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

2020 ◽  
Vol 177 (12) ◽  
pp. 5969-5992
Author(s):  
Siva Srinivas Kolukula ◽  
Balaji Baduru ◽  
P. L. N. Murty ◽  
J. Pavan Kumar ◽  
E. Pattabhi Rama Rao ◽  
...  
Keyword(s):  
Surface Current ◽  
Current Data ◽  
Empirical Orthogonal Functions ◽  
Hf Radar ◽  
Sea Surface ◽  
Orthogonal Functions

scholarly journals Gap Filling of the CALYPSO HF Radar Sea Surface Current Data through Past Measurements and Satellite Wind Observations

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Adam Gauci ◽  
Aldo Drago ◽  
John Abela
Keyword(s):  
Oil Spills ◽  
Surface Current ◽  
Radar Data ◽  
Current Data ◽  
Hf Radar ◽  
Sea Surface ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Spatial Coverage ◽  
Essential Components

High frequency (HF) radar installations are becoming essential components of operational real-time marine monitoring systems. The underlying technology is being further enhanced to fully exploit the potential of mapping sea surface currents and wave fields over wide areas with high spatial and temporal resolution, even in adverse meteo-marine conditions. Data applications are opening to many different sectors, reaching out beyond research and monitoring, targeting downstream services in support to key national and regional stakeholders. In the CALYPSO project, the HF radar system composed of CODAR SeaSonde stations installed in the Malta Channel is specifically serving to assist in the response against marine oil spills and to support search and rescue at sea. One key drawback concerns the sporadic inconsistency in the spatial coverage of radar data which is dictated by the sea state as well as by interference from unknown sources that may be competing with transmissions in the same frequency band. This work investigates the use of Machine Learning techniques to fill in missing data in a high resolution grid. Past radar data and wind vectors obtained from satellites are used to predict missing information and provide a more consistent dataset.

Directional Extreme Current Profiles Based on Complex Empirical Orthogonal Functions (C-EOF) for Offshore Design

2009 ◽  
Author(s):  
Jose Antonio Moreira Lima ◽  
Eric Oliveira Ribeiro ◽  
Wellington Ceccopieri ◽  
Guisela Grossmann Matheson
Keyword(s):  
Goodness Of Fit ◽  
Structural Reliability ◽  
Current System ◽  
Correlation Coefficients ◽  
Current Data ◽  
Empirical Orthogonal Functions ◽  
Western Boundary ◽  
Boundary Current ◽  
Orthogonal Functions ◽  
Brazil Current

This paper presents a methodology to estimate deep water design current profiles using Complex Empirical Orthogonal Function (C-EOF) and a structural reliability response based model. The advantage of C-EOF is the capability of directly obtaining directional extreme current profiles. It is estimated that most of the variability of the southeast Brazil current system can be explained by the first two EOF modes. The first mode associated with the southwestward Brazil Current (BC) and the second mode with the northeastward Intermediate Western Boundary Current (IWBC). Thus, only two series of C-EOF amplitudes can be used in the response based technique to estimate the 100-y extreme current values. The methodology can also be used with more EOF modes if required to properly represent the current data. The probabilistic cumulative functions are based on extreme value distributions such as Gumbel or Weibull, and Lognormal for conditional distributions. The evaluation of estimated distribution parameters are carried out using Kolmogorov-Smirnov goodness-of-fit hypothesis tests and correlation coefficients for each directional sector.

scholarly journals DINEOF reconstruction of clouded images including error maps – application to the Sea-Surface Temperature around Corsican Island

Ocean Science ◽  
2006 ◽  
Vol 2 (2) ◽  
pp. 183-199 ◽  
Author(s):  
J.-M. Beckers ◽  
A. Barth ◽  
A. Alvera-Azcárate
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Optimal Interpolation ◽  
Orthogonal Functions ◽  
Ligurian Sea ◽  
Additional Information ◽  
Error Covariance ◽  
Data Coverage

Abstract. We present an extension to the Data INterpolating Empirical Orthogonal Functions (DINEOF) technique which allows not only to fill in clouded images but also to provide an estimation of the error covariance of the reconstruction. This additional information is obtained by an analogy with optimal interpolation. It is shown that the error fields can be obtained with a clever rearrangement of calculations at a cost comparable to that of the interpolation itself. The method is presented on the reconstruction of sea-surface temperature in the Ligurian Sea and around the Corsican Island (Mediterranean Sea), including the calculation of inter-annual variability of average surface values and their expected errors. The application shows that the error fields are not only able to reflect the data-coverage structure but also the covariances of the physical fields.

Filling in missing sea-surface temperature satellite data over the Eastern Mediterranean Sea using the DINEOF algorithm

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Andreas Nikolaidis ◽  
Georgios Georgiou ◽  
Diofantos Hadjimitsis ◽  
Evangelos Akylas
Keyword(s):  
Missing Data ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Eastern Mediterranean ◽  
Western Mediterranean ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Temperature Fields ◽  
Special Technique ◽  
Orthogonal Functions

AbstractThe Data Interpolating Empirical Orthogonal Functions method is a special technique based on Empirical Orthogonal Functions and developed to reconstruct missing data from satellite images, which is especially useful for filling in missing data from geophysical fields. Successful experiments in the Western Mediterranean encouraged extension of the application eastwards using a similar experimental implementation. The present study summarizes the experimental work done, the implementation of the method and its ability to reconstruct the sea-surface temperature fields over the Eastern Mediterranean basin, and specifically in the Levantine Sea. L3 type Satellite Sea-surface Temperature data has been used and reprocessed in order to recover missing information from cloudy images. Data reconstruction with this method proved to be extremely effective, even when using a relatively small number of time steps, and markedly accelerated the procedure. A detailed comparison with the two oceanographic models proves the accuracy of the method and the validity of the reconstructed fields.

scholarly journals DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 126
Author(s):  
Fangyuan Liu ◽  
Hao Zhou ◽  
Biyang Wen
Keyword(s):  
Surface Current ◽  
Current Data ◽  
Hf Radar ◽  
Natural Phenomenon ◽  
High Frequency Radar ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Classical Statistics ◽  
Oceanic Eddy ◽  
Eddy Detection

Oceanic eddy is a common natural phenomenon that has large influence on human activities, and the measurement and detection of offshore eddies are significant for oceanographic research. The previous classical detecting methods, such as the Okubo–Weiss algorithm (OW), vector geometry algorithm (VG), and winding angles algorithm (WA), not only depend on expert’s experiences to set an accurate threshold, but also need heavy calculations for large detection regions. Differently from the previous works, this paper proposes a deep eddy detection neural network with pixel segmentation skeleton on high frequency radar (HFR) data, namely, the deep eddy detection network (DEDNet). An offshore eddy detection dataset is firstly constructed, which has origins from the sea surface current data measured by two HFR systems on the South China Sea. Then, a spatial globally optimum and strong detail-distinguishing pixel segmentation network is presented to automatically detect and localize offshore eddies in a flow chart. An eddy detection network based on fully convolutional networks (FCN) is also presented for comparison with DEDNet. Experimental results show that DEDNet performs better than the FCN-based eddy detection network and is competitive with the classical statistics-based methods.

scholarly journals Time–Space SST Variability in the Atlantic during 2013: Seasonal Cycle

2015 ◽  
Vol 32 (9) ◽  
pp. 1689-1705 ◽  
Author(s):  
Liyan Liu ◽  
Carlos Lozano ◽  
Dan Iredell
Keyword(s):  
Seasonal Cycle ◽  
Temporal Distribution ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Model Output ◽  
Orthogonal Functions ◽  
Cooling Period ◽  
Sst Variability ◽  
Time Space ◽  
Orthogonal Components

AbstractA 2-yr-long daily gridded field of sea surface temperature (SST) in the Atlantic centered for the year 2013 is projected onto orthogonal components: its mean, six harmonics of the year cycle, the slow-varying contribution, and the fast-varying contribution. The periodic function defined by the year harmonics, referred to here as the seasonal harmonic, contains most of the year variability in 2013. The seasonal harmonic is examined in its spatial and temporal distribution by describing the amplitude and phase of its maxima and minima, and other associated parameters. In the seasonal harmonic, the ratio of the duration of warming period to cooling period ranges from 0.2 to 2.0. There are also differences in the spatial patterns and dominance of the year harmonics—in general associated with regions with different insolation, oceanic, and atmospheric regimes. Empirical orthogonal functions (EOFs) of the seasonal harmonic allow for a succinct description of the seasonal evolution for the Atlantic and its subdomains. The decomposition can be applied to model output, allowing for a more incisive model validation and data assimilation. The decorrelation time scale of the rapidly varying signal is found to be nearly independent of the time of the year once four or more harmonics are used. The decomposition algorithm, here implemented for a single year cycle, can be applied to obtain a multiyear average of the seasonal harmonic.

scholarly journals Spatial–Temporal Variability of the Calculated Characteristics of the Ocean in the Arctic Zone of Russia by Using the NEMO Model with Altimetry Data Assimilation

2020 ◽  
Vol 8 (10) ◽  
pp. 753
Author(s):  
Konstantin Belyaev ◽  
Andrey Kuleshov ◽  
Ilya Smirnov
Keyword(s):  
Data Assimilation ◽  
Temporal Variability ◽  
Satellite Observation ◽  
Sea Surface Height ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
The Arctic ◽  
Observation Data ◽  
Arctic Zone ◽  
Orthogonal Functions

The spatial–temporal variability of the calculated characteristics of the ocean in the Arctic zone of Russia is studied. In this study, the known hydrodynamic model of the ocean Nucleus for European Modelling of the Ocean (NEMO) is used with assimilation of observation data on the sea surface height taken from the Archiving, Validating and Interpolation Satellite Observation (AVISO) archive. We use the Generalized Kalman filter (GKF) method, developed earlier by the authors of this study, in conjunction with the method of decomposition of symmetric matrices into empirical orthogonal functions (EOF, Karhunen–Loeve decomposition). The investigations are focused mostly on the northern seas of Russia. The main characteristics of the ocean, such as the current velocity, sea surface height, and sea surface temperature are calculated with data assimilation (DA) and without DA (the control calculation). The calculation results are analyzed and their spatial–temporal variability over a time period of 14 days is studied. It is shown that the main spatial variability of characteristics after DA is in good agreement with the localization of currents in the North Atlantic and in the Arctic zone of Russia. The contribution of each of the eigenvectors and eigenvalues of the covariation matrix to the spatial–temporal variability of the calculated characteristics is shown by using the EOF analysis.

scholarly journals Testing the validity of regional detail in global analyses of sea surface temperature – the case of Chinese coastal waters

Ocean Science ◽  
2019 ◽  
Vol 15 (6) ◽  
pp. 1455-1467
Author(s):  
Yan Li ◽  
Hans von Storch ◽  
Qingyuan Wang ◽  
Qingliang Zhou ◽  
Shengquan Tang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Careful Examination ◽  
Local Data ◽  
High Quality ◽  
Orthogonal Functions ◽  
Local Quality ◽  
Break Points

Abstract. We have designed a method for testing the quality of multidecadal analyses of sea surface temperature (SST) in regional seas by using a set of high-quality local SST observations. In recognizing that local data may reflect local effects, we focus on the dominant empirical orthogonal functions (EOFs) of the local data and of the localized data of the gridded SST analyses. We examine the patterns, variability, and trends of the principal components. This method is applied to examine three different SST analyses, i.e., HadISST1, ERSST, and COBE SST. They have been assessed using a newly constructed high-quality dataset of SST at 26 coastal stations along the Chinese coast in 1960–2015, which underwent careful examination with respect to quality and a number of corrections for inhomogeneities. The three gridded analyses perform generally well from 1960 to 2015, in particular since 1980. However, for the pre-satellite period prior to the 1980s, the analyses differ among each other and show some inconsistencies with the local data, such as artificial break points, periods of bias, and differences in trends. We conclude that gridded SST analyses need improvement in the pre-satellite period (prior to the 1980s) by reexamining in detail archives of local quality-controlled SST data in many data-sparse regions of the world.

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