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COMPUTATIONAL METHODS FOR SENSITIVITY AND UNCERTAINTY ANALYSIS FOR ENVIRONMENTAL AND BIOLOGICAL MODELS
Citation:
Isukapalli, S. AND P. G. Georgopoulos. COMPUTATIONAL METHODS FOR SENSITIVITY AND UNCERTAINTY ANALYSIS FOR ENVIRONMENTAL AND BIOLOGICAL MODELS. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-01/068 (NTIS PB2004-102518), 2001.
Impact/Purpose:
The objectives of this task are to continuously develop and improve the Community Multiscale Air Quality (CMAQ) modeling system, which is the science implementation within the Models-3 system framework for air quality simulation. CMAQ is a multiscale and multi-pollutant chemistry-transport model (CTM) that includes the necessary critical science process modules for atmospheric transport, deposition, cloud mixing, emissions, gas- and aqueous-phase chemical transformation processes, and aerosol dynamics and chemistry. It relies on Models-3 I/O API to support machine independent data access and maintains simple interfaces among science processor modules to provide a high-level of modularity.
Description:
This work introduces a computationally efficient alternative method for uncertainty propagation, the Stochastic Response Surface Method (SRSM). The SRSM approximates uncertainties in model outputs through a series expansion in normal random variables (polynomial chaos expansion). The unknown coefficients in series expansions are calculated using a limited number of model simulations. This method is analogous to approximation of a deterministic system by an algebraic response surface. Further improvements in the computational efficiency of the SRSM are accomplished by coupling the SRSM with ADIFOR, which facilitates automatic calculation of partial derivatives in numerical models coded in Fortran. The coupled method, SRSM-ADIFOR, uses the model outputs and their derivatives to calculate the unknown coefficients. The SRSM and the SRSM-ADIFOR are general methods, and are applicable to any model with random inputs. The SRSM has also been implemented as a black-box, web-based tool for facilitating its easy use. The SRSM and the SRSM-ADIFOR have been applied to a set of environmental and biological models. These methods directly provide sensitivity information and individual contributions of input uncertainties to output uncertainties; conventional methods require substantially larger numbers of simulations to provide such information. Thus, the SRSM and the SRSM-ADIFOR provide computationally efficient means for uncertainty and sensitivity analysis.