Impact of bio-optical data assimilation on short-term coupled physical, bio-optical model predictions
Impact of bio-optical data assimilation on short-term coupled physical, bio-optical model predictions
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生物光学数据同化对短期耦合物理、生物光学模型预测的影响
DOI：
10.1002/jgrc.20177
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发表时间：
2013
影响因子：
--
通讯作者：
S. Ladner
中科院分区：
文献类型：
--
作者：
I. Shulman;S. Frolov;S. Anderson;B. Penta;R. Gould;P. Sakalaukus;S. Ladner
[1] 介绍了蒙特利湾耦合物理、生物光学模型的数据同化实验。本研究的目的是调查卫星衍生的生物光学特性的同化是否可以改善沿海海洋中 1-5 天时间尺度的模型预测（浮游植物种群、叶绿素）。蒙特利湾模型由基于海军沿海海洋模型的物理模型和生物化学模型组成，其中包括三种营养物、两个浮游植物群（硅藻和小型浮游植物）、两组浮游动物食草动物和两个碎屑池。海军耦合海洋数据同化系统用于物理观测的同化。为了同化生物光学观测结果，我们使用具有平稳预测误差协方差的降阶卡尔曼滤波器。预测误差协方差在多元（生物光学、物理）经验正交函数的子空间中指定，该函数是根据长达一个月的模型运行估计的。通过同化卫星衍生的生物光学特性（叶绿素 a 或浮游植物的吸收），该模型能够重现在加利福尼亚州蒙特利湾水样位置观察到的地下叶绿素分布的强度和趋势。数据同化还提高了硅藻和小型浮游植物种群之间观察到的比率和模型预测的比率之间的一致性。无论是否同化卫星衍生的生物光学观测结果，模型运行都显示与水样观测结果相比硝酸盐的值被低估。我们发现，基于温度和硝酸盐之间的统计关系的硝酸盐瞬时更新纠正了多变量更新期间模型对硝酸盐场的低估。
[1] Data assimilation experiments with the coupled physical, bio-optical model of Monterey Bay are presented. The objective of this study is to investigate whether the assimilation of satellite-derived bio-optical properties can improve the model predictions (phytoplankton population, chlorophyll) in a coastal ocean on time scales of 1–5 days. The Monterey Bay model consists of a physical model based on the Navy Coastal Ocean Model and a biochemical model which includes three nutrients, two phytoplankton groups (diatoms and small phytoplankton), two groups of zooplankton grazers, and two detrital pools. The Navy Coupled Ocean Data Assimilation system is used for the assimilation of physical observations. For the assimilation of bio-optical observations, we used reduced-order Kalman filter with a stationary forecast error covariance. The forecast error covariance is specified in the subspace of the multivariate (bio-optical, physical) empirical orthogonal functions estimated from a monthlong model run. With the assimilation of satellite-derived bio-optical properties (chlorophyll a or absorption due to phytoplankton), the model was able to reproduce intensity and tendencies in subsurface chlorophyll distributions observed at water sample locations in the Monterey Bay, CA. Data assimilation also improved agreement between the observed and model-predicted ratios between diatoms and small phytoplankton populations. Model runs with or without assimilation of satellite-derived bio-optical observations show underestimated values of nitrate as compared to the water sample observations. We found that an instantaneous update of nitrate based on statistical relations between temperature and nitrate corrected the model underestimation of the nitrate fields during the multivariate update.