Articles | Volume 15, issue 20
https://doi.org/10.5194/gmd-15-7533-2022
https://doi.org/10.5194/gmd-15-7533-2022
Methods for assessment of models
 | 
17 Oct 2022
Methods for assessment of models |  | 17 Oct 2022

Recovery of sparse urban greenhouse gas emissions

Benjamin Zanger, Jia Chen, Man Sun, and Florian Dietrich

Related authors

Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich
Stavros Stagakis, Dominik Brunner, Junwei Li, Leif Backman, Anni Karvonen, Lionel Constantin, Leena Järvi, Minttu Havu, Jia Chen, Sophie Emberger, and Liisa Kulmala
Biogeosciences, 22, 2133–2161, https://doi.org/10.5194/bg-22-2133-2025,https://doi.org/10.5194/bg-22-2133-2025, 2025
Short summary
DRIVE v1.0: A data-driven framework to estimate road transport emissions and temporal profiles
Daniel Kühbacher, Jia Chen, Patrick Aigner, Mario Ilic, Ingrid Super, and Hugo Denier van der Gon
EGUsphere, https://doi.org/10.5194/egusphere-2025-753,https://doi.org/10.5194/egusphere-2025-753, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Building-resolving simulations of anthropogenic and biospheric CO2 in the city of Zurich with GRAMM/GRAL
Dominik Brunner, Ivo Suter, Leonie Bernet, Lionel Constantin, Stuart K. Grange, Pascal Rubli, Junwei Li, Jia Chen, Alessandro Bigi, and Lukas Emmenegger
EGUsphere, https://doi.org/10.5194/egusphere-2025-640,https://doi.org/10.5194/egusphere-2025-640, 2025
Short summary
Greenhouse gas column observations from a portable spectrometer in Uganda
Neil Humpage, Hartmut Boesch, William Okello, Jia Chen, Florian Dietrich, Mark F. Lunt, Liang Feng, Paul I. Palmer, and Frank Hase
Atmos. Meas. Tech., 17, 5679–5707, https://doi.org/10.5194/amt-17-5679-2024,https://doi.org/10.5194/amt-17-5679-2024, 2024
Short summary
Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors
Ayah Abu-Hani, Jia Chen, Vigneshkumar Balamurugan, Adrian Wenzel, and Alessandro Bigi
Atmos. Meas. Tech., 17, 3917–3931, https://doi.org/10.5194/amt-17-3917-2024,https://doi.org/10.5194/amt-17-3917-2024, 2024
Short summary

Related subject area

Atmospheric sciences
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025,https://doi.org/10.5194/gmd-18-3065-2025, 2025
Short summary
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025,https://doi.org/10.5194/gmd-18-2983-2025, 2025
Short summary
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025,https://doi.org/10.5194/gmd-18-2861-2025, 2025
Short summary
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025,https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025,https://doi.org/10.5194/gmd-18-2701-2025, 2025
Short summary

Cited articles

Baraniuk, R., Davenport, M., DeVore, R., and Wakin, M.: A simple proof of the restricted isometry property for random matrices, Constructive Approximation, 28, 253–263, https://doi.org/10.1007/s00365-007-9003-x, 2008. a
Boche, H., Calderbank, R., Kutyniok, G., and Vybíral, J.: A survey of compressed sensing, in: Compressed sensing and its applications, 1–39, Springer, 2015. a
Candès, E. J.: The restricted isometry property and its implications for compressed sensing, Comptes Rendus Mathematique, 346, 589–592, https://doi.org/10.1016/j.crma.2008.03.014, 2008. a, b
Candès, E. J. and Tao, T.: Decoding by linear programming, IEEE T. Inform. Theory, 51, 4203–4215, https://doi.org/10.1109/TIT.2005.858979, 2005. a
Candès, E. J., Romberg, J. K., and Tao, T.: Stable signal recovery from incomplete and inaccurate measurements, Commun. Pure Appl. Math., 59, 1207–1223, https://doi.org/10.1002/cpa.20124, 2006. a
Download
Short summary
Gaussian priors (GPs) used in least squares inversion do not reflect the true distributions of greenhouse gas emissions well. A method that does not rely on GPs is sparse reconstruction (SR). We show that necessary conditions for SR are satisfied for cities and that the application of a wavelet transform can further enhance sparsity. We apply the theory of compressed sensing to SR. Our results show that SR needs fewer measurements and is superior for assessing unknown emitters compared to GPs.
Share