ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation
ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation
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DOI：
10.5194/gmd-8-3071-2015
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发表时间：
2015-01-01
影响因子：
5.1
通讯作者：
Wunsch, C.
中科院分区：
文献类型：
--
作者：
Forget, G.;Campin, J. -M.;Wunsch, C.
本文介绍了ECCO v4非线性逆模型框架及其基线解决方案，用于1992-2011年期间不断变化的海洋状态。这两个组件都是公开的，并接受定期的自动回归测试。建模框架包括全局共形网格集、全局模型设置、数据约束和控制参数的实现、算法微分的接口以及独立于网格的功能齐全的Matlab工具箱。基线ECCO v4解决方案是动态一致的海洋状态估计，没有未识别的热源和浮力，任何感兴趣的用户都能够准确地重现。该解决方案是一个可接受的适合大多数数据，并已被发现在物理上合理的许多方面，在这里和相关出版物中记录。用户被提供了评估模型数据不适合自己的能力。建模和数据合成之间的协同作用是断言通过建模框架和状态估计的联合呈现。特别是，参数化物理的逆估计有助于改善所观察到的水文学的拟合，并成为可供一般使用的海洋模型设置的一个组成部分。更一般地说，外部，参数和结构模型误差的相对重要性的第一次评估。参数和外部模型的不确定性似乎是相当重要的，并主导结构模型的不确定性。结果一般强调的重要性，包括湍流输运参数的反问题。
This paper presents the ECCO v4 non-linear inverse modeling framework and its baseline solution for the evolving ocean state over the period 1992-2011. Both components are publicly available and subjected to regular, automated regression tests. The modeling framework includes sets of global conformal grids, a global model setup, implementations of data constraints and control parameters, an interface to algorithmic differentiation, as well as a grid-independent, fully capable Matlab toolbox. The baseline ECCO v4 solution is a dynamically consistent ocean state estimate without unidentified sources of heat and buoyancy, which any interested user will be able to reproduce accurately. The solution is an acceptable fit to most data and has been found to be physically plausible in many respects, as documented here and in related publications. Users are being provided with capabilities to assess model-data misfits for themselves. The synergy between modeling and data synthesis is asserted through the joint presentation of the modeling framework and the state estimate. In particular, the inverse estimate of parameterized physics was instrumental in improving the fit to the observed hydrography, and becomes an integral part of the ocean model setup available for general use. More generally, a first assessment of the relative importance of external, parametric and structural model errors is presented. Parametric and external model uncertainties appear to be of comparable importance and dominate over structural model uncertainty. The results generally underline the importance of including turbulent transport parameters in the inverse problem.