An Accurate, Efficient Atmospheric Radiative Transfer Algorithm for TAWS

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911QX-06-C-0007
Agency Tracking Number: A052-053-1373
Amount: $69,981.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A05-053
Solicitation Number: 2005.2
Timeline
Solicitation Year: 2005
Award Year: 2005
Award Start Date (Proposal Award Date): 2005-12-01
Award End Date (Contract End Date): 2006-06-01
Small Business Information
131 Hartwell Avenue, Lexington, MA, 02421
DUNS: 091493569
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Hilary Snell
 Sr. Staff Scientist
 (781) 761-2288
 hsnell@aer.com
Business Contact
 Cecilia Sze
Title: President and CEO
Phone: (781) 761-2288
Email: csze@aer.com
Research Institution
N/A
Abstract
A core process for sensor performance prediction is the radiative transport algorithm used to convert the scene environmental characteristics into radiance. Numeric approximations are often used to enhance execution time at the expense of overall radiometric accuracy. However, many radiative transfer approximations have limited applicability, working only for a set of atmospheric conditions, sensor configurations, or geometry. The use of approximations to the full radiative transfer solution is not always the best approach and in the ideal case the user would have the ability to tune both radiometric accuracy and the execution time to achieve the ideal balance for a particular problem. Since TAWS supports sensors in multiple wavebands, consistent physics across multiple wavebands is highly advantageous. We propose to extend our OSS radiative transfer module for TAWS to meet the radiative transfer requirements (all view angles and for a full range of scattering conditions) in an extremely computationally efficient manner without cumbersome approximations or discontinuities. Replacing the current radiative transfer model in TAWS with OSS would provide connectivity to state-of-art spectroscopic parameters for molecules, clouds, aerosols and surface properties, and would provide a straightforward path for future enhancements such as the inclusion of polarization effects within the model.

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

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