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- Title
Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL.
- Authors
Vardag, Sanam Noreen; Maiwald, Robert
- Abstract
To design a monitoring network for estimating CO 2 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 CO 2 concentration in the urban atmosphere on 10 m horizontal resolution in a 12.3 km × 12.3 km domain centred in Heidelberg, Germany. By sampling the CO 2 concentration at selected stations and feeding the measurements into a Bayesian inverse framework, CO 2 fluxes on a neighbourhood scale are estimated. Different configurations of possible measurement networks are tested to assess the precision of posterior CO 2 fluxes. We determine the trade-off between the quality and quantity of sensors by comparing the information content for different set-ups. Decisions on investing in a larger number or in 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 (CO). We find that including CO as tracer in the inversion enables the disaggregation of different emission sectors. Finally, we quantify the benefit of introducing a temporal correlation into the prior emissions. The results of this study have implications for an optimal measurement network design for a city like Heidelberg. The study showcases the general usefulness of the inverse framework developed using GRAMM/GRAL for planning and evaluating measurement networks in an urban area.
- Subjects
HEIDELBERG (Germany); SIMULATION methods &; models; CARBON dioxide; CARBON monoxide; EMISSION inventories; BIOSPHERE
- Publication
Geoscientific Model Development, 2024, Vol 17, Issue 4, p1885
- ISSN
1991-959X
- Publication type
Article
- DOI
10.5194/gmd-17-1885-2024