CASA POster Template

1
Detection of Hazardous Weather Phenomena Using
Data Assimilation Techniques
P12
Robert Fritchie, Kelvin Droegemeier, Mingjing
Tong School of Meteorology and Center for
Analysis and Prediction of Storms
Overview The automated detection of tornadoes
and other hazardous weather events involves using
algorithms to identify patterns in raw Doppler
radar reflectivity and velocity data. One such
algorithm-based system is the NSSL Warning
Decision Support System Integrated
Information (WDSS-II) One major limitation is
that new detection algorithms must be created, or
existing ones adapted, each time a new
observation system is deployed. A tornado/parent
mesocyclone will look very different when viewed
by a WSR-88D with a gate size of 1km, as supposed
to a Mobile doppler radar with a gate size of
50m. Another major limitation is that such
algorithms operate principally on data directly
measured by the radar (radial velocity and
reflectivity) and thus do not make use of other
important fields that are potentially available
to them (e.g., pressure and temperature). An
alternative approach involves using advanced data
assimilation and retrieval techniques, applied to
all available observations especially those
collected at fine scales by Doppler radar to
generate dynamically consistent, 3D gridded
analyses of all key observed and unobserved
meteorological quantities to which data mining
tools can be applied. The potential advantages
include the ability to interrogate quantities not
available from raw data and the use of
geometrically simple 3D grids. The most important
advantage, however, is that the mining algorithms
do not depend upon the data sources and do not
have to be changed when new data sources are
added (e.g., new types of radars).

• Research Objectives
• Examine tradeoffs of hazardous weather detection
  between conventional sensor-based algorithms and
  gridded data sets.
• Prove the ease of adding new instrumentation to
  detection algorithms that are based on the
  assimilated data only.
• Explore the computational requirements of data
  assimilation on a variety of scales and grid
  spacing combinations.
• Examine the physical signatures of various
  hazardous weather phenomena, particularly
  tornadoes, at various scales and grid spacings
  and compare to the detections with WDSS-II as
  well as ground truth.
• Investigate the value added to data sets through
  use of mobile or dynamic sensing platforms such
  as mobile radars or CASA radars.

• Tools and Methodology
• Compare detections produced by automated
  algorithms to features in assimilated analyses
  that are generated using ensemble Kalman
  filtering for an observed tornadic storm that
  occurred on 29 May 2004 and that was observed at
  reasonably close range by NEXRAD radar.
• Examine sensitivities to a variety of variable
  factors including, in WDSS-II, adaptable
  parameters and in ensemble Kalman filtering, grid
  spacing, data frequency, ensemble size, and
  quantities assimilated.
• Comparison between detected features, both
  through WDSS-II and Data Assimilation, and
  surveyed ground truth data will allow for a good
  assessment of relative skill of hazardous weather
  phenomena detection.
• Compute analyses with various resolutions and
  domains to compare their relative detections
  versus their computational requirements

This work is supported primarily by the National
Science Foundation under the following
cooperative agreements ATM03-31574, 31578,
31579, 31480, 31586, 31587, 31591, and 31594. Any
opinions, findings, conclusions, or
recommendations expressed in this material are
those of the authors and do not necessarily
reflect those of the National Science Foundation.
2
Detection of Hazardous Weather Phenomena Using
Data Assimilation Techniques
P12
Robert Fritchie, Kelvin Droegemeier, Mingjing
Tong School of Meteorology and Center for
Analysis and Prediction of Storms
Early Results

• 40-member ensemble of a square-root Kalman
  filter
• The filter uses observations from the KTLX radar
  of the May 29th case
• Assimilation was performed every 5 minutes (each
  volume scan) for a 1 hour period
• Built the storm to a physically consistent
  gridded set that closely resembles the actual
  storm itself
• The analysis grid, depicted in figure X, is a
  180km x 120km x 16km block, with horizontal grid
  spacing of 1 kilometer and stretched vertical
  grid spacing with a minimum of 100 meters.
• The analysis ends at 0100UTC, or 8pm CDT.
  Shortly after that time, an anticyclonic tornado
  formed north of Calumet, OK.

• On the afternoon of May 29th, 2004, a long-lived
  supercell formed in Western Oklahoma and tracked
  through Central Oklahoma, north of Oklahoma City,
  through Northeast Oklahoma, including Tulsa, and
  finally dissipated west of the Arkansas state
  line. Throughout its approximately 9 hour
  lifetime, the storm produced at least 16
  confirmed tornadoes.
• well organized cyclic supercell
• long-lived
• contained a variety of weather hazards
• produced a wide range of tornado intensities
• was very well-observed by mobile radars
• passed within close range of the WSR-88D
  located at Twin Lakes, Oklahoma.
• For these reasons and more, it is apparent that
  this would be a great case to utilize in testing
  dynamic data assimilation concepts.

• Figures 4 through 7 illustrate just a few fields
  that are available from having a dynamically
  consistent gridded data set produced by a Kalman
  filter. A Kalman filter provides the same fields
  as a numerical model, but are based on combining
  current observations from the radar with model
  physics in an optimal fashion. Highlighted
  results
• Reflectivity derived from the assimilation
  closely resembles that displayed in WDSS-II.
• Several areas of intense vorticity maxima and
  minima exist, but only one is associated with a
  strong updraft.
• A relative minimum in pressure also indicates a
  rotating updraft.
• Baroclinic zones, associated with mini-fronts
  generated by the storm, also help to identify the
  greatest risk of high winds and tornadoes at
  their intersection.

• Future Work
• Additional real-life case studies
• Perform assimilations at different resolutions
• Change domain coverages
• More quantitative comparisons between WDSS-II
  detections and phenomena indicated in assimilated
  data sets.

Figure 4 Cross-section of radar reflectivity
derived from the assimilated data. Note the
structure as compared to that displayed in figure
3. Time is 0100 UTC.
Figure 6 Low-level cross-section of pressure
with. Placement of the local minimum in pressure
is in agreement with placement of the main
anticyclonic updraft. Time is 0100 UTC.
Figure 5 Low-level cross-section of vertical
vorticity with overlaid contours of strong
positive vertical velocities (updrafts). Note the
strong updraft is associated with negative
vorticity, indicating a anti-cyclone. Several
minutes later a anticyclonic tornado touched
down. Time is 0100 UTC.
Figure 7 Cross-section of surface temperature
with overlaid contours of vertical vorticity.
Note that the intersection of the strong
temperature gradients (mini-fronts) is associated
with the strong negative vorticity area. Time is
0100 UTC.
This work is supported primarily by the National
Science Foundation under the following
cooperative agreements ATM03-31574, 31578,
31579, 31480, 31586, 31587, 31591, and 31594. Any
opinions, findings, conclusions, or
recommendations expressed in this material are
those of the authors and do not necessarily
reflect those of the National Science Foundation.