Recovery of sparse urban greenhouse gas emissions

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

doi:https://doi.org/10.5194/gmd-15-7533-2022

Articles | Volume 15, issue 20

https://doi.org/10.5194/gmd-15-7533-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-15-7533-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 20

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

Supplement

https://doi.org/10.5194/gmd-15-7533-2022-supplement

Data sets

Gaussian plume footprints Benjamin Zanger, Jia Chen, Man Sun, and Florian Dietrich https://doi.org/10.5281/zenodo.5901298

Model code and software

tum-esm/recovery_of_sparse_urban_greeenhouse_gas_emissions: v0.3.0 Benjamin Zanger, Jia Chen, Man Sun, and Florian Dietrich https://doi.org/10.5281/zenodo.6757562

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.