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An optimal estimation-based algorithm is developed to retrieve the number density of excited oxygen (O2) molecules that generate airglow emissions near 0.76 µm (b1Σg+ or A band) and 1.27 µm (a1Δg or 1Δ band) in the upper atmosphere. Both oxygen bands are important for the remote sensing of greenhouse gases. The algorithm is applied to the limb spectra observed by the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument in both the nominal (tangent heights below ∼ 90 km) and mesosphere–lower thermosphere (MLT) modes (tangent heights spanning 50–150 km). The number densities of emitting O2 in the a1Δg band are retrieved in an altitude range of 25–100 km near-daily in 2010, providing a climatology of O2 a1Δg-band airglow emission. This climatology will help disentangle the airglow from backscattered light in nadir remote sensing of the a1Δg band. The global monthly distributions of the vertical column density of emitting O2 in a1Δg state show mainly latitudinal dependence without other discernible geographical patterns. Temperature profiles are retrieved simultaneously from the spectral shapes of the a1Δg-band airglow emission in the nominal limb mode (valid altitude range of 40–100 km) and from both a1Δg- and b1Σg+-band airglow emissions in the MLT mode (valid range of 60–105 km). The temperature retrievals from both airglow bands are consistent internally and in agreement with independent observations from the Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS) and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), with the absolute mean bias near or below 5 K and root mean squared error (RMSE) near or below 10 K. The retrieved emitting O2 number density and temperature provide a unique dataset for the remote sensing of greenhouse gases and constraining the chemical and physical processes in the upper atmosphere.

The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO2) (a proxy for nitrogen oxides) tropospheric columns from ultraviolet–visible (UV–Vis) satellites has motivated many to use their ratios to gain some insights into the near-surface ozone sensitivity. Strong emphasis has been placed on the challenges that come with transforming what is being observed in the tropospheric column to what is actually in the planetary boundary layer (PBL) and near the surface; however, little attention has been paid to other sources of error such as chemistry, spatial representation, and retrieval uncertainties. Here we leverage a wide spectrum of tools and data to quantify those errors carefully. Concerning the chemistry error, a well-characterized box model constrained by more than 500 h of aircraft data from NASA's air quality campaigns is used to simulate the ratio of the chemical loss of HO2 + RO2 (LROx) to the chemical loss of NOx (LNOx). Subsequently, we challenge the predictive power of HCHO/NO2 ratios (FNRs), which are commonly applied in current research, in detecting the underlying ozone regimes by comparing them to LROx/LNOx. FNRs show a strongly linear (R2=0.94) relationship with LROx/LNOx, but only on the logarithmic scale. Following the baseline (i.e., ln(LROx/LNOx) = −1.0 ± 0.2) with the model and mechanism (CB06, r2) used for segregating NOx-sensitive from VOC-sensitive regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The transitioning ratios strictly follow a Gaussian distribution with a mean and standard deviation of 1.8 and 0.4, respectively. This implies that the FNR has an inherent 20 % standard error (1σ) resulting from not accurately describing the ROx–HOx cycle. We calculate high ozone production rates (PO3) dominated by large HCHO × NO2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO3 and HCHO × NO2 becomes more pronounced when moving towards NOx-sensitive regions due to nonlinear chemistry; our results indicate that there is fruitful information in the HCHO × NO2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO2 concentrations from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a second-order rational function permitting an analytical solution for an altitude adjustment factor to partition the tropospheric columns into the PBL region. We propose a mathematical solution to the spatial representation error based on modeling isotropic semivariograms. Based on summertime-averaged data, the Ozone Monitoring Instrument (OMI) loses 12 % of its spatial information at its native resolution with respect to a high-resolution sensor like the TROPOspheric Monitoring Instrument (TROPOMI) (> 5.5 × 3.5 km2). A pixel with a grid size of 216 km2 fails at capturing ∼ 65 % of the spatial information in FNRs at a 50 km length scale comparable to the size of a large urban center (e.g., Los Angeles). We ultimately leverage a large suite of in situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI and OMI tropospheric NO2 and HCHO columns. At a 68 % confidence interval (1σ), errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO2 columns, should be above 1.2–1.5 × 1016 molec. cm−2 to attain a 20 %–30 % standard error in the ratio. This level of error is almost non-achievable with the OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total error (40 %–90 %) in the FNRs. Due to a stronger signal in cities, the total relative error (< 50 %) tends to be mild, whereas areas with low vegetation and anthropogenic sources (e.g., the Rocky Mountains) are markedly uncertain (> 100 %). Our study suggests that continuing development in the retrieval algorithm and sensor design and calibration is essential to be able to advance the application of FNRs beyond a qualitative metric.

The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO2) (a proxy for nitrogen oxides) tropospheric columns from Ultraviolet-Visible (UV-Vis) satellites has motivated many to use their ratios to gain some insights into the near-surface ozone sensitivity. Strong emphasis has been placed on the challenges that come with transforming what is being observed in the tropospheric column to what is actually in the planetary boundary layer (PBL) and near to the surface; however, little attention has been paid to other sources of error such as chemistry, spatial representation, and retrieval uncertainties. Here we leverage a wide spectrum of tools and data to carefully quantify those errors. Concerning the chemistry error, a well-characterized box model constrained by more than 500 hours of aircraft data from NASA’s air quality campaigns is used to simulate the ratio of the chemical loss of HO2+RO2 (LROx) to the chemical loss of NOx (LNOx). Subsequently, we challenge the predictive power of HCHO / NO2 ratios (FNRs), which are commonly applied in current research, at detecting the underlying ozone regimes by comparing them to LROx / LNOx. FNRs show a strongly linear (R2=0.94) relationship to LROx / LNOx in the log-log scale. Following the baseline (i.e., ln(LROx / LNOx) = -1.0±0.2) with the model and mechanism (CB06, r2) used for segregating NOx-sensitive from VOC-sensitive regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The transitioning ratios strictly follow a Gaussian distribution with a mean and standard deviation of 1.8 and 0.4, respectively. This implies that FNR has an inherent 20 % standard error (1-sigma) resulting from not being able to fully describe the ROx-HOx cycle. We calculate high ozone production rates (PO3) dominated by large HCHO×NO2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO3 and HCHO×NO2 becomes more pronounced when moving towards NOx-sensitive regions due to non-linear chemistry; our results indicate that there is fruitful information in the HCHO×NO2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO2 concentration from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a second-order rational function permitting an analytical solution for an altitude adjustment factor to partition the tropospheric columns to the PBL region. We propose a mathematical solution to the spatial representation error based on modeling isotropic semivariograms. With respect to a high-resolution sensor like TROPOspheric Monitoring Instrument (TROPOMI) (>5.5×3.5 km2), Ozone Monitoring Instrument (OMI) loses 12 % of spatial information at its native resolution. A pixel with a grid size of 216 km2 fails at capturing ~65 % of the spatial information in FNRs at a 50 km length scale comparable to the size of a large urban center (e.g., Los Angeles). We ultimately leverage a large suite of in-situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI and OMI tropospheric NO2 and HCHO columns. At 68 % confidence interval (1 sigma) errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO2 columns should be above 1.2–1.5×1016 molec.cm-2 to attain 20–30 % standard error in the ratio. This level of error is almost non-achievable with OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total error (40–90 %) in the FNRs. Due to a stronger signal in cities, the total relative error (100 %). Our study suggests that continuing development in the retrieval algorithm and sensor design and calibration is essential to be able to advance the application of FNRs beyond a qualitative metric.

The MethaneSAT satellite mission aims at quantifying anthropogenic methane emissions by measuring reflected solar radiation in two spectral windows in the short wave infrared range. In the 1,249 nm - 1,305 nm spectral range the sensor will measure the oxygen (O2) singlet Delta band with a full width at half maximum (FWHM) of 0.17 nm. Additionally, the instrument will observe most of the 2ν3 absorption band of methane (CH4) and the P-branch of a 3ν1+ν3 carbon dioxide (CO2) band in the spectral range 1,605 nm - 1,683 nm at a spectral resolution of 0.23 nm at FWHM. Clouds and aerosols can introduce biases into the inversion of methane column concentrations from solar backscatter measurements and we therefore develop retrieval processors to filter out contaminated scenes.One processor makes use of surface pressure retrievals to infer the presence of scattering particles in the field of view of the sensor. These retrievals specifically take into account O2 airglow emission following the approach of Sun et al. (2018). Surface pressure is retrieved by fitting spectra in the O2 band, assuming a non-scattering atmosphere. We study thresholds in the variations between retrieved surface pressure and a priori meteorological databases which are suitable to screen for clouds and aerosols. A complementary algorithm takes advantage of differences in the atmospheric light path between the two spectral windows of MethaneSAT in the presence of aerosols and clouds. Here, we retrieve water vapor (H2O) column concentrations from the 1.3 µm and 1.6 µm windows under the assumption of the geometric lightpath. The ratio of the H2O retrievals from the two windows is used to construct a filter for aerosol and cloud contaminated scenes. We simulate MethaneSAT measurements with various cloud and aerosol loads to derive the retrieval configurations with highest sensitivity to scattering events.Both filtering approaches are applied to measurements of the MethaneAIR instrument (Staebell et al., 2021) to demonstrate their capacity in screening for clear scenes. Finally, we discuss our on-going efforts in developing a filter for observations affected by cloud shadows. ReferencesStaebell, C., Sun, K., Samra, J., Franklin, J., Chan Miller, C., Liu, X., Conway, E., Chance, K., Milligan, S., and Wofsy, S.: Spectral calibration of the MethaneAIR instrument, Atmospheric Measurement Techniques, 14, 3737–3753, https://doi.org/10.5194/amt-14-3737-2021, 2021.Sun, K., Gordon, I. E., Sioris, C. E., Liu, X., Chance, K., and Wofsy, S. C.: Reevaluating the use of O2 a1Δg band in spaceborne remote sensing of greenhouse gases, Geophysical Research Letters, 45, 5779–5787, https://doi.org/10.1029/2018GL077823, 2018.

Accurate reference spectroscopic information for the water molecule from the microwave to the near-ultraviolet is of paramount importance in atmospheric research. A semi-empirical potential energy surface for the ground electronic state of H 2 16 O has been created by refining almost 4000 experimentally determined energy levels. These states extend into regions with large values of rotational and vibrational excitation. For all states considered in our refinement procedure, which extend to 37 000 cm −1 and J=20 (total angular momentum), the average root-mean-square deviation is approximately 0.05 cm −1 . This potential energy surface offers significant improvements when compared to recent models by accurately predicting states possessing high values of J . This feature will offer significant improvements in calculated line positions for high-temperature spectra where transitions between high J states become more prominent. Combining this potential with the latest dipole moment surface for water vapour, a line list has been calculated which extends reliably to 37 000 cm −1 . Obtaining reliable results in the ultraviolet is of special importance as it is a challenging spectral region for the water molecule both experimentally and theoretically. Comparisons are made against several experimental sources of cross sections in the near-ultraviolet and discrepancies are observed. In the near-ultraviolet our calculations are in agreement with recent atmospheric retrievals and the upper limit obtained using broadband spectroscopy by Wilson et al. ( 2016 , p. 194) , but they do not support recent suggestions of very strong absorption in this region.

The absence of up-to-date emissions has been a major impediment to accurately simulating aspects of atmospheric chemistry and to precisely quantifying the impact of changes in emissions on air pollution. Hence, a nonlinear joint analytical inversion (Gauss–Newton method) of both volatile organic compounds (VOCs) and nitrogen oxide ( NOx ) emissions is made by exploiting the Smithsonian Astrophysical Observatory (SAO) Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) formaldehyde (HCHO) and the National Aeronautics and Space Administration (NASA) Ozone Monitoring Instrument (OMI) tropospheric nitrogen dioxide ( NO2 ) columns during the Korea–United States Air Quality (KORUS-AQ) campaign over East Asia in May–June 2016. Effects of the chemical feedback of NOx and VOCs on both NO2 and HCHO are implicitly included by iteratively optimizing the inversion. Emission uncertainties are greatly narrowed (averaging kernels > 0.8 , which is the mathematical presentation of the partition of information gained from the satellite observations with respect to the prior knowledge) over medium- to high-emitting areas such as cities and dense vegetation. The prior amount of total NOx emissions is mainly dictated by values reported in the MIX-Asia 2010 inventory. After the inversion we conclude that there is a decline in emissions (before, after, change) for China ( 87.94±44.09 Gg d −1 , 68.00±15.94 Gg d −1 , −23 %), North China Plain (NCP) ( 27.96±13.49 Gg d −1 , 19.05±2.50 Gg d −1 , −32 %), Pearl River Delta (PRD) ( 4.23±1.78 Gg d −1 , 2.70±0.32 Gg d −1 , −36 %), Yangtze River Delta (YRD) ( 9.84±4.68 Gg d −1 , 5.77±0.51 Gg d −1 , −41 %), Taiwan ( 1.26±0.57 Gg d −1 , 0.97±0.33 Gg d −1 , −23 %), and Malaysia ( 2.89±2.77 Gg d −1 , 2.25±1.34 Gg d −1 , −22 %), all of which have effectively implemented various stringent regulations. In contrast, South Korea ( 2.71±1.34 Gg d −1 , 2.95±0.58 Gg d −1 , + 9 %) and Japan ( 3.53±1.71 Gg d −1 , 3.96±1.04 Gg d −1 , +12 %) are experiencing an increase in NOx emissions, potentially due to an increased number of diesel vehicles and new thermal power plants. We revisit the well-documented positive bias (by a factor of 2 to 3) of MEGAN v2.1 (Model of Emissions of Gases and Aerosols from Nature) in terms of biogenic VOC emissions in the tropics. The inversion, however, suggests a larger growth of VOCs (mainly anthropogenic) over NCP (25 %) than previously reported (6 %) relative to 2010. The spatial variation in both the magnitude and sign of NOx and VOC emissions results in nonlinear responses of ozone production and loss. Due to a simultaneous decrease and increase in NOx∕VOC over NCP and YRD, we observe a ∼53 % reduction in the ratio of the chemical loss of NOx ( LNOx ) to the chemical loss of ROx ( RO2+HO2 ) over the surface transitioning toward NOx -sensitive regimes, which in turn reduces and increases the afternoon chemical loss and production of ozone through NO2+OH ( −0.42 ppbv h −1 ) ∕HO2 (and RO2 ) +NO ( +0.31 ppbv h −1 ). Conversely, a combined decrease in NOx and VOC emissions in Taiwan, Malaysia, and southern China suppresses the formation of ozone. Simulations using the updated emissions indicate increases in maximum daily 8 h average (MDA8) surface ozone over China (0.62 ppbv), NCP (4.56 ppbv), and YRD (5.25 ppbv), suggesting that emission control strategies on VOCs should be prioritized to curb ozone production rates in these regions. Taiwan, Malaysia, and PRD stand out as regions undergoing lower MDA8 ozone levels resulting from the NOx reductions occurring predominantly in NOx -sensitive regimes.

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).

Atmospheric aerosols are significant sources of uncertainty in air mass factor (AMF) calculations for trace gas retrievals using ultraviolet measurements from space. Current trace gas retrievals typically do not consider aerosols explicitly as cloud products partially account for aerosol effects. Here, we propose a new measurement‐based approach to correct for aerosols explicitly in the AMF calculation, apply it to Ozone Monitoring Instrument (OMI) formaldehyde (HCHO) retrievals and quantify the aerosol‐induced HCHO vertical column density difference for three aerosol types (smoke, dust, and sulfate) during 2006–2007. We use OMI aerosol retrievals for aerosol optical properties and vertical profiles to construct lookup tables of scattering weights as functions of geometry, surface pressure, surface albedo, and aerosol information. The average difference between the National Aeronautics and Space Administration operational OMI HCHO product (not considering aerosols) and the results obtained in this study on a global scale are 27%, 6%, and −0.3% for smoke, dust, and sulfate aerosols, respectively. The region with the largest aerosol effects is East China, where the explicit smoke aerosol correction enhances mean HCHO vertical column densities by 35%, with corrections to individual observations sometimes larger than 100%. The quantified aerosol effects are applicable under clear‐sky conditions. This study highlights the need to implement aerosol corrections in the AMF calculation for HCHO retrievals. This is particularly relevant in regions with high levels of pollution where aerosols interfere the most with formaldehyde satellite observations.

Atmospheric modelers and the trace gas retrieval community typically presuppose that pointwise measurements, which roughly represent the element of space, should compare well with satellite (model) pixels (grids). This assumption implies that the field of interest must possess a high degree of spatial homogeneity within the pixels (grids), which may not hold true for species with short atmospheric lifetimes or in the proximity of plumes. Results of this assumption often lead to a perception of a nonphysical discrepancy between data, resulting from different spatial scales, potentially making the comparisons prone to overinterpretation. Semivariogram is a mathematical expression of spatial variability in discrete data. Modeling the semivariogram behavior permits carrying out spatial optimal linear prediction of a random process field using kriging. Kriging can extract the spatial information (variance) pertaining to a specific scale, which in turn translating pointwise data to a grid space with quantified uncertainty such that a grid-to-grid comparison can be made. Here, using both theoretical and real-world experiments, we demonstrate that this classical geostatistical approach can be well adapted to solving problems in evaluating model-predicted or satellite-derived atmospheric trace gases. This study demonstrates that satellite validation procedures must take kriging variance and satellite spatial response functions into account. We present the comparison of Ozone Monitoring Instrument (OMI) tropospheric NO2 columns against 11 Pandora Spectrometer Instrument (PSI) systems during the DISCOVER-AQ campaign over Houston. The least-squares fit to the paired data shows a low slope (OMI=0.76×PSI+1.18×1015 molecules cm−2, r2=0.67) which is indicative of varying biases in OMI. This perceived slope, induced by the problem of spatial scale, disappears in the comparison of the convolved kriged PSI and OMI (0.96×PSI+0.66×1015 molecules cm−2, r2=0.72) illustrating that OMI possibly has a constant systematic bias over the area. To avoid gross errors in comparisons made between gridded data versus pointwise measurements, we argue that the concept of semivariogram (or spatial auto-correlation) should be taken into consideration, particularly if the field exhibits a strong degree of spatial heterogeneity at the scale of satellite and/or model footprints.

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be launched in 2019–2020 on board the GEO-KOMPSAT (GEOstationary KOrea Multi-Purpose SATellite)-2B, contributing as the Asian partner of the global geostationary constellation of air quality monitoring. To support this air quality satellite mission, we perform a cross-evaluation of simulated GEMS ozone profile retrievals from OMI (Ozone Monitoring Instrument) data based on the optimal estimation and ozonesonde measurements within the GEMS domain, covering from 5∘ S (Indonesia) to 45∘ N (south of the Russian border) and from 75 to 145∘ E. The comparison between ozonesonde and GEMS shows a significant dependence on ozonesonde types. Ozonesonde data measured by modified Brewer–Mast (MBM) at Trivandrum and New Delhi show inconsistent seasonal variabilities in tropospheric ozone compared to carbon–iodine (CI) and electrochemical condensation cell (ECC) ozonesondes at other stations in a similar latitude regime. CI ozonesonde measurements are negatively biased relative to ECC measurements by 2–4 DU; better agreement is achieved when simulated GEMS ozone retrievals are compared to ECC measurements. ECC ozone data at Hanoi, Kuala Lumpur, and Singapore show abnormally worse agreements with simulated GEMS retrievals than other ECC measurements. Therefore, ECC ozonesonde measurements at Hong Kong, Pohang, Naha, Sapporo, and Tsukuba are finally identified as an optimal reference dataset. The accuracy of simulated GEMS retrievals is estimated to be ∼5.0 % for both tropospheric and stratospheric column ozone with the precision of 15 % and 5 %, which meets the GEMS ozone requirements.

We introduce a method that accounts for errors caused by the slit function in an optimal-estimation-based spectral fitting process to improve ozone profile retrievals from the Ozone Monitoring Instrument (OMI) ultraviolet measurements (270–330 nm). Previously, a slit function was parameterized as a standard Gaussian by fitting the full width at half maximum (FWHM) of the slit function from climatological OMI solar irradiances. This cannot account for the temporal variation in slit function in irradiance, the intra-orbit changes due to thermally induced change and scene inhomogeneity, and potential differences in the slit functions of irradiance and radiance measurements. As a result, radiance simulation errors may be induced due to convolving reference spectra with incorrect slit functions. To better represent the shape of the slit functions, we implement a more generic super Gaussian slit function with two free parameters (slit width and shape factor); it becomes standard Gaussian when the shape factor is fixed to be 2. The effects of errors in slit function parameters on radiance spectra, referred to as pseudo absorbers (PAs), are linearized by convolving high-resolution cross sections or simulated radiances with the partial derivatives of the slit function with respect to the slit parameters. The PAs are included in the spectral fitting scaled by fitting coefficients that are iteratively adjusted as elements of the state vector along with ozone and other fitting parameters. The fitting coefficients vary with cross-track and along-track pixels and show sensitivity to heterogeneous scenes. The PA spectrum is quite similar in the Hartley band below 310 nm for both standard and super Gaussians, but is more distinctly structured in the Huggins band above 310 nm with the use of super Gaussian slit functions. Finally, we demonstrate that some spikes of fitting residuals are slightly smoothed by accounting for the slit function errors. Comparisons with ozonesondes demonstrate noticeable improvements when using PAs for both standard and super Gaussians, especially for reducing the systematic biases in the tropics and midlatitudes (mean biases of tropospheric column ozone reduced from -1.4∼0.7 to 0.0∼0.4 DU) and reducing the standard deviations of tropospheric ozone column differences at high latitudes (by 1 DU for the super Gaussian). Including PAs also makes the retrievals consistent between standard and super Gaussians. This study corroborates the slit function differences between radiance and irradiance, demonstrating that it is important to account for such differences in the ozone profile retrievals.

This paper presents the retrieval algorithm for the operational Ozone Monitoring Instrument (OMI) total bromine monoxide (BrO) data product (OMBRO) developed at the Smithsonian Astrophysical Observatory (SAO) and shows comparisons with correlative measurements and retrieval results. The algorithm is based on direct nonlinear least squares fitting of radiances from the spectral range 319.0–347.5 nm. Radiances are modeled from the solar irradiance, attenuated by contributions from BrO and interfering gases, and including rotational Raman scattering, additive and multiplicative closure polynomials, correction for Nyquist undersampling and the average fitting residual spectrum. The retrieval uses albedo- and wavelength-dependent air mass factors (AMFs), which have been pre-computed using a single mostly stratospheric BrO profile. The BrO cross sections are multiplied by the wavelength-dependent AMFs before fitting so that the vertical column densities (VCDs) are retrieved directly. The fitting uncertainties of BrO VCDs typically vary between 4 and 7×1012 molecules cm−2 (∼10 %–20 % of the measured BrO VCDs). Additional fitting uncertainties can be caused by the interferences from O2-O2 and H2CO and their correlation with BrO. AMF uncertainties are estimated to be around 10 % when the single stratospheric-only BrO profile is used. However, under conditions of high tropospheric concentrations, AMF errors due to this assumption of profile can be as high as 50 %. The retrievals agree well with GOME-2 observations at simultaneous nadir overpasses and with ground-based zenith-sky measurements at Harestua, Norway, with mean biases less than -0.22±1.13×1013 and 0.12±0.76×1013 molecules cm−2, respectively. Global distribution and seasonal variation of OMI BrO are generally consistent with previous satellite observations. Finally, we confirm the capacity of OMBRO retrievals to observe enhancements of BrO over the US Great Salt Lake despite the current retrieval setup considering a stratospheric profile in the AMF calculations. OMBRO retrievals also show significant BrO enhancements from the eruption of the Eyjafjallajökull volcano, although the BrO retrievals are affected under high SO2 loading conditions by the sub-optimum choice of SO2 cross sections.

Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (level 2) to a regular grid (level 3). When averaging level 2 data over a long period to a target level 3 grid that is significantly finer than the sizes of level 2 pixels, this process is referred to as. An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous methods. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g. OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g. GOSAT, IASI, and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOVs) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid size, the ground size of the FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for fine-grid oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grids finer than ∼ 16 km. The generalized 2-D super Gaussian function also enables smoothing of the level 3 results by decreasing the shape-determining exponents, which is useful for a high noise level or sparse satellite datasets. This physical oversampling approach is especially advantageous during smaller temporal windows and shows substantially improved visualization of trace gas distribution and local gradients when applied to OMI NO2 products and IASI NH3 products. There is no appreciable difference in the computational time when using the physical oversampling versus other oversampling methods., SCOPUS: ar.j, info:eu-repo/semantics/published

We used the GEOS-Chem model and its adjoint to quantify Chinese non-methane volatile organic compound (NMVOC) emissions for the year 2007, using the tropospheric column concentrations of formaldehyde and glyoxal observed by the Global Ozone Monitoring Experiment 2A (GOME-2A) instrument and the Ozone Monitoring Instrument (OMI) as quantitative constraints. We conducted a series of inversion experiments using different combinations of satellite observations to explore their impacts on the top-down emission estimates. Our top-down estimates for Chinese annual total NMVOC emissions were 30.7 to 49.5 (average 41.9) Tg yr−1, including 16.4 to 23.6 (average 20.2) Tg yr−1 from anthropogenic sources, 12.2 to 22.8 (average 19.2) Tg yr−1 from biogenic sources, and 2.08 to 3.13 (average 2.48) Tg yr−1 from biomass burning. In comparison, the a priori estimate for Chinese annual total NMVOC emissions was 38.3 Tg yr−1, including 18.8 Tg yr−1 from anthropogenic sources, 17.3 Tg yr−1 from biogenic sources, and 2.27 Tg yr−1 from biomass burning. The simultaneous use of glyoxal and formaldehyde observations helped distinguish the NMVOC species from different sources and was essential in constraining anthropogenic emissions. Our four inversion experiments consistently showed that the Chinese anthropogenic emissions of NMVOC precursors of glyoxal were larger than the a priori estimates. Our top-down estimates for Chinese annual emission of anthropogenic aromatics (benzene, toluene, and xylene) ranged from 5.5 to 7.9 Tg yr−1, 2 % to 46 % larger than the estimate of the a priori emission inventory (5.4 Tg yr−1). Three out of our four inversion experiments indicated that the seasonal variation in Chinese NMVOC emissions was significantly stronger than indicated in the a priori inventory. Model simulations driven by the average of our top-down NMVOC emission estimates (which had a stronger seasonal variation than the a priori) showed that surface afternoon ozone concentrations over eastern China increased by 1–8 ppb in June and decreased by 1–10 ppb in December relative to the simulations using the a priori emissions and were in better agreement with measurements. We concluded that the satellite observations of formaldehyde and glyoxal together provided quantitative constraints on the emissions and source types of NMVOCs over China and improved our understanding on regional chemistry.

The GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) was developed in support of NASA's decadal survey GEO-CAPE geostationary satellite mission. GCAS is an airborne push-broom remote-sensing instrument, consisting of two channels which make hyperspectral measurements in the ultraviolet/visible (optimized for air quality observations) and the visible–near infrared (optimized for ocean color observations). The GCAS instrument participated in its first intensive field campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign in Texas in September 2013. During this campaign, the instrument flew on a King Air B-200 aircraft during 21 flights on 11 days to make air quality observations over Houston, Texas. We present GCAS trace gas retrievals of nitrogen dioxide (NO2) and formaldehyde (CH2O), and compare these results with trace gas columns derived from coincident in situ profile measurements of NO2 and CH2O made by instruments on a P-3B aircraft, and with NO2 observations from ground-based Pandora spectrometers operating in direct-sun and scattered light modes. GCAS tropospheric column measurements correlate well spatially and temporally with columns estimated from the P-3B measurements for both NO2 (r2 = 0.89) and CH2O (r2 = 0.54) and with Pandora direct-sun (r2 = 0.85) and scattered light (r2 = 0.94) observed NO2 columns. Coincident GCAS columns agree in magnitude with NO2 and CH2O P-3B-observed columns to within 10 % but are larger than scattered light Pandora tropospheric NO2 columns by 33 % and direct-sun Pandora NO2 columns by 50 %.

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. This approach has another advantage in that we can, to a certain extent, disentangle the chemical and physical processes involved in the formation of ozone. Accordingly, we jointly constrain NOx and VOC emissions using well-characterized 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 NOx 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. The large variability of changes in NOx emissions is indicative of different dates and the degree of restrictions enacted to prevent the spread of the virus. For instance, NOx emissions remain at somewhat similar values or even higher in northern Germany and Moscow in March 2020 compared to the baseline. Comparisons against surface monitoring stations indicate that the model estimate of the NO2 reduction is underestimated, a picture that 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 %). Changes in VOC emissions are dominated by eastern European biomass burning activities and biogenic isoprene emissions. In March, however, TROPOMI HCHO sets an upper limit for HCHO changes such that the chemical feedback of NOx on HCHO constrained by TROPOMI NO2 reveals a non-negligible decline in anthropogenic VOC emissions in Paris (−9 %), Milan (−29 %), London (−5 %), and Rome (−5 %). 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). Results of integrated process rates of MDA8 surface ozone over central Europe in the same month suggest that physical processes (dry deposition, advection and diffusion) decrease ozone on average by −4.83 ppbv, while ozone production rates dampened by largely negative JNO2[NO2]-kNO+O3[NO][O3] become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology (mainly as air temperature and photolysis) 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 the essential character of ozone changes induced by abrupt emission anomalies.

Recently, we incorporated our new understanding of the absolute scale of the solar spectrum as measured by the Spectral Irradiance Monitor (SIM) on the Total and Spectral Solar Irradiance Sensor (TSIS-1) mission and the Compact SIM (CSIM) flight demonstration into a solar irradiance reference spectrum representing solar minimum conditions between solar cycles 24 and 25. This new reference spectrum, called the TSIS-1 Hybrid Solar Reference Spectrum (HSRS), is developed by re-normalizing independent, very high spectral resolution datasets to the TSIS-1 SIM absolute irradiance scale. The high-resolution data are from the Airforce Geophysical Laboratory (AFGL), the Quality Assurance of Ultraviolet Measurements In Europe (QASUME) campaign, the Kitt Peak National Observatory (KPNO) and the Jet Propulsion Laboratory’s (JPL) Solar Pseudo-Transmittance Spectrum (SPTS). The TSIS-1 HSRS spans 0.202 µm to 2.73 µm and has a spectral resolution of 0.01 nm or better. Uncertainties are 0.3% between 0.4 and 2.365 mm and 1.3% at wavelengths outside that rangeRecently, we have extended the long wavelength limit of the TSIS-1 HSRS from 2.73 µm to 200 µm with JPL SPTS solar line data through ~ 16 µm and theoretical understanding as represented in a computed solar irradiance spectrum by R. Kurucz. The extension expands the utility of this new solar irradiance reference spectrum to include Earth energy budget studies because it encompasses an integrated energy in excess of 99.99% of the total solar irradiance.In this work, we discuss the TSIS-1 HSRS, the extension and uncertainties, and demonstrate consistency with TSIS-1 SIM and CSIM solar spectral irradiance observations and TSIS-1 Total Irradiance Monitor (TIM) total solar irradiance observations. Additionally, we compare the TSIS-1 HSRS against independent measured and modeled solar reference spectra.

We present a new solar irradiance reference spectrum representative of solar minimum conditions between solar cycles 24 and 25. The Total and Spectral Solar Irradiance Sensor‐1 (TSIS‐1) Hybrid Solar Reference Spectrum (HSRS) is developed by applying a modified spectral ratio method to normalize very high spectral resolution solar line data to the absolute irradiance scale of the TSIS‐1 Spectral Irradiance Monitor (SIM) and the CubeSat Compact SIM (CSIM). The high spectral resolution solar line data are the Air Force Geophysical Laboratory ultraviolet solar irradiance balloon observations, the ground‐based Quality Assurance of Spectral Ultraviolet Measurements In Europe Fourier transform spectrometer solar irradiance observations, the Kitt Peak National Observatory solar transmittance atlas, and the semi‐empirical Solar Pseudo‐Transmittance Spectrum atlas. The TSIS‐1 HSRS spans 202–2730 nm at 0.01 to ∼0.001 nm spectral resolution with uncertainties of 0.3% between 460 and 2365 nm and 1.3% at wavelengths outside that range., Key Points The TSIS‐1 Spectral Irradiance Monitor and Compact SIM instruments observe the Sun's irradiance spectrum at high accuracyThe TSIS‐1 Hybrid Solar Reference Spectrum consists of high resolution solar line data normalized to the TSIS‐1 SIM irradiance spectrumThe TSIS‐1 Hybrid Solar Reference Spectrum has at least 0.01 nm spectral resolution, spans 202–2730 nm, and is accurate to 0.3%–1.3%

The largest uncertainty on measurements of dark energy using type Ia supernovae is presently due to systematics from photometry; specifically to the relative uncertainty on photometry as a function of wavelength in the optical spectrum. We show that a precise constraint on relative photometry between the visible and near-infrared can be achieved in upcoming surveys (such as in LSST at the Vera C. Rubin Observatory) via a mountaintop-located laser source tuned to the 342.78 nm vacuum excitation wavelength of neutral sodium atoms. Using a high-power (500 W) laser modified from laser guide star studies, this excitation will produce an artificial star (which we term a "laser photometric ratio star," or LPRS) of de-excitation light in the mesosphere that is observable from the ground at approximately 20 magnitude (i.e., well within the expected single-image magnitude limit of LSST) at wavelengths in vacuum of 589.16 nm, 589.76 nm, 818.55 nm, and 819.70 nm, with the sum of the numbers of 589.16 nm and 589.76 nm photons produced by this process equal to the sum of the numbers of 818.55 nm and 819.70 nm photons, establishing a precise calibration ratio between, for example, the LSST r and z filters. This technique can thus provide a novel mechanism for establishing a spectrophotometric calibration ratio of unprecedented precision, from above most of the Earth's atmosphere, for upcoming telescopic observations across astronomy and atmospheric physics. This article is the first in a pair of articles on this topic. The second article of the pair describes an alternative technique to achieve a similar, but brighter, LPRS than the technique described in this paper, by using two mountaintop-located lasers, at optical frequencies approximately 4 GHz away from resonances at wavelengths in vacuum of 589.16 nm and 819.71 nm respectively., Comment: The second article in this pair of articles is at arXiv:2010.08683 . This article has 14 pages & 7 figures. Both articles are published in MNRAS. This most recent arXiv version fixes a minor error in the caption of Figure 4

This article is the second in a pair of articles on the topic of the generation of a two-color artificial star (which we term a "laser photometric ratio star," or LPRS) of de-excitation light from neutral sodium atoms in the mesosphere, for use in precision telescopic measurements in astronomy and atmospheric physics, and more specifically for the calibration of measurements of dark energy using type Ia supernovae. The two techniques respectively described in both this and the previous article would each generate an LPRS with a precisely 1:1 ratio of yellow (589/590 nm) photons to near-infrared (819/820 nm) photons produced in the mesosphere. Both techniques would provide novel mechanisms for establishing a spectrophotometric calibration ratio of unprecedented precision, from above most of Earth's atmosphere, for upcoming telescopic observations across astronomy and atmospheric physics. The technique described in this article has the advantage of producing a much brighter (specifically, brighter by approximately a factor of 1000) LPRS, using lower-power (, The first article in this pair of articles is at arXiv:2001.10958 . This article has 15 pages and 3 figures. Both articles are published in MNRAS

In this work, we apply a principal component analysis (PCA)-based approach combined with look-up tables (LUTs) of corrections to accelerate the VLIDORT radiative transfer (RT) model used in the retrieval of ozone profiles from backscattered ultraviolet (UV) measurements by the Ozone Monitoring Instrument (OMI). The spectral binning scheme, which determines the accuracy and efficiency of the PCA–RT performance, is thoroughly optimized over the spectral range 265 to 360 nm with the assumption of a Rayleigh-scattering atmosphere above a Lambertian surface. The high level of accuracy (~ 0.03 %) is achieved from fast-PCA calculations of full radiances. In this approach, computationally expensive full multiple scattering (MS) calculations are limited to a small set of PCA-derived optical states, while fast single scattering and 2-stream multiple scattering calculations are performed, for every spectral point. The number of calls to the full MS model is only 51 in the application to OMI ozone profile retrievals with the fitting window of 270–330 nm where the RT model should be called at fine intervals (~ 0.03 nm with ~ 2000 wavelengths) to simulate OMI native measurements at 229 wavelengths (spectral resolution: 0.4–0.6 nm). We also developed a Look Up Table (LUT) to correct RT approximations performed using a scalar RT model with 4 streams (discrete ordinates) and 24 layers, thereby achieving the accuracy at the level attainable from simulations with a vector model with 12 streams and 72 layers; this speeds up the RT calculations by more than 2 orders of magnitude when ignoring other overhead. Overall, we speed up our OMI retrieval by a factor of 3.3 over the previous version, which has already been significantly sped up over line-by-line calculations due to various RT approximations. Improved treatments for RT approximation errors using LUT corrections improve spectral fitting (2–5 %) and hence retrieval errors, especially for tropospheric ozone by up to ~ 10 %; the remaining errors due to the forward model errors are within 5 % in the troposphere and 3 % in the stratosphere.

TEMPO was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (~2.1 km N/S×4.4 km E/W at 36.5°N, 100°W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O), nitrogen dioxide (NO), sulfur dioxide (SO), formaldehyde (HCO), glyoxal (CHO), bromine monoxide (BrO), IO (iodine monoxide), water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O chemistry cycle. Multi-spectral observations provide sensitivity to O in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

The primary focus of this study is to understand the contribution from excess moisture from crop transpiration to the severity of a heat wave episode that hit the Midwestern U.S. from 16 to 20 July 2011. To elucidate this, we first provide an optimal estimate of the transpiration water vapor flux using satellite total column water vapor retrievals whose accuracy and precision are characterized using independent observations. The posterior transpiration flux is estimated using a local ensemble transform Kalman filter that employs a mesoscale weather model as the forward model. The new estimation suggests that the prior values of transpiration flux from crops are biased high by 15%. We further use the constrained flux to examine the sensitivity of meteorology to the contributions from crops. Over the agricultural areas during daytime, elevated moisture (up to 40%) from crops not only increases humidity (thus the heat index) but also provides a positive radiative forcing by increasing downward longwave radiation (13 ± 4 W m(−2)) that results in even higher surface air temperature (+0.4 °C). Consequently, we find that the elevated moisture generally provides positive feedback to aggravate the heat wave, with daytime enhancements of heat index by as large as 3.3 ± 0.8 °C. Due to a strong diurnal cycle in the transpiration, the feedback tends to be stronger in the afternoon (up to 5 °C) and weaker at night. Results offer a potential basis for designing mitigation strategies for the effect of transpiration from agriculture in the future, in addition to improving the estimation of canopy transpiration.

Accurate reference spectroscopic information for the water molecule from the microwave to the near-ultraviolet is of paramount importance in atmospheric research. A semi-empirical potential energy surface for the ground electronic state of H216O has been created by refining to almost 4 000 experimentally determined energy levels. These states extend into regions with large values of rotational and vibrational excitation. For all states considered in our refinement procedure, which extend to 37 000 wavenumber and J = 20, the average root mean squared deviation is approximately 0.05 wavenumber. This potential energy surface offers significant improvements when compared to recent models by accurately predicting states possessing high values of J. This feature will offer significant improvements in calculated line positions for high temperature spectra where transitions between high J states become more prominent. Combining this potential with the latest dipole moment surface for water vapor, a line list has been calculated which extends reliably to 37 000 wavenumber. Obtaining reliable results in the ultraviolet is of special importance as it is a challenging spectral region for the water molecule both experimentally and theoretically. Comparisons are made against several experimental sources of cross sections in the near-ultraviolet and discrepancies are observed. In the near-ultraviolet our calculations are in agreement with recent atmospheric retrievals and the upper limit obtained using broad band spectroscopy by Wilson et al. (J. Quant. Spectrosc. Radiat. Transf., 2016, 170, 194) but do not support recent suggestions of very strong absorption in this region.

The absence of up-to-date emissions has been a major impediment to accurately simulate aspects of atmospheric chemistry, and to precisely quantify the impact of changes of emissions on air pollution. Hence, a non-linear joint analytical inversion (Gauss–Newton method) of both volatile organic compounds (VOC) and nitrogen oxides (NOx) emissions is made by exploiting the Smithsonian Astrophysical Observatory (SAO) Ozone Mapping and Profile Suite Nadir Mapper (OMPS-NM) formaldehyde (HCHO) and the National Aeronautics and Space Administration (NASA) Ozone Monitoring Instrument (OMI) tropospheric nitrogen dioxide (NO2) retrievals during the Korea-United States Air Quality (KORUS-AQ) campaign over East Asia in May–June 2016. Effects of the chemical feedback of NOx and VOCs on both NO2 and HCHO are implicitly included through iteratively optimizing the inversion. Emissions estimates are greatly improved (averaging kernels > 0.8) over medium- to high-emitting areas such as cities and dense vegetation. The amount of total NOx emissions is mainly dictated by values reported in the MIX-Asia 2010 inventory. After the inversion we conclude a decline in the emissions (before, after, change) for China (87.94 ± 44.09 Gg/day, 68.00 ± 15.94 Gg/day, −23 %), North China Plain (NCP) (27.96 ± 13.49 Gg/day, 19.05 ± 2.50 Gg/day, −32 %), Pearl River Delta (PRD) (4.23 ± 1.78 Gg/day, 2.70 ± 0.32 Gg/day, −36 %), Yangtze River Delta (YRD) (9.84 ± 4.68 Gg/day, 5.77 ± 0.51 Gg/day, −41 %), Taiwan (1.26 ± 0.57 Gg/day, 0.97 ± 0.33 Gg/day, −23 %), and Malaysia (2.89 ± 2.77 Gg/day, 2.25 ± 1.34 Gg/day, −22 %), all of which have effectively implemented various stringent regulations. In contrast, South Korea (2.71 ± 1.34 Gg/day, 2.95 ± 0.58 Gg/day, +9 %) and Japan (3.53 ± 1.71 Gg/day, 3.96 ± 1.04 Gg/day, +12 %) experience an increase in NOx emissions potentially due to risen number of diesel vehicles and new thermal power plants. We revisit the well-documented positive bias of the model in terms of biogenic VOC emissions in the tropics. The inversion, however, suggests a larger growth of VOC (mainly anthropogenic) over NCP (25 %) than previously reported (6 %) relative to 2010. The spatial variation in both magnitude and sign of NOx and VOC emissions results in non-linear responses of ozone production/loss. Due to simultaneous decrease/increase of NOx/VOC over NCP and YRD, we observe an ~ 53 % reduction in the ratio of the chemical loss of NOx (LNOx) to the chemical loss of ROx (RO2 + HO2) transitioning toward NOx-sensitive regimes, which in turn, reduces/increases the afternoon chemical loss/production of ozone through NO2 + OH (−0.42 ppbv hr−1)/HO2 (and RO2) + NO (+0.31 ppbv hr−1). Conversely, a combined decrease in NOx and VOC emissions in Taiwan, Malaysia, and the southern China suppresses the formation of ozone. Ultimately, model simulations indicate enhancements of maximum daily 8-hour average (MDA8) surface ozone over China (0.62 ppbv), NCP (4.56 ppbv), and YRD (5.25 ppbv) due to the non-linear ozone chemistry, suggesting that emissions standards should be extended to regulate VOCs to be able to curb ozone production rates. Taiwan, Malaysia, and PRD stand out as the regions undergoing lower MDA8 ozone levels resulting from the NOx reductions occurring predominantly in NOx-sensitive regimes.

Bromine radicals (Br + BrO) are important atmospheric species owing to their ability to catalytically destroy ozone as well as their potential impacts on the oxidative pathways of many trace gases, including dimethylsulfide and mercury. Using space-based observations of BrO, recent studies have reported rapid enhancements of tropospheric BrO over large areas (so called "BrO explosions") connected to near-surface ozone depletion occurring in polar spring. However, the source(s) of reactive bromine and mechanism(s) that initiate these BrO explosions are uncertain. In this study, we investigate the relationships between Arctic BrO explosions and two of the proposed sources of reactive bromine: sea-salt aerosol (SSA) generated from blowing snow and first-year (seasonal) sea ice. We use tropospheric column BrO derived from the Ozone Monitoring Instrument (OMI) in conjunction with the Goddard Earth Observing System Version 5 (GEOS-5) data assimilation system provided by National Aeronautics and Space Administration Global Modeling and Assimilation Office. Case studies demonstrate a strong association between the temporal and spatial extent of OMI-observed BrO explosions and the GEOS-5 simulated blowing snow-generated SSA during Arctic spring. Furthermore, the frequency of BrO explosion events observed over the 11-year record of OMI exhibits significant correlation with a time series of the simulated SSA emission flux in the Arctic and little to no correlation with a time series of satellite-based first-year sea ice area. Therefore, we conclude that SSA generated by blowing snow is an important factor in the formation of the BrO explosion observed from space during Arctic spring.

The NASA/Smithsonian Tropospheric Emissions: Monitoring of Pollution (TEMPO; tempo.si.edu) satellite instrument will measure atmospheric pollution and much more over Greater North America at high temporal resolution (hourly or better in daylight, with selected observations at 10 minute or better sampling) and high spatial resolution (10 km2 at the center of the field of regard). It will measure ozone (O3) profiles (including boundary layer O3), and columns of nitrogen dioxide (NO2), nitrous acid (HNO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), water vapor (H2O), bromine oxide (BrO), iodine oxide (IO), chlorine dioxide (OClO), as well as clouds and aerosols, foliage properties, and ultraviolet B (UVB) radiation. The instrument has been delivered and is awaiting spacecraft integration and launch in 2022. This talk describes a selection of TEMPO applications based on the TEMPO Green Paper living document (http://tempo.si.edu/publications.html). Applications to air quality and health will be summarized. Other applications presented include: biomass burning and O3 production; aerosol products including synergy with GOES infrared measurements; lightning NOx; soil NOx and fertilizer application; crop and forest damage from O3; chlorophyll and primary productivity; foliage studies; halogens in coastal and lake regions; ship tracks and drilling platform plumes; water vapor studies including atmospheric rivers, hurricanes, and corn sweat; volcanic emissions; air pollution and economic evolution; high-resolution pollution versus traffic patterns; tidal effects on estuarine circulation and outflow plumes; air quality response to power blackouts and other exceptional events.