A study on data filling from incomplete dataset of HF radar measured ocean currents — A case study of the flow field Northeast of Taiwan: Data filling from incomplete ocean currents dataset

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Abstract
The CODAR is a High-Frequency (HF) radar system. Recently, CODAR becomes widely used for monitoring ocean surface currents remotely in nearly real time. However, some environmental effects often hampers or weakens the strength of CODAR system, which would deteriorate the data quality of CODAR and result in missing data in the designated observation region. In this study, we analyzed complete CODAR datasets by using both modal decomposition methods of real-vector Empirical Orthogonal Function (EOF) and the Karhunen-Loève Expansion (KLE), respectively. More than 96% of total variances of currents in the whole observation region can be interpreted by the first 20 modes of both methods, thus the first 20 modes of both methods were further used for data reconstruction and data filling. In the data filling experiment, the incomplete dataset was generated by depleting artificially assigned grid points in the CODAR original complete dataset, then both the EOF and the KLE methods were applied, in accompany with the least square and the iteration procedures to estimate the amplitude of each mode. Results show that the real-vector EOF method in accompany with the least square procedure is the best among the four methodologies, when the percentage of occurrence of the missing data is less than 57% of the whole dataset. However, all these four methods were not adequate for filling incomplete dataset, if the percentage of occurrence of the missing data exceeds 71%.

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