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This study presents new glyoxal (CHOCHO) products from the Ozone Monitoring Instrument (OMI) by utilizing updated level 1B irradiance/radiance data (Collection 4) and an updated glyoxal retrieval algorithm. The adoption of Collection 4 contributes to the reduction of artificial signals in differential glyoxal slant column densities (dSCDs) and improved fitting root mean square, and the updated retrieval settings result in fewer negative values of glyoxal dSCDs over oceans and less noisy dSCDs in the South Atlantic Anomaly. On‐line calculations of air mass factors consider interactive physical processes between input parameters. To address persistent trends in glyoxal SCDs over the Pacific Ocean that remain despite these updates, a trend correction is implemented. We evaluate the updated OMI glyoxal products using inter‐comparisons with GOME‐2A/2B glyoxal products. OMI glyoxal products exhibit good spatial and temporal agreement with GOME‐2A/2B, with correlation coefficients of 0.75–0.78 globally and 0.84–0.85 over source regions. Small biases are observed in OMI glyoxal vertical column densities, ranging from −0.2 ± 5.7% to 9 ± 3% in low and high glyoxal conditions, respectively, against GOME‐2A/2B. These advancements contribute to the reliability and accuracy of OMI glyoxal products, enhancing their utility for atmospheric studies and enabling a 20‐year‐long data record suitable for climate studies. Plain Language Summary: This research introduces new glyoxal (CHOCHO) products obtained from a satellite instrument called the Ozone Monitoring Instrument (OMI). For new glyoxal products, we use the updated OMI irradiance/radiance data and update the way we analyze the radiance data. As a result, new OMI glyoxal products show the reduction of errors caused by the instrument and consider interactive physical processes between the surface and atmosphere. The new data matches well with similar data from other satellite instruments (GOME‐2A/B), showing that the improvements are effective. This better data set helps scientists study the air quality over a long period of time. Key Points: We improve Ozone Monitoring Instrument (OMI) glyoxal column measurements with the new radiance data (Collection 4) and updated retrieval algorithmThe new glyoxal measurements show the stable spectral fitting through OMI's lifetimeCompared to GOME‐2A/B, OMI glyoxal products show good spatial (temporal) correlation coefficients of 0.75–0.78 (0.84–0.85), respectively [ABSTRACT FROM AUTHOR]

The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 is now providing continuous daytime hourly observations of nitrogen dioxide (NO 2) columns over eastern Asia (5° S–45° N, 75–145° E) with 3.5 × 7.7 km 2 pixel resolution. These data provide unique information to improve understanding of the sources, chemistry, and transport of nitrogen oxides (NO x) with implications for atmospheric chemistry and air quality, but opportunities for direct validation are very limited. Here we correct the operational level-2 (L2) NO 2 vertical column densities (VCDs) from GEMS with a machine learning (ML) model to match the much sparser but more mature observations from the low Earth orbit TROPOspheric Monitoring Instrument (TROPOMI), preserving the data density of GEMS but making them consistent with TROPOMI. We first reprocess the GEMS and TROPOMI operational L2 products to use common prior vertical NO 2 profiles (shape factors) from the GEOS-Chem chemical transport model. This removes a major inconsistency between the two satellite products and greatly improves their agreement with ground-based Pandora NO 2 VCD data in source regions. We then apply the ML model to correct the remaining differences, Δ (GEMS–TROPOMI), using the GEMS NO 2 VCDs and retrieval parameters as predictor variables. We train the ML model with colocated GEMS and TROPOMI NO 2 VCDs, taking advantage of TROPOMI off-track viewing to cover the wide range of effective zenith angles (EZAs) observed by GEMS. The two most important predictor variables for Δ (GEMS–TROPOMI) are GEMS NO 2 VCD and EZA. The corrected GEMS product is unbiased relative to TROPOMI and shows a diurnal variation over source regions more consistent with Pandora than the operational product. [ABSTRACT FROM AUTHOR]

Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world's first geostationary-Earth-orbit (GEO) satellite instrument designed for air quality monitoring. This study describes improvements made to the GEMS aerosol retrieval (AERAOD) algorithm, including spectral binning, surface reflectance estimation, cloud masking, and post-processing, along with validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia. The adoption of spectral binning in the lookup table (LUT) approach reduces random errors and enhances the stability of satellite measurements. In addition, we introduced a new high-resolution database for surface reflectance estimation based on the minimum-reflectance method, which was adapted to the GEMS pixel resolution. Monthly background aerosol optical depth (BAOD) values were used to estimate hourly GEMS surface reflectance consistently. Advanced cloud-removal techniques have been implemented to significantly improve the effectiveness of cloud detection and enhance aerosol retrieval quality. An innovative post-processing correction method based on machine learning has been introduced to address artificial diurnal biases in aerosol optical depth (AOD) observations. In this study, we investigated selected aerosol events, highlighting the capability of GEMS in monitoring and providing insights into hourly aerosol optical properties during various atmospheric events. The performance of the GEMS AERAOD products was validated against the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data for the period from November 2021 to October 2022. GEMS AOD at 443 nm demonstrated a strong correlation with AERONET AOD at 443 nm (R = 0.792). However, it exhibited biased patterns, including the underestimation of high AOD values and overestimation of low-AOD conditions. Different aerosol types (highly absorbing fine aerosols, dust aerosols, and non-absorbing aerosols) exhibited distinct validation results. The retrievals of GEMS single-scattering albedo (SSA) at 443 nm agreed well with the AERONET SSA at 440 nm within reasonable error ranges, with variations observed among aerosol types. For GEMS AOD at 443 nm exceeding 0.4 (1.0), 42.76 % (56.61 %) and 67.25 % (85.70 %) of GEMS SSA data points fell within the ± 0.03 and ± 0.05 error bounds, respectively. Model-enforced post-processing correction improved GEMS AOD and SSA performance, thereby reducing the diurnal variation in the biases. The validation of the retrievals of GEMS aerosol layer height (ALH) against the CALIOP data demonstrates good agreement, with a mean bias of - 0.225 km and 55.29 % (71.70 %) of data points falling within ± 1 km (1.5 km). [ABSTRACT FROM AUTHOR]

Nitrogen oxide radicals (NOx≡NO+NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO 2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO 2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx , but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation in NO 2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations in NO 2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are 4 times higher in the daytime than at night, driving an accumulation of NO 2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO 2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with a negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO 2 column at 13:00–14:00 local time (LT). Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO 2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation in NO 2 reflects a dynamic balance between emission and chemical loss. [ABSTRACT FROM AUTHOR]

Quantifying the global bromine monoxide (BrO) budget is essential to understand ozone chemistry better. In particular, the tropospheric BrO budget has not been well characterized. Here, we retrieve nearly a decade (February 2012–July 2021) of stratospheric and tropospheric BrO vertical columns from the Ozone Mapping and Profiling Suite Nadir Mapper (OMPS-NM) on board the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite. In quantifying tropospheric BrO enhancements from total slant columns, the key aspects involve segregating them from stratospheric enhancements and applying appropriate air mass factors. To address this concern and improve upon the existing methods, our study proposes an approach that applies distinct BrO vertical profiles based on the presence or absence of tropospheric BrO enhancement at each pixel, identifying it dynamically using a satellite-derived stratospheric-ozone–BrO relationship. We demonstrate good agreement for both stratosphere (r = 0.81–0.83) and troposphere (r = 0.50–0.70) by comparing monthly mean BrO vertical columns from OMPS-NM with ground-based observations from three stations (Lauder, Utqiaġvik, and Harestua). Although algorithm performance is primarily assessed at high latitudes, the OMPS-NM BrO retrievals successfully capture tropospheric enhancements not only in polar regions but also in extrapolar areas, such as the Rann of Kutch and the Great Salt Lake. We also estimate random uncertainties in the retrievals pixel by pixel, which can assist in quantitative applications of the OMPS-NM BrO dataset. Our BrO retrieval algorithm is designed for cross-sensor applications and can be adapted to other space-borne ultraviolet spectrometers, contributing to the creation of continuous long-term satellite BrO observation records. [ABSTRACT FROM AUTHOR]

The Geostationary Environment Monitoring Spectrometer (GEMS) is the first geostationary earth orbit (GEO) environmental instrument, onboard the Geostationary Korea Multi-Purpose Satellite–2B (GEO-KOMPSAT-2B) launched on 19 February 2020, and is measuring reflected radiance from the earth's surface and atmosphere system in the range of 300–500 nm in the ultraviolet–visible (UV–Vis) region. The radiometric response of a satellite sensor that measures the UV–Vis wavelength region can depend on the polarization states of the incoming light. To reduce the sensitivity due to polarization, many current low earth orbit (LEO) satellites are equipped with a scrambler to depolarize the signals or a polarization measurement device (PMD) that simultaneously measures the polarization state of the atmosphere, then utilizes it for a polarization correction. However, a novel polarization correction algorithm is required since GEMS does not have a scrambler or a PMD. Therefore, this study aims to improve the radiometric accuracy of GEMS by developing a polarization correction algorithm optimized for GEMS that simultaneously considers the atmosphere's polarization state and the instrument's polarization sensitivity characteristics. The polarization factor and axis were derived by the preflight test on the ground as a function of wavelengths, showing a polarization sensitivity of more than 2 % at some specific wavelengths. The polarization states of the atmosphere are configured as a look-up table (LUT) using the Vector Linearized Discrete Ordinate Radiative-Transfer model (VLIDORT). Depending on the observation geometry and atmospheric conditions, the observed radiance spectrum can include a polarization error of 2 %. The performance of the proposed GEMS polarization algorithm was assessed using synthetic data, and the errors due to polarization were found to be larger in clear regions than in cloudy regions. After the polarization correction, polarization errors were reduced close to zero for almost all wavelengths, including the wavelength regions with high peaks and curvatures in the GEMS polarization factor, which sufficiently demonstrates the effectiveness of the proposed polarization correction algorithm. From the actual observation data after the launch of GEMS, the diurnal variation for the spatial distribution of polarization error was confirmed to be minimum at noon and maximum at sunrise/sunset. This can be used to improve the quality of GEMS measurements, the first geostationary environmental satellite, and then contribute to the retrieved accuracy of various Level-2 products, such as trace gases and aerosols in the atmosphere. [ABSTRACT FROM AUTHOR]

Nitrogen oxide radicals (NOx ≡ NO + NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx,but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation of NO2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations of NO2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are four times higher in the daytime than at night, driving an accumulation of NO2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO2 column at 13–14 local time. Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation of NO2 reflects a dynamic balance between emission and chemical loss. [ABSTRACT FROM AUTHOR]

Satellite retrievals of tropospheric-column formaldehyde (HCHO) and nitrogen dioxide (NO 2) are frequently used to investigate the sensitivity of ozone (O 3) production to emissions of nitrogen oxides and volatile organic carbon compounds. This study inter-compared the systematic biases and uncertainties in retrievals of NO 2 and HCHO, as well as resulting HCHO–NO 2 ratios (FNRs), from two commonly applied satellite sensors to investigate O 3 production sensitivities (Ozone Monitoring Instrument, OMI, and TROPOspheric Monitoring Instrument, TROPOMI) using airborne remote-sensing data taken during the Long Island Sound Tropospheric Ozone Study 2018 between 25 June and 6 September 2018. Compared to aircraft-based HCHO and NO 2 observations, the accuracy of OMI and TROPOMI were magnitude-dependent with high biases in clean environments and a tendency towards more accurate comparisons to even low biases in moderately polluted to polluted regions. OMI and TROPOMI NO 2 systematic biases were similar in magnitude (normalized median bias, NMB = 5 %–6 %; linear regression slope ≈ 0.5–0.6), with OMI having a high median bias and TROPOMI resulting in small low biases. Campaign-averaged uncertainties in the three satellite retrievals (NASA OMI; Quality Assurance for Essential Climate Variables, QA4ECV OMI; and TROPOMI) of NO 2 were generally similar, with TROPOMI retrievals having slightly less spread in the data compared to OMI. The three satellite products differed more when evaluating HCHO retrievals. Campaign-averaged tropospheric HCHO retrievals all had linear regression slopes ∼0.5 and NMBs of 39 %, 17 %, 13 %, and 23 % for NASA OMI, QA4ECV OMI, and TROPOMI at finer (0.05∘×0.05∘) and coarser (0.15∘×0.15∘) spatial resolution, respectively. Campaign-averaged uncertainty values (root mean square error, RMSE) in NASA and QA4ECV OMI HCHO retrievals were ∼9.0×1015 molecules cm -2 (∼ 50 %–55 % of mean column abundance), and the higher-spatial-resolution retrievals from TROPOMI resulted in RMSE values ∼30 % lower. Spatially averaging TROPOMI tropospheric-column HCHO, along with NO 2 and FNRs, to resolutions similar to the OMI reduced the uncertainty in these retrievals. Systematic biases in OMI and TROPOMI NO 2 and HCHO retrievals tended to cancel out, resulting in all three satellite products comparing well to observed FNRs. However, while satellite-derived FNRs had minimal campaign-averaged median biases, unresolved errors in the indicator species did not cancel out in FNR calculations, resulting in large RMSE values compared to observations. Uncertainties in HCHO retrievals were determined to drive the unresolved biases in FNR retrievals. [ABSTRACT FROM AUTHOR]

Nitrogen oxides (NO x≡ NO + NO 2) are of central importance for air quality, climate forcing, and nitrogen deposition to ecosystems. The Geostationary Environment Monitoring Spectrometer (GEMS) is now providing hourly NO 2 satellite observations over East Asia, offering the first direct measurements of NO 2 diurnal variation from space to guide understanding of NO x emissions and chemistry. The NO 2 retrieval requires independent vertical profile information from a chemical transport model (CTM) to compute the air mass factor (AMF) that relates the NO 2 column measured along the line of sight to the NO 2 vertical column. Here, we use aircraft observations from the Korea-United States Air Quality (KORUS-AQ) campaign over the Seoul metropolitan area (SMA) and around the Korean Peninsula in May–June 2016 to better understand the factors controlling the NO 2 vertical profile, its diurnal variation, the implications for the AMFs, and the ability of the GEOS-Chem CTM to compute the NO 2 vertical profiles used for AMFs. Proper representation of oxidant chemistry is critical for the CTM simulation of NO 2 vertical profiles and is achieved in GEOS-Chem through new model developments, including aerosol nitrate photolysis, reduced uptake of hydroperoxy (HO 2) radicals by aerosols, and accounting for atmospheric oxidation of volatile chemical products (VCPs). We find that the tropospheric NO 2 columns measured from space in the SMA are mainly contributed by the planetary boundary layer (PBL) below 2 km altitude, reflecting the highly polluted conditions. Repeated measurements of NO 2 vertical profiles over the SMA at different times of day show that diurnal change in mixing depth affecting the NO 2 vertical profile induces a diurnal variation in AMFs of comparable magnitude to the diurnal variation in the NO 2 column. GEOS-Chem captures this diurnal variation in AMFs and more generally the variability in the AMFs for the KORUS-AQ NO 2 vertical profiles (2.7 % mean bias, 7.6 % precision), with some outliers in the morning due to errors in the timing of mixed-layer growth. [ABSTRACT FROM AUTHOR]

Tropospheric nitrogen dioxide (NO2) is of central importance for air quality, climate forcing, and nitrogen deposition to ecosystems. The Geostationary Environment Monitoring Spectrometer (GEMS) is now providing high-density NO2 satellite data including diurnal variation over East Asia. The NO2 retrieval requires independent vertical profile information from a chemical transport model (CTM) to compute the air mass factor (AMF) that relates the NO2 column along the line of sight to the NO2 vertical column. Here, we use aircraft observations from the Korea-United States Air Quality (KORUS-AQ) campaign over the Seoul Metropolitan Area (SMA) and around the Korean peninsula to better understand the factors controlling the NO2 vertical profile, its diurnal variation, the implications for the AMF, and the ability of the GEOS-Chem CTM to compute the AMF and its variability. Proper representation of oxidant chemistry is critical for the CTM simulation of NO2 vertical profiles and is achieved in GEOS-Chem through new model developments including aerosol nitrate photolysis, reduced uptake of hydroperoxy (HO2) radicals by aerosols, and accounting for atmospheric oxidation of volatile chemical products (VCPs). We find that the tropospheric NO2 columns measured from space are mainly contributed by the planetary boundary layer (PBL) below 2 km altitude, reflecting the highly polluted conditions. Repeated measurements of NO2 vertical profiles over SMA at different times of day show that diurnal change in mixing depth affecting the NO2 vertical profile induces a diurnal variation in AMF of comparable magnitude to the diurnal variation in the NO2 column. GEOS-Chem captures this diurnal variation in AMF and more generally the variability in the AMF for the KORUS-AQ NO2 vertical profiles (2.7 % mean bias, 7.6 % precision), with some outliers in the morning due to non-systematic errors in the timing of mixed layer growth. [ABSTRACT FROM AUTHOR]

Vertical column density (VCD) of nitrogen dioxide (NO2) was measured using Pandora spectrometers at six sites on the Korean Peninsula during the Megacity Air Pollution Studies-Seoul (MAPS-Seoul) campaign from May to June 2015. To estimate the tropospheric NO 2 VCD, the stratospheric NO 2 VCD from the Ozone Monitoring Instrument (OMI) was subtracted from the total NO 2 VCD from Pandora. European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis wind data was used to analyze variations in tropospheric NO 2 VCD caused by wind patterns at each site. The Yonsei/SEO site was found to have the largest tropospheric NO 2 VCD (1.49 DU on average) from a statistical analysis of hourly tropospheric NO 2 VCD measurements. At rural sites, remarkably low NO 2 VCDs were observed. However, a wind field analysis showed that trans-boundary transport and emissions from domestic sources lead to an increase in tropospheric NO 2 VCD at NIER/BYI and KMA/AMY, respectively. At urban sites, high NO 2 VCD values were observed under conditions of low wind speed, which were influenced by local urban emissions. Tropospheric NO 2 VCD at HUFS/Yongin increases under conditions of significant transport from urban area of Seoul according to a correlation analysis that considers the transport time lag. Significant diurnal variations were found at urban sites during the MAPS-Seoul campaign, but not at rural sites, indicating that it is associated with diurnal patterns of NO 2 emissions from dense traffic.