Interpolation of the gaps in current maps generated by high-frequency radar

ABSTRACT High-frequency (HF) radars sense the surface of ocean using electromagnetic waves and provide data of current vectors over a large spatial domain in the form of current maps at short intervals of time. Due to reasons such as failure of hardware or software, vandalism, or environmental issues, small or large gaps can always be found in collected data. In-filling of such missing information calls for special procedures owing to the typical sensing and reporting style of these current maps. In this article HF radar observations made at five different locations in India are analysed with the aim of providing consistent and synthesized information after noticing that there were significant missing values. This task is accomplished by selecting the most appropriate and state of the art methods of spatial data interpolation. An exhaustive experimentation in this regard showed that the statistical method of ‘inverse distance weighting’ or the soft computing technique of artificial neural network can work satisfactorily for this purpose.

[1]  Hrvoje Kalinic,et al.  Sensitivity of Self-Organizing Map surface current patterns to the use of radial vs. Cartesian input vectors measured by high-frequency radars , 2015, Comput. Geosci..

[2]  Robert H. Weisberg,et al.  Coastal Ocean Observing Systems , 2015 .

[3]  Eric Terrill,et al.  Objectively mapping HF radar-derived surface current data using measured and idealized data covariance matrices , 2007 .

[4]  Simone Cosoli,et al.  Surface current variability and wind influence in the northeastern Adriatic Sea as observed from high-frequency (HF) radar measurements , 2012 .

[5]  Hans C. Graber,et al.  Introduction to High-Frequency Radar: Reality and Myth , 1997 .

[6]  M. C. Deo,et al.  Filling up gaps in wave data with genetic programming , 2008 .

[7]  S. Kim.  Quality Assessment Techniques Applied to Surface Radial Velocity Maps Obtained from High-Frequency Radars , 2015 .

[8]  Clifford R. Merz,et al.  HF Radar Performance in a Low-Energy Environment: CODAR SeaSonde Experience on the West Florida Shelf , 2010 .

[9]  Oleg Makarynskyy,et al.  Wave Prediction and Data Supplementation with Artificial Neural Networks , 2007 .

[10]  A. Sentchev,et al.  A Combined EOF/Variational Approach for Mapping Radar-Derived Sea Surface Currents , 2011, CSR 2011.

[11]  Wilton Sturges,et al.  On interpolating gappy records for time‐series analysis , 1983 .

[12]  Christos Stefanakos,et al.  A unified methodology for the analysis, completion and simulation of nonstationary time series with missing values, with application to wave data , 2001 .

[13]  Lucy R. Wyatt,et al.  Assimilation of HF radar data into the SWAN wave model , 2009 .

[14]  S. Cosoli,et al.  Surface circulation in the Gulf of Trieste (northern Adriatic Sea) from radar, model, and ADCP comparisons , 2013 .

[15]  Jian-Kang Wu,et al.  Neural networks and simulation methods , 1993 .

[16]  Clifford R. Merz,et al.  Assessment of CODAR SeaSonde and WERA HF Radars in Mapping Surface Currents on the West Florida Shelf , 2014 .

[17]  William J. Emery,et al.  Data Analysis Methods in Physical Oceanography , 1998 .

[18]  Ronald J. Classen,et al.  Introduction to Geographic Information Systems , 2001 .

[19]  O. Makarynskyy,et al.  Improving wave predictions with artificial neural networks , 2004 .

[20]  Oyvind Breivik,et al.  Real time assimilation of HF radar currents into a coastal ocean model , 2001, 1209.6467.

[21]  P. Challenor,et al.  Using PPCA to Estimate EOFs in the Presence of Missing Values , 2004 .

[22]  Damien Sirjacobs,et al.  Data Interpolating Empirical Orthogonal Functions (DINEOF): a tool for geophysical data analyses , 2011 .

[23]  P. Krause,et al.  COMPARISON OF DIFFERENT EFFICIENCY CRITERIA FOR HYDROLOGICAL MODEL ASSESSMENT , 2005 .

[24]  D. Crombie,et al.  Doppler Spectrum of Sea Echo at 13.56 Mc./s. , 1955, Nature.

[25]  H. Statscewich,et al.  Quality Assessment of HF Radar–Derived Surface Currents Using Optimal Interpolation , 2015 .

[26]  G. Ciraolo,et al.  Tidal currents in the Malta – Sicily Channel from high-frequency radar observations , 2015 .

[27]  Klaus-Werner Gurgel,et al.  HF surface wave radar for oceanography - a review of activities in Germany , 2003, 2003 Proceedings of the International Conference on Radar (IEEE Cat. No.03EX695).

[28]  S. Glenn,et al.  Automated quality control of High Frequency radar data , 2012, 2012 Oceans.

[29]  Alexander Barth,et al.  Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF , 2009 .

[30]  Philip D. Wasserman,et al.  Advanced methods in neural computing , 1993, VNR computer library.

[31]  Josep R. Medina,et al.  Discussion of "Predictions of Missing Wave Data by Recurrent Neuronets" , 2004 .

[32]  Clifford R. Merz,et al.  Real-time quality control of current velocity data on individual grid cells in WERA HF radar , 2014, OCEANS 2014 - TAIPEI.

[33]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

[34]  David M. Kaplan,et al.  Spatial interpolation and filtering of surface current data based on open‐boundary modal analysis , 2007 .

[35]  Saad Bennis,et al.  Improving single-variable and multivariable techniques for estimating missing hydrological data , 1997 .

[36]  Rory O. R. Y. Thompson,et al.  Spectral Estimation from Irregularly Spaced Data , 1971 .

[37]  K. Gurgel,et al.  High-frequency radars : physical limitations and recent developments , 1999 .

[38]  M. Deo,et al.  Genetic programming for retrieving missing information in wave records along the west coast of India , 2007 .

[39]  Donald E. Barrick,et al.  A coastal radar system for tsunami warning , 1979 .

[40]  S. Glenn,et al.  Surface current and wave validation of a nested regional HF radar Network in the Mid-Atlantic Bight , 2008, 2008 IEEE/OES 9th Working Conference on Current Measurement Technology.

[41]  Brunello Tirozzi,et al.  Neural Network Approach to the Problem of Recovering Lost Data In a Network of Marine Buoys , 2001 .