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Flexible nonstationary spatiotemporal modeling of high‐frequency monitoring data
Author(s) -
Geoga Christopher J.,
Anitescu Mihai,
Stein Michael L.
Publication year - 2021
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2670
Subject(s) - covariance , estimator , computer science , algorithm , computation , boundary (topology) , covariance function , process (computing) , doppler effect , mathematics , statistics , mathematical analysis , physics , astronomy , operating system
Many physical datasets are generated by collections of instruments that make measurements at regular time intervals. For such regular monitoring data, we extend the framework of half‐spectral covariance functions to the case of nonstationarity in space and time and demonstrate that this method provides a natural and tractable way to incorporate complex behaviors into a covariance model. Further, we use this method with fully time‐domain computations to obtain bona fide maximum likelihood estimators—as opposed to using Whittle‐type likelihood approximations, for example—that can still be computed conveniently. We apply this method to very high‐frequency Doppler LIDAR vertical wind velocity measurements, demonstrating that the model can expressively capture the extreme nonstationarity of dynamics above and below the atmospheric boundary layer and, more importantly, the interaction of the process dynamics across it.

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