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Ozone pollution is secondarily produced through a complex, non-linear chemical process. Our understanding of the spatiotemporal variations in photochemically produced ozone (i.e., PO3) is limited to sparse aircraft campaigns and chemical transport models, which often carry significant biases. Hence, we present a novel satellite-derived PO3 product informed by bias-corrected TROPOspheric Monitoring Instrument (TROPOMI) HCHO, NO2, surface albedo data, and various models. These data are integrated into a parameterization that relies on HCHO, NO2, HCHO / NO2, j NO2, and j O1D. Despite its simplicity, it can reproduce ∼ 90 % of the variance in observationally constrained PO3, with minimal biases in moderately to highly polluted regions. We map PO3 across various regions with respect to July 2019 at a 0.1° × 0.1° spatial resolution, revealing accelerated values (> 8 ppbv h−1) for numerous cities throughout Asia and the Middle East, resulting from elevated ozone precursors and enhanced photochemistry. In Europe and the United States, such high levels are only detected over Benelux, Los Angeles, and New York City. PO3 maxima are observed in various seasons and are attributed to changes in photolysis rates, non-linear ozone chemistry, and fluctuations in HCHO and NO2. Satellite errors result in moderate errors (10 %–20 %) in PO3 estimates over cities on a monthly average basis, while these errors exceed 50 % in clean areas and under low light conditions. Using the current algorithm, we demonstrate that satellite data can provide valuable information for robust PO3 estimation. This capability expands future research through the application of data to address significant scientific questions about locally produced ozone hotspots, seasonality, and long-term trends. [ABSTRACT FROM AUTHOR]

Formaldehyde (HCHO) in the atmosphere is an intermediate product from the oxidation of methane and non-methane volatile organic compounds. In remote marine regions, HCHO variability is closely related to atmospheric oxidation capacity, and modeled HCHO in these regions is usually added as a global satellite HCHO background. Thus, it is important to understand and validate the levels of satellite HCHO over the remote oceans. Here we intercompare three satellite retrievals of total HCHO columns from the Ozone Monitoring Instrument Smithsonian Astrophysical Observatory (OMI SAO (v004)) algorithm, Ozone Mapping and Profiler Suite on Suomi National Polar-orbiting Partnership Smithsonian Astrophysical Observatory (OMPS-NPP SAO) algorithm, and Ozone Monitoring Instrument Belgian Institute for Space Aeronomy (OMI BIRA) algorithm and validate them against in situ observations from the NASA Atmospheric Tomography Mission (ATom) mission. All retrievals are correlated with ATom-integrated columns over remote oceans, with OMI SAO (v004) showing the best agreement. This is also reflected in the mean bias (MB) for OMI SAO (- 0.73 ± 0.87) × 1015 molec. cm−2, OMPS SAO (- 0.76 ± 0.88) × 1015 molec. cm−2, and OMI BIRA (- 1.40 ± 1.11) × 1015 molec. cm−2. We recommend the OMI-SAO (v004) retrieval for remote-ocean atmosphere studies. Three satellite HCHO retrievals and in situ ATom columns all generally captured the spatial and seasonal distributions of HCHO in the remote-ocean atmosphere. Retrieval bias varies by latitude and season, but a persistent low bias is found in all products at high latitudes, and the general low bias is most severe for the OMI BIRA product. Examination of retrieval components reveals that slant column corrections have a larger impact on the retrievals over remote marine regions, while AMFs play a smaller role. This study informs us that the potential latitude-dependent biases in the retrievals require further investigation for improvement and should be considered when using marine HCHO satellite data, and vertical profiles from in situ instruments are crucial for validating satellite retrievals. [ABSTRACT FROM AUTHOR]

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]

Formaldehyde (HCHO) in the atmosphere is an intermediate product from the oxidation of methane and non-methane volatile organic compounds. In remote marine regions, HCHO variability is closely related to atmospheric oxidation capacity and modeled HCHO in these regions is usually added as a global satellite HCHO background. Thus, it is important to understand and validate the levels of satellite HCHO over the remote oceans. Here we intercompare three satellite retrievals of total HCHO columns (OMI-SAO (v004), OMPS-NPP SAO, and OMI BIRA) and validate them against in situ observations from the NASA Atmospheric Tomography Mission (ATom) mission. All retrievals are correlated with ATom integrated columns over remote oceans, with OMI SAO (v004) showing the best agreement. Three satellite HCHO retrievals and in situ ATom columns all generally captured the spatial and seasonal distributions of HCHO in the remote ocean atmosphere. Retrieval bias varies by latitude and season, but a persistent low bias is found in all products at high latitudes and the general low bias is most severe for the OMI BIRA product. Examination of retrieval components reveals slant column corrections have a larger impact on the retrievals over remote marine regions while AMFs play a smaller role. This study informs that the potential latitude-dependent biases in the retrievals require further investigation for improvement and should be considered when using marine HCHO satellite data, and vertical profiles from in situ instruments are crucial for validating satellite retrievals. [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]

Despite its importance for the global oxidative capacity, spatially resolved trends and variability of the hydroxyl radical (OH) are poorly constrained. We demonstrate the utility of a tropospheric column OH (TCOH) product, created from machine learning and satellite proxy data, in determining the spatial variability in trends of tropical OH over the oceans during September through November. While OH increases domain‐wide by 2.1%/decade from 2005–2019, we find significant spatial heterogeneity in regional trends, with decreases in some areas of 2.5%/decade. Our analysis of the trends in the proxy data indicate anthropogenic‐driven changes in emissions of OH drivers as well as increasing temperatures cause these trends. This OH product is potentially a significant advance in constraining OH spatial variability and serves as a useful complement to existing tools in understanding the atmospheric oxidative capacity. Comprehensive observations of TCOH are required to assess the fidelity of this method. Plain Language Summary: Hydroxyl is a chemical that removes many gases from the atmosphere, including methane, an important greenhouse gas. To understand recent trends in methane, we must also understand recent trends in hydroxyl. Because of various limitations, we unfortunately do not have long‐term, direct observations of hydroxyl. To address this problem, we have developed a machine learning model that uses satellite observations of variables relevant to hydroxyl chemistry and variability to calculate hydroxyl. We demonstrate that this product can be used to understand trends and variability of hydroxyl over the tropical oceans. While, on average, hydroxyl increases from 2005–2019, we show that this is not a universal trend and that hydroxyl actually decreases in multiple regions over the same time period. Using satellite observations of various chemicals, we demonstrate that changes in emissions due to human activity and increases in temperature cause many of these trends. This product is potentially a significant advance in understanding changes in hydroxyl and could be a useful complement to more traditional methods in understanding atmospheric methane. Key Points: Satellite proxies can constrain the spatial distribution of trends and variability of the hydroxyl radical in the tropicsChanges in biomass burning, temperature, and other emissions lead to large spatial heterogeneity in hydroxyl radical trendsMore direct observations of the hydroxyl radical and its drivers are needed to further validate the fidelity of our methodology [ABSTRACT FROM AUTHOR]

We describe the new and improved version 2 of the ozone profile research product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. One of the major changes is to switch the OMI L1b data from collection 3 to the recent collection 4 as well as the accompanying auxiliary datasets. The algorithm details are updated on radiative transfer model calculation and measurement calibrations, along with the input changes in meteorological data, and with the use of a tropopause-based ozone profile climatology, an improved high-resolution solar reference spectrum, and a recent ozone absorption cross-section dataset. A super Gaussian is applied to better represent OMI slit functions instead of a normal Gaussian. The effect of slit function errors on the spectral residuals is further accounted for as pseudo-absorbers in the iterative fit process. The OMI irradiances are averaged into monthly composites to reduce noise uncertainties in OMI daily measurements and to cancel out the temporal variations of instrument characteristics that are common in both radiance and irradiance measurements, which was previously neglected due to use of climatological composites. The empirical soft calibration spectra are re-derived to be consistent with the updated implementations and derived annually to remove the time-varying systematic biases between measured and simulated radiances. The "common mode" correction spectra are derived from remaining residual spectra after soft calibration as a function of solar zenith angle. The common mode is included as a pseudo-absorber in the iterative fit process, which helps to reduce the discrepancies of ozone retrieval accuracy between lower and higher solar zenith angles and between nadir and off-nadir pixels. Validation with ozonesonde measurements demonstrates the improvements of ozone profile retrievals in the troposphere, especially around the tropopause. The retrieval quality of tropospheric column ozone is improved with respect to the seasonal consistency between winter and summer as well as the long-term consistency before and after the row-anomaly occurrence. [ABSTRACT FROM AUTHOR]

We investigate the benefit of assimilating high spatial‐temporal resolution nitrogen dioxide (NO2) measurements from a geostationary (GEO) instrument such as Tropospheric Emissions: Monitoring of Pollution (TEMPO) versus a low‐earth orbit (LEO) platform like TROPOspheric Monitoring Instrument (TROPOMI) on the inverse modeling of nitrogen oxides (NOx) emissions. We generated synthetic TEMPO and TROPOMI NO2 measurements based on emissions from the COVID‐19 lockdown period. Starting with emissions levels prior to the lockdown, we use the Weather Research and Forecasting Model coupled with Chemistry/Data Assimilation Research Testbed (WRF‐Chem/DART) to assimilate these pseudo‐observations in Observing System Simulation Experiments to adjust NOx emissions and quantify how well the assimilation of TEMPO versus TROPOMI measurements recovers the lockdown‐induced emissions changes. We find that NOx emission biases can be ameliorated using half as many simulation days when assimilating GEO observations, and the estimated NOx emissions in 23 out of 29 major urban regions in the US are more accurate. The root mean square error and coefficient of determination of posterior NOx emissions are reduced by 12.5%–41.5% and 1.5%–17.1%, respectively, across different regions. We conduct sensitivity experiments that use different data assimilation (DA) configurations to assimilate synthetic GEO observations. Results demonstrate that the temporal width of the DA window introduces −10% to −20% biases in the emissions inversion and constraining both NOx concentrations and emissions simultaneously yields the most accurate NOx emissions estimates. Our work serves as a valuable reference on how to appropriately assimilate GEO observations for constraining NOx emissions in future studies. Plain Language Summary: Nitrogen oxides (NOx) are major air pollutants and precursors to tropospheric ozone and secondary inorganic aerosols. The diverse natural and anthropogenic sources of NOx pose a challenge for NOx emissions estimates. Inverse modeling techniques which use observations to infer emissions can be applied to improve our understanding of anthropogenic NOx emissions. This study aims to compare the ability of the new geostationary (GEO) instrument Tropospheric Emissions: Monitoring of Pollution (TEMPO) and the existing low‐earth orbit instrument TROPOspheric Monitoring Instrument (TROPOMI) to constrain NOx emissions. Synthetic TEMPO and TROPOMI NO2 measurements are generated and assimilated to constrain NOx emissions in an idealized experiment in which the "true" emissions are known. The results show the true NOx emissions can be retrieved using half as many simulation days when assimilating GEO NO2 observations. Moreover, the experiment that assimilates GEO NO2 observations improves the accuracy of estimated NOx emissions by 12.5%–41.5% and 1.5%–17.1% in terms of root mean square error and coefficient of determination, respectively, across different air quality regions. The NOx emissions in most urban regions are better constrained when assimilating GEO NO2 data. We also propose best practices for assimilating GEO NO2 observations, which can serve as reference for future research. Key Points: True NOx emissions can be recovered using half as many simulation days when assimilating synthetic Tropospheric Emissions: Monitoring of Pollution (TEMPO) observations rather than TROPOspheric Monitoring InstrumentAssimilating synthetic TEMPO observations improve emissions inversion accuracy by 13%–42% across different regions of USThe best estimates of NOx emissions are achieved by using short data assimilation window (e.g., 30 min) and updating concentrations/emissions jointly [ABSTRACT FROM AUTHOR]