scholarly journals Blending Surface Currents from HF Radar Observations and Numerical Modeling: Tidal Hindcasts and Forecasts

2015 ◽  
Vol 32 (2) ◽  
pp. 256-281 ◽  
Author(s):  
E. V. Stanev ◽  
F. Ziemer ◽  
J. Schulz-Stellenfleth ◽  
J. Seemann ◽  
J. Staneva ◽  
...  
Keyword(s):  
Numerical Modeling ◽  
German Bight ◽  
Spatial Scales ◽  
Surface Current ◽  
Hf Radar ◽  
Optimal Interpolation ◽  
Kelvin Wave ◽  
Arctic Seas ◽  
Current Estimate ◽  
Radar Observations

AbstractAn observation network operating three Wellen Radars (WERAs) in the German Bight, which are part of the Coastal Observing System for Northern and Arctic Seas (COSYNA), is presented in detail. Major consideration is given to expanding the patchy observations over the entire German Bight on a 1-km grid and producing state estimates at intratidal scales, and 6- and 12-h forecasts. This was achieved with the help of the proposed spatiotemporal optimal interpolation (STOI) method, which efficiently uses observations and simulations from a free model run within an analysis window of one or two tidal cycles. In this way the method maximizes the use of available observations and can be considered as a step toward the “best surface current estimate.” The performance of the analysis was investigated based on the achieved reduction of the misfit between model and observations. The complex dynamics of the study domain was illustrated based on the spatial and temporal changes of tidal ellipses for the M2 and M4 constituents from HF radar observations. It was demonstrated that blending observations and numerical modeling facilitates physical interpretation of processes such as the nonlinear distortion of the Kelvin wave in the coastal zone and in particular in front of the Elbe and Weser estuaries. Comparisons with in situ data acquired outside the area covered by the HF radar demonstrated that the analysis method is able to propagate the HF radar information to larger spatial scales.

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.

Ocean surface current prediction based on HF radar observations using trajectory-oriented association rule mining

2017 ◽  
Author(s):  
Kulsawasd Jitkajornwanich ◽  
Peerapon Vateekul ◽  
Upa Gupta ◽  
Teeranai Kormongkolkul ◽  
Arnon Jirakittayakorn ◽  
...  
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Surface Current ◽  
Ocean Surface ◽  
Hf Radar ◽  
Rule Mining ◽  
Radar Observations ◽  
Ocean Surface Current

scholarly journals Ocean forecasting for the German Bight: from regional to coastal scales

Ocean Science ◽  
2016 ◽  
Vol 12 (5) ◽  
pp. 1105-1136 ◽  
Author(s):  
Emil V. Stanev ◽  
Johannes Schulz-Stellenfleth ◽  
Joanna Staneva ◽  
Sebastian Grayek ◽  
Sebastian Grashorn ◽  
...  
Keyword(s):  
German Bight ◽  
Unstructured Grid ◽  
Wind Waves ◽  
Radar Data ◽  
Coastal Ocean ◽  
Hf Radar ◽  
Regional Models ◽  
Arctic Seas ◽  
The North ◽  
Ocean Forecasting

Abstract. This paper describes recent developments based on advances in coastal ocean forecasting in the fields of numerical modeling, data assimilation, and observational array design, exemplified by the Coastal Observing System for the North and Arctic Seas (COSYNA). The region of interest is the North and Baltic seas, and most of the coastal examples are for the German Bight. Several pre-operational applications are presented to demonstrate the outcome of using the best available science in coastal ocean predictions. The applications address the nonlinear behavior of the coastal ocean, which for the studied region is manifested by the tidal distortion and generation of shallow-water tides. Led by the motivation to maximize the benefits of the observations, this study focuses on the integration of observations and modeling using advanced statistical methods. Coastal and regional ocean forecasting systems do not operate in isolation but are linked, either weakly by using forcing data or interactively using two-way nesting or unstructured-grid models. Therefore, the problems of downscaling and upscaling are addressed, along with a discussion of the potential influence of the information from coastal observatories or coastal forecasting systems on the regional models. One example of coupling coarse-resolution regional models with a fine-resolution model interface in the area of straits connecting the North and Baltic seas using a two-way nesting method is presented. Illustrations from the assimilation of remote sensing, in situ and high-frequency (HF) radar data, the prediction of wind waves and storm surges, and possible applications to search and rescue operations are also presented. Concepts for seamless approaches to link coastal and regional forecasting systems are exemplified by the application of an unstructured-grid model for the Ems Estuary.

HF radar observations in the German Bight: Measurements and quality control

2011 ◽  
Author(s):  
Klaus-Werner Gurgel ◽  
Thomas Schlick ◽  
George Voulgaris ◽  
Jorg Seemann ◽  
Friedwart Ziemer
Keyword(s):  
Quality Control ◽  
German Bight ◽  
Hf Radar ◽  
Radar Observations

HF radar observations in the northern Adriatic: surface current field in front of the Venetian Lagoon

2004 ◽  
Vol 51 (1-4) ◽  
pp. 95-122 ◽  
Author(s):  
V. Kovačević ◽  
M. Gačić ◽  
I. Mancero Mosquera ◽  
A. Mazzoldi ◽  
S. Marinetti
Keyword(s):  
Surface Current ◽  
Hf Radar ◽  
Current Field ◽  
Northern Adriatic ◽  
Radar Observations ◽  
Venetian Lagoon

Multi-year assessment of ocean surface currents from Copernicus Sentinel-1 and HF radar in the German Bight

2021 ◽  
Author(s):  
Christine Gommenginger ◽  
Adrien C. H. Martin ◽  
Benjamin Jacob ◽  
Joanna Staneva
Keyword(s):  
German Bight ◽  
Surface Current ◽  
Ground Truth ◽  
Ocean Surface ◽  
Hf Radar ◽  
Surface Velocity ◽  
Coastal Current ◽  
Direct Estimate ◽  
Simple Method ◽  
Surface Motion

<p>Direct estimate of ocean surface motion sensed by the Doppler shift of the surface includes ocean surface current and a wind-wave induced artefact surface velocity (WASV). The Sentinel-1 (S1) C-band SAR mission includes direct ocean surface motion estimates as an operational Level-2 Ocean (OCN) Radial VeLocity (RVL) product. The existing operational RVL products suffer from significant uncorrected platform and instrument effects that presently prevent exploitation of the data. This paper proposes a simple method to calibrate and correct for these effects and evaluate the benefit of these corrections over 2.5 years S1A acquisition against ground truth measurements. A specific geometry for S1 has been chosen for S1-A over the HF radar (HFR) instrumented site in the German Bight. The 78 S1A snapshots end in 56 match-ups within 20 minutes of HFR measurements. HFR velocity fields were projected in the same radial direction as S1A. Land calibration corrects for constant snapshot biases of the operational products up to 2 m/s. Besides these constant biases there is persistent relative biases within snapshots between up to 0.4 m/s in addition to the TOPSAR uncorrected scalloping effect with an amplitude of 0.1 m/s. After calibration, corrected RVL are compared against HFR with various WASV correction. Applying WASV correction with a reduced 70% C-Dop model, gives the best results with a precision of 0.25 m/s and correlation in time of 0.9. This might be due to C-Dop amplitude in up/downwind being too strong for a coastal environment as encountered in the German Bight. Quadratic mean of all 78 S1A snapshots after all corrections applied exhibits coastal current jets in good agreement with bathymetry channels and is promising as a cheap way to infer local bathymetry channels.</p>

scholarly journals HF radar observations of small-scale surface current variability in the Straits of Florida

2009 ◽  
Vol 114 (C8) ◽  
Author(s):  
A. B. Parks ◽  
L. K. Shay ◽  
W. E. Johns ◽  
J. Martinez-Pedraja ◽  
K.-W. Gurgel
Keyword(s):  
Surface Current ◽  
Hf Radar ◽  
Small Scale ◽  
Radar Observations ◽  
Straits Of Florida ◽  
Scale Surface

scholarly journals On the generation of internal waves by river plumes in subcritical initial conditions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
R. Mendes ◽  
J. C. B. da Silva ◽  
J. M. Magalhaes ◽  
B. St-Denis ◽  
D. Bourgault ◽  
...  
Keyword(s):  
Numerical Modeling ◽  
Internal Waves ◽  
Coastal Waters ◽  
Initial Conditions ◽  
Spatial Scales ◽  
River Plumes ◽  
Wide Range ◽  
Near Shore ◽  
Generation Mechanisms ◽  
Douro River

AbstractInternal waves (IWs) in the ocean span across a wide range of time and spatial scales and are now acknowledged as important sources of turbulence and mixing, with the largest observations having 200 m in amplitude and vertical velocities close to 0.5 m s−1. Their origin is mostly tidal, but an increasing number of non-tidal generation mechanisms have also been observed. For instance, river plumes provide horizontally propagating density fronts, which were observed to generate IWs when transitioning from supercritical to subcritical flow. In this study, satellite imagery and autonomous underwater measurements are combined with numerical modeling to investigate IW generation from an initial subcritical density front originating at the Douro River plume (western Iberian coast). These unprecedented results may have important implications in near-shore dynamics since that suggest that rivers of moderate flow may play an important role in IW generation between fresh riverine and coastal waters.

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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