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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;line-height:115%"><b><span style="font-size:28.0pt;line-height:115%;font-family:"Monotype Corsiva"">Frederick Soster</span></b><span style="font-size:28.0pt;line-height:115%;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:12.0pt"><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> EVALUATING FRONTAL PRECIPITATION CONSISTENCY WITHIN REANALYSIS DATASETS<span style="font-size:14.0pt"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt"><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. Rhys Parfitt</span></b><b><u><span style="font-size:14.0pt;font-family:"Times",serif"><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"> August 11th, 2020
<b><u>Time</u>: 3:00 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">Zoom Meeting URL: <a href="https://fsu.zoom.us/j/99506435144">
https://fsu.zoom.us/j/99506435144</a><o:p></o:p></span></p>
<p class="MsoNormal" align="center" style="text-align:center"><b><span style="font-size:12.0pt"><o:p> </o:p></span></b></p>
<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">Precipitation from atmospheric fronts accounts for a significant portion of the total precipitation in the mid-latitudes, with some locations receiving the majority of their precipitation from atmospheric fronts.
In addition, a significant proportion of extreme precipitation events coincide with a frontal passage in the mid-latitudes, and some of these events lead to extreme flooding which can have important and costly socio-economic consequences. Climatological studies
regarding both atmospheric fronts and precipitation frequently use global reanalysis datasets due to their cohesive record of many atmospheric variables over a temporal range of generally 40 years or longer. Differences between these reanalyses regarding observations
assimilated, atmospheric model used, and grid size contribute to differences in regional precipitation accumulations and the structure and frequency of identified atmospheric fronts.
<o:p></o:p></p>
<p class="MsoNormal" style="text-indent:.5in"><o:p> </o:p></p>
<p class="MsoNormal" style="text-indent:.5in">It is therefore important to understand how frontal precipitation is represented in global reanalysis datasets. As much of the literature on atmospheric fronts and frontal precipitation are limited to the use of
a single global reanalysis or regional model, this thesis seeks to investigate the consistency among frontal identification and frontal precipitation within multiple reanalyses. The following reanalyses were used based on data availability, spatial and temporal
resolution, and use within the literature: ERA-20C, ERA-40, ERA-Interim, ERA5, JRA-55, MERRA-2, NCEP-CFSR, and NOAA20C V2C. Two satellite precipitation products, CMORPH and TRMM, were also used for comparison of frontal precipitation with ERA5, as these three
datasets shared the same grid and grid spacing. There are numerous methods to identify atmospheric fronts that rely on different parameters involving temperature and/or wind. While different front diagnostics give similar results geographically in terms of
frequency and structure, each diagnostic has its own strengths and weaknesses. <o:p>
</o:p></p>
<p class="MsoNormal" style="text-indent:.5in"><o:p> </o:p></p>
<p class="MsoNormal" style="text-indent:.5in">As the choice of front diagnostic has been shown to result in differences frontal frequency when using the same reanalysis, two different front diagnostics are used. Results show that reanalyses with a finer grid
spacing (i.e. less than 0.5<sup>o</sup> x 0.5<sup>o</sup>) contain a 200% increase in globally averaged mean annual frontal frequency for one diagnostic and a 450% increase in globally averaged mean annual frontal frequency for the other diagnostic compared
to reanalyses with coarse grid spacing (i.e. 2.0<sup>o</sup> x 2.0<sup>o</sup>). Results also show that reanalyses with a finer grid spacing see a 150% increase in globally averaged mean annual frontal precipitation proportion for one diagnostic and a 460%
increase in globally averaged mean annual frontal precipitation proportion compared to coarser grid-spaced reanalyses. The largest differences between reanalyses in both frontal frequency and frontal precipitation proportion exist in the tropics for both diagnostics.
Differences between reanalyses regarding both frontal frequency and frontal precipitation proportion are indicated to be strongly related to the differing grid spacings of each reanalysis. To account for differing grid spacings, the reanalyses and satellite
precipitation products are regridded to the same coarser grid spacing, and both diagnostics and their frontal precipitation are recalculated on this grid to attribute differences in both frontal frequency and frontal precipitation to either differing grid
spacings between reanalyses or differences inherent to reanalyses. <o:p></o:p></p>
<p class="MsoNormal" style="text-indent:.5in"><o:p> </o:p></p>
<p class="MsoNormal" style="text-indent:.5in">The regridded reanalyses have much more consistency regarding both frontal frequency and frontal precipitation proportion. Globally, 30% of the difference in the means of mean annual frontal frequency of all eight
reanalyses is attributed to grid size for one diagnostic, while 59% of the difference is attributed to grid size for the other diagnostic. Allocation of frontal precipitation closely follows the frequency of identified fronts. Globally, 28% of the difference
in the means of mean annual frontal precipitation proportion of all eight reanalyses is attributed to grid size for one diagnostic while 61% of the difference is attributed to grid size for the other diagnostic. Both diagnostics show that the percent difference
in frontal frequency and frontal precipitation proportion is highly dependent on geographical area. Objective frontal identification and frontal precipitation proportion is highly dependent on the choice of diagnostic, the region under consideration, the grid
spacing of the reanalysis, and the reanalysis or reanalyses used. These results strongly suggest that research regarding both frontal identification and frontal precipitation should use more than one reanalysis.<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">1011 Academic Way, 2019 EOA Building<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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