scholarly journals Mitigation of Model Bias Influences on Wave Data Assimilation with Multiple Assimilation Systems Using WaveWatch III v5.16 and SWAN v41.20

2019 ◽  
Author(s):  
Jiangyu Li ◽  
Shaoqing Zhang
Keyword(s):  
Data Assimilation ◽  
Statistical Approach ◽  
Wave Model ◽  
Model Bias ◽  
Wave Analysis ◽  
Physical Processes ◽  
Wave Data ◽  
Numerical Wave ◽  
Wave Models ◽  
Biased Assimilation

Abstract. 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 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 model bias on WDA. 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.

scholarly journals Mitigation of model bias influences on wave data assimilation with multiple assimilation systems using WaveWatch III v5.16 and SWAN v41.20

2020 ◽  
Vol 13 (3) ◽  
pp. 1035-1054 ◽  
Author(s):  
Jiangyu Li ◽  
Shaoqing Zhang
Keyword(s):  
Data Assimilation ◽  
Statistical Approach ◽  
Wave Model ◽  
Model Bias ◽  
Wave Analysis ◽  
Physical Processes ◽  
Wave Data ◽  
Numerical Wave ◽  
Wave Models ◽  
Biased Assimilation

Abstract. 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 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 model bias on WDA. 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.

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>

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

scholarly journals Spatially Tracking Wave Events in Partitioned Numerical Wave Model Outputs

2019 ◽  
Vol 36 (10) ◽  
pp. 1933-1944 ◽  
Author(s):  
Haoyu Jiang
Keyword(s):  
Grid Point ◽  
Wave Model ◽  
Ocean Waves ◽  
Model Verification ◽  
Analysis Model ◽  
Wave Parameters ◽  
Numerical Wave ◽  
Wave Models ◽  
Spectral Partitioning ◽  
The Impact

AbstractNumerical wave models can output partitioned wave parameters at each grid point using a spectral partitioning technique. Because these wave partitions are usually organized according to the magnitude of their wave energy without considering the coherence of wave parameters in space, it can be difficult to observe the spatial distributions of wave field features from these outputs. In this study, an approach for spatially tracking coherent wave events (which means a cluster of partitions originating from the same meteorological event) from partitioned numerical wave model outputs is presented to solve this problem. First, an efficient traverse algorithm applicable for different types of grids, termed breadth-first search, is employed to track wave events using the continuity of wave parameters. Second, to reduce the impact of the garden sprinkler effect on tracking, tracked wave events are merged if their boundary outlines and wave parameters on these boundaries are both in good agreement. Partitioned wave information from the Integrated Ocean Waves for Geophysical and other Applications dataset is used to test the performance of this spatial tracking approach. The test results indicate that this approach is able to capture the primary features of partitioned wave fields, demonstrating its potential for wave data analysis, model verification, and data assimilation.

scholarly journals Assimilation of decomposed in-situ directional wave spectra into a numerical wave model on typhoon wave

2013 ◽  
Vol 1 (4) ◽  
pp. 3967-3989
Author(s):  
Y. M. Fan ◽  
H. Günther ◽  
C. C. Kao ◽  
B. C. Lee
Keyword(s):  
Data Assimilation ◽  
Forecast Accuracy ◽  
Wave Model ◽  
Sequential Data ◽  
Wave Spectra ◽  
Typhoon Wave ◽  
Independent Observations ◽  
Directional Wave ◽  
Numerical Wave

Abstract. The purpose of this study was to enhance the accuracy of numerical wave forecasts through data assimilation during typhoon period. A sequential data assimilation scheme was modified to enable its use with partitions of directional wave spectra. The performance of the system was investigated with respect to operational applications specifically for typhoon wave. Two typhoons that occurred in 2006 around Taiwan (Kaemi and Shanshan) were used for this case study. The proposed data assimilation method increased the forecast accuracy in terms of wave parameters, such as wave height and period. After assimilation, the shapes of directional spectra were much closer to those reported from independent observations.

scholarly journals Assimilation of decomposed in situ directional wave spectra into a numerical wave model of typhoon waves

2014 ◽  
Vol 14 (1) ◽  
pp. 73-80
Author(s):  
Y. M. Fan ◽  
H. Günther ◽  
C. C. Kao ◽  
B. C. Lee
Keyword(s):  
Data Assimilation ◽  
Forecast Accuracy ◽  
Wave Model ◽  
Sequential Data ◽  
Wave Spectra ◽  
Independent Observations ◽  
Directional Wave ◽  
Numerical Wave ◽  
Typhoon Waves

Abstract. The purpose of this study was to enhance the accuracy of numerical wave forecasts through data assimilation during typhoon periods. A sequential data assimilation scheme was modified to enable its use with partitions of directional wave spectra. The performance of the system was investigated with respect to operational applications, specifically for typhoon waves. Two typhoons that occurred in 2006 around Taiwan (Kaemi and Shanshan) were used for this case study. The proposed data assimilation method increased the forecast accuracy in terms of wave parameters, such as wave height and period. After assimilation, the shapes of directional spectra were much closer to those reported from independent observations.

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