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An adjoint data assimilation approach for estimating parameters in a three-dimensional ecosystem model

机译:一种追溯数据同化方法,用于估算三维生态系统模型中的参数

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In this paper an ecosystem model, including phytoplankton, zooplankton, nitrate, ammonium, phosphate and detritus, is described. The model is driven by physical fields derived from a three-dimensional physical transport model. Simulation includes nitrate input from a river. Simulated results are then sampled and the sampled data are used in sequential numerical experiments to assess the ability of using an adjoint data assimilation approach for estimating the poorly known parameters of the ecosystem model, such as growth and death rate, half-saturation constant of nutrients, etc. Data with different spatial and temporal resolution over I week are assimilated into the ecosystem model. Assimilation of data at 30 grid stations with a sampling interval of 6 h is proved to be adequate for recovering all the parameters of the ecosystem model. Both the spatial and temporal resolution of the data are mutually complementary in the assimilative model. Thus, improvement of either of them can result in improvement of model parameter recoveries. The assimilation of phytoplankton data is essential to recover the model parameters. Phytoplankton is the core of the food web and without the information on phytoplankton, the structure of the ecosystem cannot be constructed correctly. The adjoint method can work well with the noisy data. In the twin experiments with noisy data, the parameters can be recovered but the error is increased. The results of the model and parameter recovery are sensitive to the initial conditions of state variables, so the determination of the initial condition is as important as that of the model parameter. The spatial and temporal resolution and the data type of the observations in Analysis and Modelling Research of the Ecosystem in the Bohai Sea (AMREB) are suitable for the recovery of the model parameters used in this study. (c) 2005 Elsevier B.V. All rights reserved.
机译:在本文中,描述了一种生态系统模型,包括浮游植物,浮游动物,硝酸盐,铵,磷酸盐和碎屑。该模型由来自三维物理传输模型的物理字段驱动。仿真包括来自河流的硝酸盐输入。然后采样模拟结果,并采样数据用于顺序数值实验,以评估使用伴随数据同化方法来估算生态系统模型的众所周知的参数的能力,例如生长和死亡率,营养成分的半饱和度常数等等的数据与I周上不同的空间和时间分辨率的数据被同化到生态系统模型中。有关6小时的采样间隔的30个网格站的辅助被证明是足以恢复生态系统模型的所有参数。数据的空间和时间分辨率都在同化模型中相互互补。因此,它们中的任何一个的改进都会导致模型参数恢复的改善。浮游植物数据的同化对于恢复模型参数至关重要。 Phytoplankton是食品网的核心,没有关于浮游植物的信息,无法正确构建生态系统的结构。伴随方法可以很好地使用嘈杂的数据。在具有嘈杂数据的双实验中,可以恢复参数,但误差增加。模型和参数恢复的结果对状态变量的初始条件敏感,因此初始条件的确定与模型参数一样重要。渤海(AMREB)生态系统的分析和建模研究的空间和时间分辨率和数据类型适用于恢复本研究中使用的模型参数。 (c)2005 Elsevier B.V.保留所有权利。

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