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An alternative approach to detecting hazardous weather phenomena
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).
In order to analyze the abilities of this new detection technique, we are comparing
detections from the WDSS-II system to features in a data set derived through Dynamic
Data Assimilation. Here is a quick overview of the assimilation technique used:
Assimilation Overview:
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.
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Figure 1: 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.
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Figure 2: 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.
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Figure 3: 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.
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Figure 4: 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.
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Figures 1 through 4 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.
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