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AN OVERVIEW OF THE UNCERTAINTY ANALYSIS, SENSITIVITY ANALYSIS, AND PARAMETER ESTIMATION (UA/SA/PE) API AND HOW TO IMPLEMENT IT
Citation:
Castleton, K., J E. Babendreier, S Fine, E. R. Banta, M. Hill, S. Markstrom, AND G. Leavesley. AN OVERVIEW OF THE UNCERTAINTY ANALYSIS, SENSITIVITY ANALYSIS, AND PARAMETER ESTIMATION (UA/SA/PE) API AND HOW TO IMPLEMENT IT. Presented at International Workshop on Uncertainty, Sensitivity, and Parameter Estimation for Multimedia Environmental Modeling, Rockville, MD, August 19-21, 2003.
Impact/Purpose:
The primary goals are to: (1) Construct a 400-node PC-based supercomputing cluster supporting Windows and Linux computer operating systems (i.e. SuperMUSE: Supercomputer for Model Uncertainty and Sensitivity Evaluation); (2) Develop platform-independent system software for the management of SuperMUSE and parallelization of EPA models and modeling systems for implementation on SuperMUSE (and other PC-based clusters); (3) Conduct uncertainty and sensitivity analyses of the 3MRA modeling system; (4) Develop advanced algorithmic software for advanced statistical sampling methods, and screening, localized, and global sensitivity analyses; and (5) Provide customer-oriented model applications for probabilistic risk assessment supporting quality assurance in multimedia decision-making.
Description:
The Application Programming Interface (API) for Uncertainty Analysis, Sensitivity Analysis, and
Parameter Estimation (UA/SA/PE API) (also known as Calibration, Optimization and Sensitivity and Uncertainty (CUSO)) was developed in a joint effort between several members of both the Framework Software Workgroup and the Uncertainty and Parameter Estimation Workgroup of the Federal Interagency Steering Committee on Multimedia Environmental Modeling (ISCMEM). The primary purpose for its undertaking, the development of the current draft UA/SA/PE API presented here today attempts to initiate discussion and increase cooperation among the various Federal Agencies in moving towards a common software programming approach for the future development of sharable tools and methods for conducting uncertainty analysis, sensitivity analysis, and parameter estimation.