A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An ensemble Kalman filter for atmospheric data assimilation: Application to wind tunnel data
AbstractIn the previous work (Zheng et al., 2007, 2009), a data assimilation method, based on ensemble Kalman filter, has been applied to a Monte Carlo Dispersion Model (MCDM). The results were encouraging when the method was tested by the twin experiment and a short-range field experiment. In this technical note, the measured data collected in a wind tunnel experiment have been assimilated into the Monte Carlo dispersion model. The uncertain parameters in the dispersion model, including source term, release height, turbulence intensity and wind direction have been considered. The 3D parameters, i.e. the turbulence intensity and wind direction, have been perturbed by 3D random fields. In order to find the factors which may influence the assimilation results, eight tests with different specifications were carried out. Two strategies of constructing the 3D perturbation field of wind direction were proposed, and the result shows that the two level strategy performs better than the one level strategy. It is also found that proper standard deviation and the correlation radius of the perturbation field play an important role for the data assimilation results.
An ensemble Kalman filter for atmospheric data assimilation: Application to wind tunnel data
AbstractIn the previous work (Zheng et al., 2007, 2009), a data assimilation method, based on ensemble Kalman filter, has been applied to a Monte Carlo Dispersion Model (MCDM). The results were encouraging when the method was tested by the twin experiment and a short-range field experiment. In this technical note, the measured data collected in a wind tunnel experiment have been assimilated into the Monte Carlo dispersion model. The uncertain parameters in the dispersion model, including source term, release height, turbulence intensity and wind direction have been considered. The 3D parameters, i.e. the turbulence intensity and wind direction, have been perturbed by 3D random fields. In order to find the factors which may influence the assimilation results, eight tests with different specifications were carried out. Two strategies of constructing the 3D perturbation field of wind direction were proposed, and the result shows that the two level strategy performs better than the one level strategy. It is also found that proper standard deviation and the correlation radius of the perturbation field play an important role for the data assimilation results.
An ensemble Kalman filter for atmospheric data assimilation: Application to wind tunnel data
Zheng, D.Q. (author) / Leung, J.K.C. (author) / Lee, B.Y. (author)
Atmospheric Environment ; 44 ; 1699-1705
2010-01-18
7 pages
Article (Journal)
Electronic Resource
English
Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter
| British Library Online Contents | 2010
Data assimilation for transient flow in geologic formations via ensemble Kalman filter
| British Library Online Contents | 2006
Data assimilation for phase-field models based on the ensemble Kalman filter
| British Library Online Contents | 2018
Data assimilation for phase-field models based on the ensemble Kalman filter
| British Library Online Contents | 2018
Data assimilation for phase-field models based on the ensemble Kalman filter
| British Library Online Contents | 2018