Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale

TL;DR

This study compares three tree-based ensemble algorithms—random forests, gradient boosting machines, and XGBoost—for merging satellite and ground-based precipitation data at a daily scale across the US, finding XGBoost performs best.

Contribution

It provides the first comprehensive comparison of these algorithms for satellite precipitation correction at the daily scale in the US.

Findings

XGBoost outperforms random forests and gbm in accuracy.

Tree-based ensemble methods improve satellite precipitation data quality.

Linear regression serves as a baseline for comparison.

Abstract

Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products. Machine and statistical learning regression algorithms are regularly utilized in this endeavour. At the same time, tree-based ensemble algorithms are adopted in various fields for solving regression problems with high accuracy and low computational cost. Still, information on which tree-based ensemble algorithm to select for correcting satellite precipitation products for the contiguous United States (US) at the daily time scale is missing from the literature. In this study, we worked towards filling this methodological gap by conducting an extensive comparison between three algorithms of the category of interest, specifically between random forests, gradient boosting machines (gbm) and extreme gradient boosting (XGBoost). We used daily data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and the IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets. We also used earth-observed precipitation data from the Global Historical Climatology Network daily (GHCNd) database. The experiments referred to the entire contiguous US and additionally included the application of the linear regression algorithm for benchmarking purposes. The results suggest that XGBoost is the best-performing tree-based ensemble algorithm among those compared...