SBIR/STTR Research Award

Predicting the Combined Impact of Social and Environmental Models (PreCISE Models)

Award identifier a54df78321e273fc02ca2d09ad1a56e0e4d19f3db325c54e77f9ae259db4c3f0Awarding agency Department of Defense · Army

Award amount

$100,000

Program and phase

SBIR · Phase I

Award year

2010

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Award details

Awarding agency
Department of Defense
Branch
Army
Recipient company
APTIMA INC
Proposal award date
2010-03-29
Contract end date
2010-08-31
UEI
K1GCPNNZKEN7
Solicitation number
2009.2
Topic code
OSD09-HS1

Research abstract

While long-term, global climate change has received the attention and concern of the world’s governments and populations, less consideration has been given to the short-term seasonal and inter-annual changes in climate likely to greatly influence the management of water, food, and energy resources. The Office of the Secretary of Defense and the United States Army Corps of Engineers seek to develop a web-based tool to facilitate planning stability operations based on predictions of climate change. In response, Aptima proposes to develop PreCISE Models, a web-based decision aid that presents the predictions of uniquely-linked models of weather and human social behavior in an intuitive way that will promote planning stability operations.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

520 SBIR/STTR records share this UEI (1997–2025). Reported award amounts total $208,057,185. Reported amounts are not federal obligations.

Phases: Phase I, Phase II, Phase II – Direct to Phase II

Agencies: Department of Defense, National Aeronautics and Space Administration, Department of Health and Human Services, Department of Transportation, Department of Energy, Department of Homeland Security

Agency intelligence

Department of Defense

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