Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles by Hristos Tyralis, Georgia Papacharalampous, Nikolaos Doulamis, Anastasios Doulamis

“…To assess LightGBM, we contribute a large-scale application that includes merging daily precipitation measurements in contiguous United States with PERSIANN and GPM-IMERG satellite precipitation data. …”
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Comparison of Machine Learning Algorithms for Merging Gridded Satellite and Earth-Observed Precipitation Data by Georgia Papacharalampous, Hristos Tyralis, Anastasios Doulamis, Nikolaos Doulamis

“…To provide results that are generalizable and to contribute to the delivery of best practices, we here compare eight state-of-the-art machine learning algorithms in correcting satellite precipitation data for the entire contiguous United States and for a 15-year period. We use monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) gridded dataset, together with monthly earth-observed precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). …”
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Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale by Georgia Papacharalampous, Hristos Tyralis, Anastasios Doulamis, Nikolaos Doulamis

“…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. …”
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Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data by Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis

“…We apply the ensemble learners to monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets that span over a 15-year period and over the entire contiguous United States (CONUS). We also use gauge-measured precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). …”
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