Uncertainty estimation in satellite precipitation interpolation with machine learning

arXiv:2311.07511v2 Announce Type: replace-cross
Abstract: Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We address this gap by benchmarking six algorithms, mostly novel for this task, for quantifying predictive uncertainty in spatial interpolation. On 15 years of monthly data over the contiguous United States (CONUS), we compared quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machines (LightGBM), and quantile regression …

abstract algorithms arxiv benchmarking cs.lg data datasets gap machine machine learning merging novel physics.ao-ph precipitation predictive satellite six spatial stat.ap stat.me stat.ml type uncertainty