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PhD.html
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<!DOCTYPE html>
<html>
<head>
<title>Brian Blaylock, Ph.D.</title>
<link rel="stylesheet" href="./css/brian_style.css" />
<script src="./js/site/siteopen.js"></script>
</head>
<body>
<a name="TOP"></a>
<script src="./js/site/sitemenu.js"></script>
<div id="content">
<h1 align="center">
<i class="fa fa-graduation-cap fa-fw" aria-hidden="true"></i> Ph.D. Research</h1>
<hr>
<p>My PhD research utilized new technology for archiving and accessing output
from the High-Resolution Rapid Refresh model and observations made on
the GOES-16/17 satellite. High-throughput computing was used to rapidly compute
statistical information about these data sets and was then applied to
assist situational awareness of weather conditions in the wildland fire environment.
<!-- Tabs -->
<ul class="nav nav-tabs">
<li class="active"><a data-toggle="tab" href="#tab1">JFSP Project</a></li>
<li><a data-toggle="tab" href="#tab2">NASA Fellowship Applications</a></li>
</ul>
<div class="tab-content">
<div id="tab1" class="tab-pane fade in active">
<img src="./images/jfsp.png" align=right width="150px" style="padding:20px 20px 5px 5px;">
<br>
<h3>Assessment of HRRR Model Forecasts of Convective
Outflows in the Fire Environment</h3>
<h4>Join Fire Science Program</h4>
<h5>Status: Accepted</h5>
<p>The proposed work will evaluate the ability of operational and experimental versions of the High
Resolution Rapid Refresh (HRRR) modeling system for the continental United State and Alaska to
forecast the characteristics of mesoscale atmospheric boundaries arising from thunderstorm
outflows, gust fronts, and downburst winds (referred collectively as convective outflows). The
objective is to lead to enhanced situational awareness within the operational fire weather
community of the ability of the HRRR model and predictive tools that rely on its output to nowcast
and forecast convective outflows.
<hr>
<a class='btn btn-success' href="http://hrrr.chpc.utah.edu" >
<i class="fa fa-database fa-fw" ></i> HRRR Archive Access</a>
<a class='btn btn-success' href="https://1drv.ms/w/s!AtJL0JL_rT9jtItjapRfnnPXUdDuXw" >
PhD General Exam</a>
<a class='btn btn-danger' href="https://home.chpc.utah.edu/~u0553130/Brian_Blaylock/hrrr_fires.html">
<i class="fas fa-fire-extinguisher"></i> HRRR Fire Forecasts</a>
<a class='btn btn-primary' href="http://meso1.chpc.utah.edu/jfsp_convective/" >
Project Home Page</a>
<a class='btn btn-warning' href="https://1drv.ms/b/s!AtJL0JL_rT9juvJrYNEuJ6VlqHqY6w?e=TY1xrR" >
<i class="fas fa-file-pdf"></i> Full Dissertation</a>
<a class='btn btn-warning' href="https://1drv.ms/p/s!AtJL0JL_rT9juflZhDBFvUG0ncVEkQ?e=C7o8k9" >
<i class="fas fa-file-powerpoint"></i> Defense Presentation</a>
<hr>
<div class='row'>
<div class="col-md-4">
<div class="panel panel-default">
<div class="panel-heading">Part 1: HRRR Archive</div>
<div class="panel-body" style="height:120px">
<h4 style="text-align: center">Cloud archiving and data mining of High-Resolution Rapid Refresh forecast model output</h4>
</div>
<div class="panel-footer">
<a href="https://doi.org/10.1016/j.cageo.2017.08.005"><i class="fas fa-external-link-alt"></i> Link</a>
</div>
</div>
</div>
<div class="col-md-4">
<div class="panel panel-default">
<div class="panel-heading">Part 2: HRRR Climatology</div>
<div class="panel-body" style="height:120px">
<h4 style="text-align: center">High-Resolution Rapid Refresh Model Data Analytics Derived on the Open Science Grid to Assist Wildland Fire Weather Assessment</h4>
</div>
<div class="panel-footer">
<a href="https://journals.ametsoc.org/doi/abs/10.1175/JTECH-D-18-0073.1"><i class="fas fa-external-link-alt"></i> Link</a>
</div>
</div>
</div>
<div class="col-md-4">
<div class="panel panel-default">
<div class="panel-heading">Part 3: HRRR and GOES Lightning</div>
<div class="panel-body" style="height:120px">
<h4 style="text-align: center">Comparison of Lightning Forecasts from the High-Resolution Rapid Refresh Model to Geostationary Lightning Mapper Observations</h4>
</div>
<div class="panel-footer">
Submitted to <i>Weather and Forecasting</i>
</div>
</div>
</div>
</div>
<hr>
<div class='col-md-12'>
<p style="text-align:center;">
<img src="./images/PoleCreek.png" width=95% class='style11'>
<figcaption style="text-align:center;">14 September 2018<br><span style='font-size:12px'>GOES-16 True Color and Fire Temperature RGB Composite</span></figcaption>
</p>
</div>
<div class='row'>
<div class='col-md-6'>
<p style="text-align:center;">
<img src="./images/fires_forecast_funnel.png" width="95%" class='style11'>
<figcaption style="text-align:center;">Fire Forecast Funnel</figcaption>
</p>
</div>
<div class='col-md-6'>
<p style="text-align:center;">
<img src="./images/BrianHead_2017-06_Ethan.png" width="350px" class='style11'>
<figcaption style="text-align:center;">Brian Head Fire, June 2017</figcaption>
</p>
</div>
</div>
</div>
<div id="tab2" class="tab-pane fade">
<img src="./images/nasa.png" align=right width="150px" style="padding:20px 20px 5px 5px;">
<br>
<h3>Sensitivity of Numerical Simulations of Coastal Atmospheric Boundary Layers to Remote Sensing Estimates of Surface State </h3>
<h4>2016 NASA's Earth and Space Science Fellowship program</h4>
<h5>Status: Declined</h5>
<p>Residents of many coastal metropolitan areas are often exposed to unhealthy levels of ozone during summer. High ozone levels in
coastal zones are common because offshore shallow stable boundary layers tend to decrease vertical mixing and concentrate ozone
and precursor pollutants in a shallow layer near the surface. Thermally-driven flows between lakes or oceans and coastal urban and
suburban areas subsequently may recirculate ozone and ozone precursors within the stable boundary-layer. Forecasting the evolution
of coastal ozone events is a current challenge to air quality forecasters requiring high resolution numerical models. Preliminary work
shows that these models are sensitive to the surface state, such as coastal water temperature and urban land cover properties.
<p>Many operational weather and air quality models poorly initialize the underlying surface state in coastal areas, which can adversely
affect the ability of numerical simulations to properly forecast boundary-layer depth and the timing and intensity of thermally-
driven flows. In this proposed work, remote sensing estimates of the surface state will be used to improve simulations of the Weather
Research and Forecast model. The sensitivity of coastal boundary layer characteristics to water surface temperature, green vegetation
fraction, land use, urban properties, and albedo will be tested. The model simulations will be compared to observations from recent
and upcoming ozone monitoring field studies. Upon completion, this proposed work will fulfill NASA's Science Mission Directorate
to advance knowledge of our changing environment and improve life by using NASA remote sensing products to improve weather
simulations and air quality forecasts in coastal environments.
<br>
<hr>
<img src="./images/nasa.png" align=right width="150px" style="padding:20px 20px 5px 5px;">
<br>
<h3>Boundary Layer Sensitivity to Dynamically-Changing Surface States</h3>
<h4>2017 NASA's Earth and Space Science Fellowship program</h4>
<h5>Status: Declined</h5>
<p>High resolution numerical weather prediction models provide invaluable
information for many applications, which include aviation, air quality,
wildfire management, flooding hazards, solar and wind energy, and general
weather forecasting. The accuracy of these numerical models are affected
by their representation of the underlying surface state. Surface characteristics
vary spatially and temporally on all scales due to anthropogenic and
natural factors. For example, drought, irrigation, deforestation, wildfire
burn scars, vegetation regrowth, urban development, and changes in coastlines
and water covered areas affect albedo, surface roughness, soil moisture,
and latent and sensible heat fluxes that alter atmospheric boundary layer
processes.
<p>Current operational weather prediction models tend to rely on static,
outdated, and simplified land use classifications for the parameterization
of heat, moisture, and momentum fluxes near the surface. Remote sensing
estimates of surface state from a small sample of images are often used
to develop those generalized land use classifications and their properties
(e.g., albedo and surface roughness). More attention is now being placed
on incorporating into research models remotely-sensed estimates of
substantive local changes in the surface state that may have transpired
on temporal scales within a few years to as recently as within the current season.
<p>The objective of the proposed work is to investigate the sensitivity
of numerical simulations of summertime atmospheric boundary layers in the
western United States to improved characterization of the surface state
derived from NASA remote sensing products. Of particular interest is to
contrast the effects of early to late summer-season changes in: (1) areal
extent and temperature of reservoirs and lakes and (2) extent and characteristics
of vegetation and soil in rural and urban areas. Reliance in numerical simulations
on static or seasonally-evolving climatologies of surface state characteristics
may introduce substantive errors in boundary-layer depths and transport winds
that affect simulations of poor air quality episodes and wildfire outbreaks
that are common during summer in the West. First, literature will be reviewed
to identify appropriate specifications of surface characteristics, such as
albedo and surface roughness, for more diverse ranges of land cover and
vegetation than are commonly used for parameterizing surface fluxes in
land surface models. Second, the extent to which those properties can be
specified based on NASA remote-sensing products and utilized in the Weather
Research and Forecast model will be assessed. Third, sensitivity of the
atmospheric boundary layer to improved treatment of land use and cover
will be investigated for selected case studies of high interest events
(e.g., poor air quality episodes and after major wildfires). This work
is intended to benefit the utility of high-resolution weather forecasts
for diverse applications, including air quality, agriculture and water and forest management.
</div>
</div>
<br>
<hr>
<h3>NOAA Big Data Request for Information</h3>
<p>Since I use GOES-16 data acquired from Amazon AWS S3 buckets,
I responded to the <a href="https://www.noaa.gov/big-data-project">NOAA Big Data Project</a>
Request for Information.
<br><a class='btn btn-primary' href='https://1drv.ms/b/s!AtJL0JL_rT9jtfxoIWfdPm9Ze0qt9g'>
<i class="fas fa-external-link-alt"></i> Response Document
</a>
</div>
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