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Forecasting IR Satellite Imagery for Adaptive Sensor Tasking

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: W9113M-08-C-0084
Agency Tracking Number: B073-033-0415
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: MDA07-033
Solicitation Number: 2007.3
Timeline
Solicitation Year: 2007
Award Year: 2008
Award Start Date (Proposal Award Date): 2008-02-12
Award End Date (Contract End Date): 2008-08-13
Small Business Information
131 Hartwell Avenue
Lexington, MA 02421
United States
DUNS: 091493569
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Robert d'Entremont
 Principal Investigator
 (781) 761-2288
 rpd@aer.com
Business Contact
 Cecilia Sze
Title: President and CEO
Phone: (781) 761-2288
Email: csze@aer.com
Research Institution
N/A
Abstract

Accurate forecasts of infrared satellite imagery are needed to optimize tasking of valuable and limited space borne surveillance assets. Atmospheric constituents such as clouds, aerosols, and water vapor can negatively impact the ability of IR surveillance systems to monitor activities on the ground. Due to their chaotic nature, atmospheric impacts are particularly severe for applications that require ground monitoring over extended time periods. In our Phase I effort we will develop a prototype 0-72 hour IR imagery forecast system with particular emphasis on characterizing the impacts of clouds. We will build on our expertise and existing capabilities in a number of relevant areas including: analysis and short-term cloud forecasting from Meteorological Satellite (MetSat) imagery; use of mesoscale Numerical Weather Prediction (NWP) models to forecast atmospheric state and diagnose cloud physical properties; application of Mie and Modified Anomalous Diffraction Approximation (MADA) theory to estimate cloud radiative properties from forecasted physical properties; calculation of in-band Top Of Atmosphere (TOA) radiance values from knowledge of the surface characteristics and forecasts of the atmospheric state and cloud properties; and visualizations of forecasted scene elements derived from the TOA radiance estimates. A final demonstration of the prototype system will be conducts using live data inputs.

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

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