Surf zone bathymetry and circulation predictions via data assimilation of remote sensing observations
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Bathymetry is a major factor in determining nearshore and surf zone wave transformation and currents, yet is often poorly known. This can lead to inaccuracy in numerical model predictions. Here bathymetry is estimated as an uncertain parameter in a data assimilation system, using the ensemble Kalman filter (EnKF). The system is tested by assimilating several remote sensing data products, which were collected in September 2010 as part of a field experiment at the U.S. Army Corps of Engineers Field Research Facility (FRF) in Duck, NC. The results show that by assimilating remote sensing data alone, nearshore bathymetry can be estimated with good accuracy, and nearshore forecasts (e.g., the prediction of a rip current) can be improved. This suggests an application where a nearshore forecasting model could be implemented using only remote sensing data, without the explicit need for in situ data collection.Keywords:
Rip current
Surf zone
Surf zone
Rip current
Current meter
Drifter
Neutral buoyancy
Stokes drift
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Bathymetry, or the measurement of depth in any body of water, has been an area of research since man began to venture out onto the open waters. Historically, researching the near-shore surf zone has been a time consuming and expensive process. The tools and methods used to gather data points in the surf zone are either time inefficient, expensive, or both. This is an issue considering how dynamic the surf zone environment can be. It is possible that by the time the surf zone bathymetry measurements have been completed, they are already out of date. This project utilizes unmanned aerial systems (UAS) to gather high-quality video of the near-shore surf zone waves crest. This footage is then processed using particle image velocimetry (PIV), a method for determining the velocity of particles in sequential images. This velocity is then processed using linear-wave theory shallow water approximations for calculating wave celerity from depth, but ran in reverse, to obtain the bathymetry itself. Ground-truth field measurements are used to verify the resulting velocity and depth.
Surf zone
Wave setup
Wave height
Ground truth
Rip current
Crest
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Abstract X‐band radar observations from the 2017 Inner Shelf Dynamics Experiment (ISDE) in central California show multiple persistent and pulsatory rip currents on a relatively straight coastline with alongshore‐varying bathymetry. Although past studies have assessed the characteristics of transient rip currents on alongshore uniform beaches, the relative balance of transient versus steady rip current behavior on nonuniform beaches in realistic wave conditions remains poorly understood. Here, a phase‐resolving Boussinesq‐type wave model ( funwaveC ) is used to assess the role of alongshore‐varying bathymetry and incident conditions in controlling mean and transient surf zone vorticity and velocity fields and their effect on surf zone exchange. The model simulates wave conditions chosen from the ISDE observations and utilizes both an alongshore‐varying bathymetry estimated from the ISDE radar observations and a uniform bathymetry. Results show that the variable bathymetry significantly increases the alongshore‐ and time‐averaged kinetic energy but that this increase is primarily due to the increase in the standing component resulting from mean circulation patterns, with only small changes in the transient component. A variable bathymetry also increases the spectral energy of surf zone vorticity and time‐averaged vorticity forcing at large spatial scales (>100 m). Wave directional spreading has a large impact on the alongshore‐ and time‐averaged enstrophy and on the spectral energy of surf zone vorticity and vorticity forcing at smaller spatial scales (<100 m). In the presence of a directionally spread wavefield, an alongshore‐varying bathymetry slightly increases the total exchange velocity but has little effect on its transient component.
Surf zone
Rip current
Forcing (mathematics)
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Recent progress in the particle filter has made it possible to use it for nonlinear or non-Gaussian data assimilation in high-dimensional systems, but a relatively large ensemble is still needed to outperform the ensemble Kalman filter (EnKF) in terms of accuracy. An alternative ensemble data assimilation method based on deep learning is presented, in which deep neural networks are locally embedded in the EnKF. This method is named the deep learning-ensemble Kalman filter (DL-EnKF). The DL-EnKF analysis ensemble is generated from the DL-EnKF analysis and the EnKF analysis deviation ensemble. The performance of the DL-EnKF is investigated through data assimilation experiments in both perfect and imperfect model scenarios using three versions of the Lorenz 96 model and a deterministic EnKF with an ensemble size of 10. Nonlinearity in data assimilation is controlled by changing the time interval between observations. Results demonstrate that despite being such a small ensemble, the DL-EnKF is superior to the EnKF in terms of accuracy in strongly nonlinear regimes and that the DL-EnKF analysis is more accurate than the output of deep learning because of positive feedback in assimilation cycles. Even if the target of training is an EnKF analysis with a large ensemble or a simulation by an imperfect model, the improvement introduced by the DL-EnKF is not very different from the case where the target of training is the true state.
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Ensemble forecasting
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Rip currents are the main cause of beach rescues and fatalities. Key drivers of rip current hazard are: (1) fast current speeds; and (2) the exit rate of floating material from inside to outside of the surf zone. Exit rates may vary temporally, such as due to Very Low Frequency (VLF) motions, which have a period on the order of 10 minutes. However, there is little field data to determine the driver(s) of exit rate. Therefore, the aim of this research was to determine rip current circulation patterns, and specifically, determine their relationship to surf zone exits, on a high-energy dissipative beach. Three days of field measurements were undertaken at Ngarunui Beach, New Zealand. Three daily surf zone flow patterns were found: (1) alongshore; (2) surf zone eddy with high exit rate; and (3) surf zone eddy with no exits. There were strong infragravity peaks in energy within the surf zone, at 30-45s, although none at VLF (around 10 minute) frequencies. Further research is underway to determine what drove the high surf zone exit rate observed at Ngarunui Beach.
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Surf zone
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Rip current
Surf zone
Wave setup
Plage
Forcing (mathematics)
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Abstract The ensemble Kalman filter (EnKF) has been extensively applied in sequential soil moisture data assimilation to improve the land surface model performance and in turn weather forecast capability. Usually, the ensemble size of EnKF is determined with limited sensitivity experiments. Thus, the optimal ensemble size may have never been reached. In this work, based on a series of mathematical derivations, we demonstrate that the maximum efficiency of the EnKF for assimilating observations into the models could be reached when the ensemble size is set to 12. Simulation experiments are designed in this study under ensemble size cases 2, 5, 12, 30, 50, 100, and 300 to support the mathematical derivations. All the simulations are conducted from 1 June to 30 September 2012 over southeast USA (from −90°W, 30°N to −80°W, 40°N) at 25 km resolution. We found that the simulations are perfectly consistent with the mathematical derivation. This optical ensemble size may have theoretical implications on the implementation of EnKF in other sequential data assimilation problems.
Assimilation (phonology)
Ensemble average
Ensemble forecasting
Ensemble Learning
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Bathymetry is a major factor in determining nearshore and surf zone wave transformation and currents, yet is often poorly known. This can lead to inaccuracy in numerical model predictions. Here bathymetry is estimated as an uncertain parameter in a data assimilation system, using the ensemble Kalman filter (EnKF). The system is tested by assimilating several remote sensing data products, which were collected in September 2010 as part of a field experiment at the U.S. Army Corps of Engineers Field Research Facility (FRF) in Duck, NC. The results show that by assimilating remote sensing data alone, nearshore bathymetry can be estimated with good accuracy, and nearshore forecasts (e.g., the prediction of a rip current) can be improved. This suggests an application where a nearshore forecasting model could be implemented using only remote sensing data, without the explicit need for in situ data collection.
Rip current
Surf zone
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Citations (61)
We demonstrate the implementation and validation of a surf zone forecasting system, which uses remote sensing observations to control errors in surf zone bathymetry. This system uses ensemble-based sequential data assimilation techniques, which are adaptable to arbitrary geophysical observations, and/or arbitrary improvements to model physics. The system is validated using data from a 2010 field experiment at Duck, NC (U.S.A.), and is shown to produce accurate corrections to bathymetry, leading to improvements in prediction of currents.
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Rip current
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Gallop, S.L.; Bryan, K.R.; Pitman, S.J.; Ranasinghe, R., and Sandwell, D., 2016. Pulsations in surf zone currents on a high energy mesotidal beach in New Zealand. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 378–382. Coconut Creek (Florida), ISSN 0749-0208.The exchange of material between the surf zone and continental shelf can be driven by pulsations in rip current velocities. However, there is a poor understanding of the relationship of these pulsations to surf zone morphology and material exchange. Moreover, understanding of rip current dynamics has focused mainly on single-barred beaches in an intermediate state, and there have been few studies on high energy beaches. Therefore, this paper undertakes preliminary research on surf zone current velocity pulsations, on a high energy beach in New Zealand. This initial analysis presents results from two days of measurements using Acoustic Doppler Velocimeters and Lagrangian GPS drifters. Drifters revealed pulsations in current velocities on the order of ∼0.5–2 m s−1 throughout the surf zone, whether inside a rip current circulation cell or not. More infragravity wave energy was associated with constant pulsations in current velocity, and lower infragravity energy with pulsation bursts, lasting 5–10 minutes, interspersed with periods of relatively constant velocity lasting 15–25 minutes. However, higher wave conditions also reduced the exit rate from the surf zone.
Surf zone
Rip current
Energy exchange
Energy current
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