Title: Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones.
Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate TC intensity forecasts and for understanding the role TCs play in the global climate system, yet that characterization is complicated by the high-noise ocean environment, correlations inherent in spatio-temporal data, relative scarcity of in situ observations and the entanglement of the TC-induced signal with seasonal signals. We present a general methodological framework that addresses these difficulties, integrating existing techniques in seasonal mean field estimation, Gaussian process modeling, and nonparametric regression into a functional ANOVA model. Importantly, we improve upon past work by properly handling seasonality, providing rigorous uncertainty quantification, and treating time as a continuous variable, rather than producing estimates that are binned in time. This functional ANOVA model is estimated using in situ subsurface temperature profiles from the Argo fleet of autonomous floats through a multi-step procedure, which (1) characterizes the upper ocean seasonal shift during the TC season; (2) models the variability in the temperature observations; (3) fits a thin plate spline using the variability estimates to account for heteroskedasticity and correlation between the observations. This spline fit reveals the ocean thermal response to TC passage. Through this framework, we obtain new scientific insights into the interaction between TCs and the ocean on a global scale, including a three-dimensional characterization of the near-surface and subsurface cooling along the TC storm track and the mixing-induced subsurface warming on the track’s right side. Joint work with Addison Hu, Ann Lee, Donata Giglio and Kimberly Wood.
Dr. Mikael Kuusela is an Assistant Professor of Statistics and Data Science at Carnegie Mellon University. His research focuses on developing statistical methods for analyzing large and complex datasets in the physical sciences. He is particularly interested in questions related to ill-posed inverse problems, spatio-temporal data, uncertainty quantification and statistical learning in climate science, oceanography, remote sensing and particle physics.
Meeting ID: 939 8331 3215