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<p class="HTMLBody" align="center" style="text-align:center;line-height:115%"><b><u><span style="font-size:26.0pt;line-height:115%;font-family:"Monotype Corsiva"">Meteorology Seminar<o:p></o:p></span></u></b></p>
<p class="HTMLBody" align="center" style="text-align:center"><span style="font-size:28.0pt;font-family:"Monotype Corsiva"">Caitlin Dirkes<o:p></o:p></span></p>
<p class="HTMLBody" align="center" style="text-align:center"><span style="font-size:28.0pt;font-family:"Monotype Corsiva"">Master’s Meteorology Candidate<o:p></o:p></span></p>
<p class="HTMLBody"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
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<b><u><span style="font-size:14.0pt">Title</span></u>:</b> <span style="color:black">
Process Oriented Diagnostics of Tropical Cyclones in Reanalyses Using Moist Static Energy Budgets</span><span style="font-size:14.0pt"><o:p></o:p></span></p>
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<p class="MsoNormal"><b><u><span style="font-size:14.0pt">Major Professor</span></u></b><b><span style="font-size:14.0pt">: Dr. Allison Wing</span></b><b><u><span style="font-size:14.0pt;font-family:"Times",serif"><o:p></o:p></span></u></b></p>
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<p class="MsoNormal"><b><u><span style="font-size:14.0pt">Date</span></u></b><b><span style="font-size:14.0pt">:</span></b><span style="font-size:14.0pt"> June 24, 2021
<b><u>Time</u>: 3:30</b><o:p></o:p></span></p>
<p class="HTMLBody"><b><o:p> </o:p></b></p>
<p class="MsoNormal"><b><u><span style="font-size:14.0pt">Location</span></u></b><b><span style="font-size:14.0pt">:
</span></b><a href="https://fsu.zoom.us/j/99621735560?pwd=MmJtQ1BQL3NuV0JHUGVsK2JGb1hxdz09">https://fsu.zoom.us/j/99621735560?pwd=MmJtQ1BQL3NuV0JHUGVsK2JGb1hxdz09</a> (Meeting ID: 996 2173 5560 Passcode: 587772)<o:p></o:p></p>
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<p class="HTMLBody"><b><o:p> </o:p></b></p>
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<p class="MsoNormal" align="center" style="text-align:center"><b>ABSTRACT<o:p></o:p></b></p>
<p class="MsoNormal" align="center" style="text-align:center"><b><o:p> </o:p></b></p>
<p class="MsoNormal" style="text-indent:.5in"><span style="color:black">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.</span><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black"><o:p> </o:p></span></p>
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