In geostatistical analysis, we are faced with the formidable challenge of specifying a valid
spatio-temporal covariance function, either directly or through the construction of processes.
This task is di cult as these functions should yield positive de nite covariance matrices. In
recent years, we have seen a
ourishing of methods and theories on constructing spatiotemporal
covariance functions satisfying the positive de niteness requirement. The current
state-of-the-art when modeling environmental processes are those that embed the associated
physical laws of the system. The class of Lagrangian spatio-temporal covariance functions
ful lls this requirement. Moreover, this class possesses the allure that they turn already
established purely spatial covariance functions into spatio-temporal covariance functions by
a direct application of the concept of Lagrangian reference frame. In the three main chapters
that comprise this dissertation, several developments are proposed and new features
are provided to this special class. First, the application of the Lagrangian reference frame
on transported purely spatial random elds with second-order nonstationarity is explored,
an appropriate estimation methodology is proposed, and the consequences of model misspeci
cation is tackled. Furthermore, the new Lagrangian models and the new estimation
technique are used to analyze particulate matter concentrations over Saudi Arabia. Second,
a multivariate version of the Lagrangian framework is established, catering to both secondorder
stationary and nonstationary spatio-temporal random elds. The capabilities of the
Lagrangian spatio-temporal cross-covariance functions are demonstrated on a bivariate reanalysis
climate model output dataset previously analyzed using purely spatial covariance functions. Lastly, the class of Lagrangian spatio-temporal cross-covariance functions with
multiple transport behaviors is presented, its properties are explored, and its use is demonstrated
on a bivariate pollutant dataset of particulate matter in Saudi Arabia. Moreover,
the importance of accounting for multiple transport behaviors is discussed and validated
via numerical experiments. Together, these three extensions to the Lagrangian framework
makes it a more viable geostatistical approach in modeling realistic transport scenarios.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/669674 |
| Date | 14 June 2021 |
| Creators | Salvaña, Mary Lai O. |
| Contributors | Genton, Marc G., Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Ombao, Hernando, Sang, Huiyan, Stenchikov, Georgiy L. |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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