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Impact of Clouds on CO2 Satellite Measurements
From Dr. Ned Snell, AER:
We are interested in evaluating the impact of clouds on
the measurement strategy of potential satellite-based sensors to measure the
column CO2 amount. The project would be to expand the method and software developed
last summer as part of the REU program to analyze our archived global cloud
data to provide a measure of the sensor's ability to measure the space-to-surface
column for a given orbit and measurement geometry (both the field-of-view of
the sensor and also the time of day it makes a measurement at a given location).
This
project would give someone IDL experience, insight into global cloud cover,
and an understanding of issues related to satellite sensor design and measurement
strategy. It would also provide the student with some visibility within our
team which includes industry and academia.
Note! This student may opt to be located either in Norman or the Lexington,
MA, area. If you choose Norman, you will travel to MA for week 5. If you
choose MA, you will spend weeks 1 and 10 in Norman. There are extra funds
to cover the travel.
Update and QC the SPC severe weather database
From Mr. Greg Carbin and Dr. Joe Schaefer, SPC:
- Review SPC severe weather database with the objective being a thorough
understanding of the data fields.
- Describe the strengths and weaknesses of
the current database structure and discuss ideas for improvement.
- Develop
a scheme to check the accuracy of the records within the database. Comparisons
must be made with NCDC data and discrepancies noted.
- Research database/data
management alternatives. What other options are available to SPC to store
and retrieve this type of data?
- Analyze the process by which the following
objectives may be attained:
- Error correction to the existing data/database
(time, county information, boundary crossing data, other statistics
should match NCDC).
- Migration
of the existing database to a new form of database (preferably easily
accessible and web-based. Other options include
GIS/mapping capability).
- Describe the entire database review/QC process
and the database transition proposal as part of a semester research paper.
This would be a challenging project for an ambitious student. We're
prepared to work closely with the intern selected to conduct this research.
Identifying Operational Deficiencies in Weather Radar
From Dr. Pam Heinselman, CIMMS teamed
with NSSL, and Ms.
Daphne LaDue, CAPS:
Given that the WSR-88D is approaching its 20-year life span, options
for replacement are under consideration. By identifying operational deficiencies
of the current technology for key stakeholder groups, better business decisions
may be made regarding choice of replacement systems. This project will
investigate how National Weather Service forecasters and broadcast meteorologists
think and work when weather is affecting their operations. Using the critical
incident methodology, at least three major operational deficiencies will
be identified for each group. The
student will join Ms. LaDue on any remaining interviews when REU starts
and then be a full co-investigator on the transcription and analysis of
the interviews. We'll review the critical incident methodology, qualitative
research methods generally, and guide the student through conducting this
interdisciplinary project.
A CASA-Related Project
From Dr.
Kevin Kloesel, A&GS:
Dr. Kloesel will mentor on a project of mutual interest that involves the
CASA radars in some way. This project
could be meteorology or social science. In the past Dr. Kloesel mentored
a student on a proposal
development project and a few years ago mentored for a project
involving how to schedule
priorities for CASA radars. This student needs to be pretty independent
because Kevin is a very busy person.
NWS Tornado Watch and Warning Performance since the deployment of the WSR-88D
From Mr. Paul Schlatter, WDTB, and Mr. Jack Hales, SPC:
This study will analyze all killer and F3 or stronger tornadoes starting
with 2007 and working backwards. Time permitting, the study will work backwards
all the way to the mid 1990s when the WSR-88D was deployed across the U.S.
Given this database of tornado reports, each one will be closely examined
to see if a tornado watch was issued, and if a tornado warning was issued,
and given a warning, what was the lead time. This study will be directly
compared to a previous study by Jack Hales that was done prior to deployment
of the WSR-88D. One end result of the study will be to find out in what ways
the WSR-88D has affected watches and warnings for siginificant tornadic events.
WAS*IS Related Project
From Dr. Matt
Biddle, OU:
To foster sustainable interdisciplinary working-relationships for social
scientists (geographers, anthropologists, political scientists, economics
and communications majors etc) with physical scientists (geographers, meteorologists,
geologists, environmental scientists, etc). We will produce more effective
ways to communicate metrics and human ecology within technical and non-technical
communities. He (Matt) was in the first WAS*IS.
Burn Conditions and Wildfire Management
From Dr. Mark
Shafer, OCS:
Prescribed burning is a land management tool often employed in Oklahoma.
The best time to conduct these burns is in late winter, before vegetation "greens
up" and when humidity is low. Unfortunately, this is also the time of
year at which wildfire danger is the highest. Sudden wind shifts or increases
in wind speed can quickly turn a controlled burn into a wildfire. Furthermore,
burn piles, such as tree limbs and other dead vegetation, may remain "hot" for
several days after a burn, sometimes re-igniting on a windy day when fire
suppression apparatus is no longer on-site.
The Oklahoma Climatological Survey would like to examine the probability of
having several consecutive low-wind days during the prime burn period. The
student project will use wind data from the Oklahoma Mesonet sites and products
from the Oklahoma Fire Danger Model. The final project will form the basis
for a wildfire / burn climatology that will be published as a technical report
by the Oklahoma Climatological Survey.
Ice Storm Frequency in Oklahoma
From Dr. Mark Shafer, OCS; mentor will be either Mr.
Deke Arndt, OCS,
and/or Mr. Gary
McManus, Assistant Oklahoma State Climatologist & OCS:
Could global warming be causing ice storms? As contradictory as it may seem,
there appears the possibility that a warming atmosphere has turned would-be
snowfall events into ice events in Oklahoma. Since 2000, Oklahoma has experienced
five major ice storms - events over such a short time span that had been almost
unheard of in our history. Since the 1980s, winter surface temperatures, in
particular, have also been warming. Is this a coincidence or is there a real
relationship here? The proposed project would examine atmospheric profiles
from winter events to examine the plausibility of a warmed troposphere with
cold surface layer leading to this sharp increase in ice events.
See related
news article from The Oklahoman.
Cloud Scale Numerical Simulation
From Dr. Ted Mansell, NSSL:
Cloud resolving models are useful tools for understanding and investigating
thunderstorm processes. Our particular interests are in microphysics, thunderstorm
electrification and lightning, and data assimilation. A variety of projects
are possible, depending on your particular interests.
Programming experience in Fortran is not required, but could allow for more
project possibilities. Experience will be gained in cloud modeling, 3-D visualization
(Vis5D), and UNIX-style command interaction.
For some examples of electrification research, see http://cimms.ou.edu/~mansell/
Finding Better Hail Warning Criteria
From Dr. Lak and Kiel Ortega, both CIMMS teamed
with NSSL:
We are interested in evaluating the potential application of using time
trends of various radar parameters to diagnose severe hail fall at the surface.
It seems logical that a pulse type storm has a lower chance of severe hail
than a more steady state storm. However, current techniques, like the Donovan
technique, and algorithms, like the Hail Detection Algorithm, use single
time steps to diagnose hail size and/or the probability of severe hail.
The
project will utilize high resolution data from the Severe Hazards Analysis
and Verification Experiment (SHAVE). These data provide for an enhanced database
of hail which ideally cover a storm's entire life cycle and include non-severe
and "no hail" reports.
An interest in severe storms is a plus. The project will involve some light
scripting in perl, but hard-core coding experience is not necessary.
Ensemble Data Assimilation Simulation Experiments for the Coastal
Ocean: Towards Automatic Localization
From Dr. Ross Hoffman, AER:
Data assimilation is part art, part science and every data assimilation system
has certain parameters that must be set properly for the problem at hand. Our
project combines a coastal ocean model called ECOM with a general purpose ensemble
data assimilation system called LETKF. For the LETKF the localization parameters
are critical. The REU project will process data from our data assimilation
experiments to come up with a more automatic way of setting these parameters.
If successful the approach might also be applied to the atmosphere. Details
about ECOM/LETKF follow and then a bit more about the work planned.
A coastal ocean data assimilation system is being developed. The goal is to
combine large and disparate datasets with ocean numerical models, producing
accurate analyses, forecasts, and respective uncertainty estimates for any
littoral region. A modular interface combines the Estuarine and Coastal Ocean
Model (ECOM) and the Local Ensemble Transform Kalman Filter (LETKF) into a
highly scalable, portable and efficient ocean data assimilation system. The
ECOM is a state-of-the-art, three- dimensional, hydrodynamic ocean model developed
as a derivative of the Princeton Ocean Model [1]. The LETKF, a recent adaptation
of ensemble Kalman filtering techniques, works particularly well for very large
non-linear dynamical systems in both sparse and dense data regimes, and provides
efficient algorithms for error estimation and quality control [2]. In simulation
experiments for highly idealized data distributions in the the New York Harbor
Observing and Prediction System (NYHOPS) the filter quickly converges, eliminating
bias and greatly reducing rms errors [3]. This behavior is robust to changes
in ensemble size, data coverage, and data error.
Ensemble data assimilation provides a promising path for making use of remotely
sensed ocean data such as sea surface temperature, ocean color, turbidity,
surface currents, free surface elevation, and sea surface salinity. In theory,
the ensemble approach provides the best way for observations of one variable
to affect the analysis of other correlated variables, either collocated or
nearby, at the same level or through the depth of the water column. With many
sub-models available in the ECOM for biogeochemistry, sediment transport, water
quality, waves, and particle tracking, there are opportunities to extend the
assimilation to non-standard data such as ocean color and turbidity, chemical
tracers, wave energy, and locations of drifting buoys and autonomous underwater
vehicles. These opportunities exist because the LETKF method is completely
general in the sense that when the observation errors can be assumed to be
Gaussian, any observation of a physical parameter that has a know functional
dependence on the variables of the dynamical model, can potentially be usefully
assimilated.
In ensemble data assimilation due to a limited sample we expect many distant
correlations to appear to be significant. But they are not. Localization limits
the region considered, eliminating these spurious correlations. But how big
should the localization region be? In this approach we will increase the sample
size by considering samples over the length of the data assimilation experiment
(at least a few days, and maybe a month). For these very large samples correlations
are expected to die down with distance in a smooth matter and the e- folding
distance can be used to set the localization domain. We will explore how these
parameters vary with depth, location, and variable. Because the NYHOPS geometry
includes rivers, bays, coastal areas, and open ocean shelf regions, the correlation
structures are expected to be quite varied. The method requires simulated data
only, and can be applied in situations with few observations. Several extensions
to this work are possible. Of particular interest would be application to unusual
observing systems that integrate over space or time where the best approach
to localization is not straight forward. Example observing systems of this
type include for example stream flow, GPS soundings, and radiances.
[1] A. F. Blumberg, L. A. Khan, and J. P. St. John, “Three- dimensional
hydrodynamic simulations of the New York Harbor, Long Island Sound and the
New York Bight,” J. Hydrologic Eng., vol. 125, pp. 799–816, 1999.
[2] Istvan Szunyogh, Eric J. Kostelich, Gyorgyi Gyarmati,
Eugenia Kalnay, Brian R. Hunt, Edward Ott, Elizabeth Satterfield, and James
A. Yorke, “A
local ensemble transform Kalman filter data assimilation system for the NCEP
global model,” Tellus A, vol. 60, no. 1, pp. 113–130, 2008, doi:10.1111/j.
1600-0870.2007.00274.x.
[3] Ross N. Hoffman, Rui M. Ponte, Eric J. Kostelich,
Alan Blumberg, Istvan Szunyogh, Sergey Vinogradov, and John M. Henderson, “A simulation study
using a local ensemble transform Kalman filter for data assimilation in New
York Harbor,” J. Atmos. Oceanic Technol., 2008, Accepted.
Note! This student may opt to be located either in Norman or the Lexington,
MA, area. If you choose Norman, you will travel to MA for week 5. If you
choose MA, you will spend weeks 1 and 10 in Norman. There are extra funds
to cover the travel.
Last Updated: March 27, 2008
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