Articles | Volume 4, issue 1
https://doi.org/10.5194/gi-4-121-2015
© 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/gi-4-121-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
16 Jun 2015
Research article |
| 16 Jun 2015
Designing optimal greenhouse gas observing networks that consider performance and cost
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
C. Yver Kwok
Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France
P. Cameron-Smith
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
H. Graven
Department of Physics and Grantham Institute, Imperial College London, London, UK
Scripps Institution of Oceanography, UC San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0244, USA
D. Bergmann
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
T. P. Guilderson
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
R. Weiss
Scripps Institution of Oceanography, UC San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0244, USA
R. Keeling
Scripps Institution of Oceanography, UC San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0244, USA
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- Impact of meteorological inflow uncertainty on tracer transport and source estimation in urban atmospheres D. Lucas et al. 10.1016/j.atmosenv.2016.08.019
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19 citations as recorded by crossref.
- A layer-wise neural network for multi-item single-output quality estimation E. Yapp et al. 10.1007/s10845-022-01995-0
- Designing optimal greenhouse gas monitoring networks for Australia T. Ziehn et al. 10.5194/gi-5-1-2016
- Data imputation in IoT using Spatio-Temporal Variational Auto-Encoder S. Zhang et al. 10.1016/j.neucom.2023.01.022
- Impact of meteorological inflow uncertainty on tracer transport and source estimation in urban atmospheres D. Lucas et al. 10.1016/j.atmosenv.2016.08.019
- Prediction of time series using an analysis filter bank of LSTM units J. Mejia et al. 10.1016/j.cie.2021.107371
- Nonsmooth Optimization-Based Hyperparameter-Free Neural Networks for Large-Scale Regression N. Karmitsa et al. 10.3390/a16090444
- Enabling Highly Efficient k-Means Computations on the SW26010 Many-Core Processor of Sunway TaihuLight M. Li et al. 10.1007/s11390-019-1900-5
- Enhancing Time Series Predictors with Generalized Extreme Value Loss M. Zhang et al. 10.1109/TKDE.2021.3108831
- COUNT AND DURATION TIME SERIES WITH EQUAL CONDITIONAL STOCHASTIC AND MEAN ORDERS A. Aknouche & C. Francq 10.1017/S0266466620000134
- 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
- Opportunities for an African greenhouse gas observation system L. Merbold et al. 10.1007/s10113-021-01823-w
- Evaluation of Carbon Pricing Policy in Hydrous Ethanol Transport Sector in Brazil R. Santos et al. 10.2139/ssrn.4119545
- A Weighting Scheme for One-Shot Federated Learning M. Garin et al. 10.2139/ssrn.4035732
- Prediction of time series using wavelet Gaussian process for wireless sensor networks J. Mejia et al. 10.1007/s11276-020-02250-1
- Conformal Prediction for Time Series C. Xu & Y. Xie 10.1109/TPAMI.2023.3272339
- A flexible algorithm for network design based on information theory R. Thompson & I. Pisso 10.5194/amt-16-235-2023
- 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
- Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant D. Lucas et al. 10.5194/acp-17-13521-2017
- Novel approach to observing system simulation experiments improves information gain of surface–atmosphere field measurements S. Metzger et al. 10.5194/amt-14-6929-2021
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Latest update: 24 Apr 2024
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...
