competitions. To assess LightGBM, we contribute a large-scale application that includes merging daily precipitation measurements in contiguous US with PERSIANN and GPM-IMERG satellite precipitation data. We focus on extreme quantiles of the probability distribution of the dependent variable, where LightGBM outperforms quantile regression forests (QRF, a variant of random forests) in terms of quantile score at extreme quantiles. Our study offers understanding of probabilistic predictions in spatial settings using machine learning.

翻译：了解降水的时空实际分布对于水文建模应用至关重要，但由于经济限制，雨量站的空间覆盖范围有限。网格化卫星降水数据集通过均匀覆盖大面积区域，为估算实际降水提供了替代方案，但其相关估算并不准确。为了改进降水估算，机器学习被应用于融合基于雨量计的测量数据与网格化卫星降水产品。在此背景下，观测降水作为因变量，而卫星数据作为预测变量。随机森林是相关应用中最主流的机器学习算法。在这些空间预测情境中，通常输出因变量的点预测（主要是条件分布的均值或中位数）。本文旨在解决空间插值设置中侧重极端分位数的降水概率预测问题。我们在此提出使用轻量梯度提升机（LightGBM）进行降水的概率空间预测。LightGBM是一种提升算法，在预测和预报竞赛中因获奖成绩而备受瞩目。为评估LightGBM，我们开展了一项大规模应用，内容涉及将美国本土的日降水测量数据与PERSIANN和GPM-IMERG卫星降水数据进行融合。我们重点关注因变量概率分布的极端分位数，在此方面，LightGBM在极端分位数的分位数评分上优于分位数回归森林（QRF，随机森林的一种变体）。我们的研究为利用机器学习进行空间情境下的概率预测提供了理解。