scholarly journals Dispersion of Particles Released into a Neutral Planetary Boundary Layer Using a Markov Chain–Monte Carlo Model

2005 ◽  
Vol 44 (7) ◽  
pp. 1106-1115 ◽  
Author(s):  
R. Avila ◽  
S. S. Raza
Keyword(s):  
Boundary Layer ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Planetary Boundary Layer ◽  
Monte Carlo Model ◽  
Measurement Data ◽  
Gaussian Model ◽  
Neutral Atmosphere ◽  
Numerical Predictions

Abstract The dispersion and concentration of particles (fluid elements) that are continuously released into a neutral planetary boundary layer is presented. The velocity fluctuations of the particles are generated using a Markov chain–Monte Carlo (MCMC) process at random time intervals with a one-step memory. The local mean concentration of the particles is calculated by using a fully Lagrangian method, which performs an efficient near-neighbor search and employs a smoothing kernel for eliminating the statistical noise. The predicted vertical and transversal root-mean-square of the particles’ deviation from their mean position [()1/2 and ()1/2] for an elevated continuous release source in a neutral atmosphere are compared with empirical parameters like the Pasquill–Gifford σz and σy. The numerical predictions of the particle concentration are compared with a Gaussian model and field measurement data on the ground concentration obtained during the Green Glow Program. The comparison between the numerical predictions and the field data shows that the MCMC model can successfully predict the particle dispersion and concentration in a neutral atmosphere.

scholarly journals Implementation of a Markov Chain Monte Carlo Method to inorganic aerosol modeling of observations from the MCMA-2003 Campaign. Part II: Model application to the CENICA, Pedregal and Santa Ana sites

2006 ◽  
Vol 6 (4) ◽  
pp. 5999-6040
Author(s):  
F. M. San Martini ◽  
E. J. Dunlea ◽  
R. Volkamer ◽  
T. B. Onasch ◽  
J. T. Jayne ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Nitric Acid ◽  
Gas Phase ◽  
Mexico City ◽  
Monte Carlo Model ◽  
Rural Site ◽  
Inorganic Species ◽  
Inorganic Aerosol

Abstract. A Markov Chain Monte Carlo model for integrating the observations of inorganic species with a thermodynamic equilibrium model was presented in Part I of this series. Using observations taken at three ground sites, i.e. a residential, industrial and rural site, during the MCMA-2003 campaign in Mexico City, the model is used to analyze the inorganic aerosol and ammonia data and predict gas phase concentrations of nitric and hydrochloric acid. In general the model is able to accurately predict the observed inorganic aerosol concentrations at all three sites. The agreement between the predicted and observed gas phase ammonia concentration is excellent. The NOz concentration calculated from the NOy, NO and NO2 observations is of limited use in constraining the gas phase nitric acid concentration given the large uncertainties in this measure of nitric acid and additional reactive nitrogen species. Focusing on the acidic period of 9–11 April identified by Salcedo et al. (2006), the model accurately predicts the aerosol phase observations during this period with the exception of the nitrate predictions after 10:00 a.m. (CDT) on 9 April, where the model underpredicts the observations by, on average, 20%. For periods when the aerosol chloride observations are consistently above the detection limit, the model is able to both accurately predict the aerosol chloride predictions and provide well-constrained HCl (g) concentrations. When the aerosols are aqueous, the most likely concentrations of HCl (g) are in the sub-ppbv range. The most likely predicted concentration of HCl (g) was found to reach concentrations of order 10 ppbv if the aerosols are dry. Finally, the atmospheric relevance of HCl (g) is discussed in terms of its indicator properties for the possible influence of chlorine-mediated photochemistry in Mexico City.

Parameter Estimation of an Electrohydraulic Servo System Using a Markov Chain Monte Carlo Method

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Junhong Liu ◽  
Huapeng Wu ◽  
Heikki Handroos ◽  
Heikki Haario
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Measurement Data ◽  
Estimation Method ◽  
Operating Conditions ◽  
Point Estimate ◽  
Best Fitting ◽  
The One

A parameter estimation method is presented by an example of an electrohydraulic position servo. The method is based on the Markov chain Monte Carlo approach. The method allows utilization of noisy measurement data in identification process, making use of original physical data possible without the requirement of a filter. The method seeks for the best fitting point estimate of the unknown model parameter vector, but the solution to the parameter estimation problem is given as a statistical distribution that contains “all” the possible parameter combinations. The robustness of the model developed with the proposed method is further demonstrated by verification in operating conditions that are independent of each other and the one used in the identification step. Results show that the system model with the hybrid leakage formula for the studied valve describes the system dynamics more precisely and matches the real responses better.

scholarly journals Implementation of a Markov Chain Monte Carlo method to inorganic aerosol modeling of observations from the MCMA-2003 campaign – Part II: Model application to the CENICA, Pedregal and Santa Ana sites

2006 ◽  
Vol 6 (12) ◽  
pp. 4889-4904 ◽  
Author(s):  
F. M. San Martini ◽  
E. J. Dunlea ◽  
R. Volkamer ◽  
T. B. Onasch ◽  
J. T. Jayne ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Nitric Acid ◽  
Gas Phase ◽  
Mexico City ◽  
Monte Carlo Model ◽  
Rural Site ◽  
Inorganic Particle ◽  
Inorganic Species

Abstract. A Markov Chain Monte Carlo model for integrating the observations of inorganic species with a thermodynamic equilibrium model was presented in Part I of this series. Using observations taken at three ground sites, i.e. a residential, industrial and rural site, during the MCMA-2003 campaign in Mexico City, the model is used to analyze the inorganic particle and ammonia data and to predict gas phase concentrations of nitric and hydrochloric acid. In general, the model is able to accurately predict the observed inorganic particle concentrations at all three sites. The agreement between the predicted and observed gas phase ammonia concentration is excellent. The NOz concentration calculated from the NOy, NO and NO2 observations is of limited use in constraining the gas phase nitric acid concentration given the large uncertainties in this measure of nitric acid and additional reactive nitrogen species. Focusing on the acidic period of 9–11 April identified by Salcedo et al. (2006), the model accurately predicts the particle phase observations during this period with the exception of the nitrate predictions after 10:00 a.m. (Central Daylight Time, CDT) on 9 April, where the model underpredicts the observations by, on average, 20%. This period had a low planetary boundary layer, very high particle concentrations, and higher than expected nitrogen dioxide concentrations. For periods when the particle chloride observations are consistently above the detection limit, the model is able to both accurately predict the particle chloride mass concentrations and provide well-constrained HCl (g) concentrations. The availability of gas-phase ammonia observations helps constrain the predicted HCl (g) concentrations. When the particles are aqueous, the most likely concentrations of HCl (g) are in the sub-ppbv range. The most likely predicted concentration of HCl (g) was found to reach concentrations of order 10 ppbv if the particles are dry. Finally, the atmospheric relevance of HCl (g) is discussed in terms of its indicator properties for the possible influence of chlorine-mediated photochemistry in Mexico City.

scholarly journals Markov Chain Monte Carlo Model Analysis of Cardiac Mitochondrial VDAC1 Kinetics

2014 ◽  
Vol 106 (2) ◽  
pp. 761a
Author(s):  
Shivendra G. Tewari ◽  
Bradley J. Otto ◽  
Qunli Cheng ◽  
YiFan Zhou ◽  
Ranjan K. Dash ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Model ◽  
Model Analysis
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