Abstract An assessment of variance in ocean current signal and noise shared by in situ observations (drifters) and a large gridded analysis (GlobCurrent) is sought as a function of day of the year for 1993–2015 and across a broad spectrum of current speed. Regardless of the division of collocations, it is difficult to claim that any synoptic assessment can be based on independent observations. Instead, a measurement model that departs from ordinary linear regression by accommodating error correlation is proposed. The interpretation of independence is explored by applying Fuller's (1987) concept of equation and measurement error to a division of error into shared (correlated) and unshared (uncorrelated) components, respectively. The resulting division of variance in the new model favours noise. Ocean current shared (equation) error is of comparable magnitude to unshared (measurement) error and the latter is, for GlobCurrent and drifters respectively, comparable to ordinary and reverse linear regression. Although signal variance appears to be small, its utility as a measure of agreement between two variates is highlighted. Sparse collocations that sample a dense (high resolution) grid permit a first order autoregressive form of measurement model to be considered, including parameterizations of analysis-in situ error cross-correlation and analysis temporal error autocorrelation. The former (cross-correlation) is an equation error term that accommodates error shared by both GlobCurrent and drifters. The latter (autocorrelation) facilitates an identification and retrieval of all model parameters. Solutions are sought using a prescribed calibration between GlobCurrent and drifters (by variance matching). Because the true current variance of GlobCurrent and drifters is small, signal to noise ratio is near zero at best. This is particularly evident for moderate current speed and for the meridional current component.

中文翻译：

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在大型分析验证中利用误差相关性：GlobCurrent 案例研究

摘要 原位观测（漂流器）和大型网格分析（GlobCurrent）共享的洋流信号和噪声的方差评估被寻求作为 1993-2015 年和在广泛的当前速度范围内的一年中的一天的函数。不管搭配的划分如何，都很难说任何天气评估都可以基于独立的观察。相反，提出了一种通过适应误差相关性而脱离普通线性回归的测量模型。通过将 Fuller (1987) 的方程和测量误差概念应用于将误差分别划分为共享（相关）和非共享（不相关）分量，可以探索对独立性的解释。新模型中产生的方差划分有利于噪声。洋流共享（方程）误差与非共享（测量）误差具有可比性，后者对于 GlobCurrent 和漂流器而言分别与普通和反向线性回归相当。尽管信号方差看起来很小，但它作为两个变量之间一致性的度量的实用性得到了强调。对密集（高分辨率）网格进行采样的稀疏搭配允许考虑测量模型的一阶自回归形式，包括原位分析误差互相关和分析时间误差自相关的参数化。前者（互相关）是一个方程误差项，它适应 GlobCurrent 和漂移器共享的误差。后者（自相关）有助于识别和检索所有模型参数。使用 GlobCurrent 和漂移器之间的规定校准（通过方差匹配）来寻找解决方案。由于 GlobCurrent 和漂移器的真实电流方差很小，因此信噪比充其量接近于零。这对于中等水流速度和子午水流分量尤其明显。