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

Award Information
Agency:
Department of Defense
Branch:
Defense Threat Reduction Agency
Amount:
$749,998.00
Award Year:
2007
Program:
SBIR
Phase:
Phase II
Contract:
HDTRA1-07-C-0121
Agency Tracking Number:
RDI060003362
Solicitation Year:
2006
Solicitation Topic Code:
DTRA06-004
Solicitation Number:
2006.1
Small Business Information
ATMOSPHERIC & ENVIRONMENTAL RESEARCH, IN
131 Hartwell Avenue, Lexington, MA, 02421
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
091493569
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 search technique demonstrated during Phase 1 focuses 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. AER proposes a Phase 2 program in which the search technique is implemented in a production environment to identify typical days for all seasons and locations over the entire globe. Deliverables will include a complete "typical day" dataset suitable for ATD modeling for a wide range of spatial and temporal scales, and an extensible software system for generating similar datasets using alternate input data sources and/or search algorithms.

* information listed above is at the time of submission.

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