scholarly journals A Simple Data Assimilation Method to Improve Atmospheric Dispersion Based on Lagrangian Puff Model

2021 ◽  
Author(s):  
Ke Li ◽  
Weihua Chen ◽  
Manchun Liang ◽  
Jianqiu Zhou ◽  
Yunfu Wang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Dispersion

Data assimilation in meteorological pre-processors: Effects on atmospheric dispersion simulations

2007 ◽  
Vol 41 (14) ◽  
pp. 2917-2932 ◽  
Author(s):  
E. Davakis ◽  
S. Andronopoulos ◽  
I. Kovalets ◽  
N. Gounaris ◽  
J.G. Bartzis ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Dispersion

scholarly journals Data Assimilation in the Air Contaminant Dispersion Using Particle Filter and Expectation-Maximization Algorithm with UAV Observations

2017 ◽  
Author(s):  
Rongxiao Wang ◽  
Bin Chen ◽  
Sihang Qiu ◽  
Zhengqiu Zhu ◽  
Xiaogang Qiu
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Expectation Maximization ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Expectation Maximization Algorithm ◽  
Air Quality Monitoring ◽  
State Variables ◽  
Precise Prediction ◽  
Air Contaminant

The precise prediction of air contaminant dispersion is essential to the air quality monitoring and the emergency management of the contaminant gases leakage incidents in the chemical industry park. The conventional atmospheric dispersion models can seldom give precise prediction due to inaccurate input parameters. In order to improve the prediction accuracy of dispersion model, two data assimilation methods (i.e. one is merely based on the typical particle filter while the other is a combination of particle filter and expectation-maximization algorithm) are proposed to assimilate the UAV observations into the atmospheric dispersion model. Two emission cases are taken into consideration, the difference between which is the different dimensions of state variables. To test the performances of the proposed methods, experiments corresponding to the two emission cases are designed and implemented. The results show that the particle filter can effectively estimate the model parameters and improve the accuracy of model prediction when the dimension of state variables is low. In contrast, when the dimension of state variables becomes higher, the method of particle filter combining expectation-maximization algorithm performs better in the parameter estimation accuracy and warm-up time. Therefore, the data assimilation methods are able to effectively support the air quality monitoring and emergency management in chemical industry parks.

Sign in / Sign up

Export Citation Format

Share Document