Strategic design of methane observation networks to improve emission estimates: A case study in Africa

Abstract

Ground-based and satellite atmospheric observations are essential for reducing uncertainties in methane (CH4) emissions by atmospheric inversion, particularly in data-sparse regions such as Africa. However, adding new observation sites does not yield linear improvements of emission uncertainties because overlapping transport sensitivities reduces marginal information gain. Here we develop a Bayesian framework to strategically optimize CH4 observation network design for column retrievals from upward-looking Fourier Transform Infrared (FTIR) spectrometers (e.g., EM27/SUN), jointly identifying the optimal number of sites and their spatial configuration. The framework quantifies uncertainty reduction for grid-point (1°) total and sectoral emissions while accounting for transport redundancy, cloud screening, and observational errors. Using January and July as representative months, we find that uncertainty reduction increases rapidly during early network expansion but gradually saturates beyond a certain number of additional sites. An optimized configuration of ten new sites added to the existing network achieves over 65 % reduction in prior uncertainty for total African CH4 emissions in both months, with comparable improvements across fire, wetland, and anthropogenic sectors. Sensitivity analyses indicate that while the optimal number of sites varies with assumptions about cloud filtering, the spatial configuration remains robust, supporting cost-effective observation network design in data-sparse regions.

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BibTeX RIS

BibTeX

@article{li2026strategic,
  title = {{Strategic design of methane observation networks to improve emission estimates: A case study in Africa}},
  author = {Hui Li and Philippe Ciais and Frédéric Chevallier and Bo Zheng and Paul Palmer and Frank Hase and Morgan Lopez and Elsa Ordway and Shushi Peng and Danielle Monteverde and Michel Ramonet and Jason St. Clair and Le Bienfaiteur Sagang and Benjamin Poulter},
  journal = {Atmospheric Chemistry and Physics},
  year = {2026},
  month = aug,
  doi = {10.5194/acp-26-11893-2026},
  url = {https://hal.science/hal-05734824v1},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - Strategic design of methane observation networks to improve emission estimates: A case study in Africa
AU  - Hui Li
AU  - Philippe Ciais
AU  - Frédéric Chevallier
AU  - Bo Zheng
AU  - Paul Palmer
AU  - Frank Hase
AU  - Morgan Lopez
AU  - Elsa Ordway
AU  - Shushi Peng
AU  - Danielle Monteverde
AU  - Michel Ramonet
AU  - Jason St. Clair
AU  - Le Bienfaiteur Sagang
AU  - Benjamin Poulter
PY  - 2026
DA  - 2026/08/21
JO  - Atmospheric Chemistry and Physics
DO  - 10.5194/acp-26-11893-2026
UR  - https://hal.science/hal-05734824v1
AB  - Ground-based and satellite atmospheric observations are essential for reducing uncertainties in methane (CH4) emissions by atmospheric inversion, particularly in data-sparse regions such as Africa. However, adding new observation sites does not yield linear improvements of emission uncertainties because overlapping transport sensitivities reduces marginal information gain. Here we develop a Bayesian framework to strategically optimize CH4 observation network design for column retrievals from upward-looking Fourier Transform Infrared (FTIR) spectrometers (e.g., EM27/SUN), jointly identifying the optimal number of sites and their spatial configuration. The framework quantifies uncertainty reduction for grid-point (1°) total and sectoral emissions while accounting for transport redundancy, cloud screening, and observational errors. Using January and July as representative months, we find that uncertainty reduction increases rapidly during early network expansion but gradually saturates beyond a certain number of additional sites. An optimized configuration of ten new sites added to the existing network achieves over 65 % reduction in prior uncertainty for total African CH4 emissions in both months, with comparable improvements across fire, wetland, and anthropogenic sectors. Sensitivity analyses indicate that while the optimal number of sites varies with assumptions about cloud filtering, the spatial configuration remains robust, supporting cost-effective observation network design in data-sparse regions.
SN  - 1680-7316
SN  - 1680-7324
ER  -

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