Assessing the impact of atmospheric CO2 and NO2 measurements from space on estimating city-scale fossil fuel CO2 emissions in a data assimilation system
article
The European Copernicus programme plans to install a constellation of multiple polar
orbiting satellites (Copernicus Anthropogenic CO2 Monitoring Mission, CO2M mission) for
observing atmospheric CO2 content with the aim to estimate fossil fuel CO2 emissions. We
explore the impact of potential CO2M observations of column-averaged CO2 (XCO2),
nitrogen dioxide (NO2), and aerosols in a 200 × 200 km2 domain around Berlin. For the
quantification of anticipated XCO2 random and systematic errors we developed and
applied new error parameterisation formulae based on artificial neural networks. For the
interpretation of these data, we further established a CCFFDAS modelling chain from
parameters of emission models to XCO2 and NO2 observations to simulate the 24 h
periods preceeding simulated CO2M overpasses over the study area. For one overpass in
winter and one in summer, we present a number of assessments of observation impact in
terms of the posterior uncertainty in fossil fuel emissions on scales ranging from 2 to
200 km. This means the assessments include temporal and spatial scales typically not
covered by inventories. The assessments differentiate the fossil fuel CO2 emissions into
two sectors, an energy generation sector (power plants) and the complement, which we
call “other sector.” We find that combined measurements of XCO2 and aerosols provide a
powerful constraint on emissions from larger power plants; the uncertainty in fossil fuel
emissions from the largest three power plants in the domain was reduced by 60%–90%
after assimilating the observations. Likewise, these measurements achieve an uncertainty
reduction for the other sector that increases when aggregated to larger spatial scales.
When aggregated over Berlin the uncertainty reduction for the other sector varies between
28% and 48%. Our assessments show a considerable contribution of aerosol
observations onboard CO2M to the constraint of the XCO2 measurements on emissions from all power plants and for the other sector on all spatial scales. NO2
measurements onboard CO2M provide a powerful additional constraint on the
emissions from power plants and from the other sector. We further apply a Jacobian
representation of the CCFFDAS modelling chain to decompose a simulated CO2 column in
terms of spatial emission impact. This analysis reveals the complex structure of the
footprint of an observed CO2 column, which indicates the limits of simple mass balances
approaches for interpretation of such observations.
orbiting satellites (Copernicus Anthropogenic CO2 Monitoring Mission, CO2M mission) for
observing atmospheric CO2 content with the aim to estimate fossil fuel CO2 emissions. We
explore the impact of potential CO2M observations of column-averaged CO2 (XCO2),
nitrogen dioxide (NO2), and aerosols in a 200 × 200 km2 domain around Berlin. For the
quantification of anticipated XCO2 random and systematic errors we developed and
applied new error parameterisation formulae based on artificial neural networks. For the
interpretation of these data, we further established a CCFFDAS modelling chain from
parameters of emission models to XCO2 and NO2 observations to simulate the 24 h
periods preceeding simulated CO2M overpasses over the study area. For one overpass in
winter and one in summer, we present a number of assessments of observation impact in
terms of the posterior uncertainty in fossil fuel emissions on scales ranging from 2 to
200 km. This means the assessments include temporal and spatial scales typically not
covered by inventories. The assessments differentiate the fossil fuel CO2 emissions into
two sectors, an energy generation sector (power plants) and the complement, which we
call “other sector.” We find that combined measurements of XCO2 and aerosols provide a
powerful constraint on emissions from larger power plants; the uncertainty in fossil fuel
emissions from the largest three power plants in the domain was reduced by 60%–90%
after assimilating the observations. Likewise, these measurements achieve an uncertainty
reduction for the other sector that increases when aggregated to larger spatial scales.
When aggregated over Berlin the uncertainty reduction for the other sector varies between
28% and 48%. Our assessments show a considerable contribution of aerosol
observations onboard CO2M to the constraint of the XCO2 measurements on emissions from all power plants and for the other sector on all spatial scales. NO2
measurements onboard CO2M provide a powerful additional constraint on the
emissions from power plants and from the other sector. We further apply a Jacobian
representation of the CCFFDAS modelling chain to decompose a simulated CO2 column in
terms of spatial emission impact. This analysis reveals the complex structure of the
footprint of an observed CO2 column, which indicates the limits of simple mass balances
approaches for interpretation of such observations.
Topics
TNO Identifier
973477
Source
Frontiers in Remote Sensing, pp. 1-21.
Pages
1-21