COMET: a Lagrangian transport model for greenhouse gas emission estimation – forward model technique and performance for methane

Abstract. The Lagrangian transport model COMET has been developed to evaluate emission estimates based on atmospheric concentration observations. This paper describes the model and its application in modelling the methane concentrations at the European stations Cabauw and Macehead. The COMET model captures in most cases both synoptic and diurnal variations of the concentrations as a function of time and in absolute size quite well. The explained variability by COMET of the mixed layer concentration for Cabauw varies from 50% to 84%; for all hourly observations in 2002 the explained variability is 71% with a RMSE of 112 ppb. The explained variability for Macehead is 48%. The most important model parameters were tested for their influence on model performance, but in general the model is not very sensitive to variations within acceptable limits. For a regionally and locally polluted continental site the COMET model shows only a small bias and a moderate random error, and therefore is considered to capture the influence of the sources on the concentration variations quite well. It is therefore concluded that inverse methods and more specifically the COMET model is suitable to be applied in deriving independent estimates of greenhouse gas emissions using Source-Receptor relationships.

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Greenhouse gas emission estimation for temperature-controlled food distribution systems

Journal of Cleaner Production ◽

10.1016/j.jclepro.2015.05.038 ◽

2015 ◽

Vol 104 ◽

pp. 139-147 ◽

Cited By ~ 6

Author(s):

Wei-Ting Chen ◽

Chaug-Ing Hsu

Keyword(s):

Greenhouse Gas ◽

Distribution Systems ◽

Greenhouse Gas Emission ◽

Food Distribution ◽

Gas Emission ◽

Emission Estimation ◽

Temperature Controlled

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Applicability of Different Hydrological Model Concepts on Small Catchments: Case Study of Bükkös Creek, Hungary

Acta Silvatica et Lignaria Hungarica ◽

10.2478/aslh-2014-0006 ◽

2014 ◽

Vol 10 (1) ◽

pp. 77-90 ◽

Cited By ~ 2

Author(s):

Péter Torma ◽

Borbála Széles ◽

Géza Hajnal

Keyword(s):

Model Comparison ◽

Hydrological Model ◽

Model Performance ◽

Digital Terrain Model ◽

Model Parameters ◽

Lumped Model ◽

Terrain Model ◽

Distributed Approach ◽

Model Setup ◽

And Performance

Abstract This study aims to test and compare the applicability and performance of two different hydrological model concepts on a small Hungarian watershed. The lumped model of HEC-HMS and the semi-distributed TOPMODEL have been implemented to predict streamflow of Bükkös Creek. Models were calibrated against the highest flood event recorded in the basin in May, 2010. Validation was done in an extended interval when smaller floods were observed. Acceptable results can be achieved with the semi-distributed approach. Model comparison is made by means of sensitivity analysis of model parameters. For TOPMODEL the effect of spatial resolution of the digital terrain model while for HMS the complexity of the model setup was further explored. The results were quantified with model performance indices.

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Characterization of an Instrument Model for Exoplanet Transit Spectrum Estimation through Wide-scale Analysis on HST Data

The Astronomical Journal ◽

10.3847/1538-3881/ac341a ◽

2021 ◽

Vol 163 (1) ◽

pp. 22

Author(s):

Noah Huber-Feely ◽

Mark R. Swain ◽

Gael Roudier ◽

Raissa Estrela

Keyword(s):

Light Curve ◽

Model Performance ◽

Distribution Functions ◽

Model Parameters ◽

Spectrum Estimation ◽

Parent Distribution ◽

Wide Scale ◽

And Performance ◽

In Transit

Abstract Instrument models (IMs) enable the reduction of systematic error in transit spectroscopy light-curve data, but, since the model formulation can influence the estimation of science model parameters, characterization of the instrument model effects is crucial to the interpretation of the reduced data. We analyze a simple instrument model and assess its validity and performance across Hubble WFC3 and STIS instruments. Over a large, n = 63, sample of observed targets, a Markov chain Monte Carlo sampler computes the parent distribution of each instrument model parameter. Possible parent distribution functions are then fit and tested against the empirical IM distribution. Correlation and other analyses are then performed to find IM relationships. The model is shown to perform well across the two instruments and three filters analyzed and, further, the Student’s t distribution is shown to closely fit the empirical parent distribution of IM parameters and the Gaussian distribution is shown to poorly model the observed distribution. This parent distribution can be used in the MCMC prior fitting and demonstrates IM consistency for wide-scale atmospheric analysis using this model. Finally, we propose a simple metric based on light-curve residuals to determine model performance, and we demonstrate its ability to determine whether a derived spectrum under this IM is high quality and robust.

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