Designing optimal greenhouse gas observing networks that consider performance and cost
Autor: | Ray F. Weiss, Heather Graven, Philip Cameron-Smith, T. Guilderson, Dan Bergmann, Ralph F. Keeling, Donald D. Lucas, C. Yver Kwok |
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Přispěvatelé: | Institut de Recherche sur l'Enseignement des Sciences (IRES), Université d'Orléans (UO), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), ICOS-ATC (ICOS-ATC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Lawrence Livermore National Laboratory (LLNL), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS) |
Jazyk: | angličtina |
Rok vydání: | 2015 |
Předmět: |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean
Atmosphere Atmospheric Science Engineering Operations research 010504 meteorology & atmospheric sciences business.industry lcsh:QC801-809 Pareto principle Geology Inversion (meteorology) 02 engineering and technology Oceanography 01 natural sciences lcsh:Geophysics. Cosmic physics 13. Climate action Greenhouse gas 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Diminishing returns business [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment 0105 earth and related environmental sciences |
Zdroj: | Geoscientific Instrumentation, Methods and Data Systems Geoscientific Instrumentation, Methods and Data Systems, European Geosciences Union 2015, 4 (1), pp.121-137. ⟨10.5194/gi-4-121-2015⟩ Geoscientific Instrumentation, Methods and Data Systems, Vol 4, Iss 1, Pp 121-137 (2015) Geoscientific Instrumentation, Methods and Data Systems, 2015, 4 (1), pp.121-137. ⟨10.5194/gi-4-121-2015⟩ |
ISSN: | 2193-0856 2193-0864 |
DOI: | 10.5194/gi-4-121-2015⟩ |
Popis: | 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. |
Databáze: | OpenAIRE |
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