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Parameter estimation in a three-dimensional marine ecosystem model using the adjoint technique

机译:使用伴随技术的三维海洋生态系统模型参数估计

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An adjoint data assimilation method is applied to estimate the poorly known ecological parameters in a coupled physical-biological model of the Bohai Sea and the Yellow Sea (BYS). The biological model includes five state variables: dissolved inorganic nitrogen, dissolved inorganic phosphate, phytoplankton, zooplankton and detritus. For the selection of control variables, a new sensitivity analysis method is introduced. This method is based upon the gradient equations of cost function with respect to model parameters. By analyzing the linear combination of Lagrange multiplies in these equations, an unrelated parameter set is selected. In twin experiments, the feasibility of estimated control variables is tested. The results indicate phytoplankton data are the strongest constraints during the parameter estimation process. Climatology daily mean chlorophyll concentrations derived from Modis-Aqua and SeaWiFS are also assimilated into the model to simulate the seasonal cycle of the distributions of phytoplankton. The results indicate a two-bloom characteristic in the BYS, a major bloom at spring and a minor bloom at fall. The simulated horizontal distributions of surface phytoplankton are consistent with the biological roles of seasonal stratification. The concentrations of the nutrients at the surface are lowest in spring, after the phytoplankton bloom. The highest concentrations occur in winter.
机译:在渤海和黄海的物理-生物耦合模型中,采用了一种伴随数据同化的方法来估计鲜为人知的生态参数。生物学模型包括五个状态变量:溶解的无机氮,溶解的无机磷酸盐,浮游植物,浮游动物和碎屑。为了选择控制变量,引入了一种新的灵敏度分析方法。该方法基于成本函数相对于模型参数的梯度方程。通过分析这些方程式中拉格朗日乘积的线性组合,可以选择不相关的参数集。在双实验中,测试了估计控制变量的可行性。结果表明,浮游植物数据是参数估计过程中最强的约束条件。来自Modis-Aqua和SeaWiFS的气候日平均叶绿素浓度也被吸收到该模型中,以模拟浮游植物分布的季节性周期。结果表明,BYS具有两朵花的特征,春季主要开花,秋季少量开花。表层浮游植物的模拟水平分布与季节分层的生物学作用相一致。浮游植物开花后,春季的表面养分浓度最低。最高浓度发生在冬季。

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