We use the GEOS‐Chem four‐dimensional variational data assimilation system to estimate emissions of carbon monoxide (CO) and nitrogen oxides (NO_x) in November 2009 and July 2010. We assimilated CO retrievals from The Measurements of Pollution In The Troposphere (MOPITT), ozone (O₃) retrievals from the Tropospheric Emission Spectrometer (TES) and the Optical Spectrograph and InfraRed Imager System (OSIRIS), and NO₂ columns from the Ozone Monitoring Instrument (OMI). By assimilating data for multiple chemical species with the four‐dimensional variational scheme we obtain a consistent chemical state over the assimilation period. The modeled O₃ bias was reduced to less than 3.5 ppbv everywhere, relative to independent aircraft O₃ data, except in the high‐latitude upper troposphere and lower stratosphere. We found that in November, the CO and NO_x emission estimates obtained with the multiple species assimilation were generally comparable to those inferred from assimilating only MOPITT or OMI data, respectively, whereas in July the differences were larger. For the main anthropogenic source regions, the inferred North American and European emissions responded most strongly to the multispecies information in the assimilation. In July, North American and European CO emission estimates differed by 31% and 65% relative to the MOPITT‐only estimates, respectively. We also found large differences for the North American and European NO_x emission estimates, which differed by 27% and 16% relative to the OMI‐only estimates, respectively. Our results highlight the potential benefit of exploiting the additional constraints offered by multispecies chemical data assimilation.

We examine data assimilation coupling between meteorology and chemistry in the stratosphere from both weak and strong coupling strategies. The study was performed with the Canadian operational weather prediction Global Environmental Multiscale (GEM) model coupled online with the photochemical stratospheric chemistry model developed at the Belgian Institute for Space Aeronomy, described in Part I. Here, the Canadian Meteorological Centre’s operational variational assimilation system was extended to include errors of chemical variables and cross-covariances between meteorological and chemical variables in a 3D-Var configuration, and we added the adjoint of tracer advection in the 4D-Var configuration. Our results show that the assimilation of limb sounding observations from the MIPAS instrument on board Envisat can be used to anchor the AMSU-A radiance bias correction scheme. Additionally, the added value of limb sounding temperature observations on meteorology and transport is shown to be significant. Weak coupling data assimilation with ozone–radiation interaction is shown to give comparable results on meteorology whether a simplified linearized or comprehensive ozone chemistry scheme is used. Strong coupling data assimilation, using static error cross-covariances between ozone and temperature in a 3D-Var context, produced inconclusive results with the approximations we used. We have also conducted the assimilation of long-lived species observations using 4D-Var to infer winds. Our results showed the added value of assimilating several long-lived species, and an improvement in the zonal wind in the Tropics within the troposphere and lower stratosphere. 4D-Var assimilation also induced a correction of zonal wind in the surf zone and a temperature bias in the lower tropical stratosphere.

A coupled stratospheric chemistry–meteorology model was developed by combining the Canadian operational weather prediction model Global Environmental Multiscale (GEM) with a comprehensive stratospheric photochemistry model from the Belgian Assimilation System for Chemical ObsErvations (BASCOE). The coupled model was called GEM-BACH for GEM-Belgian Atmospheric CHemistry. The coupling was made across a chemical interface that preserves time-splitting while being modular, allowing GEM to run with or without chemistry. An evaluation of the coupling was performed by comparing the coupled model, refreshed by meteorological analyses every 6 h, against the standard offline chemical transport model (CTM) approach. Results show that the dynamical meteorological consistency between meteorological analysis times far outweighs the error created by the jump resulting from the meteorological analysis increments at regular time intervals, irrespective of whether a 3D-Var or 4D-Var meteorological analysis is used. Arguments in favor of using the same horizontal resolution for chemistry, meteorology, and meteorological and chemical analysis increments are also presented. GEM-BACH forecasts refreshed by meteorological analyses every 6 h were compared against independent measurements of temperature, long-lived species, ozone and water vapor. The comparison showed a relatively good agreement throughout the stratosphere except for an upper-level warm temperature bias and an ozone deficit of nearly 15%. In particular, the coupled model simulation during an ozone hole event gives better ozone concentrations than a 4D-Var chemical assimilation at a lower resolution.

Bias estimations and corrections of total column measurements are applied and evaluated with ozone data from satellite instruments providing near-real-time products during summer 2014 and 2015 and winter 2015. The developed standalone bias-correction system can be applied in near-real-time chemical data assimilation and long-term reanalysis. The instruments to which these bias corrections were applied include the Global Ozone Monitoring Experiment-2 instruments on the MetOp-A and MetOp-B satellites (GOME-2A and GOME-2B), the total column ozone mapping instrument of the Ozone Mapping Profiler Suite (OMPS-NM) on the Suomi National Polar-orbiting Partnership (S-NPP) satellite, and the Ozone Monitoring Instrument (OMI) instrument on the Aura research satellite. The OMI data set based on the TOMS version 8.5 retrieval algorithm was chosen as the reference used in the bias correction of the other satellite-based total column ozone data sets. OMI data were chosen for this purpose instead of ground-based observations due to OMI's significantly better spatial and temporal coverage, as well as interest in near-real-time assimilation. Ground-based Brewer and Dobson spectrophotometers, and filter ozonometers, as well as the Solar Backscatter Ultraviolet satellite instrument (SBUV/2), served as independent validation sources of total column ozone data. Regional and global mean differences of the OMI-TOMS data with measurements from the three ground-based instrument types for the three evaluated 2-month periods were found to be within 1 %, except for the polar regions, where the largest differences from the comparatively small data set in Antarctica exceeded 3 %. Values from SBUV/2 summed partial columns were typically larger than OMI-TOMS on average by 0.6 % to 1.2 %, with smaller differences than with ground-based observations over Antarctica. Bias corrections as a function of latitude and solar zenith angle were performed for GOME-2A/B and OMPS-NM using colocation with OMI-TOMS and three variants of differences with short-term model forecasts. These approaches were shown to yield residual biases of less than 1 %, with the rare exceptions associated with bins with less data. These results were compared to a time-independent bias-correction estimation that used colocations as a function of ozone effective temperature and solar zenith angle which, for the time period examined, resulted in larger residual biases for bins whose bias varies more in time. The impact of assimilating total column ozone data from single and multiple satellite data sources with and without bias correction was examined with a version of the Environment and Climate Change Canada variational assimilation and forecasting system. Assimilation experiments for July–August 2014 show a reduction of global mean biases for short-term forecasts relative to ground-based Brewer and Dobson observations from a maximum of about 2.3 % in the absence of bias correction to less than 0.3 % in size when bias correction is included. Both temporally averaged and time-varying mean differences of forecasts with OMI-TOMS were reduced to within 1 % for nearly all cases when bias-corrected observations are assimilated for the latitudes where satellite data are present.

This paper is about the retrieval of horizontal wind and ozone number density from measurement simulations for the Stratospheric Wind Interferometer for Transport Studies (SWIFT). This instrument relies on the concept of imaging Doppler Michelson interferometry applied to thermal infrared emission originating from the stratosphere. The instrument and measurement simulations are described in detail in the first of this series of two papers. In this second paper, a summary of the measurement simulations and a data retrieval method suited to these measurements are first presented. The inversion method consists of the maximum a posteriori solution approach with added differential regularization and, when required, iterations performed with the Gauss–Newton method. Inversion characterization and an error analysis have been performed. Retrieval noise estimates have been obtained both from derived covariance matrices and sample inversions. Retrieval noise levels for wind and ozone number density of ∼1–3 m s−1 and <1% have been obtained over the altitude range of 20–45 km with Backus–Gilbert resolving lengths of ∼1.5 km. Retrieval noise levels over the extended altitude range of 15–55 km are less than 10 m s−1 and 2%. The sensitivity to other error sources has been examined through a few sample realizations. The contributions from these other errors can be as important as or more so than retrieval noise. An error budget identifying contributing wind and ozone error levels to total errors of 5 m s−1 and 5% for altitudes of 20–45 km has been prepared relying on the retrieval errors and knowledge of the instrument design.