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On the Estimation of Lagrangian Diffusivity: Influence of Nonstationary Mean Flow

机译：关于拉格朗日扩散率的估计：非平稳平均流的影响

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摘要

Eddy mean flow decomposition is crucial to the estimation of Lagrangian diffusivity based on drifter data. Previous studies have shown that inhomogeneous mean flow induces shear dispersion that increases the estimated diffusivity with time. In the present study, the influences of nonstationary mean flows on the estimation of Lagrangian diffusivity, especially the asymptotic behavior, are investigated using a first-order stochastic model, with both idealized and satellite-based oceanic mean flows. Results from both experiments show that, in addition to inhomogeneity, nonstationarity of mean flows that contain slowly varying signals, such as a seasonal cycle, also cause large biases in the estimates of diffusivity within a time lag of 2 months if a traditional binning method is used. Therefore, when assessing Lagrangian diffusivity over regions where a seasonal cycle is significant [e.g., the Indian Ocean (IO) dominated by monsoon winds], inhomogeneity and nonstationarity of the mean flow should be simultaneously taken into account in eddy mean flow decomposition. A temporally and spatially continuous fit through the Gauss Markov (GM) estimator turns out to be very efficient in isolating the effects of inhomogeneity and nonstationarity of the mean flow, resulting in estimates that are closest to the true diffusivity, especially in regions where strong seasonal cycles exist such as the eastern coast of Somalia and the equatorial IO.
机译：涡流平均流分解对于基于漂移数据估算拉格朗日扩散率至关重要。先前的研究表明，不均匀的平均流量会引起剪切分散，从而随时间增加估计的扩散率。在本研究中，使用一阶随机模型，结合理想化和基于卫星的海洋平均流量，研究了非平稳平均流量对拉格朗日扩散率（尤其是渐近行为）估计的影响。这两个实验的结果表明，除了不均匀性之外，如果包含传统的分箱方法，则包含两个缓慢变化的信号（例如季节周期）的平均流量的非平稳性也会在2个月的时滞内导致扩散率估计值出现较大偏差。用过的。因此，在评估季节周期明显的区域（例如，以季风为主的印度洋（IO））上的拉格朗日扩散率时，在涡流平均流量分解中应同时考虑平均流量的不均匀性和非平稳性。通过高斯马尔可夫（GM）估计器进行的时空连续拟合在隔离平均流的不均匀性和非平稳性的影响方面非常有效，因此得出的估计值与真实扩散率最接近，尤其是在季节性强的地区存在着一些周期，例如索马里东海岸和赤道IO。

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  • 来源
    《Journal of Physical Oceanography》 |2014年第10期|2796-2811|共16页
  • 作者

    Qian Yu-Kun; Peng Shiqiu; Liang Chang-Xia; Lumpkin Rick;

  • 作者单位

    Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Guangzhou, Guangdong, Peoples R China;

    Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Guangzhou, Guangdong, Peoples R China;

    State Ocean Adm, South China Sea Marine Predict Ctr, Guangzhou, Guangdong, Peoples R China;

    NOAA, Atlantic Oceanog & Meteorol Lab, Miami, FL 33149 USA;

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