Restoration of incomplete oceanographic datasets

Remote sensing of oceanographic data often yields incomplete coverage of
the measurement domain. This can limit interpretability of the data and
identification of coherent features informative of ocean
dynamics. Several methods exist to fill gaps of missing
oceanographic data, and are often based on projecting the
measurements onto basis functions or a statistical model. Herein, we use
an information transport approach inspired from an image processing
algorithm. This approach aims to restore gaps in data by advecting and
diffusing information of features as opposed to the field itself.
Since this method does not involve fitting or projection, the portions of
the domain containing measurements can remain unaltered, and the method
offers control over the extent of local information transfer. This method
is applied to measurements of ocean surface currents by high frequency
radars. This is a relevant application because data coverage can be
sporadic and filling data gaps can be essential to data usability.
Application to two regions with differing spatial scale is considered. The
accuracy and robustness of the method is tested by systematically blinding
measurements and comparing the restored data at these locations to the
actual measurements. These results demonstrate that even for locally large
percentages of missing data points, the restored velocities have errors
within the native error of the original data (e.g., <10% for
velocity magnitude and <3% for velocity direction). Results were
relatively insensitive to model parameters, facilitating a
priori selection of default parameters for de
novo applications.

海洋学数据的遥感观测通常会出现测量域覆盖不完整的情况。这会限制数据的可解释性，以及对能够反映海洋动力过程的连贯特征的识别。
目前已有多种方法可用于填补海洋学数据的缺失间隙，此类方法通常基于将测量数据投影至基函数或统计模型的思路。本文采用一种受图像处理算法启发的信息传输方法，该方法旨在通过对特征信息进行平流与扩散来填补数据间隙，而非直接对数据场本身进行操作。
由于该方法无需进行拟合或投影操作，测量域中存在实测数据的区域可保持不变，且该方法可对局部信息传输的范围进行调控。
本文将该方法应用于高频雷达获取的海洋表层流观测数据。该应用场景具有实际意义，因为实测数据的覆盖往往存在间歇性，填补数据间隙对提升数据可用性至关重要。
本文选取两个空间尺度各异的研究区域开展方法应用测试。通过系统性遮蔽部分测量点，并将修复后的该位置数据与真实实测数据进行对比，以此验证该方法的精度与鲁棒性。
实验结果表明，即便在局部缺失大量数据点的情况下，修复后的流速数据误差仍处于原始数据的固有误差范围内：例如流速幅值误差小于10%，流速方向误差小于3%。
实验结果对模型参数的敏感度较低，这便于为全新应用场景预先选择默认参数。