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Projects
Multiple
Radar Comparison and Analysis of the 8 May 2003 Oklahoma City Tornadic
Supercell by Michael E. Charles, and mentors
David L. Andra Jr., Michael P. Foster, and Daniel J. Miller
"This paper will examine the structure and evolution of the
8 May 2003 Central Oklahoma tornadic supercell using two different
radars: the KTLX WSR-88D and the Central Oklahoma TDWR. Measurements
of the vertical vorticity and convergence of each of three scales
of rotation (mesocyclone, tornado cyclone signature [TCS], and tornadic
vortex signature [TVS]) were made by subjectively choosing maximum
outbound and inbound velocities from each time/elevation angle of
each radar dataset. Data was graphed in the form of time-height
plots of mesocyclone and TCS vorticity, and TVS delta-velocity,
for both radars. Temporal variation of low-level convergence associated
with the supercell was also analyzed. It was found that the TDWR,
with much higher spatial and temporal resolution, was superior to
the WSR-88D in resolving the evolution of small-scale storm features.
The TCS was always more easily discernable in the TDWR velocity
data. The TDWR velocity couplet associated with the TVS tracked
very close to the tornado damage path, while the KTLX data was much
less accurate at times. The TDWR was also able to resolve one or
more surges on the rear-flank downdraft (RFD) that descended into
the mesocyclone region and eventually was tied to the development
of intense low-level convergence and the TCS. The KTLX WSR-88D did
resolve an RFD surge, but it was difficult to track and observe
in great detail because of the radar's lower temporal and spatial
resolution. The TDWR data was also found to at times be difficult
to interpret because of its ability to resolve detailed structures
and its tendency to suffer velocity dealiasing failure. Nevertheless,
the TDWR was shown to have advantages over the WSR-88D in observing
important small-scale storm features."
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Reviewing
the SPC/NSSL Spring Program 2003:An Evaluation of the Use of Short-Range
Ensemble Forecasting Systems and New High Resolution Deterministic
Models in the Prediction of Severe Thunderstorms by
Marc R. Dahmer, and mentors Steven J. Weiss and David Bright
"The SPC/NSSL Spring Program is typically held during the
heart of the severe convective weather season in Norman, Oklahoma.
This is an opportunity for researchers and operational meteorologists
to interact and collaborate on a variety of experimental forecast
and other operationally relevant research programs. This year's
program focus was two-fold. The primary objectives were to explore
the use of Short-Range Ensemble Forecasting (SREF) systems to provide
meaningful guidance in severe weather forecasting, and to examine
the ability of new high-resolution deterministic models to predict
convective initiation and evolution. These objectives were subjectively
analyzed by participants and evaluated for its operational forecasting
uses. The participants of the program were also surveyed to glean
insight into the program's utility. Through the evaluation of the
objectives, it was found that the SREF output does have positive
use operationally. It was also found that just because a model's
QPF is initially misplaced or missing, does not mean the model should
be discounted as a tool in the prediction of severe weather as it
pertains to watch lead time."
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Analyzing
Statistical Models of Hourly Precipitation Events by
Jennifer Esker, and mentor Harold Brooks
"Understanding the national precipitation distribution can
be useful in many fields of study, but finding those patterns is
not easy. Overwhelming amounts of data create roadblocks for detailed
analysis, but constructing statistical models can reduce the mount
of data needed. This study applied gamma distributions to a year's
worth of processed hourly precipitation data to examine the national
precipitation. The set represents all precipitation events of the
contiguous United States as elliptical objects and produced a precipitation
regime classification based on the gamma parameters assigned to
the precipitation within the object. Starting with a general model
of the national precipitation the analysis continues to categorize
the data by location, season and precipitation regime to produce
detailed relationships. Examining plots of the gamma parameters
also provides insights into the variability of these categories
and additionally confirms that these models present an accurate
representation of annual precipitation."
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Summertime
Precipitation Variability and Atmospheric Circulation over the South
American Altiplano: Effects of Lake Titicaca and Salar de Uyuni
by Maura Hahnenberger, and mentors Michael Douglas and Jose
Galvez
"The South American Altiplano is a high altitude plateau located
between 15°S and 22°S and lying between two mountain chains of the
Central Andes. Within the plateau are two large features of surface
discontinuity that influence local circulations. Lake Titicaca,
at the north end of the Altiplano is has an area of 8,300 square
km making it the 2nd largest lake in South America. The Salar de
Uyuni is the largest dry salt lake in the world with a surface area
of 9000 square km. These features and their influences on local
circulation can possibly change weather and climate of their surrounding
areas. The aim of this study is to use pilot balloon soundings and
rain gauge measurements to describe the influence of these features
on their surroundings, and the impact on precipitation. Large-scale
results showed a relationship between upper-level easterly flow
and wet days on the Altiplano, in addition to the opposite; westerly
flow and dry days on the altiplano. On the local scale we observed
a tendency for increased precipitation with proximity to the lake.
The difference in flow on wet and dry days also modified the diurnal
breeze circulation of the lake. Analysis of morning and afternoon
near-surface winds at the lake and salar indicate morning confluence
and afternoon difluence, which may be linked to increased morning
convection over the lake indicated by satellite imagery. Comparison
of the Salar and Lake indicated a stronger breeze signal at the
Salar, possibly a result of the resistance of the salt surface to
heating, and reduction of daytime surface heating surrounding Lake
Titicaca due to increased vegetation."
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A Validation
of the National Centers for Environmental Prediction's Short Range
Ensemble Forecast by Andrew Hamm, and mentor
Kimberly Elmore
"This paper investigates the performance of soundings generated
from the National Centers for Environmental Prediction's Short Range
Ensemble Forecast (NCEP SREF). The NCEP SREF is an operational ensemble
forecast model with 15 members. Rank histograms are used as the
primary tool to investigate consistent bias problems as well as
ensemble dispersal. For the period spanning 1 May 2003 and 19 July
2003, nine different locations scattered about the continental U.S.
are validated with rawinsonde data. Ensembles modified by a lagged
bias correction and ensembles modified by both a lagged bias correction
and the addition of observational errors are considered. Rank histograms
constructed from the unmodified ensemble imply either severe bias
problems in the ensemble or a significantly underdispersed ensemble,
depending on the variable examined, forecast time, pressure level,
and location. Because forecasts between the different locations
are poorly correlated, the assumption of independence is acceptable
and rank histograms for each location are merged into combined rank
histograms for all cities for a given variable, forecast time, and
pressure level to produce adequate sample sizes. Combined rank histograms
constructed from the bias corrected ensemble are U-shaped, which
may be caused either by an under-dispersed ensemble, a non-homogeneous
bias structure, or observational errors. However, including observational
errors with the bias correction often results in uniform, or occasionally
over-dispersed, rank histograms. Analysis of other factors, including
the non-homogeneous biases of the ensemble, is shown to help understand
the combined rank histograms. Without the bias correction, this
ensemble if of limited utility, but the lagged bias correction greatly
enhances the ensemble performance."
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Intercomparison
of Cloud Base Height at the ARM Southern Great Plains Site by
Christina P. Kalb, and mentors Andy Dean, Randy Peppler, and Karen
Sonntag
"Instruments that measure cloud base height at the Atmospheric
Radiation Measurement (ARM) Program site in Lamont and Blackwell
Oklahoma are examined. These instruments include, the Micropulse
Lidar, Belfort Laser Ceilometer, Vaisala Ceilometer, and Millimeter-Wavelength
Cloud Radar. Instruments at the ARM sites record information regarding
cloud radiative forcing and feedback effects, variables that represent
a great amount of uncertainty in climate prediction. However, flawed
observations and dissimilarities in instrument performance when
reporting cloud types hinder our ability to fully understand these
processes. Also, users of ARM data assume these instruments are
interchangeable, but this may not be the case. The purpose of this
paper is to address the observed differences between these instruments
under different atmospheric conditions and cloud types both qualitatively
and statistically, and to test a method that may be useful to identify
outliers. Qualitative analysis revealed that the Micropulse Lidar
is superior in reporting high cloud bases and jagged cloud bases,
but inferior to both ceilometers when reporting low clouds. However,
statistical results were inconclusive, due to large standard deviations
encountered in all cloud episodes. Histograms used to identify outliers
gave reasonable results when cloud bases were visibly similar, but
resulted in skewed or bimodal distributions for other cases. These
results are discussed for observations taken during the Spring 2000
Cloud Intensive Observing Period."
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Conditions
Associated with Derechos Occurring in Dry Boundary Layer Environments
by Allen L. Logan, and mentors David A. Imy and Stephen
F. Corfidi
"The purpose of this study is to determine environmental conditions
that are most favorable for the development of widespread convectively
induced windstorms that occur within relatively dry boundary layer
conditions. Events such as this are difficult to forecast, as are
most organized convective windstorms occur within a moist boundary
layer. In this study, a dataset composed of 8 dry boundary layer
organized wind events that occurred during the months of March,
April, May, July and November for a 12-year period of 1989-2001."
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Quality
Control of Radar Data to Improve Mesocyclone Detection
by Rebecca J. Mazur, and mentors Greg J. Stumpf and V. Lakshmanan
"Real-time severe weather algorithms that are used to identify
various storm attributes can be adversely affected by the presence
of meteorological and non-meteorological contaminants such as anomalous
propagation (AP), ground clutter (GC), clear-air return or biological
scatters in the radar reflectivity data. We examine the Quality
Control Neural Network, a new algorithm which classifies precipitation
and non-precipitation returns from radar data and provides reflectivity
tilts where the majority of contaminants are removed. We demonstrate
that using the reflectivity tilts from the QCNN rather than the
unedited reflectivity data improves the skill of the NSSL Mesocyclone
Detection Algorithm (MDA). In order to determine a positive effect
at classifying radar echoes, the MDA is run both without and with
the QCNN filtering the original data. Results using 15 nationwide
storm events show that the application of the QCNN effectively removes
false MDA detection in clear air return while essentially not impacting
the ability to detect mesocyclones in precipitation and storm regions."
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Extratropical
Cyclones with Multiple Baroclinic Zones and their Relationship to
Severe Weather by Nicholas Metz, and mentors
David M. Schultz and Robert H. Johns
"Cyclones from the central United States and south-central
Canada were examined from 1982 and 1989 to determine how often they
contained more than one baroclinic zone. A baroclinic zone was defined
if a gradient of 8°F (4.4°C) per 220 km was found and a length of
440 km was achieved. Forty-three percent of cyclones were found
to have multiple baroclinic zones. The greatest frequency of cyclones
with multiple baroclinic zones occurred during the transition months
of April, May, August, and September. In addition, the baroclinic
zones appeared to follow a seasonal progression. Ninety-four percent
of all baroclinic zones were coincident with a moisture gradient
that was apparent through isodrosotherm analysis every 4°F (2.2°C),
and 73% contained a veering wind shift across them of at least 20°.
Of cyclones with multiple baroclinic zones, severe weather was found
to occur along 57% of southern baroclinic zones, significant severe
weather along 41%, tornadoes along 35%, and significant tornadoes
along 24%. During the spring and summer, severe weather occurred
along 83% of southern baroclinic zones, significant severe weather
along 65%, tornadoes along 57%, and significant tornadoes along
39%. The occurrence of severe weather, significant severe weather,
tornadoes, and significant tornadoes was relatively consistent along
the southern baroclinic zones between 1982 and 1989. Finally, the
formation of multiple baroclinic zones was examined and two main
forms were found. A second baroclinic zone can be the result of
an interaction with a historical cold/stationary front, or can result
through the attachment of a baroclinic zone from the north."
Click here for Nick's figures.
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Analysis
of Mesocyclone Detection Algorithm Attributes to Increase Tornado
Detection by Christina M. Nestlerode, and mentor
Michael B. Richman
"The Mesocyclone Detection Algorithm (MDA) is used in the
Weather Surveillance Radar -1988 Doppler (WSR-88D) to detect rotation
associated with tornadoes and other severe weather. The MDA analyzes
Doppler radar radial velocity volume scans to compose a number of
attributes thought to be related to mesocyclone formation. The 23
attributes of the MDA are compared to truthed tornado data in exploratory
and diagnostic analyses to examine the underlying structure of the
MDA. Results of these analyses indicate that the MDA is a highly
correlated system with a wide variety of complexity in those correlations.
This multicollinearity can hinder statistical prediction. Measured
associations between the attributes vary from near zero correlation
to complex correlations with values greater than 0.8, binding up
to nine MDA attributes. In diagnostic analyses, linear and logistic
regressions are performed on various sets of MDA attributes in an
attempt to distinguish tornado events from non-tornado events. Logistic
regression is found to be the most successful model due to its parsimony
and ability to classify correctly tornado versus non-tornado cases.
This research has shown that the number of attributes in the MDA
can be decreased by projecting the 23 correlated attributes on a
number of uncorrelated dimensions. Using principal component analysis
(PCA), multivariate exploration of the data determines that 9 dimensions
are needed to describe 85% of the variability of the MDA attributes.
While the MDA is currently an improvement over older algorithms,
this research shows that it is advantageous to reduce the redundancy
of the MDA to make it a more useful tool."
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A Study
of Proximity Sounding Derived Parameters Associated with Significant
Severe Weather by Corey K. Potvin, and mentors
Steven J. Weiss and Sarah J. Taylor
"This study focuses on the sensitivity of significant severe
weather climatology to proximity criteria. Six independent definitions
of proximity are used. These criteria are then used to develop a
climatology of several sounding derived parameters for significant
wind, hail, and tornado cases. Geographical and significant severe
type comparisons are made. One of the major findings is that little
variance occurs in distributions of the parameters studied over
the range of proximity criteria considered, namely, from 40 km and
30 min to 185 km and 3 h. Therefore, criteria on the upper end of
this range can be confidently applied to significant severe storm
climatologies in order to maximize sample size. Substantial differences
between the climatological significant severe thunderstorm environment
in the High Plains and that of other regions of the country are
noted. However, significant tornado cases in all the regions studied
are found to be associated with higher values of wind shear between
the surface and 1 km, and lower mean layer LCL heights. The climatology
compiled in this study describes mean significant severe weather
environments for eight regions of the United States."
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The Impact
of High Wind Events on the Central Business District of Oklahoma
City by Dustin Rapp, and mentor Jeff Basara
"It is critical to understand airflow through cities due to
the possibilities of biological and chemical terrorist attacks,
pollution, and accidental chemical spills. Currently very few studies
have used field measurements of wind conditions within a city to
study urban air flow. This paper investigates the airflow at specific
locations within Oklahoma City during two synoptic high wind events
using data collected at fifteen different sites within the central
business district. Wind speed and direction were averaged for each
site before and after the frontal passages. The wind shifts and
changes in the magnitude of the wind vectors were analyzed at specific
locations and time periods to understand air flow based on street
orientation and building structures within the city."
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A Comparison
of Sounding Parameters for the Southeastern United States During
El Niño, La Niña, and Neutral Winters by Victoria
Sankovich, and mentors Joe Schaefer and Jason Levit
"Previous research based upon examinations of previous weather
events speculates that the El Nino/Southern Oscillation affects
severe weather in the United States. However, in this study, thermodynamic
and kinematic parameters associated with severe weather are calculated
from rawinsonde data to explore differences in the atmospheric stratification
during the El Nino, La Nina, and Neutral ENSO phases. The soundings
used in this investigation are taken over the southeastern United
States during the winter season. Two separate datasets are examined:
one of soundings from severe weather events and another of all 00UTC
soundings. Surface-3km Storm Relative Helicity, Surface CAPE, and
Surface-6km Bulk Shear are analyzed for the severe weather dataset,
and results show that severe weather occurs under the same atmospheric
conditions regardless of ENSO phase. For the dataset of all weather
soundings, three thermodynamic parameters (Mean Layer CAPE, Surface
Convective Inhibition, and Mean Layer 300mb Lifted Index) and three
kinematic parameters (Surface-6km Bulk Shear, Surface-1km Storm-related
Helicity, and Surface-3km Storm-Related Helicity) are examined.
The results from this analysis reveal that the thermodynamic parameters
favor storm development during the La Nina ENSO phase and that the
dynamic parameters favor the El Nino and Neutral phases for severe
thunderstorms."
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