Structured Covariate-Informed Empirical Orthogonal Functions for Spatio-Temporal Environmental Fields
Abstract
Low-rank representations such as empirical orthogonal function (EOF) decompositions are widely used for analyzing large spatio-temporal environmental fields. However, conventional EOF identifies latent modes solely from covariance structure and does not utilize observed environmental covariates, limiting its ability to incorporate external information into low-rank representations. This study introduces Structured Covariate-Informed EOF (SCIEOF), a covariate-informed extension of EOF that bridges low-rank dimension reduction and prediction-oriented spatio-temporal modeling. SCIEOF embeds spatial and temporal covariates into the latent bases while incorporating spatio-temporal covariates through an additive component, yielding low-rank representations with latent modes informed by observed covariates. Estimation procedures are developed and evaluated through simulation studies and an application to global near-surface air temperature from the MERRA-2 reanalysis. Simulation studies demonstrate that incorporating informative covariates improves latent structure recovery and predictive accuracy, particularly when the spatial basis is appropriately specified. The advantage is more pronounced at moderate-to-large sample sizes, while methods with stronger structural assumptions remain competitive when data are limited. In the MERRA-2 application, SCIEOF achieves competitive or improved predictive performance relative to commonly used methods while providing a compact and physically interpretable low-rank representation. Overall, SCIEOF provides a flexible and computationally scalable framework for integrating structural covariate information into low-rank spatio-temporal representations, extending EOF toward predictive environmental modeling.
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BibTeX
@misc{huang2026structured,
title = {{Structured Covariate-Informed Empirical Orthogonal Functions for Spatio-Temporal Environmental Fields}},
author = {Hao-Yun Huang and ShengLi Tzeng},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.13850},
eprint = {2609.13850},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.13850},
}
RIS
TY - UNPB
TI - Structured Covariate-Informed Empirical Orthogonal Functions for Spatio-Temporal Environmental Fields
AU - Hao-Yun Huang
AU - ShengLi Tzeng
PY - 2026
DA - 2026/09/12
PB - arXiv
DO - 10.48550/arxiv.2609.13850
UR - https://arxiv.org/abs/2609.13850
AB - Low-rank representations such as empirical orthogonal function (EOF) decompositions are widely used for analyzing large spatio-temporal environmental fields. However, conventional EOF identifies latent modes solely from covariance structure and does not utilize observed environmental covariates, limiting its ability to incorporate external information into low-rank representations. This study introduces Structured Covariate-Informed EOF (SCIEOF), a covariate-informed extension of EOF that bridges low-rank dimension reduction and prediction-oriented spatio-temporal modeling. SCIEOF embeds spatial and temporal covariates into the latent bases while incorporating spatio-temporal covariates through an additive component, yielding low-rank representations with latent modes informed by observed covariates. Estimation procedures are developed and evaluated through simulation studies and an application to global near-surface air temperature from the MERRA-2 reanalysis. Simulation studies demonstrate that incorporating informative covariates improves latent structure recovery and predictive accuracy, particularly when the spatial basis is appropriately specified. The advantage is more pronounced at moderate-to-large sample sizes, while methods with stronger structural assumptions remain competitive when data are limited. In the MERRA-2 application, SCIEOF achieves competitive or improved predictive performance relative to commonly used methods while providing a compact and physically interpretable low-rank representation. Overall, SCIEOF provides a flexible and computationally scalable framework for integrating structural covariate information into low-rank spatio-temporal representations, extending EOF toward predictive environmental modeling.
ER -