On the flushing of Port Phillip Bay: an application of HF ocean radar

1999 ◽  
Vol 50 (6) ◽  
pp. 483 ◽  
Author(s):  
A. Prytz ◽  
M. L. Heron
Keyword(s):  
Numerical Models ◽  
Surface Current ◽  
Coastal Ocean ◽  
Current Data ◽  
Surface Currents ◽  
Tidal Forcing ◽  
Residual Currents ◽  
Tidal Component ◽  
The Matrix ◽  
Ocean Radar

HF ocean radar can produce maps of surface current in coastal ocean and estuarine waters by providing coverage in both the space and time dimensions. The deployment of COSRAD in Port Phillip Bay for two successive five-day periods provided hourly values of surface currents over the topographically complex area at the south end of the bay. Analysis of the current data provided tidal ellipses for the validation of numerical models, with resultant residual currents of the order of 0·05 m s–1. The repeated hourly maps were the basis for producing Lagrangian tracks; most tracks resulted in trapped paths which remained for long periods of time in the matrix of channels and sand-banks. A ‘tidal run’ technique was developed to calculate the length of Lagrangian tracks over one phase (ebb or flood) of the main tidal component. All tidal runs were about equal to, or shorter than, the length of the relevant channel; this indicates that tidal forcing is not effective in flushing the bay. In contrast, the observed residual currents can be an effective flushing agent if they persist for three days or longer. It is suggested that phenomena on the scale of meteorological to seasonal forcing are the effective flushing agents for Port Phillip Bay.

Gap Filling of the Coastal Ocean Surface Currents from HFR Data: Application to the Mid-Atlantic Bight HFR Network

2016 ◽  
Vol 33 (6) ◽  
pp. 1097-1111 ◽  
Author(s):  
Erick Fredj ◽  
Hugh Roarty ◽  
Josh Kohut ◽  
Michael Smith ◽  
Scott Glenn
Keyword(s):  
Numerical Models ◽  
Spatial Scales ◽  
Surface Current ◽  
Ocean Surface ◽  
The United States ◽  
Coastal Ocean ◽  
Current Data ◽  
Memory Storage ◽  
Nearshore Processes ◽  
Data Gaps

AbstractHigh-frequency radar (HFR) surface current data are an increasingly utilized tool for capturing complex dynamics of coastal ocean systems worldwide. The radar is uniquely capable of sampling relevant temporal and spatial scales of nearshore processes that impact event response activities and basic coastal ocean research. HFR is a shore-based remote sensing system and is therefore subject to data gaps, which are predominately due to environmental effects, like increased external noise or low signal due to ocean surface conditions. Many applications of these surface current data require that these gaps be filled, such as Lagrangian numerical models, to estimate material transport and dispersion. This study introduces a new penalized least squares regression method based on a three-dimensional discrete cosine transform method to reconstruct hourly HFR surface current data with a horizontal resolution of 6 km. The method explicitly uses both time and space variability to predict the missing value. Furthermore, the method is fast, robust, and requires relatively low computer memory storage. This paper evaluates the method against two scenarios of common data gaps found in HFR networks currently deployed around the world. The validation is based on observed surface current maps along the mid-Atlantic coast of the United States with specific vectors removed to replicate these common gap scenarios. The evaluation shows that the new method is robust and particularly well suited to fill a more common scenario with complete data coverage surrounding an isolated data gap. It is shown that the real-time application of the method is suitable for filling data gaps in large oceanography datasets with high accuracy.

scholarly journals Surface currents in the Porsanger fjord in northern Norway

2016 ◽  
Vol 37 (3) ◽  
pp. 337-360 ◽  
Author(s):  
Malgorzata Stramska ◽  
Andrzej Jankowski ◽  
Agata Cieszyńska
Keyword(s):  
Sea Level ◽  
Barents Sea ◽  
Surface Current ◽  
Current Data ◽  
Tidal Range ◽  
Surface Currents ◽  
Tidal Component ◽  
Level Data ◽  
Sea Level Oscillations ◽  
Tidal Constituents

Abstract We describe surface currents in the Porsanger fjord (Porsangerfjorden) located in the European Arctic in the vicinity of the Barents Sea. Our analysis is based on surface current data collected in the summer of 2014 using High Frequency (WERA, Helzel Messtechnik GmbH) radar system. One of our objectives was to separate out the tidal from the nontidal components of the currents and to determine the most important tidal constituents. Tides in the Porsanger fjord are substantial, with tidal range on the order of about 3 m. Tidal analysis attributes to tides about 99% of variance in sea level time series recorded in Honningsvaag. The most important tidal component in sea level data is the M2 component, with amplitude of ~90 cm. The S2 and N2 constituents (amplitude of ~20 cm) also play a significant role in the semidiurnal sea level oscillations. The most important diurnal component is K1 with amplitude of about 8 cm. The most important tidal component in analyzed surface currents records is the M2 component. The second most important component is the S2. Our results indicate that in contrast to sea level, only about 10-30% of variance in surface currents can be attributed to tidal currents. This means that about 70-90% of variance is due to wind-induced and geostrophic currents.

scholarly journals Coastal observing and forecasting system for the German Bight – estimates of hydrophysical states

Ocean Science ◽  
2011 ◽  
Vol 7 (5) ◽  
pp. 569-583 ◽  
Author(s):  
E. V. Stanev ◽  
J. Schulz-Stellenfleth ◽  
J. Staneva ◽  
S. Grayek ◽  
J. Seemann ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
German Bight ◽  
Numerical Models ◽  
Coastal Ocean ◽  
Hf Radar ◽  
Optimal Interpolation ◽  
Surface Currents ◽  
Arctic Seas

Abstract. A coastal observing system for Northern and Arctic Seas (COSYNA) aims at construction of a long-term observatory for the German part of the North Sea, elements of which will be deployed as prototype modules in Arctic coastal waters. At present a coastal prediction system deployed in the area of the German Bight integrates near real-time measurements with numerical models in a pre-operational way and provides continuously state estimates and forecasts of coastal ocean state. The measurement suite contributing to the pre-operational set up includes in situ time series from stationary stations, a High-Frequency (HF) radar system measuring surface currents, a FerryBox system and remote sensing data from satellites. The forecasting suite includes nested 3-D hydrodynamic models running in a data-assimilation mode, which are forced with up-to-date meteorological forecast data. This paper reviews the present status of the system and its recent upgrades focusing on developments in the field of coastal data assimilation. Model supported data analysis and state estimates are illustrated using HF radar and FerryBox observations as examples. A new method combining radial surface current measurements from a single HF radar with a priori information from a hydrodynamic model is presented, which optimally relates tidal ellipses parameters of the 2-D current field and the M2 phase and magnitude of the radials. The method presents a robust and helpful first step towards the implementation of a more sophisticated assimilation system and demonstrates that even using only radials from one station can substantially benefit state estimates for surface currents. Assimilation of FerryBox data based on an optimal interpolation approach using a Kalman filter with a stationary background covariance matrix derived from a preliminary model run which was validated against remote sensing and in situ data demonstrated the capabilities of the pre-operational system. Data assimilation significantly improved the performance of the model with respect to both SST and SSS and demonstrated a good skill not only in the vicinity of the Ferry track, but also over larger model areas. The examples provided in this study are considered as initial steps in establishing new coastal ocean products enhanced by the integrated COSYNA-observations and numerical modelling.

scholarly journals Coastal observing and forecasting system for the German Bight – estimates of hydrophysical states

2011 ◽  
Vol 8 (2) ◽  
pp. 829-872 ◽  
Author(s):  
E. V. Stanev ◽  
J. Schulz-Stellenfleth ◽  
J. Staneva ◽  
S. Grayek ◽  
J. Seemann ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
German Bight ◽  
Numerical Models ◽  
Coastal Ocean ◽  
Hf Radar ◽  
Optimal Interpolation ◽  
Surface Currents ◽  
Arctic Seas

Abstract. A coastal observing system for Northern and Arctic Seas (COSYNA) aims at construction of a long-term observatory for the German part of the North Sea, elements of which will be deployed as prototype modules in Arctic coastal waters. At present a coastal prediction system deployed in the area of the German Bight integrates near real-time measurements with numerical models in a pre-operational way and provides continuously state estimates and forecasts of coastal ocean state. The measurement suite contributing to the pre-operational set up includes in situ time series from stationary stations, a High-Frequency (HF) radar system measuring surface currents, a FerryBox system and remote sensing data from satellites. The forecasting suite includes nested 3-D hydrodynamic models running in a data-assimilation mode, which are forced with up-to-date meteorological forecast data. This paper reviews the present status of the system and its recent upgrades focusing on developments in the field of coastal data assimilation. Model supported data analysis and state estimates are illustrated using HF radar and FerryBox observations as examples. A new method combining radial surface current measurements from a single HF radar with a priori information from a hydrodynamic model is presented, which optimally relates tidal ellipses parameters of the 2-D current field and the M2 phase and magnitude of the radials. The method presents a robust and helpful first step towards the implementation of a more sophisticated assimilation system and demonstrates that even using only radials from one station can substantially benefit state estimates for surface currents. Assimilation of FerryBox data based on an optimal interpolation approach using a Kalman filter with a stationary background covariance matrix derived from a preliminary model run which was validated against remote sensing and in situ data demonstrated the capabilities of the pre-operational system. Data assimilation significantly improved the performance of the model with respect to both SST and SSS and demonstrated a good skill not only in the vicinity of the Ferry track, but also over larger model areas. The examples provided in this study are considered as initial steps in establishing new coastal ocean products enhanced by the integrated COSYNA-observations and numerical modelling.

scholarly journals Prediction of geostrophic currents using big weather data archive and neural networks for the Aegean Sea

2019 ◽  
Vol 9 (1) ◽  
pp. 10-20
Author(s):  
Timur İnan ◽  
Ahmet Fevzi BABA
Keyword(s):  
Neural Network ◽  
Surface Current ◽  
Aegean Sea ◽  
Current Data ◽  
Sea Surface ◽  
Weather Data ◽  
Time Of Arrival ◽  
Surface Currents ◽  
Data Archive ◽  
Wave Direction

Prediction of sea and weather environment variables like wind speed, wind direction, wave height, wave direction, sea surface current direction and magnitude has always been an important subject in marine engineering as they effect on ship speed and effect the time of arrival to destination point as well. In this study, we propose a neural network that can predict the latitudinal and longitudinal components of sea surface currents in the Aegean Sea. The system can predict the sea surface currents components using the wind components which are gathered from the INMARSAT weather report system. The neural network is trained using the historical data which is gathered from UCAR historical weather database and historical surface current data which is gathered from IFREMER database. Keywords: Sea surface current, weather report, prediction, neural network, big data archive.

scholarly journals The Influence of Fetch on the Response of Surface Currents to Wind Studied by HF Ocean Surface Radar

2008 ◽  
Vol 38 (5) ◽  
pp. 1107-1121 ◽  
Author(s):  
Yadan Mao ◽  
Malcolm L. Heron
Keyword(s):  
Wind Speed ◽  
Momentum Transfer ◽  
Surface Current ◽  
Ocean Surface ◽  
Growth Function ◽  
Current Data ◽  
Hf Radar ◽  
Stokes Drift ◽  
Surface Currents ◽  
Sea State

Abstract The momentum transfer from wind to sea generates surface currents through both the wind shear stress and the Stokes drift induced by waves. This paper addresses issues in the interpretation of HF radar measurements of surface currents and momentum transfer from air to sea. Surface current data over a 30-day period from HF ocean surface radar are used to study the response of surface currents to wind. Two periods of relatively constant wind are identified—one for the short-fetch condition and the other for the long-fetch condition. Results suggest that the ratio of surface current speed to wind speed is larger under the long-fetch condition, while the angle between the surface current vector and wind vector is larger under the short-fetch condition. Data analysis shows that the Stokes drift dominates the surface currents under the long-fetch condition when the sea state is more mature, while the Stokes drifts and Ekman-type currents play almost equally important roles in the total currents under the short-fetch condition. The ratios of Stokes drift to wind speed under these two fetch conditions are shown to agree well with results derived from the empirical wave growth function. These results suggest that fetch, and therefore sea state, significantly influences the total response of surface current to wind in both the magnitude and direction by variations in the significance of Stokes drift. Furthermore, this work provides observational evidence that surface currents measured by HF radar include Stokes drift. It demonstrates the potential of HF radar in addressing the issue of momentum transfer from air to sea under various environmental conditions.

Comparisons of Scatterometer and TAO Winds Reveal Time-Varying Surface Currents for the Tropical Pacific Ocean*

2005 ◽  
Vol 22 (6) ◽  
pp. 735-745 ◽  
Author(s):  
Kathryn A. Kelly ◽  
Suzanne Dickinson ◽  
Gregory C. Johnson
Keyword(s):  
Southern Oscillation ◽  
Surface Current ◽  
Current Data ◽  
Surface Currents ◽  
Radar Altimeter ◽  
Near Surface ◽  
The Mean ◽  
Divergence Pattern ◽  
Acoustic Doppler Current Profilers ◽  
Good Agreement

Abstract The differences between Tropical Atmosphere Ocean (TAO) anemometer and QuikSCAT scatterometer winds are analyzed over a period of 3 yr. Systematic differences are expected owing to ocean currents because the anemometer measures absolute air motion, whereas a radar measures the motion of the air relative to the ocean. Monthly averaged collocated wind differences (CWDs) are compared with available near-surface current data at 15-m depth from drifters, at 25-m depth from acoustic Doppler current profilers (ADCPs), and at 10-m depth from current meters and with geostrophic currents at the surface from the TOPEX/Poseidon radar altimeter. Because direct current observations are so sparse, comparisons are also made with climatological currents from these same sources. Zonal CWDs are in good agreement with the zonal current observations, particularly from 2°S to 2°N where there are strong currents and a robust seasonal cycle, with the altimeter-derived anomalous currents giving the best match. At higher latitudes there is qualitative agreement at buoys with relatively large currents. The overall variance of the zonal component of the CWDs is reduced by approximately 25% by subtracting an estimate of the zonal currents. The meridional CWDs are nearly as large as the zonal CWDs but are unpredictable. The mean CWDs show a robust divergence pattern about the equator, which is suggestive of Ekman currents, but with unexpectedly large magnitudes. Coefficients for estimating climatological zonal surface currents from the altimeter at the TAO buoys are tabulated: the amplitudes and phases for the annual and semiannual harmonics, and a linear regression against the Southern Oscillation index, are combined with the mean from the drifter currents. Examples are shown of the application of these estimators to data from SeaWinds on the Midori satellite. These estimators are also useful for deriving air–sea fluxes from TAO winds.

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.

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