Improved monitoring of surface ozone by joint assimilation of geostationary satellite observations of ozone and CO

Future geostationary satellite observations of tropospheric ozone aim to improve monitoring of surface ozone air quality. However, ozone retrievals from space have limited sensitivity in the lower troposphere (boundary layer). Data assimilation in a chemical transport model can propagate the information from the satellite observations to provide useful constraints on surface ozone. This may be aided by correlated satellite observations of carbon monoxide (CO), for which boundary layer sensitivity is easier to achieve. We examine the potential of concurrent geostationary observations of ozone and CO to improve constraints on surface ozone air quality through exploitation of ozoneeCO model error correlations in a joint data assimilation framework. The hypothesis is that model transport errors diagnosed for CO provide information on corresponding errors in ozone. A paired-model analysis of ozoneeCO error correlations in the boundary layer over North America in summer indicates positive error correlations in continental outflow but negative regional-scale error correlations over land, the latter reflecting opposite sensitivities of ozone and CO to boundary layer depth. Aircraft observations from the ICARTT campaign are consistent with this pattern but also indicate strong positive error correlations in fine-scale pollution plumes. We develop a joint ozoneeCO data assimilation system and apply it to a regional-scale Observing System Simulation Experiment (OSSE) of the planned NASA GEO-CAPE geostationary mission over North America. We find substantial benefit from joint ozoneeCO data assimilation in informing US ozone air quality if the instrument sensitivity for CO in the boundary layer is greater than that for ozone. A high-quality geostationary measurement of CO could potentially relax the requirements for boundary layer sensitivity of the ozone measurement. This is contingent on accurate characterization of ozoneeCO error correlations. A finer-resolution data assimilation system resolving the urban scale would need to account for the change in sign of the ozoneeCO error correlations between urban pollution plumes and the regional atmosphere.