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Towards uncertainty quantification and parameter estimation for Earth system models in a component-based modeling framework
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DOI：10.1016/j.cageo.2016.03.005.png)
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Abstract
En 中文
Component-based modeling frameworks make it easier for users to access, configure, couple, run and test numerical models. However, they do not typically provide tools for uncertainty quantification or data-based model verification and calibration. To better address these important issues, modeling frameworks should be integrated with existing, general-purpose toolkits for optimization, parameter estimation and uncertainty quantification. This paper identifies and then examines the key issues that must be addressed in order to make a component-based modeling framework interoperable with general-purpose packages for model analysis. As a motivating example, one of these packages, DAKOTA, is applied to a representative but nontrivial surface process problem of comparing two models for the longitudinal elevation profile of a river to observational data. Results from a new mathematical analysis of the resulting nonlinear least squares problem are given and then compared to results from several different optimization algorithms in DAKOTA. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Model uncertainty
Modeling frameworks
Component-based modeling
Optimization
Inverse problems
Nonlinear least squares
Parameter estimation
Longitudinal river elevation profiles
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