scholarly journals Impact of modelled particle characteristics on emissions inferred by inversion of tracer transport

2013 ◽  
Vol 13 (2) ◽  
pp. 4391-4432 ◽  
Author(s):  
S. M. Burrows ◽  
P. J. Rayner ◽  
T. Butler ◽  
M. G. Lawrence
Keyword(s):  
Particle Size ◽  
Monte Carlo ◽  
Atmospheric Chemistry ◽  
Ice Nucleation ◽  
Model Parameters ◽  
Source Estimation ◽  
Nucleation Scavenging ◽  
Observation Uncertainty ◽  
Global Mean

Abstract. Model-simulated transport of atmospheric trace components can be combined with observed concentrations to obtain estimates of ground-based sources using various inversion techniques. These approaches have been applied in the past primarily to obtain source estimates for long-lived trace gases such as CO2. We consider the application of similar techniques to source estimation for atmospheric aerosols, using as a case study the estimation of bacteria emissions from different ecosystem regions in the global atmospheric chemistry and climate model ECHAM5/MESSy-Atmospheric Chemistry (EMAC). Source estimation via Monte Carlo Markov Chain is applied to a suite of sensitivity simulations and the global mean emissions are estimated. We present an analysis of the uncertainties in the global mean emissions, and a partitioning of the uncertainties that are attributable to particle size, activity as cloud condensation nuclei (CCN), the ice nucleation scavenging ratios for mixed-phase and cold clouds, and measurement error. Uncertainty due to CCN activity or to a 1 μm error in particle size is typically between 10% and 40% of the uncertainty due to observation uncertainty, as measured by the 5%-ile to 95%-ile range of the Monte Carlo ensemble. Uncertainty attributable to the ice nucleation scavenging ratio in mixed-phase clouds is as high as 10% to 20% of that attributable to observation uncertainty. Taken together, the four model parameters examined contribute about half as much to the uncertainty in the estimated emissions as do the observations. This was a surprisingly large contribution from model uncertainty in light of the substantial observation uncertainty, which ranges from 81% to 870% of the mean for each of ten ecosystems for this case study. The effects of these and other model parameters in contributing to the uncertainties in the transport of atmospheric aerosol particles should be treated explicitly and systematically in both forward and inverse modelling studies.

scholarly journals Estimating bacteria emissions from inversion of atmospheric transport: sensitivity to modelled particle characteristics

2013 ◽  
Vol 13 (11) ◽  
pp. 5473-5488 ◽  
Author(s):  
S. M. Burrows ◽  
P. J. Rayner ◽  
T. Butler ◽  
M. G. Lawrence
Keyword(s):  
Particle Size ◽  
Atmospheric Chemistry ◽  
Aerosol Particles ◽  
Ice Nucleation ◽  
Model Parameters ◽  
Source Estimation ◽  
Nucleation Scavenging ◽  
Observation Uncertainty ◽  
Global Mean

Abstract. Model-simulated transport of atmospheric trace components can be combined with observed concentrations to obtain estimates of ground-based sources using various inversion techniques. These approaches have been applied in the past primarily to obtain source estimates for long-lived trace gases such as CO2. We consider the application of similar techniques to source estimation for atmospheric aerosols, using as a case study the estimation of bacteria emissions from different ecosystem regions in the global atmospheric chemistry and climate model ECHAM5/MESSy-Atmospheric Chemistry (EMAC). Source estimation via Markov Chain Monte Carlo is applied to a suite of sensitivity simulations, and the global mean emissions are estimated for the example problem of bacteria-containing aerosol particles. We present an analysis of the uncertainties in the global mean emissions, and a partitioning of the uncertainties that are attributable to particle size, activity as cloud condensation nuclei (CCN), the ice nucleation scavenging ratios for mixed-phase and cold clouds, and measurement error. For this example, uncertainty due to CCN activity or to a 1 μm error in particle size is typically between 10% and 40% of the uncertainty due to observation uncertainty, as measured by the 5–95th percentile range of the Monte Carlo ensemble. Uncertainty attributable to the ice nucleation scavenging ratio in mixed-phase clouds is as high as 10–20% of that attributable to observation uncertainty. Taken together, the four model parameters examined contribute about half as much to the uncertainty in the estimated emissions as do the observations. This was a surprisingly large contribution from model uncertainty in light of the substantial observation uncertainty, which ranges from 81–870% of the mean for each of ten ecosystems for this case study. The effects of these and other model parameters in contributing to the uncertainties in the transport of atmospheric aerosol particles should be treated explicitly and systematically in both forward and inverse modelling studies.

scholarly journals Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Stability ◽  
Small Uncertainty ◽  
Best Fit ◽  
Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

scholarly journals Produção de dados de tráfego sintéticos através de algoritmo genético e simulação microscópica

TRANSPORTES ◽  
2010 ◽  
Vol 18 (3) ◽  
Author(s):  
José Elievam Bessa Júnior ◽  
José Reynaldo Setti
Keyword(s):  
Monte Carlo ◽  
Simulation Model ◽  
Synthetic Data ◽  
Monte Carlo Sampling ◽  
Sao Paulo ◽  
Model Parameters ◽  
São Paulo ◽  
Traffic Data ◽  
Wide Range

<p><strong>Resumo: </strong>Neste artigo, propõe-se um método para produzir, por meio de um simulador, uma série de dados de tráfego sintéticos que representem diversas condições operacionais e comportamentais, como os fornecidos por sensores instalados nas vias. O método proposto baseia-se num algoritmo genético (AG), que automaticamente calibra um modelo de simulação microscópico. Entre os cromossomos (soluções) gerados pelo AG na calibração do simulador, são escolhidos os que produzem resultados marginalmente inferiores ao ótimo. As distribuições de valores dos parâmetros que formam os cromossomos deste subconjunto são usadas para gerar, através de uma amostragem de Monte Carlo, parâmetros para as replicações da simulação que são usadas para produzir os dados sintéticos. A viabilidade do método proposto é demonstrada através de um estudo de caso, no qual o simulador usado foi o TWOPAS e os dados de tráfego foram obtidos de sensores instalados no km 98 da SP98, uma rodovia de pista simples no estado de São Paulo.</p><strong>Abstract: </strong>The paper proposes a method for generating, by a simulator, synthetic traffic data which represents a wide range of operational and behavioral conditions, such as those obtained from detectors installed on roads. The proposed method uses a genetic algorithm (GA) to calibrate a traffic simulation model automatically. A subset of chromosomes (solutions) generated by the GA during the calibration of the simulator containing those that produce results marginally inferior to the best solution is used to produce distributions of feasible values for the simulation model parameters. The distributions of simulation model parameters contained in the chromosomes of this subset of solutions are used to produce, by Monte Carlo sampling, parameters for the simulations that generate the synthetic data. The feasibility of the proposed approach is demonstrated by a case study using TWOPAS and data from detectors installed on SP98, a two-lane highway in the state of São Paulo, Brazil.

Pyrolysis of brown algae (Bifurcaria Bifurcata) in a stainless steel tubular reactor: From a review to a case study

2018 ◽  
Vol 14 (1) ◽  
pp. 31-60 ◽  
Author(s):  
M. Y. Guida ◽  
F. E. Laghchioua ◽  
A. Hannioui
Keyword(s):  
Particle Size ◽  
Stainless Steel ◽  
Flow Rate ◽  
Brown Algae ◽  
Tubular Reactor ◽  
Nitrogen Flow ◽  
Nitrogen Flow Rate ◽  
Bifurcaria Bifurcata ◽  
Bio Oil

This article deals with fast pyrolysis of brown algae, such as Bifurcaria Bifurcata at the range of temperature 300–800 °C in a stainless steel tubular reactor. After a literature review on algae and its importance in renewable sector, a case study was done on pyrolysis of brown algae especially, Bifurcaria Bifurcata. The aim was to experimentally investigate how the temperature, the particle size, the nitrogen flow rate (N2) and the heating rate affect bio-oil, bio-char and gaseous products. These parameters were varied in the ranges of 5–50 °C/min, below 0.2–1 mm and 20–200 mL. min–1, respectively. The maximum bio-oil yield of 41.3wt% was obtained at a pyrolysis temperature of 600 °C, particle size between 0.2–0.5 mm, nitrogen flow rate (N2) of 100 mL. min–1 and heating rate of 5 °C/min. Liquid product obtained under the most suitable and optimal condition was characterized by elemental analysis, 1H-NMR, FT-IR and GC-MS. The analysis of bio-oil showed that bio-oil from Bifurcaria Bifurcata could be a potential source of renewable fuel production and value added chemicals.

scholarly journals A combined Monte Carlo/ DFT approach to simulate UV ‐vis spectra of molecules and aggregates: Merocyanine dyes as a case study

2021 ◽  
Author(s):  
Bernardino Tirri ◽  
Gloria Mazzone ◽  
Alistar Ottochian ◽  
Jerôme Gomar ◽  
Umberto Raucci ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Merocyanine Dyes

scholarly journals Toward a systematic improvement of the fixed-node approximation in diffusion Monte Carlo for solids—A case study in diamond

2020 ◽  
Vol 153 (18) ◽  
pp. 184111
Author(s):  
Anouar Benali ◽  
Kevin Gasperich ◽  
Kenneth D. Jordan ◽  
Thomas Applencourt ◽  
Ye Luo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Diffusion Monte Carlo ◽  
Systematic Improvement ◽  
Fixed Node

scholarly journals Statistical Analysis of Tritium Breeding Ratio Deviations in the DEMO Due to Nuclear Data Uncertainties

2021 ◽  
Vol 11 (11) ◽  
pp. 5234
Author(s):  
Jin Hun Park ◽  
Pavel Pereslavtsev ◽  
Alexandre Konobeev ◽  
Christian Wegmann
Keyword(s):  
Monte Carlo ◽  
Fusion Reactor ◽  
Nuclear Data ◽  
Data File ◽  
Nuclear Model ◽  
Model Parameters ◽  
Tritium Breeding ◽  
Tritium Breeding Ratio ◽  
Nuclear Data Library ◽  
Breeding Ratio

For the stable and self-sufficient functioning of the DEMO fusion reactor, one of the most important parameters that must be demonstrated is the Tritium Breeding Ratio (TBR). The reliable assessment of the TBR with safety margins is a matter of fusion reactor viability. The uncertainty of the TBR in the neutronic simulations includes many different aspects such as the uncertainty due to the simplification of the geometry models used, the uncertainty of the reactor layout and the uncertainty introduced due to neutronic calculations. The last one can be reduced by applying high fidelity Monte Carlo simulations for TBR estimations. Nevertheless, these calculations have inherent statistical errors controlled by the number of neutron histories, straightforward for a quantity such as that of TBR underlying errors due to nuclear data uncertainties. In fact, every evaluated nuclear data file involved in the MCNP calculations can be replaced with the set of the random data files representing the particular deviation of the nuclear model parameters, each of them being correct and valid for applications. To account for the uncertainty of the nuclear model parameters introduced in the evaluated data file, a total Monte Carlo (TMC) method can be used to analyze the uncertainty of TBR owing to the nuclear data used for calculations. To this end, two 3D fully heterogeneous geometry models of the helium cooled pebble bed (HCPB) and water cooled lithium lead (WCLL) European DEMOs were utilized for the calculations of the TBR. The TMC calculations were performed, making use of the TENDL-2017 nuclear data library random files with high enough statistics providing a well-resolved Gaussian distribution of the TBR value. The assessment was done for the estimation of the TBR uncertainty due to the nuclear data for entire material compositions and for separate materials: structural, breeder and neutron multipliers. The overall TBR uncertainty for the nuclear data was estimated to be 3~4% for the HCPB and WCLL DEMOs, respectively.

scholarly journals Estimates of Uncertainty in Predictions of Global Mean Surface Temperature

2007 ◽  
Vol 20 (5) ◽  
pp. 843-855 ◽  
Author(s):  
J. A. Kettleborough ◽  
B. B. B. Booth ◽  
P. A. Stott ◽  
M. R. Allen
Keyword(s):  
Twentieth Century ◽  
Energy Balance ◽  
Temperature Change ◽  
Radiative Forcing ◽  
Balance Model ◽  
Future Climate Change ◽  
Model Parameters ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

Abstract A method for estimating uncertainty in future climate change is discussed in detail and applied to predictions of global mean temperature change. The method uses optimal fingerprinting to make estimates of uncertainty in model simulations of twentieth-century warming. These estimates are then projected forward in time using a linear, compact relationship between twentieth-century warming and twenty-first-century warming. This relationship is established from a large ensemble of energy balance models. By varying the energy balance model parameters an estimate is made of the error associated with using the linear relationship in forecasts of twentieth-century global mean temperature. Including this error has very little impact on the forecasts. There is a 50% chance that the global mean temperature change between 1995 and 2035 will be greater than 1.5 K for the Special Report on Emissions Scenarios (SRES) A1FI scenario. Under SRES B2 the same threshold is not exceeded until 2055. These results should be relatively robust to model developments for a given radiative forcing history.

Probabilistic Assessment of Wind Farm Active Power Based on Monte-Carlo Simulation

2013 ◽  
Vol 291-294 ◽  
pp. 536-540 ◽  
Author(s):  
Xin Wei Wang ◽  
Jian Hua Zhang ◽  
Cheng Jiang ◽  
Lei Yu
Keyword(s):  
Monte Carlo ◽  
Power Output ◽  
Wind Farm ◽  
Active Power ◽  
Assessment Methods ◽  
Probabilistic Methods ◽  
Model Parameters ◽  
Probability Assessment ◽  
Reliability Models ◽  
Reliability Parameters

The conventional deterministic methods have been unable to accurately assess the active power output of the wind farm being the random and intermittent of wind power, and the probabilistic methods commonly used to solve this problem. In this paper the multi-state fault model is built considering run, outage and derating state of wind turbine, and then the reliability model of the wind farm is established considering the randomness of the wind speed, the wind farm wake effects and turbine failure. The active wind farm output probability assessment methods and processes based on the Monte Carlo method. The related programs are written in MATLAB, and the probability assessment for active power output of a wind farm in carried out, the effectiveness and adaptability of built reliability models and assessment methods are illustrated by analysis of the effects of reliability parameters and model parameters on assessment results.

Sign in / Sign up

Export Citation Format

Share Document