Space-borne ozone (O₃) measurements have indicated consistent positive trends across the entire Asia–Pacific region despite the considerable reduction of NO_x since 2000s. The rate of increase in O₃ derived from lower free tropospheric column measurements was observed to be 0.21 ± 0.05 DU/decade during 2005–2018. Our space-borne-based diagnosis of the nonlinear photochemical formation regimes, NO_x-limited and NO_x-saturated, show that O₃ chemistry is undergoing a transitional process to the NO_x-limited regime throughout most of the Asian region. Nevertheless, NO_x-saturated conditions persist at present in and over eight major megacities. These NO_x-saturated conditions in megacities contribute to the increased O₃ due to NO_x reduction, which could also affect the enhanced O₃ concentrations throughout the Asia–Pacific region via long-range transport. This indicates that VOC limits along with NO_x reductions are needed in megacities in the short term to reduce O₃ levels. Moreover, NO_x saturation in major megacities will continue until 2025, according to the forecast emission scenarios from the Intergovernmental Panel on Climate Change (IPCC). These scenarios gradually shift nearly all cities to the NO_x-limited regime by 2050 with the exception of few cities under IPCC RCP8.5. Thus, continued reductions in NO_x will be a key factor in reducing O₃ in the long term.

Motivated by the need of obtaining a more accurate global ozone distribution in the upper troposphere and lower stratosphere (UTLS), we have investigated the use of a tropopause-based (TB) ozone climatology in ozone profile retrieval from the Ozone Monitoring Instrument (OMI). Due to the limited vertical ozone information in the UTLS region from OMI backscattered ultraviolet radiances, better climatological a priori information is important for improving ozone profile retrievals. We present the new TB climatology and evaluate the result of retrievals against previous work. The TB climatology is created using ozonesonde profiles from 1983 through 2008 extended with climatological ozone data above sonde burst altitude (~35 km) with the corresponding temperature profiles used to identify the thermal tropopause. The TB climatology consists of the mean states and 1σ standard deviations for every month for each 10° latitude band. Compared to the previous TB climatology by Wei et al. (2010), three additional processes are applied in deriving our climatology: (1) using a variable shifting offset to define the TB coordinate, (2) separating ozonesonde profiles into tropical and extratropical regimes based on a threshold of 14 km in the thermal tropopause height, and (3) merging with an existing climatology from 5–10 km above the tropopause. The first process changes the reference of profiles to a variable position between local and mean tropopause heights within ±5 km of the tropopause and to the mean tropopause elsewhere. The second helps to preserve characteristics of either tropical or extratropical ozone structures depending on tropopause height, especially in the subtropical region. The third improves the climatology above ozonesonde burst altitudes and in the stratosphere by using climatology derived from many more satellite observations of ozone profiles. With aid from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) tropopause height, the new climatology and retrieval can better represent the dynamical variability of ozone in the tropopause region. The new retrieval result demonstrates significant improvement of UTLS ozone, especially in the extratropical UTLS, when evaluated using ozonesonde measurements and the meteorological data. The use of TB climatology significantly enhances the spatial consistency and the statistical relationship between ozone and potential vorticity/tropopause height in the extratropical UTLS region. Comparisons with ozonesonde measurements show substantial improvements in both mean biases and their standard deviations over the extratropical lowermost stratosphere and upper troposphere. Overall, OMI retrievals with the TB climatology show improved ability in capturing ozone gradients across the tropopause found in tropical/extratropical ozonesonde measurements.

This paper verifies and corrects the Ozone Mapping and Profiler Suite (OMPS) nadir mapper (NM) level 1B v2.0 measurements with the aim of producing accurate ozone profile retrievals using an optimal-estimation-based inversion method to fit measurements in the spectral range 302.5–340 nm. The evaluation of available slit functions demonstrates that preflight-measured slit functions represent OMPS measurements well compared to derived Gaussian slit functions. Our initial OMPS fitting residuals contain significant wavelength and cross-track-dependent biases, resulting in serious cross-track striping errors in the tropospheric ozone retrievals. To eliminate the systematic component of the fitting residuals, we apply soft calibration to OMPS radiances. With the soft calibration the amplitude of fitting residuals decreases from ∼ 1 to 0.2 % over low and middle latitudes, and thereby the consistency of tropospheric ozone retrievals between OMPS and the Ozone Monitoring Instrument (OMI) is substantially improved. A common mode correction is also implemented for additional radiometric calibration; it improves retrievals especially at high latitudes where the amplitude of fitting residuals decreases by a factor of ∼ 2. We estimate the noise floor error of OMPS measurements from standard deviations of the fitting residuals. The derived error in the Huggins band ( ∼ 0.1 %) is twice the OMPS L1B measurement error. OMPS noise floor errors constrain our retrievals better, leading to improving information content of ozone and reducing fitting residuals. The final precision of the fitting residuals is less than 0.1 % in the low and middle latitudes, with ∼ 1 degrees of freedom for signal for the tropospheric ozone, meeting the general requirements for successful tropospheric ozone retrievals.

The continued interest in air pollution and stratospheric ozone variability has motivated the development of a Geostationary Environmental Monitoring Spectrometer (GEMS) for hourly ozone monitoring. This paper provides the atmospheric science community with the world's first assessment of GEMS total column ozone (TCO) retrieval performance and diurnal ozone variation. The algorithm used for GEMS is a more advanced version of its predecessor, the Total Ozone Mapping Spectrometer (TOMS) V8, that incorporates several improvements, including a new lookup table, a simple Lambertian-equivalent reflectivity model, and a spectral dependence correction. The GEMS algorithm also uses the optimal estimation method (OEM) to make error analysis more accessible and robust. The estimated retrieval errors range from 1.5 to 2 DU in September and 2 DU in December, with a constant degree of freedom of the signal (DFS) of 1 in September and a variable DFS of 1.25 to 1.4 in December throughout the day, depending on solar zenith angle (SZA). To assess the performance of the GEMS algorithm, the hourly GEMS total ozone was compared with ground-based measurements from Pandora instruments and other satellite platforms from TROPOMI (TROPOspheric Monitoring Instrument) and OMPS (Ozone Mapping and Profiler Suite Nadir Mapper). GEMS has a high correlation of 0.97 and small RMSE values compared to Pandora TCO at Busan and Seoul in South Korea. It is notable that despite exhibiting seasonal dependence in the mean bias of GEMS with Pandora, GEMS is capable of observing daily variations in ozone that are highly consistent with Pandora measurements, with a bias of approximately 1 %. The comparison of GEMS TCO data with TROPOMI and OMPS TCO data shows a high correlation of 0.99 and low RMSE compared to TROPOMI and OMPS TCO data, but the data have a negative bias of −2.38 % and −2.17 %, with standard deviations of 1.33 % and 1.57 %, respectively. Similar to OMPS, the influence of SO₂ from volcanic eruptions is not properly removed in some regions, leading to GEMS overestimating TCO in those areas. The mean biases of GEMS TCO data with TROPOMI and OMPS TCO are within ±1 % at low latitudes but become negative at midlatitudes, with an increasingly negative dependence on latitude. Furthermore, this dependence becomes more prominent from summer to winter. The empirical correction applied to the GEMS irradiance data improves the dependence of the mean bias on season and latitude, but a consistent bias still remains, and a marginal positive trend was observed in December. Therefore, further investigation into correction methods is needed. The results are a meaningful scientific advance by providing the first validated, hourly UV ozone retrievals from a satellite in geostationary orbit. This experience can be used to advance research with future geostationary environmental satellite missions, including the incoming TEMPO (Tropospheric Emissions: Monitoring of Pollution) and Sentinel-4.

Questions about how emissions are changing during the COVID-19 lockdown periods cannot be answered by observations of atmospheric trace gas concentrations alone, in part due to simultaneous changes in atmospheric transport, emissions, dynamics, photochemistry, and chemical feedback. A chemical transport model simulation benefiting from a multi-species inversion framework using well-characterized observations should differentiate those influences enabling to closely examine changes in emissions. Accordingly, we jointly constrain NO_x and VOC emissions using well-characterized TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO₂ columns during the months of March, April, and May 2020 (lockdown) and 2019 (baseline). We observe a noticeable decline in the magnitude of NO_x emissions in March 2020 (14 %–31 %) in several major cities including Paris, London, Madrid, and Milan, expanding further to Rome, Brussels, Frankfurt, Warsaw, Belgrade, Kyiv, and Moscow (34 %–51 %) in April. However, NO_x emissions remain at somewhat similar values or even higher in some portions of the UK, Poland, and Moscow in March 2020 compared to the baseline, possibly due to the timeline of restrictions. Comparisons against surface monitoring stations indicate that the constrained model underrepresents the reduction in surface NO₂. This underrepresentation correlates with the TROPOMI frequency impacted by cloudiness. During the month of April, when ample TROPOMI samples are present, the surface NO₂ reductions occurring in polluted areas are described fairly well by the model (model: −21 ± 17 %, observation: −29 ± 21 %). The observational constraint on VOC emissions is found to be generally weak except for lower latitudes. Results support an increase in surface ozone during the lockdown. In April, the constrained model features a reasonable agreement with maximum daily 8 h average (MDA8) ozone changes observed at the surface (r=0.43), specifically over central Europe where ozone enhancements prevail (model: +3.73 ± 3.94 %, +1.79 ppbv, observation: +7.35 ± 11.27 %, +3.76 ppbv). The model suggests that physical processes (dry deposition, advection, and diffusion) decrease MDA8 surface ozone in the same month on average by −4.83 ppbv, while ozone production rates dampened by largely negative $J_{{\mathrm{NO}}_{\mathrm{2}}}$ [NO₂]-$k_{\mathrm{NO}+{\mathrm{O}}_{\mathrm{3}}}$ [NO][O₃] become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology contributes to 42 % enhancement in MDA8 surface ozone over the same region with the remaining part (58 %) coming from changes in anthropogenic emissions. Results illustrate the capability of satellite data of major ozone precursors to help atmospheric models capture ozone changes induced by abrupt emission anomalies.

South Korea is planning to launch the GEMS (Geostationary Environment Monitoring Spectrometer) instrument into the GeoKOMPSAT (Geostationary Korea Multi-Purpose SATellite) platform in 2018 to monitor tropospheric air pollutants on an hourly basis over East Asia. GEMS will measure backscattered UV radiances covering the 300–500 nm wavelength range with a spectral resolution of 0.6 nm. The main objective of this study is to evaluate ozone profiles and stratospheric column ozone amounts retrieved from simulated GEMS measurements. Ozone Monitoring Instrument (OMI) Level 1B radiances, which have the spectral range 270–500 nm at spectral resolution of 0.42–0.63 nm, are used to simulate the GEMS radiances. An optimal estimation-based ozone profile algorithm is used to retrieve ozone profiles from simulated GEMS radiances. Firstly, we compare the retrieval characteristics (including averaging kernels, degrees of freedom for signal, and retrieval error) derived from the 270–330 nm (OMI) and 300–330 nm (GEMS) wavelength ranges. This comparison shows that the effect of not using measurements below 300 nm on retrieval characteristics in the troposphere is insignificant. However, the stratospheric ozone information in terms of DFS decreases greatly from OMI to GEMS, by a factor of ∼2. The number of the independent pieces of information available from GEMS measurements is estimated to 3 on average in the stratosphere, with associated retrieval errors of ~1% in stratospheric column ozone. The difference between OMI and GEMS retrieval characteristics is apparent for retrieving ozone layers above ~20 km, with a reduction in the sensitivity and an increase in the retrieval errors for GEMS. We further investigate whether GEMS can resolve the stratospheric ozone variation observed from high vertical resolution Earth Observing System (EOS) Microwave Limb Sounder (MLS). The differences in stratospheric ozone profiles between GEMS and MLS are comparable to those between OMI and MLS below ~3 hPa (~40 km), except with slightly larger biases and larger standard deviations by up to 5%. At pressure altitudes above ~3 hPa, GEMS retrievals show strong influence of a priori and large differences with MLS, which, however, can be sufficiently improved by using better a priori information. The GEMS-MLS differences show negative biases of less than 4% for stratospheric column ozone, with standard deviations of 1–3%, while OMI retrievals show similar agreements with MLS except for 1% smaller biases at middle and high latitudes. Based on the comparisons, we conclude that GEMS will measure tropospheric ozone and stratospheric ozone columns with accuracy comparable to that of OMI and ozone profiles with slightly worse performance than that of OMI below ~3 hPa.