Citation:

HILL, M. C., E. P. POETER, E. R. BANTA, S. CHRISTENSEN, R. L. COOLEY, D. M. ELY, J. E. BABENDREIER, G. LEAVESLEY, M. TONKIN, AND R. JULICH. NEW PROGRAMMING ENVIRONMENTS FOR UNCERTAINTY ANALYSIS. Presented at American Geophysical Union Fall Meeting, San Francisco, CA, December 05 - 09, 2005.

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:

We live in a world of faster computers, better GUI's and visualization technology, increasing international cooperation made possible by new digital infrastructure, new agreements between US federal agencies (such as ISCMEM), new European Union programs (such as Harmoniqua), and greater collaboration between US university scientists through CUAHSI. These changes provide new resources for tackling the difficult job of quantifying how well our models perform. This talk introduces new programming environments that take advantage of these new developments and will change the paradigm of how we develop methods for uncertainty evaluation. For example, the programming environments provided by COSU API, JUPITER API, and Sensitivity/Optimization Toolbox provide enormous opportunities for faster and more meaningful evaluation of uncertainties. Instead of waiting years for ideas and theories to be compared in the complex circumstances of interest to resource managers, these new programming environments will expedite the process. In the new paradigm, unproductive ideas and theories will be revealed more quickly, productive ideas and theories will more

quickly be used to address our increasingly difficult water resources problems. As examples, two ideas in JUPITER API applications are presented: uncertainty correction factors that account for system complexities not represented in models, and PPR and OPR statistics used to identify new data needed to reduce prediction uncertainty.

Record Details: