Evaluating Lower Computational Burden Approaches for Calibration of Large Environmental Models.

ABSTRACT: Realistic environmental models used for decision making typically require a highly parameterized approach. Calibration of such models is computationally intensive because widely used parameter estimation approaches require individual forward runs for each parameter adjusted. These runs construct a parameter-to-observation sensitivity, or Jacobian, matrix used to develop candidate parameter upgrades. Parameter estimation algorithms are also commonly adversely affected by numerical noise in the calculated sensitivities within the Jacobian matrix, which can result in unnecessary parameter estimation iterations and less model-to-measurement fit. Ideally, approaches to reduce the computational burden of parameter estimation will also increase the signal-to-noise ratio related to observations in

SUBMITTER: Hunt RJ 

PROVIDER: S-EPMC9292030 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature