Articles | Volume 15, issue 4
https://doi.org/10.5194/acp-15-2051-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-15-2051-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
25 Feb 2015
Research article |
| 25 Feb 2015
Greenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 2: Sensitivity analyses and South African test case
Department of Statistical Sciences, University of Cape Town, Cape Town, 7701, South Africa
Global Change and Ecosystem Dynamics, CSIR, Pretoria, 0005, South Africa
Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric Research, Aspendale, VIC 3195, Australia
P.J. Rayner
School of Earth Sciences, University of Melbourne, Melbourne, VIC 3010, Australia
R.J. Scholes
Global Change and Ecosystem Dynamics, CSIR, Pretoria, 0005, South Africa
F. Engelbrecht
Climate Studies and Modelling and Environmental Health, CSIR, Pretoria, 0005, South Africa
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Cited
21 citations as recorded by crossref.
- Designing an Atmospheric Monitoring Network to Verify National CO2 Emissions S. Sim et al. 10.1007/s13143-023-00343-3
- An atmospheric inversion over the city of Cape Town: sensitivity analyses A. Nickless et al. 10.5194/acp-19-7789-2019
- Site selection and effects of background towers on urban CO2 estimates: A case study from central downtown Zhengzhou in China G. Ren et al. 10.1016/j.envres.2024.120169
- Comparison of the genetic algorithm and incremental optimisation routines for a Bayesian inverse modelling based network design A. Nickless et al. 10.1088/1361-6420/aab46c
- Reviews and syntheses: guiding the evolution of the observing system for the carbon cycle through quantitative network design T. Kaminski & P. Rayner 10.5194/bg-14-4755-2017
- Designing surface CO2 monitoring network to constrain the Indian land fluxes K. Nalini et al. 10.1016/j.atmosenv.2019.117003
- Opportunities for an African greenhouse gas observation system L. Merbold et al. 10.1007/s10113-021-01823-w
- Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm Y. Hao & S. Xie 10.1016/j.atmosenv.2018.01.011
- The first national scale spatial and temporal analysis of surface CO2 over South Africa utilising satellite retrievals X. Ncipha & V. Sivakumar 10.1080/03736245.2021.1934093
- A flexible algorithm for network design based on information theory R. Thompson & I. Pisso 10.5194/amt-16-235-2023
- The Influence of Meteorology and Air Transport on CO2 Atmospheric Distribution over South Africa X. Ncipha et al. 10.3390/atmos11030287
- Design and evaluation of CO<sub>2</sub> observation network to optimize surface CO<sub>2</sub> fluxes in Asia using observation system simulation experiments J. Park & H. Kim 10.5194/acp-20-5175-2020
- Designing optimal greenhouse gas monitoring networks for Australia T. Ziehn et al. 10.5194/gi-5-1-2016
- Designing optimal greenhouse gas observing networks that consider performance and cost D. Lucas et al. 10.5194/gi-4-121-2015
- Greenhouse gas observation network design for Africa A. Nickless et al. 10.1080/16000889.2020.1824486
- Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling A. Nickless et al. 10.5194/acp-18-4765-2018
- Designing additional CO2 in-situ surface observation networks over South Korea using bayesian inversion coupled with Lagrangian modelling S. Takele Kenea et al. 10.1016/j.atmosenv.2024.120471
- Exploring the utility of quantitative network design in evaluating Arctic sea ice thickness sampling strategies T. Kaminski et al. 10.5194/tc-9-1721-2015
- Spatial and temporal disaggregation of anthropogenic CO2 emissions from the City of Cape Town A. Nickless et al. 10.17159/sajs.2015/20140387
- Towards a feasible and representative pan-African research infrastructure network for GHG observations A. López-Ballesteros et al. 10.1088/1748-9326/aad66c
- Greenhouse gas network design using backward Lagrangian particle dispersion modelling − Part 1: Methodology and Australian test case T. Ziehn et al. 10.5194/acp-14-9363-2014
20 citations as recorded by crossref.
- Designing an Atmospheric Monitoring Network to Verify National CO2 Emissions S. Sim et al. 10.1007/s13143-023-00343-3
- An atmospheric inversion over the city of Cape Town: sensitivity analyses A. Nickless et al. 10.5194/acp-19-7789-2019
- Site selection and effects of background towers on urban CO2 estimates: A case study from central downtown Zhengzhou in China G. Ren et al. 10.1016/j.envres.2024.120169
- Comparison of the genetic algorithm and incremental optimisation routines for a Bayesian inverse modelling based network design A. Nickless et al. 10.1088/1361-6420/aab46c
- Reviews and syntheses: guiding the evolution of the observing system for the carbon cycle through quantitative network design T. Kaminski & P. Rayner 10.5194/bg-14-4755-2017
- Designing surface CO2 monitoring network to constrain the Indian land fluxes K. Nalini et al. 10.1016/j.atmosenv.2019.117003
- Opportunities for an African greenhouse gas observation system L. Merbold et al. 10.1007/s10113-021-01823-w
- Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm Y. Hao & S. Xie 10.1016/j.atmosenv.2018.01.011
- The first national scale spatial and temporal analysis of surface CO2 over South Africa utilising satellite retrievals X. Ncipha & V. Sivakumar 10.1080/03736245.2021.1934093
- A flexible algorithm for network design based on information theory R. Thompson & I. Pisso 10.5194/amt-16-235-2023
- The Influence of Meteorology and Air Transport on CO2 Atmospheric Distribution over South Africa X. Ncipha et al. 10.3390/atmos11030287
- Design and evaluation of CO<sub>2</sub> observation network to optimize surface CO<sub>2</sub> fluxes in Asia using observation system simulation experiments J. Park & H. Kim 10.5194/acp-20-5175-2020
- Designing optimal greenhouse gas monitoring networks for Australia T. Ziehn et al. 10.5194/gi-5-1-2016
- Designing optimal greenhouse gas observing networks that consider performance and cost D. Lucas et al. 10.5194/gi-4-121-2015
- Greenhouse gas observation network design for Africa A. Nickless et al. 10.1080/16000889.2020.1824486
- Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling A. Nickless et al. 10.5194/acp-18-4765-2018
- Designing additional CO2 in-situ surface observation networks over South Korea using bayesian inversion coupled with Lagrangian modelling S. Takele Kenea et al. 10.1016/j.atmosenv.2024.120471
- Exploring the utility of quantitative network design in evaluating Arctic sea ice thickness sampling strategies T. Kaminski et al. 10.5194/tc-9-1721-2015
- Spatial and temporal disaggregation of anthropogenic CO2 emissions from the City of Cape Town A. Nickless et al. 10.17159/sajs.2015/20140387
- Towards a feasible and representative pan-African research infrastructure network for GHG observations A. López-Ballesteros et al. 10.1088/1748-9326/aad66c
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Latest update: 23 Nov 2024
Short summary
This study aims to provide an optimal network design for the placement of new atmospheric monitoring stations around South Africa, to best estimate the emission and uptake of carbon dioxide fluxes due to both anthropogenic and natural sources. In addition, a sensitivity analysis was performed on the impact that certain parameters would have on the final network solution, considering the inverse modelling framework, the transport model and the use of a different optimisation routine.
This study aims to provide an optimal network design for the placement of new atmospheric...
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