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

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Latest update: 14 Jul 2024
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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.