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Ekman模型时间变化风应力系数的伴随参数估计
李国庆1, 高艳秋2, 张继才①1
1.浙江大学海洋学院 物理海洋研究所 舟山;2.国家海洋局第二海洋研究所 卫星海洋环境动力学国家重点实验室 杭州
摘要:
本文基于时间分布参数设置的假设,利用伴随同化技术建立相关算法,反演了Ekman模型中随时间变化的风应力拖曳系数,并运行孪生实验和实际实验对该方法进行了验证。在孪生实验中,研究了参数反演结果对不同影响因素的响应,包括:风速分布、风应力系数分布、风应力系数初始猜测值、风应力系数独立变量个数、观测数据误差和观测数据层位;孪生实验结果验证了伴随同化技术反演Ekman模型中时变风应力系数的有效性,具体包括如下五个方面结论:1)不同风速分布下均能成功反演出不同风应力拖曳系数分布;2)反演结果对初始猜测值较为敏感,风应力系数初始猜测值越接近给定值,反演结果越好;3)独立风应力个数和位置的选取会显著影响反演结果,合理的选择有利于提高反演效率及减少观测数据误差;4)观测误差能够影响反演结果,结果表明观测数据误差在20%以下能取得合理的反演结果;5)反演结果对观测数据的表层和次表层流速更为敏感,这是由Ekman流的物理性质决定的。在实际实验中,利用百慕大锚系试验平台的风速和流速数据,去除周期性潮流和地转流成分后得到Ekman流成分,并作为观测输入该同化模型,反演出了适用于该区域和该时段的随时间变化的风应力系数。通过比较模拟流速和观测流速,证明利用伴随同化技术能从实测数据中反演出合理的时变风应力系数,对于海洋模型风应力系数的确定是一项有益的尝试。
关键词:  伴随同化  风应力系数  时间变化  Ekman模型  参数估计
DOI:10.11693/hyhz20190100029
分类号:
基金项目:国家重点研发计划;国家自然科学基金项目
ADJOINT PARAMETER ESTIMATION OF TIME-VARYING WIND DRAG COEFFICIENT FOR AN EKMAN MODEL
LI Guo-Qing1, GAO Yan-Qiu2, ZHANG Ji-Cai1
1.Institute of Physical Oceanography,Ocean College,Zhejiang University,Zhoushan;2.State Key Laboratory of Satellite Ocean Environment Dynamics,Second Institute of Oceanography,Hangzhou
Abstract:
Based on the assumption of time varying parameterse, this paper uses the adjoint assimilation technique to invert the time-varying wind drag coefficient in an Ekman model. The method is testified by running twin experiments and practical experiments. In the twin experiments, the response of the parameter inversion results to different influencing factors is studied, including: distributions of wind speed, distributions of wind drag coefficient, initial guess values, number of independent parameters, observation errors and layers of observations. The results of twin experiments have verified the effectiveness of the time-varying wind drag coefficient in the Ekman model, which includes the following five conclusions: 1) The different distributions of wind drag coefficient can be successfully inverted under different wind speed distributions. 2) The inversion result is sensitive to the values of initial guess, therefore the initial guess should be as reasonable as possible to improve the results and reduce the convergence time. 3) The selection of the number and location of the independent wind drag coefficient can significantly affect the inversion results. Reasonable selection is beneficial to improve the inversion efficiency. 4) The observation error can affect the inversion results, and reasonable inversion results can be obtained with maximum error below 20%; 5) The inversion results are more sensitive to the surface and subsurface observed current velocities, which is determined by the physical dynamics of the Ekman model. In the practical experiments, the observed Ekman current components are obtained from Bermuda Testbed Moorings (BTM) by removing the periodic tidal components and the geostrophic components.. Then the observed Ekman currents are assimilated into the model to invert the time varying wind drag coefficients during the observing time. By comparing the simulated results and the observed velocities, it is proved that the adjoint assimilation technique can derive a reasonable time-varying wind drag coefficients from measured data, which is a useful attempt to determine the wind drag coefficient for ocean models.
Key words:  adjoint assimilation  wind drag coefficient  time varying  Ekman layer model  parameter estimation
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