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DESCRIPTION:\n	\n		\n			Title: Spatio-temporal methods for estimating subsurface 
 ocean thermal response to tropical cyclones.\n\n			\n				\n					Abstract:\n\n					Tropical 
 cyclones (TCs)\, driven by heat exchange between the air and sea\, pose a 
 substantial risk to many communities around the world. Accurate characteri
 zation of the subsurface ocean thermal response to TC passage is crucial f
 or accurate TC intensity forecasts and for understanding the role TCs play
  in the global climate system\, yet that characterization is complicated b
 y the high-noise ocean environment\, correlations inherent in spatio-tempo
 ral data\, relative scarcity of in situ observations and the entanglement 
 of the TC-induced signal with seasonal signals. We present a general metho
 dological framework that addresses these difficulties\, integrating existi
 ng techniques in seasonal mean field estimation\, Gaussian process modelin
 g\, and nonparametric regression into a functional ANOVA model. Importantl
 y\, we improve upon past work by properly handling seasonality\, providing
  rigorous uncertainty quantification\, and treating time as a continuous v
 ariable\, rather than producing estimates that are binned in time. This fu
 nctional ANOVA model is estimated using in situ subsurface temperature pro
 files from the Argo fleet of autonomous floats through a multi-step proced
 ure\, 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 h
 eteroskedasticity and correlation between the observations. This spline fi
 t 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 characterizati
 on 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 wo
 rk with Addison Hu\, Ann Lee\, Donata Giglio and Kimberly Wood.\n\n					Speaker
 \n\n					Dr. Mikael Kuusela is an Assistant Professor of Statistics and Data Sc
 ience at Carnegie Mellon University. His research focuses on developing st
 atistical 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 an
 d particle physics.\n				\n			\n\n			\n				\n					\n						\n							Zoom Link\n\n							Meeting ID: 939 8331 3215
 \n\n							Passcode: 096952\n						\n					\n\n					 \n\n					 \n\n					 \n				\n			\n		\n	\n\n
DTSTART:20210212T203000Z
DTEND:20210212T213000Z
SUMMARY:Mikael Kuusela (Carnegie Mellon University)
URL:/mathstat/channels/event/mikael-kuusela-carnegie-m
 ellon-university-328538
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