Submitted as: development and technical paper
05 Oct 2023
Submitted as: development and technical paper |  | 05 Oct 2023
Status: a revised version of this preprint is currently under review for the journal GMD.

Optimising Urban Measurement Networks for CO2 Flux Estimation: A High-Resolution Observing System Simulation Experiment using GRAMM/GRAL

Sanam N. Vardag and Robert Maiwald

Abstract. To design a monitoring network for estimating CO2 fluxes in an urban area, a high-resolution Observing System Simulation Experiment (OSSE) is performed using the transport model Graz Mesoscale Model (GRAMMv19.1) coupled to the Graz Lagrangian Model (GRALv19.1). First, a high-resolution anthropogenic emission inventory, which is considered as the truth serves as input to the model to simulate CO2 concentration in the urban atmosphere on 10 m horizontal resolution in a 12.3 km x 12.3 km domain centered in Heidelberg, Germany. By sampling the CO2 concentration at selected stations and feeding the measurements into a Bayesian inverse framework, CO2 fluxes on neighbourhood scale are estimated. Different configurations of possible measurement networks are tested to assess the precision of posterior CO2 fluxes. We determine the trade-off of between quality and quantity of sensors by comparing the information content for different set-ups. Decisions on investing in a larger number or more precise sensors can be based on this result. We further analyse optimal sensor locations for flux estimation using a Monte Carlo approach. We examine the benefit of additionally measuring carbon monoxide. We find that including CO as tracer in the inversion allows the disaggregation of different emission sectors such as traffic emissions. Finally, we quantify the benefit of introducing a temporal correlation into the prior emissions. The results of this study give implications for an optimal measurement network design for a city like Heidelberg. The study showcases the general usefulness of the developed inverse framework using GRAMM/GRAL for planning and evaluating measurement networks in an urban area.

Sanam N. Vardag and Robert Maiwald

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-192', Anonymous Referee #1, 22 Oct 2023
    • AC3: 'Reply on RC1', Sanam Noreen Vardag, 01 Dec 2023
  • CEC1: 'Executive editor comment on gmd-2023-192', Astrid Kerkweg, 23 Oct 2023
    • AC1: 'Reply on CEC1', Sanam Noreen Vardag, 24 Oct 2023
  • RC2: 'Comment on gmd-2023-192', Gerrit H. de Rooij, 17 Nov 2023
    • AC2: 'Reply on RC2', Sanam Noreen Vardag, 01 Dec 2023

Sanam N. Vardag and Robert Maiwald

Model code and software

Bayesian inversion R. Maiwald

Experiments R. Maiwald

Processing GRAMM/GRAL output R. Maiwald

Sanam N. Vardag and Robert Maiwald


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Short summary
We use the atmospheric transport model GRAMM/GRAL in an inversion to estimate urban CO2 emissions on 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 into the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.