P2.3
Dispersion Modeling with a Dense Network of Meteorological Observations using ARMADA (Army RDT&E Meteorological Architecture for Data Archival) at Dugway Proving Ground
PAPER WITHDRAWN
Susan Krippner, U.S. Army, Dugway Proving Ground, Dugway, UT; and S. F. Halvorson, D. Storwold, and E. Argenta
The US Army Dugway Proving Ground (DPG) West Desert Test Center conducts outdoor developmental tests of point and remote detectors for both chemical and biological agents using simulants for the agents. During these tests, real-time quantitative dispersion model output is provided to the test officer prior to, during, and after any dissemination of simulated material. Because these tests are typically conducted on a spatial scale of 100 meters to several kilometers and a temporal scale of 5 to 60 minutes, the model meteorological inputs must be derived from micro-scale meteorological observations. Consequently, in addition to a fixed mesoscale network of remote automated weather stations, DPG deploys dense arrays of portable weather stations, sodars, and other meteorological instrumentation to support specific tests. The Army Research, Development, Test and Evaluation (RDT&E) Meteorological Architecture for Data Archival (ARMADA), a repository that collects and archives all of these data, was first deployed in 2007. An important feature of ARMADA is that it can integrate both surface and vertical meteorological measurements in near real time for input to a dispersion model such as the Defense Threat Reduction Agency's Hazard Prediction and Assessment Capability (HPAC) Second Order Closure Integrated Puff (SCIPUFF) dispersion model. Furthermore, ARMADA is able to integrate historical meteorological data, which allows modelers to reexamine test data with future or updated dispersion models. This paper will show an example of this integration using data from the DTRA Fusing Sensor Information from Observing Networks (FUSION) Field Trial 2007 (FFT07) experiment conducted at DPG in September 2007. FFT07 will provide a comprehensive dispersion and meteorological dataset with a high spatial and temporal resolution for single and multiple puff and continuous tracer releases. This paper will compare SCIPUFF predictions made using data from a dense network of meteorological stations with predictions made using data from a standard mesoscale network against the observed downwind concentrations.
Poster Session P2, Poster Session P2
Wednesday, 23 January 2008, 2:30 PM-4:00 PM, Exhibit Hall B
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