# Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles

arXiv: https://arxiv.org/abs/2302.03606 · HTML: https://mlsanity.com/paper/2302.03606/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 90 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [A review of predictive uncertainty estimation with machine learning](https://mlsanity.com/paper/2209.08307/) (2022)
- [Boosting algorithms in energy research: A systematic review](https://mlsanity.com/paper/2004.07049/) (2020)
- [A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting](https://mlsanity.com/paper/2206.08998/) (2022)
- [Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability](https://mlsanity.com/paper/2001.00811/) (2020)
- [Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data](https://mlsanity.com/paper/2301.01252/) (2022)
- [Hydrological post-processing for predicting extreme quantiles](https://mlsanity.com/paper/2202.13166/) (2022)
- [Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale](https://mlsanity.com/paper/2301.01214/) (2022)
- [Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets](https://mlsanity.com/paper/2204.06540/) (2022)

## Built on by (most cited first, 5 of 5)

- [Ensemble learning for blending gridded satellite and gauge-measured precipitation data](https://mlsanity.com/paper/2307.06840/) (2023)
- [Ensemble learning for uncertainty estimation with application to the correction of satellite precipitation products](https://mlsanity.com/paper/2403.10567/) (2024)
- [Uncertainty estimation of machine learning spatial precipitation predictions from satellite data](https://mlsanity.com/paper/2311.07511/) (2023)
- [Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products](https://mlsanity.com/paper/2407.01623/) (2024)
- [TRUST-ESD: A Risk-Calibrated and Governance-Aware AI Framework for Enterprise Strategic Decision Support Under Uncertainty](https://mlsanity.com/paper/2607.20065/) (2026)

## Abstract (arXiv)

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.