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@article{Lucas:2015:10.5194/gi-4-121-2015,
author = {Lucas, DD and Kwok, CY and Cameron-Smith, P and Graven, H and Bergmann, D and Guilderson, TP and Weiss, R and Keeling, R},
doi = {10.5194/gi-4-121-2015},
journal = {Geoscientific Instrumentation, Methods and Data Systems},
pages = {121--137},
title = {Designing optimal greenhouse gas observing networks that consider performance and cost},
url = {http://dx.doi.org/10.5194/gi-4-121-2015},
volume = {4},
year = {2015}
}

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TY  - JOUR
AB  - Emission rates of greenhouse gases (GHGs) enteringinto the atmosphere can be inferred using mathematicalinverse approaches that combine observations from a networkof stations with forward atmospheric transport models.Some locations for collecting observations are better thanothers for constraining GHG emissions through the inversion,but the best locations for the inversion may be inaccessibleor limited by economic and other non-scientific factors.We present a method to design an optimal GHG observingnetwork in the presence of multiple objectives that may bein conflict with each other. As a demonstration, we use ourmethod to design a prototype network of six stations to monitorsummertime emissions in California of the potent GHG1,1,1,2-tetrafluoroethane (CH2FCF3, HFC-134a). We use amultiobjective genetic algorithm to evolve network configurationsthat seek to jointly maximize the scientific accuracyof the inferred HFC-134a emissions and minimize the associatedcosts of making the measurements. The genetic algorithmeffectively determines a set of “optimal” observingnetworks for HFC-134a that satisfy both objectives (i.e., thePareto frontier). The Pareto frontier is convex, and clearlyshows the tradeoffs between performance and cost, and thediminishing returns in trading one for the other. Without dif-ficulty, our method can be extended to design optimal networksto monitor two or more GHGs with different emissionspatterns, or to incorporate other objectives and constraintsthat are important in the practical design of atmosphericmonitoring networks.
AU  - Lucas,DD
AU  - Kwok,CY
AU  - Cameron-Smith,P
AU  - Graven,H
AU  - Bergmann,D
AU  - Guilderson,TP
AU  - Weiss,R
AU  - Keeling,R
DO  - 10.5194/gi-4-121-2015
EP  - 137
PY  - 2015///
SN  - 2193-0864
SP  - 121
TI  - Designing optimal greenhouse gas observing networks that consider performance and cost
T2  - Geoscientific Instrumentation, Methods and Data Systems
UR  - http://dx.doi.org/10.5194/gi-4-121-2015
UR  - http://hdl.handle.net/10044/1/25618
VL  - 4
ER  -

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