| Summary: | 碩士 === 國立臺灣大學 === 海洋研究所 === 101 === The Coastal Ocean Dynamics Applications Radar (CODAR) is a High-Frequency (HF) radar system with compact antennas. Recently, CODAR becomes widely used, for monitoring ocean surface currents remotely in nearly real time; its ability of large coverage on ocean surface and high resolution both in time and in space, makes CODAR an ideal tool for the operational oceanography, such as now-cast of currents and data-assimilation tasks. However, environmental effects such as interferences from obstacles, and/or from ionospheric disturbances, often hampers or weakens the strength of CODAR system, might deteriorate the data quality of CODAR, inducing incomplete datasets with missing data or holes in the designated observation region. The development of appropriate methodology for filling missing data is therefore a necessity deserving for further studies, in prior to the development of operational framework.
In the context, we have analyzed a nearly two-year long dataset of CODAR, which was observed by two HF radars located at Suao and Han-Ben, respectively, and provided by the Surface Current Observations at North-East Taiwan (SCONET) project; basic statistics of the measurements show that the SCONET dataset satisfies the normality and weakly stationary condition. Further processing, by using both modal decomposition methods of Real-vector Empirical Orthogonal Function (REOF) and the Karhuren-Loeve Expansion (KLE), respectively, reveals that almost more than 96% of total variances of currents in the whole observation region can be interpreted by the first 20 modes of both methods. Therefore the first 20 modes of both methods are used for data reconstruction and data filling later.
An independent month-long time series of CODAR measurements is adopted for the data filling experiment, by which the incomplete dataset is generated by depleting artificially assigned grid points in the original complete dataset, the measurements are therefore treated as the ground truth for later comparison. Monte Carlo simulation is used for the tests of data filling experiment when the missing points of data exceed 3 points. We have used both the EOF and the KLE methods, in accompany with the least square and the iteration procedures for the estimation of the amplitude of each modes, for the study of data filling experiment. Results show that the 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 are not adequate for filling incomplete dataset, if the percentage of occurrence of the missing data exceeds 71%.
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