Data Assimilation for Phase-Resolved Ocean Wave Forecast

2021 ◽  
Author(s):  
Guangyao Wang ◽  
Yulin Pan
Keyword(s):  
Data Assimilation ◽  
Ocean Wave ◽  
Wave Forecast

Neural-Network-Based Data Assimilation to Improve Numerical Ocean Wave Forecast

2016 ◽  
Vol 41 (4) ◽  
pp. 944-953 ◽  
Author(s):  
Aditya N. Deshmukh ◽  
M. C. Deo ◽  
Prasad K. Bhaskaran ◽  
T. M. Balakrishnan Nair ◽  
K. G. Sandhya
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
Ocean Wave ◽  
Wave Forecast

Wave data assimilation using ensemble error covariances for operational wave forecast

2006 ◽  
Vol 14 (1-2) ◽  
pp. 102-121 ◽  
Author(s):  
S.A. Sannasiraj ◽  
Vladan Babovic ◽  
Eng Soon Chan
Keyword(s):  
Data Assimilation ◽  
Wave Data ◽  
Wave Forecast

Ocean Wave Modeling from Satellite Images Using Data Assimilation

2021 ◽  
pp. 77-87
Author(s):  
M. Sai Pravallika ◽  
B. Naga Varun ◽  
S. Vasavi ◽  
N. Sandeep ◽  
M. Jaya Priya ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Satellite Images ◽  
Ocean Wave ◽  
Wave Modeling ◽  
Using Data

The Propagation of Wind Errors Through Ocean Wave Hindcasts

1996 ◽  
Vol 118 (3) ◽  
pp. 184-189 ◽  
Author(s):  
L. H. Holthuijsen ◽  
N. Booij ◽  
L. Bertotti
Keyword(s):  
Sensitivity Analysis ◽  
Mediterranean Sea ◽  
Wind Field ◽  
Ocean Wave ◽  
Wind Analysis ◽  
Wave Forecast ◽  
The Mediterranean ◽  
Field Errors

To estimate uncertainties in wave forecast and hindcasts, computations have been carried out for a location in the Mediterranean Sea using three different analyses of one historic wind field. These computations involve a systematic sensitivity analysis and estimated wind field errors. This technique enables a wave modeler to estimate such uncertainties in other forecasts and hindcasts if only one wind analysis is available.

A new methodology for the extension of the impact of data assimilation on ocean wave prediction

2009 ◽  
Vol 59 (3) ◽  
pp. 523-535 ◽  
Author(s):  
George Galanis ◽  
George Emmanouil ◽  
Peter C. Chu ◽  
George Kallos
Keyword(s):  
Data Assimilation ◽  
Ocean Wave ◽  
Wave Prediction ◽  
The Impact

Data Assimilation for Phase-Resolved Ocean Wave Forecast

2020 ◽  
Author(s):  
Guangyao Wang ◽  
Yulin Pan
Keyword(s):  
Data Assimilation ◽  
Nonlinear Wave ◽  
Ocean Surface ◽  
Ocean Waves ◽  
Nonlinear Wave Equations ◽  
Stokes Wave ◽  
Initial Condition ◽  
Wave Forecast ◽  
Radar Measurements ◽  
Wave Models

Abstract The phase-resolved prediction of ocean waves is crucial for the safety of offshore operations. With the development of the remote sensing technology, it is now possible to reconstruct the phase-resolved ocean surface from radar measurements in real time. Using the reconstructed ocean surface as the initial condition, nonlinear wave models such as the high-order spectral (HOS) method can be applied to predict the evolution of the ocean waves. However, due to the error in the initial condition (associated with the radar measurements and reconstruction algorithm) and the chaotic nature of the nonlinear wave equations, the prediction by HOS can deviate quickly from the true surface evolution (in order of one minute). To solve this problem, the capability to regularly incorporate measured data into the HOS simulation through data assimilation is desirable. In this work, we develop the data assimilation capability for nonlinear wave models, through the coupling of an ensemble Kalman filter (EnKF) with HOS. The developed algorithm is validated and tested using a synthetic problem on the simulation of a propagating Stokes wave with random initial errors. We show that the EnKF-HOS method achieves much higher accuracy in the long-term simulation of nonlinear waves compared to the HOS-only method.

Validation of the CMEMS-IBI wave model with data assimilation in a high resolution regional configuration.

2020 ◽  
Author(s):  
Malek Ghantous ◽  
Lotfi Aouf ◽  
Alice Dalphinet ◽  
Cristina Toledano ◽  
Lorea García San Martín ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Ocean Circulation ◽  
Circulation Model ◽  
Wave Model ◽  
Optimal Interpolation ◽  
Wave Data ◽  
Wave Forecast ◽  
Buoy Data ◽  
The Impact

<p>One of the challenges of the Iberia-Biscay-Ireland (IBI) Monitoring Forecasting Centre in CMEMS phase 2 is the implementation of the assimilation of altimeter wave data in the wave forecast system.  In this work we explored the impact of the assimilation of altimeter wave data in the IBI domain.  We ran the Météo France version of the WAM wave model (MFWAM) in the IBI domain for 2018 and 2019, with data assimilated from the Jason 2 and 3, Saral, Cryosat 2 and Sentinel 3 altimeters.  This high-resolution (0.05 degree) configuration was forced by 0.05 degree ECMWF winds, and boundary conditions were provided by a 0.1 degree global model run.  We also included refraction from currents generated with the NEMO-IBI ocean circulation model.  We present results with and without wave–current interactions.  Validation against both buoy data and the HaiYang 2 altimeter shows that the assimilation of data leads to a marked reduction in scatter index and model bias compared to the run without data assimilation; the gains from including currents meanwhile are modest.  </p><p>The data assimilation scheme presently implemented in MFWAM uses an optimal interpolation algorithm where constant model and observational errors are assumed.  To add some sophistication, we experimented with non-constant background errors derived from a model ensemble.  Though the effect was small, the method suggests a way to improve the data assimilation performance without substantially altering the algorithm.</p>

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