Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 4
Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1885-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1885-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 4
Development and technical paper
 | 
01 Mar 2024
Development and technical paper |  | 01 Mar 2024

Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL

Sanam Noreen Vardag and Robert Maiwald

Data sets

Optimising Urban Measurement Networks for CO2 Flux Estimation: A High-Resolution Observing System Simulation Experiment using GRAMM/GRAL [data] S. N. Vardag and R. Maiwald https://doi.org/10.11588/data/NHIVDO

Model code and software

ATMO-IUP-UHEI/BayesInverse: V.1.1 release of BayesInverse (v.1.1) R. Maiwald and C. Lüken-Winkels https://doi.org/10.5281/zenodo.8354902

Processing GRAMM/GRAL output R. Maiwald https://doi.org/10.5281/zenodo.8375169

ATMO-IUP-UHEI/Experiments: OSSE experiments with GRAMM-GRAL (v1.0.0) R. Maiwald https://doi.org/10.5281/zenodo.8370230

Download
Short summary
We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
Read more