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Geeta Nain, PhD Student, Atmospheric Sciences, Michigan Technological University

Implication of different parametric hurricane wind methods on storm surge over US coastline

Abstract:
Rapid coastal development coupled with sea level rise and increasing hurricane activity in changing climate pose risks to coastal communities.  Storm surges caused by high wind speeds and low atmospheric pressure push ocean water into coastal regions, and can be responsible for catastrophe destruction to properties, injuries and loss of life. Robust estimation of TC induced hazard is required for risk assessments and effective mitigation strategies. Model based surge methods require surface wind fields to estimate surge by coupling hydrodynamic models with hurricane models. Parametric methods approximate surface wind fields by combining parametric vortex models and background wind fields; these methods are very efficient for risk estimates but also limited due to simplicity in estimating overall surface wind. In this presentation, I will present our investigation on the limitations of different parametric wind methods from a coastal risk perspective. Limitation of parametric wind methods can cause poor representation of storm properties and asymmetry of spatial wind field which is crucial for identifying high-risk areas. Surface wind asymmetry has been considered crucial for the spatial distribution of rainfall, relative influence of wind asymmetry on storm surge has not been investigated much. The implications of storm surge predictions due to different combinations of parametric models involving vortex and background wind methods will be discussed.

Bio:
A PhD student in Atmospheric Sciences program with Prof. Pengfei Xue in the CEGE department, Geeta Nain is jointly affiliated with the Environment Science Division at Argonne National Lab for her doctoral studies on Climate informed risk for tropical cyclone induced storm surge risk for the U.S. Coasts and wind risk for Offshore wind farms. Her research involves Tropical cyclone modeling, hydrodynamic modeling of storm surge and risk assessment with synthetic tropical cyclone hazard models in changing climate. She gained four years of work experience in catastrophe modeling at Risk Management Solutions, India and pursued her Masters in Earth, Atmosphere and Planetary Sciences (EAPS) with a computational sciences track in a computational interdisciplinary grad program (CIGP) from Purdue University.

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Chuyan Zhao, Postdoctoral Scholar, Michigan Technological University

Improved thermal structure and lake surface temperature simulation for Lake Superior using a data assimilative model

Abstract:
For ecological health, it is crucial to understand a lake’s biophysical characteristics, which significantly rely on its thermal structure and surface temperature variations. While three-dimensional (3D) hydrodynamic models have been utilized for hindcasting and forecasting water temperature in Great Lakes systems, existing models exhibit noticeable biases. In this study, a data assimilation (DA) method was applied to a 3D hydrodynamic model of Lake Superior, with the goal of enhancing lake-surface temperature (LST) and thermal structure simulations. Numerical experiments were conducted to investigate the impact of DA on both hindcast and forecast of the lake's temperature structure. The study validated the efficacy of DA in improving LST hindcast, extending its positive influence not only to open areas but also along the lake's coastlines. Furthermore, the efficiency of DA in enhancing thermal structure hindcast within the vicinity of moored instruments was demonstrated by assimilating moored thermal data into the model. The study also confirmed the positive effect of DA using satellite LST data on improving thermal structure hindcast, capable of enhancing the entire horizontal thermal structure of the lake in shallow water. Evaluation of the DA model's prediction skill revealed varying performance during different phases of the year. Notably, the DA model exhibited proficient prediction capabilities for LST and thermal structure during all periods, except for the well-stratified phase, which largely depended on the initial error. The findings underscored the significance of the proposed DA strategy in improving hindcast and forecast accuracy in the complex freshwater system of Lake Superior.

Bio:
Chuyan Zhao is a postdoctoral scholar at Michigan Technological University’s Great Lakes Research Center, where she focuses on Great Lakes hydrodynamics and ecosystem modeling. With a Ph.D. from a joint program of Dalian University of Technology and the Massachusetts Institute of Technology in Harbor Coastal and Offshore Engineering, Dr. Zhao has developed expertise in wave-vegetation interactions, sediment transport in vegetated regions, and coastal disaster mitigation. Her research also explores data assimilation techniques to improve hydrodynamic simulations in large inland water bodies like Lake Superior.