Development of a Highly Efficient and Accurate Wind-Wave Simulation Framework for Operational Data Assimilation

2010 ◽  
Author(s):  
Lian Shen
Keyword(s):  
Data Assimilation ◽  
Wind Wave ◽  
Simulation Framework ◽  
Wave Simulation ◽  
Highly Efficient ◽  
Operational 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

scholarly journals Using Canonical Correlation Analysis to produce dynamically-based highly-efficient statistical observation operators

2019 ◽  
Author(s):  
Eric Jansen ◽  
Sam Pimentel ◽  
Wang-Hung Tse ◽  
Dimitra Denaxa ◽  
Gerasimos Korres ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Diurnal Cycle ◽  
Canonical Correlation ◽  
General Circulation ◽  
Computational Cost ◽  
Dynamical Model ◽  
Central Component ◽  
Highly Efficient

Abstract. Observation operators (OOs) are a central component of any data assimilation system. As they project the state variables of a numerical model into the space of the observations, they also provide an ideal opportunity to correct for effects that are not or not sufficiently described by the model. In such cases a dynamical OO, an OO that interfaces to a secondary and more specialised model, often provides the best results. However, given the large number of observations to be assimilated in a typical atmospheric or oceanographic model, the computational resources needed for using a fully dynamical OO mean that this option is usually not feasible. This paper presents a method, based on canonical correlation analysis (CCA), that can be used to generate highly-efficient statistical OOs that are based on a dynamical model. These OOs can provide an approximation to the dynamical model at a fraction of the computational cost. One possible application of such an OO is the modelling of the diurnal cycle of sea surface temperature (SST) in ocean general circulation models (OGCMs). Satellites that measure SST measure the temperature of the thin uppermost layer of the ocean. This layer is strongly affected by the atmospheric conditions and its temperature can differ significantly from the water below. This causes a discrepancy between the SST measurements and the upper layer of the OGCM, which typically has a thickness of around 1 m. The CCA OO method is used to parametrise the diurnal cycle of SST. The CCA OO is based on an input dataset from the General Ocean Turbulence Model (GOTM), a high-resolution water column model that has been specifically tuned for this purpose. The parameterisations of the CCA OO are found to be in good agreement with the results from GOTM, showing the potential of this method for use in data assimilation systems.

Application of Data Assimilation for a Spectral Wave Model on Unstructured Meshes

2006 ◽  
Author(s):  
Tai-Wen Hsu ◽  
Shan-Hwei Ou ◽  
Jian-Ming Liau ◽  
Jaw-Guei Lin ◽  
Chia-Chuen Kao ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Wind Wave ◽  
Wave Spectrum ◽  
Wave Model ◽  
Wave Period ◽  
Optimal Interpolation ◽  
Sequential Method ◽  
Significant Wave ◽  
Spectral Wave Model ◽  
Buoy Data

The effect of the data assimilation of buoy data in the wind wave model (WWM) for wind wave simulations in both deep and shallow water regions developed by Hsu et al. [2005] is investigated. Following Lionello et al. [1992], the sequential method is implemented, where analyzed wave spectra and significant wave fields were assimilated by optimal interpolation (OI), then the analyzed values were used to reconstruct the wave spectrum. This paper examines the results of the assimilation of wave spectrum, significant wave height and significant wave period in a nearshore WWM model. The WWM model underestimates the wave period because it incorrectly applies past wave field data. The analysis has provided useful indications of the shortcomings of the WWM model. In summary, the OI approach is shown to be a reliable assimilation scheme in the WWM model.

Wave Simulation in Tokyo Bay under Typhoon Faxai with Different Wind Sources Generated Through Typhoon-bogussing and 4DVar Data Assimilation

2020 ◽  
Vol 76 (2) ◽  
pp. I_217-I_222
Author(s):  
Chathura MANAWASEKARA ◽  
Katsuyuki SUZUYAMA ◽  
Yoji TANAKA ◽  
Yiqing XIA ◽  
Mangala AMUNUGAMA
Keyword(s):  
Data Assimilation ◽  
Tokyo Bay ◽  
Wave Simulation
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