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<p class="HTMLBody" align="center" style="text-align:center"><b><u><span style="font-size:26.0pt;font-family:"Monotype Corsiva"">Meteorology Seminar<o:p></o:p></span></u></b></p>
<p class="HTMLBody" align="center" style="text-align:center"><b><span style="font-size:28.0pt;font-family:"Monotype Corsiva""><o:p> </o:p></span></b></p>
<p class="HTMLBody" align="center" style="text-align:center"><b><span style="font-size:28.0pt;font-family:"Monotype Corsiva"">Shannon Shields</span></b><span style="font-size:28.0pt;font-family:"Monotype Corsiva""><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"">M.S. Meteorology Candidate<o:p></o:p></span></p>
<p class="HTMLBody"><span style="font-size:14.0pt"><o:p> </o:p></span></p>
<p class="doublespacedcaps" align="left" style="text-align:left;line-height:normal">
<b><u><span style="font-size:14.0pt">Title</span></u>:</b> <b><span style="font-size:14.0pt">Potential Intensity and Lifetime Maximum Intensity Interannual Variability of Tropical Cyclones: An Analysis on Influential Factors</span></b><span style="font-size:14.0pt"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt"><o:p> </o:p></span></p>
<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"><o:p></o:p></span></u></b></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Tahoma",sans-serif;color:black"><o:p> </o:p></span></p>
<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">
<b>April 2, 2019</b> <b><u>Time</u>: 3:30-5:00 PM</b></span><span style="font-size:14.0pt;font-family:"Times New Roman",serif"><o:p></o:p></span></p>
<p class="HTMLBody"><b><o:p> </o:p></b></p>
<p class="HTMLBody"><b><u><span style="font-size:14.0pt">Location</span></u></b><b><span style="font-size:14.0pt">:
</span></b><span style="font-size:14.0pt">Werner A. Baum Seminar Room (353 </span>
<span style="font-size:14.0pt">Love Building)<b><o:p></o:p></b></span></p>
<p class="MsoNormal"><b>(Please join us for refreshments served outside room 353 Love @ 3:00 PM)</b><b><span style="font-size:12.0pt"><o:p></o:p></span></b></p>
<p class="HTMLBody" style="text-autospace:ideograph-other"><span style="font-size:9.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal" align="center" style="text-align:center"><b>ABSTRACT</b><b><span style="font-size:12.0pt"><o:p></o:p></span></b></p>
<p style="margin:0in;margin-bottom:.0001pt;text-indent:.5in">Many tropical cyclone studies have been conducted on basin-averaged potential intensity trends and the influence of sea surface temperatures (SSTs) on tropical cyclone intensity, but there is less
research on along-track potential intensity (PI) and lifetime maximum intensity (referred to here as actual intensity or AI) and the factors that influence their variability. Potential intensity is a theory that predicts the maximum intensity that a tropical
cyclone can achieve given certain large-scale environmental variables. Understanding interannual variability in PI and its relation to AI interannual variability is of great importance for assessing the impact of future climate conditions on tropical cyclones.
Theoretically, warmer SSTs and cooler outflow temperatures would cause an increase in overall PI which in turn would lead to an increase in AI. This thesis examined the relationship between PI and AI on interannual time scales and the factors affecting PI
variability: thermodynamic efficiency and air-sea enthalpy disequilibrium.<o:p></o:p></p>
<p class="MsoNormal" style="text-indent:.5in">Using best-track data and three reanalysis products, variability in PI and AI was examined for the North Atlantic, North Indian, South Indian, South Pacific, Eastern North Pacific, and Western North Pacific basins
from 1980-2013. Overall, the Western North Pacific was the only basin that yielded high and consistently significant correlations between AI and PI. Despite the expectation from a previous study, the North Atlantic did not yield any significant correlations.
Multiple tests were then conducted to determine the sensitivity of the North Atlantic correlations to different datasets and time periods. Ultimately, it was determined that the North Atlantic AI vs. PI correlation results were very dependent upon the time
period and the individual years within the time period. In the comparison of all contributors, air-sea disequilibrium was the dominant contributor to PI variability. When AI variability was correlated with PI variability, disequilibrium (which is largely controlled
by SSTs) was also the dominant contributor to AI variability. Although disequilibrium was the dominant factor in PI and AI interannual variability, efficiency also played a role. In fact, this study found that variances in efficiency explained 13-93% of PI
interannual variability, indicating that variability in outflow temperatures (which cause much of the variability in efficiency) must be taken into account.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Shel McGuire<o:p></o:p></p>
<p class="MsoNormal">Florida State University<o:p></o:p></p>
<p class="MsoNormal">Academic Program Specialist<o:p></o:p></p>
<p class="MsoNormal">Department of Earth, Ocean, & Atmospheric Science<o:p></o:p></p>
<p class="MsoNormal">1017 Academic Way, 410 Love Building (Meteorology)<o:p></o:p></p>
<p class="MsoNormal">Tallahassee, FL 32306<o:p></o:p></p>
<p class="MsoNormal">850-644-8582<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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