Optimization of Parameters and InitialValues in a Marine NPZD-Type Ecosystem Model

Parameters and initial values of a one-dimensional marine ecosystemmodel are optimized using a gradient-based optimization algorithm takinginto account parameter bounds. Sensitivities of the optimized parametersw.r.t. errors in observations and initial values are studied numerically andfound to yield parameter ranges narrow relative to the a priori parameteruncertainty reflected in upper and lower bounds on the permitted pa-rameter range. This means, that optimal parameters can be determinedaccurately. We find, that optimizing for the initial values along with theparameters can greatly improve the model’s fit to the observation

Reducing the model-data misfit in a marine ecosystem model using periodic parameters and linear quadratic optimal control

This paper presents the application of the Linear Quadratic Optimal Control (LQOC) method to a parameter optimization problem for a one-dimensional marine ecosystem model of NPZD (N for dissolved inorganic nitrogen, P for phytoplankton, Z for zooplankton and D for detritus) type. This ecosystem model, developed by Oschlies and Garcon, simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean circulation data. The LQOC method is used to introduce annually periodic model parameters in a linearized version of the model. We show that the obtained version of the model gives a significant reduction of the model-data misfit, compared to the one obtained for the original model with optimized constant parameters. The found inner-annual variability of the optimized parameters provides hints for improvement of the original model. We use the obtained optimal periodic parameters also in validation and prediction experiments with the original non-linear version of the model. In both cases, the results are significantly better than those obtained with optimized constant parameters

Parameter Optimization and Validation of a Marine Biogeochemical Model using a Hybrid Algorithm

Sensitivity computations, parameter identification and optimization for an 1-D marine biogeochemical model of $NPZD$ type are presented. For the optimization a hybrid algorithm combining quantum-evolutionary and local gradient-based search methods is used. It turns out to be an efficient and flexible tool for optimization and can be easily adopted for other simulation models. For the model under investigation attainable data could be exactly identified. For realistic measure ment data we argue that a certain parameter set leading to a non-optimal fit cannot be improved. Moreover we show that data uncertainty leads to a significant parameter spread. Thus we conclude that the $NPZD$ model needs to be modified or extended, maybe including a modification of external forcings and/or initial conditions