Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.509 IF 5.509
  • IF 5-year value: 5.689 IF 5-year
  • CiteScore value: 5.44 CiteScore
  • SNIP value: 1.519 SNIP 1.519
  • SJR value: 3.032 SJR 3.032
  • IPP value: 5.37 IPP 5.37
  • h5-index value: 86 h5-index 86
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 161 Scimago H
    index 161
Volume 15, issue 4
Atmos. Chem. Phys., 15, 2051-2069, 2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: 9th International Carbon Dioxide Conference (ICDC9) (ESD/ACP/AMT/BG...

Atmos. Chem. Phys., 15, 2051-2069, 2015
© Author(s) 2015. This work is distributed under
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

A. Nickless2,1, T. Ziehn3, P.J. Rayner4, R.J. Scholes1, and F. Engelbrecht5 A. Nickless et al.
  • 1Global Change and Ecosystem Dynamics, CSIR, Pretoria, 0005, South Africa
  • 2Department of Statistical Sciences, University of Cape Town, Cape Town, 7701, South Africa
  • 3Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric Research, Aspendale, VIC 3195, Australia
  • 4School of Earth Sciences, University of Melbourne, Melbourne, VIC 3010, Australia
  • 5Climate Studies and Modelling and Environmental Health, CSIR, Pretoria, 0005, South Africa

Abstract. This is the second part of a two-part paper considering a measurement network design based on a stochastic Lagrangian particle dispersion model (LPDM) developed by Marek Uliasz, in this case for South Africa. A sensitivity analysis was performed for different specifications of the network design parameters which were applied to this South African test case. The LPDM, which can be used to derive the sensitivity matrix used in an atmospheric inversion, was run for each candidate station for the months of July (representative of the Southern Hemisphere winter) and January (summer). The network optimisation procedure was carried out under a standard set of conditions, similar to those applied to the Australian test case in Part 1, for both months and for the combined 2 months, using the incremental optimisation (IO) routine. The optimal network design setup was subtly changed, one parameter at a time, and the optimisation routine was re-run under each set of modified conditions and compared to the original optimal network design. The assessment of the similarity between network solutions showed that changing the height of the surface grid cells, including an uncertainty estimate for the ocean fluxes, or increasing the night-time observation error uncertainty did not result in any significant changes in the positioning of the stations relative to the standard design. However, changing the prior flux error covariance matrix, or increasing the spatial resolution, did.

Large aggregation errors were calculated for a number of candidate measurement sites using the resolution of the standard network design. Spatial resolution of the prior fluxes should be kept as close to the resolution of the transport model as the computing system can manage, to mitigate the exclusion of sites which could potentially be beneficial to the network. Including a generic correlation structure in the prior flux error covariance matrix led to pronounced changes in the network solution. The genetic algorithm (GA) was able to find a marginally better solution than the IO procedure, increasing uncertainty reduction by 0.3 %, but still included the most influential stations from the standard network design. In addition, the computational cost of the GA compared to IO was much higher. Overall the results suggest that a good improvement in knowledge of South African fluxes is available from a feasible atmospheric network, and that the general features of this network are invariable under several reasonable choices in a network design study.

Publications Copernicus
Special issue
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...