Title page for ETD etd-05092011-210249

Type of Document

Thesis

Author

Thompson, Aarolyn

URN

etd-05092011-210249

Title

Psi Chi Interactions In Hurricane Model Forecasts With Rapidly Changing Intensity

Degree

Master of Science

Department

Earth, Ocean & Atmospheric Science, Department of

Advisory Committee

Advisor Name	Title
Tiruvalam Krishnamurti	Committee Chair
Robert Hart	Committee Member
Vasubandhu Misra	Committee Member

Keywords

Hurricane
Psi Chi
Intensity

Date of Defense

2011-03-08

Availability

unrestricted

Abstract

Rapid intensity changes in hurricanes have been the focus of many publications. Understanding how and why these storms intensify has been the goal of many meteorologists over the last few years. This study explores the energetics of Psi-Chi interactions in modeled hurricanes of rapidly changing intensity. The energy exchanged within the storm as the flow changes from divergent to rotational is known as Psi-Chi interactions. Processes associated with latent heant and sensible heat exchanges are important when it comes to identifying changes in hurricane intensity. Understanding this interaction with the environment is crucial to understanding how the intensity changes. Model data derived from the fifth generation model from NCAR/Penn State known as the MM5 is used to calculate each term within the Psi-Chi equation. Each term is then calculated within the storm and its maximum values as well as location of maximum values within the storm are noted. The maximum value of each term and location of each term allows for an investigation of a connection between the energy exchange of the storm and its intensity. The maximum values of the terms of Psi-Chi interactions are also compared to the model intensity of each storm. Based on the comparison of model intensity and maximum term values there appears to be a strong relationship between the two (correlation values near r = 0.87), although further studt is needed to determine if this simultaneous relationship can be extended to a lagged relationship to provide predictive value to forecasters.

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