[Eoas-seminar] Reminder: Meteorology Masters Defense - Caitlin Dirkes - June 24, 3:30pm - Zoom

[eoas-seminar at lists.fsu.edu]

Meteorology Seminar

Caitlin Dirkes

Master's Meteorology Candidate



Title:  Process Oriented Diagnostics of Tropical Cyclones in Reanalyses Using Moist Static Energy Budgets

Major Professor:  Dr. Allison Wing

Date: June 24, 2021                Time:  3:30


Location: https://fsu.zoom.us/j/99621735560?pwd=MmJtQ1BQL3NuV0JHUGVsK2JGb1hxdz09  (Meeting ID: 996 2173 5560   Passcode: 587772)




ABSTRACT

Global models have certain biases that are not yet fully understood. Since they are used to represent tropical cyclones (TCs), we need to have a solid understanding of these biases in order to accurately study TCs. The motivation behind this project is to provide an observation-based reference for the processes involved in a good simulation of TCs. Using the column-integrated moist static energy variance budget, we analyze radiative and surface flux feedback terms across five different reanalysis datasets:  MERRA-2, CFSR, JRA-55, ERA5, and ERA-Interim. Our goal is to diagnose the physical mechanisms that cause models to simulate a TC to better understand model biases. This work is a continuation of Wing et al. 2019, where process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled were developed. These diagnostics allow us to evaluate models against observations, which tells us the specific processes to target for model improvement. We consider two different kinds of composites over TC snapshots. We construct a composite relative to the time of lifetime maximum intensity to compare storms at the same lifecycle stage and we construct an intensity bin composite to compare storms of similar intensity. Our results point to some fundamental differences among reanalysis datasets in how they represent surface flux and radiative feedbacks in TCs. These process-oriented diagnostics for TCs can be used by future model developers as a reference tool. Our results leave room for future work to validate climate model simulations against this observation-based reference, which will further help model development efforts.


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