Journal cover Journal topic
Geoscientific Instrumentation, Methods and Data Systems An interactive open-access journal of the European Geosciences Union
Geosci. Instrum. Method. Data Syst., 4, 121-137, 2015
http://www.geosci-instrum-method-data-syst.net/4/121/2015/
doi:10.5194/gi-4-121-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
16 Jun 2015
Designing optimal greenhouse gas observing networks that consider performance and cost
D. D. Lucas1, C. Yver Kwok2, P. Cameron-Smith1, H. Graven3,4, D. Bergmann1, T. P. Guilderson1, R. Weiss4, and R. Keeling4 1Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
2Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France
3Department of Physics and Grantham Institute, Imperial College London, London, UK
4Scripps Institution of Oceanography, UC San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0244, USA
Abstract. Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototype network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH2FCF3, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.

Citation: Lucas, D. D., Yver Kwok, C., Cameron-Smith, P., Graven, H., Bergmann, D., Guilderson, T. P., Weiss, R., and Keeling, R.: Designing optimal greenhouse gas observing networks that consider performance and cost, Geosci. Instrum. Method. Data Syst., 4, 121-137, doi:10.5194/gi-4-121-2015, 2015.
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Short summary
Multiobjective optimization is used to design Pareto optimal greenhouse gas (GHG) observing networks. A prototype GHG network is designed to optimize scientific performance and measurement costs. The Pareto frontier is convex, showing the trade-offs between performance and cost and the diminishing returns in trading one for the other. Other objectives and constraints that are important in the design of practical GHG monitoring networks can be incorporated into our method.
Multiobjective optimization is used to design Pareto optimal greenhouse gas (GHG) observing...
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