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arXiv Machine Learning By Ryan Solgi, Rohan Shankar, Hugo A. Loaiciga

Low-rank tensor structure of precipitation and its application to satellite-reference merging

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The study shows that daily precipitation over the contiguous United States can be represented as a low‑rank spatiotemporal tensor, and that preserving native spatial and temporal modes via CANDECOMP/PARAFAC factorization yields more accurate reconstructions than treating daily fields independently. Building on this, the authors developed TMerge, a tensor‑based framework that merges satellite precipitation with sparse reference observations through shared low‑rank factors. Applied to the IMERG Final Run product, TMerge raised correlation from 0.53 to 0.85 and cut root‑mean‑square error and mean absolute error by 48.2 % and 29.3 %, outperforming linear bias correction, quantile mapping, and neural networks across seasons, intensity regimes, and regions, especially in coastal areas.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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