The efficacy of an ocean observing, analysis, and forecasting system for the Mid-Atlantic Bight and the Gulf of Maine is explored using the concept of array modes. The analysis-forecast system is based on a triply nested configuration of the Regional Ocean Modeling System (ROMS) in conjunction with 4-dimensional variational (4D-Var) data assimilation. The array modes identify the degrees of freedom (df) of the signal and of the noise resolved by the observations, and are used here to quantify the extent to which the existing network of platforms and instruments are able to observe the ocean across different dynamical regimes ranging from quasi-geostrophic through the mesoscale and down to the sub-mesoscale. The ocean observing system includes the U.S. National Science Foundation’s Ocean Observatories Initiative Pioneer Array. In general, it is found that the df of the signal are largely associated with in situ observations from the Pioneer Array. On the other hand, a combination of satellite remote sensing and in situ observations potentially contribute to the df of the noise associated with uncertainties in the measurements. The array modes also provide information about the reduction in the expected analysis and forecast error covariance due to assimilating the observations. Here too observations from the Pioneer Array are found to significantly influence the veracity of the analyses and forecasts, and the circulation is instrumental in propagating observational information to other parts of the model domain. An approach is presented in which the array modes are used to quantify the impact of data assimilation on the expected forecast error covariance of forecasts initialized from the 4D-Var ocean state estimates. The advantage of this approach over others in common use is that it is independent of forecast error norm and circumvents the need for generating potentially large and costly ensembles.

使用阵列模式的概念探索了用于中大西洋湾和缅因湾的海洋观测、分析和预报系统的功效。分析预测系统基于区域海洋建模系统 (ROMS) 的三重嵌套配置以及 4 维变分 (4D-Var) 数据同化。阵列模式识别自由度 ( df) 的信号和由观测解决的噪声，并在此用于量化现有平台和仪器网络能够在从准地转到中尺度和向下的不同动力状态下观测海洋的程度到亚中尺度。海洋观测系统包括美国国家科学基金会的海洋观测站倡议先锋阵列。一般而言，发现信号的df在很大程度上与先锋阵列的原位观测有关。另一方面，卫星遥感和原位观测的结合可能有助于df与测量中的不确定性相关的噪声。阵列模式还提供有关由于同化观测而导致的预期分析和预测误差协方差减少的信息。在这里，先锋阵列的观测也被发现显着影响分析和预测的准确性，并且环流有助于将观测信息传播到模型域的其他部分。提出了一种方法，其中阵列模式用于量化数据同化对从 4D-Var 海洋状态估计初始化的预测的预期预测误差协方差的影响。这种方法相对于其他常用方法的优势在于它独立于预测误差范数，并且不需要生成潜在的大型和昂贵的集合。