  Regression algorithms are regularly used for improving the accuracy of
satellite precipitation products. In this context, satellite precipitation and
topography data are the predictor variables, and gauged-measured precipitation
data are the dependent variables. Alongside this, it is increasingly recognised
in many fields that combinations of algorithms through ensemble learning can
lead to substantial predictive performance improvements. Still, a sufficient
number of ensemble learners for improving the accuracy of satellite
precipitation products and their large-scale comparison are currently missing
from the literature. In this study, we work towards filling in this specific
gap by proposing 11 new ensemble learners in the field and by extensively
comparing them. 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
the contiguous United States (CONUS). We also use gauge-measured precipitation
data from the Global Historical Climatology Network monthly database, version 2
(GHCNm). The ensemble learners combine the predictions of six machine learning
regression algorithms (base learners), namely the multivariate adaptive
regression splines (MARS), multivariate adaptive polynomial splines
(poly-MARS), random forests (RF), gradient boosting machines (GBM), extreme
gradient boosting (XGBoost) and Bayesian regularized neural networks (BRNN),
and each of them is based on a different combiner. The combiners include the
equal-weight combiner, the median combiner, two best learners and seven
variants of a sophisticated stacking method. The latter stacks a regression
algorithm on top of the base learners to combine their independent
predictions...
