Gaps Filling in HF Radar Sea Surface Current Data Using Complex Empirical Orthogonal Functions

The knowledge of speed and direction of coastal ocean currents are critical for carrying out search and rescue at sea, to respond to the hazardous marine oil spills, to estimate the dispersal pattern of pollutants, to issue warnings against tsunamis and cyclones etc. Such operations need uninterrupted observational data of sea surface currents over wide area with high spatial and temporal resolution. The High frequency (HF) radars installed at the shore, sense the surface of ocean using electromagnetic waves, and provide the speed and direction of ocean currents over a specific spatial domain at high temporal resolution. However, the frequent gaps in the data are a concern for its usage. The gaps arise due to its dependence on the roughness of the sea other electromagnetic interferences from environment sources and due to data acquisition failures. Hence, it is necessary to fill the gaps for successful usage of HF radar data. The present work describes a method to fill the gaps in the HF radar data using complex empirical orthogonal functions (CEOF). Before filling the missing data, HF radar data is assessed with available observations. The CEOFs are calculated iteratively using singular value decomposition. The number of statistically significant CEOFs required to construct the missing HF radar currents data accurately along the Indian coast are obtained through a cross-validation procedure. The proposed method is first validated for various rates of synthetic data gaps created in synthetic data and in model derived cyclonic wind fields and comparisons are illustrated, the reconstructed data and the original data are in good match