arXiv：cs.LG· Ryan Solgi, Rohan Shankar, Hugo A. Loaiciga·· 4 小时前
利用低秩张量结构实现卫星降水与稀疏参考观测的融合：TMerge 框架
Low-rank tensor structure of precipitation and its application to satellite-reference merging
AI 导读
研究提出 TMerge 张量框架，通过共享低秩时空因子将卫星降水与稀疏参考观测融合，在美国本土对 IMERG Final Run 进行校正。2019-2022 年间，TMerge 将相关系数从 0.53 提升至 0.85，均方根误差和平均绝对误差分别降低 48.2% 和 29.3%，在各季节、降水强度区间和区域上均优于线性偏差校正、分位数映射和神经网络。
正文
Abstract:The intermittent and variable nature of precipitation makes its accurate estimation over extended domains difficult, yet its spatiotemporal structure suggests that a low-rank representation may be possible. This work represents daily precipitation over the contiguous United States (CONUS) as spatiotemporal tensors and applies CANDECOMP/PARAFAC factorization, showing that preserving the native spatial and temporal modes yields more accurate reconstruction than factorizing independent daily fields or unfolded space--time matrices. Building on this finding, this work presents TMerge, a tensor-based framework that integrates satellite precipitation with sparse reference observations through shared low-rank spatial and temporal factors. TMerge was applied to correct the IMERG Final Run product with climate prediction center reference observations over CONUS. During 2019-2022, TMerge increased correlation from 0.53 to 0.85 and reduced root-mean-square error and mean absolute error by 48.2% and 29.3%, respectively. TMerge consistently outperformed linear bias correction, quantile mapping, and neural networks across seasons, precipitation-intensity regimes, and regions. Improvements were spatially coherent and largest in coastal regions where IMERG errors were greatest. These results demonstrate that low-rank tensor structure parsimoniously approximates the dominant spatiotemporal variability of precipitation and provides a practical mechanism for improving satellite estimates under limited reference observations over extended domains.
| Subjects: | Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph) |
| Cite as: | arXiv:2610.11000 [cs.LG] |
| (or arXiv:2610.11000v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11000 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ryan Solgi [view email]
[v1]
Wed, 7 Oct 2026 23:35:02 UTC (7,745 KB)
来源：arXiv：cs.LG · arxiv.org