Articles | Volume 10, issue 2
https://doi.org/10.5194/ascmo-10-69-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-10-69-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
22 Jul 2024
| 22 Jul 2024
Spatiotemporal methods for estimating subsurface ocean thermal response to tropical cyclones
Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
Mikael Kuusela
Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
Ann B. Lee
Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
Donata Giglio
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA
Kimberly M. Wood
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
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Short summary
We introduce a new statistical framework to estimate the change in subsurface ocean temperature following the passage of a tropical cyclone (TC). Our approach combines tools handling seasonal variations and spatial dependence in the data, culminating in a three-dimensional characterization of the interaction between TCs and the ocean. Our work allows us to obtain new scientific insights, and we expect it to be generally applicable to studying the impact of TCs on other ocean phenomena.
We introduce a new statistical framework to estimate the change in subsurface ocean temperature...
