A transport method for restoring incomplete ocean current measurements

TL;DR

This paper introduces an information transport method inspired by image processing to restore incomplete ocean current measurements, effectively filling data gaps without fitting or projection, and demonstrating high accuracy even with large missing data portions.

Contribution

The novel approach applies an information transport technique to oceanographic data, offering a flexible, non-intrusive way to fill gaps while preserving original measurement regions.

Findings

Restored data errors are within the native measurement error (<10% for magnitude, <3% for direction).

Method remains robust across different parameters and data coverage scenarios.

Effective even with large percentages of missing data points.

Abstract

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.