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"Typical day" Meteorological Data for Atmospheric Transport and Dispersion (ATD) Modeling

Award Information
Agency: Department of Defense
Branch: Defense Threat Reduction Agency
Contract: HDTRA1-06-P-0099
Agency Tracking Number: RDI060003362
Amount: $99,943.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: DTRA06-004
Solicitation Number: 2006.1
Timeline
Solicitation Year: 2006
Award Year: 2006
Award Start Date (Proposal Award Date): 2006-06-19
Award End Date (Contract End Date): 2006-12-19
Small Business Information
131 Hartwell Avenue, Lexington, MA, 02421
DUNS: 091493569
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Steve Lowe
 Principal Investigator
 (781) 377-2362
 slowe@aer.com
Business Contact
 Cecilia Sze
Title: President and CEO
Phone: (781) 761-2288
Email: csze@aer.com
Research Institution
N/A
Abstract
The complexity of Atmospheric Transport and Dispersion (ATD) modeling requires physically consistent weather data evolving in both space and time. Spatially and/or temporally averaged data found in climatological products is not suitable for ATD modeling. The proposed solution for providing “typical day” meteorological data couples innovative search techniques applied to long-range historical archives and standard Numerical Weather Prediction (NWP) mesoscale models to recreate a selected historical event identified as representative of typical for a region and season. The proposed search techniques focus on the application of frequency distribution analysis of parameters of relevance to ATD modeling, and can be applied for not only the search for “typical” days, but also significant “atypical” days that may be of relevance to ATD planners. The application of standard NWP models provides for “typical” weather data sets of the same content and resolution as is used from operational forecast sources. AER proposes a Phase 1 program to develop and demonstrate a methodology for providing “typical day” meteorological data that is ready for direct use by HPAC. The proposed work is low-risk in that it leverages existing technologies, and applies standard statistical techniques in innovative ways relevant to the ATD problem space.

* Information listed above is at the time of submission. *

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