scholarly journals On the specification of background errors for wave data assimilation systems

2016 ◽  
Vol 121 (1) ◽  
pp. 209-223 ◽  
Author(s):  
Jesús Portilla‐Yandún ◽  
Luigi Cavaleri
Keyword(s):  
Data Assimilation ◽  
Wave Data

scholarly journals Non-linear wave data assimilation with an ANN-type wind-wave model and Ensemble Kalman Filter (EnKF)

2010 ◽  
Vol 34 (8) ◽  
pp. 1984-1999 ◽  
Author(s):  
Ahmadreza Zamani ◽  
Ahmadreza Azimian ◽  
Arnold Heemink ◽  
Dimitri Solomatine
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Wind Wave ◽  
Wave Model ◽  
Linear Wave ◽  
Wave Data ◽  
Non Linear

Variational Wave Data Assimilation in a Third-Generation Wave Model

1994 ◽  
Vol 11 (5) ◽  
pp. 1350-1369 ◽  
Author(s):  
Miriam M. De Las Heras ◽  
Gerrit Burgers ◽  
Peter A. E. M. Janssen
Keyword(s):  
Data Assimilation ◽  
Wave Model ◽  
Third Generation ◽  
Wave Data ◽  
Variational Wave

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

INVERSE METHODOLOGY FOR COASTAL WAVE DATA ASSIMILATION

2007 ◽  
Author(s):  
Alexandre Coli ◽  
João Alfredo Santos ◽  
António A. Pires-Silva
Keyword(s):  
Data Assimilation ◽  
Wave Data

Data assimilation of partitioned HF radar wave data into Wavewatch III

2013 ◽  
Vol 72 ◽  
pp. 17-31 ◽  
Author(s):  
Jennifer Waters ◽  
Lucy R. Wyatt ◽  
Judith Wolf ◽  
Adrian Hines
Keyword(s):  
Data Assimilation ◽  
Hf Radar ◽  
Wavewatch Iii ◽  
Wave Data ◽  
Radar Wave

scholarly journals Wave Data Assimilation in Support of Wave Energy Converter Power Prediction: Yakutat, Alaska Case Study

2020 ◽  
Author(s):  
Ann Dallman ◽  
Mohammad Khalil ◽  
Kaus Raghukumar ◽  
Craig Jones ◽  
Jeremy Kasper ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Wave Energy ◽  
Wave Energy Converter ◽  
Power Prediction ◽  
Energy Converter ◽  
Wave Data

Global Wave Data Assimilation Model using Optimal Interpolation Method

2020 ◽  
Author(s):  
Kyeong Ok Kim ◽  
Hanna Kim ◽  
Kyung Tae Jung ◽  
Young Ho Kim
Keyword(s):  
Data Assimilation ◽  
Wave Energy ◽  
Interpolation Method ◽  
Energy Spectra ◽  
Global Ocean ◽  
Optimal Interpolation ◽  
Significant Wave ◽  
Wave Data ◽  
Wave Heights ◽  
Significant Wave Heights

<p>To construct a reanalyzed global ocean wave data set with improved accuracy, which is important for the better understanding and simulation of various near-surface ocean dynamics, a data assimilation method has been embedded to the global spectral wave model based on WW3. The major factors controlling the wave simulation accuracy are the wind condition and the parameterization on the wave energy development, dissipation and nonlinear processes between wave components. However, the atmospheric prediction accuracy is still not sufficient, and the parameterization cannot be generalized due to the local geographic conditions.</p><p>In detail, the data assimilation using the optimal interpolation method has been applied, verification through the comparison with satellite altimeters and buoy observations has been made with examination of the data assimilation effects. The significant wave heights computed by the integration of wave energy spectra are showed to be quite similar with observed results. However, the wave periods and directions related to the shape of wave energy spectra are not sufficiently comparable. Generally there have been difficulties in predicting the propagation of long period waves such as swells.</p><p>The wave energy spectra on wave number and direction domains was multiplied by optimal interpolation method with the ratio of observed significant wave heights on first guessed simulated results. The energy spectra was thereafter shifted by the difference between simulated and observed peak wave periods and directions. From then examination of the reanalysis simulation during 1 year, it could be seen that the accuracy of the model with the data assimilation shows better results than that without data assimilation.</p>

Mitigation of Model Bias Influences on Wave Data Assimilation with Multiple Assimilation Systems

2020 ◽  
Author(s):  
jiangyu li ◽  
shaoqing zhang
Keyword(s):  
Data Assimilation ◽  
Statistical Approach ◽  
Wave Model ◽  
Wind Forcing ◽  
Initial Condition ◽  
Model Bias ◽  
Wave Analysis ◽  
Physical Processes ◽  
Wave Data ◽  
Wave Models

<p>High-quality wave prediction with a numerical wave model is of societal value. To initialize the wave model, wave data assimilation (WDA) is necessary to combine the model and observations. Due to inaccurate wind forcing, imperfect numerical schemes, and approximated physical processes, a wave model is always biased in relation to the real world. In this study, two assimilation systems are first developed using two nearly independent wave models; then, “perfect” and “biased” assimilation frameworks based on the two assimilation systems are designed to reveal the uncertainties of WDA. A series of “biased” assimilation experiments is conducted to systematically examine the adverse impact of initial condition, boundary forcing, and model bias on WDA, then model bias play a strongest role among them . A statistical approach based on the results from multiple assimilation systems is explored to carry out bias correction, by which the final wave analysis is significantly improved with the merits of individual assimilation systems. The framework with multiple assimilation systems provides an effective platform to improve wave analyses and predictions and help identify model deficits, thereby improving the model.</p>

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