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Nitrogen oxides (NOx = NO + NO2) play a crucial role in the formation of ozone and secondary inorganic and organic aerosols, thus affecting human health, global radiation budget, and climate. The diurnal and spatial variations of NO2 are functions of emissions, advection, deposition, vertical mixing, and chemistry. Their observations, therefore, provide useful constraints in our understanding of these factors. We employ a Regional chEmical and trAnsport model (REAM) to analyze the observed temporal (diurnal cycles) and spatial distributions of NO2 concentrations and tropospheric vertical column densities (TVCDs) using aircraft in situ measurements, surface EPA Air Quality System (AQS) observations, as well as the measurements of TVCDs by satellite instruments (OMI: the Ozone Monitoring Instrument; and GOME-2A: Global Ozone Monitoring Experiment - 2A), ground-based Pandora, and the Airborne Compact Atmospheric Mapper (ACAM) instrument, in July 2011 during the DISCOVER-AQ campaign over the Baltimore-Washington region. The model simulations at 36- and 4-km resolutions are in reasonably good agreement with the temporospatial NO2 observations in the daytime. However, nighttime mixing in the model needs to be enhanced to reproduce the observed NO2 diurnal cycle in the model. Another discrepancy is that Pandora measured NO2 TVCDs show much less variation in the late afternoon than simulated in the model. Relative to the 36-km model simulations, the 4-km model results show larger biases compared to the observations due largely to the larger spatial variations of NO2 in the model when the spatial resolution is increased from 36 to 4 km, although the biases are often comparable to the ranges of the observations. The high-resolution aircraft ACAM observations show a more dispersed distribution of NO2 vertical column densities (VCDs) and lower VCDs in urban regions than 4-km model simulations, reflecting likely the spatial distribution bias of NOx emissions in the National Emissions Inventory (NEI) 2011 at high resolution. [ABSTRACT FROM AUTHOR]

Abundant NO[sub 2] column measurements from airborne and ground-based Pandora spectrometers were collected as part of the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region and coincided with early measurements from the Sentinel-5P TROPOMI instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOspheric Monitoring Instrument (TROPOMI) NO[sub 2] Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO[sub 2]. First, airborne and Pandora TrVCs are compared to evaluate the uncertainty of the airborne TrVC and establish the spatial representativeness of the Pandora observations. The 171 coincidences between Pandora and airborne TrVCs are found to be highly correlated (r[sup 2]=0.92 and slope of 1.03) with the biggest individual differences being associated with high temporal and/or spatial variability. These reference measurements (Pandora and airborne) are complementary with respect to temporal coverage and spatial representivity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representivity when operated in direct-sun mode. Airborne spectrometers are typically only deployed for short periods of time, but their observations are more spatially representative of the satellite measurements with the added capability of retrieving at subpixel resolutions of 250 m × 250 m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements (r[sup 2]=0.96) than Pandora measurements are with TROPOMI (r[sup 2]=0.84). The largest outliers between TROPOMI and the reference measurements are caused by errors in the TROPOMI retrieval of cloud pressure impacting the calculation of tropospheric air mass factors in cloud-free scenes. This factor causes a high bias in TROPOMI TrVCs of 4-11 %. Excluding these cloud-impacted points, TROPOMI has an overall low bias of 19-33% during the LISTOS timeframe of June-September 2018. Part of this low bias is caused by coarse a priori profile input from TM5-MP model; replacing these profiles with those from a 12 km NAMCMAQ analysis results in a 12-14 % increase in the TrVCs. Even with this improvement, the TROPOMI-NAMCMAQ TrVCs have a 7-19 % low bias, indicating needed improvement in a priori assumptions in the air mass factor calculation. Future work should explore additional impacts of a priori inputs to further assess the remaining low biases in TROPOMI using these datasets. [ABSTRACT FROM AUTHOR]

NASA deployed an airborne UV/Visible spectrometer, GeoTASO, in May-June 2017 to produce high resolution (approximately 250 x 250 m), gapless NO2 datasets over the western shore of Lake Michigan and over the Los Angeles Basin. Results show that the airborne tropospheric vertical column retrievals compare well with ground-based Pandora spectrometer column NO2 observations (r² = 0.91 and slope of 1.03). Apparent disagreements between the two measurements can be sensitive to the coincidence criteria and are often associated with large local variability, including rapid temporal changes and also spatial heterogeneity that may be observed differently by the sunward viewing Pandora observations. The gapless mapping strategy executed during the 2017 GeoTASO flights provides data suitable for averaging to coarser areal resolutions to simulate satellite retrievals. As simulated satellite pixel area increases to values typical of TEMPO, TROPOMI, and OMI, the agreement with Pandora measurements is degraded as localized polluted plumes observed by Pandora are spatially averaged over larger areas (aircraft-to-Pandora slope: TEMPO scale = 0.88; TROPOMI scale = 0.77; OMI scale = 0.57). This behavior suggests that satellite products are representative of individual Pandora observations up to a certain pollution scale that depends on satellite spatial resolution. In these two regions, Pandora and TEMPO or TROPOMI have the potential to compare well up to pollution scales of 30 x 1015 molecules cm-2. Two publicly available OMI tropospheric NO2 retrievals are both found to be biased low with respect to Pandora observations (NASA V3 Standard Product slope = 0.18 and Berkeley High Resolution Product slope = 0.30). However, the agreement improves when higher resolution a priori inputs are used for the tropospheric air mass factor calculation. Overall, this work explores best practices for satellite validation strategies by showing the sensitivity to product spatial resolution and demonstrates how the high spatial resolution NO2 data retrieved from airborne spectrometers, such as GeoTASO, can be used with high temporal resolution surface observations to evaluate the influence of spatial heterogeneity on validation results. [ABSTRACT FROM AUTHOR]

Abstract: A number of satellite‐based instruments have become an essential part of monitoring emissions. Despite sound theoretical inversion techniques, the insufficient samples and the footprint size of current observations have introduced an obstacle to narrow the inversion window for regional models. These key limitations can be partially resolved by a set of modest high‐quality measurements from airborne remote sensing. This study illustrates the feasibility of nitrogen dioxide (NO2) columns from the Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) to constrain anthropogenic NOx emissions in the Houston‐Galveston‐Brazoria area. We convert slant column densities to vertical columns using a radiative transfer model with (i) NO2 profiles from a high‐resolution regional model (1 × 1 km2) constrained by P‐3B aircraft measurements, (ii) the consideration of aerosol optical thickness impacts on radiance at NO2 absorption line, and (iii) high‐resolution surface albedo constrained by ground‐based spectrometers. We characterize errors in the GCAS NO2 columns by comparing them to Pandora measurements and find a striking correlation (r > 0.74) with an uncertainty of 3.5 × 1015 molecules cm−2. On 9 of 10 total days, the constrained anthropogenic emissions by a Kalman filter yield an overall 2–50% reduction in polluted areas, partly counterbalancing the well‐documented positive bias of the model. The inversion, however, boosts emissions by 94% in the same areas on a day when an unprecedented local emissions event potentially occurred, significantly mitigating the bias of the model. The capability of GCAS at detecting such an event ensures the significance of forthcoming geostationary satellites for timely estimates of top‐down emissions. [ABSTRACT FROM AUTHOR]

The Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) instrument has been used as a precursor for a hyperspectral instrument on the future geostationary satellite, yet its ability to "measure" ocean reflectance needs to be evaluated. Here, we demonstrate its capacity through vicarious calibration and atmospheric correction of data collected during flight campaigns over the Louisiana shelf in September 2013 and over the North Atlantic Ocean in November 2015. GCAS-measured at-sensor radiance was first vicariously calibrated using concurrent measurements by the Moderate Resolution Imaging Spectrometer (MODIS) and radiative transfer simulations with the MODerate resolution atmospheric TRANsmission (MODTRAN). Then, atmospheric correction has been implemented using MODTRAN-developed lookup tables and the traditional Gordon and Wang "black pixel" approach but with nonzero water-leaving radiance in the near-infrared accounted for through iteration. The atmospheric correction algorithm was applied to the vicariously calibrated GCAS imagery, with resulting Rrs compared with concurrent MODIS Rrs and in situ Rrs. The comparison shows a mean relative difference of about 25% (N = 11) between GCAS and in situ Rrs in the blue-green bands for clear to moderately turbid waters. [ABSTRACT FROM AUTHOR]

Nitrogen oxides (NO x =NO+NO 2 ) play a crucial role in the formation of ozone and secondary inorganic and organic aerosols, thus affecting human health, global radiation budget, and climate. The diurnal and spatial variations in NO 2 are functions of emissions, advection, deposition, vertical mixing, and chemistry. Their observations, therefore, provide useful constraints in our understanding of these factors. We employ a Regional chEmical and trAnsport model (REAM) to analyze the observed temporal (diurnal cycles) and spatial distributions of NO 2 concentrations and tropospheric vertical column densities (TVCDs) using aircraft in situ measurements and surface EPA Air Quality System (AQS) observations as well as the measurements of TVCDs by satellite instruments (OMI: the Ozone Monitoring Instrument; GOME-2A: Global Ozone Monitoring Experiment - 2A), ground-based Pandora, and the Airborne Compact Atmospheric Mapper (ACAM) instrument in July 2011 during the DISCOVER-AQ campaign over the Baltimore-Washington region. The model simulations at 36 and 4 km resolutions are in reasonably good agreement with the regional mean temporospatial NO 2 observations in the daytime. However, we find significant overestimations (underestimations) of model-simulated NO 2 (O 3 ) surface concentrations during night-time, which can be mitigated by enhancing nocturnal vertical mixing in the model. Another discrepancy is that Pandora-measured NO 2 TVCDs show much less variation in the late afternoon than simulated in the model. The higher-resolution 4 km simulations tend to show larger biases compared to the observations due largely to the larger spatial variations in NO x emissions in the model when the model spatial resolution is increased from 36 to 4 km. OMI, GOME-2A, and the high-resolution aircraft ACAM observations show a more dispersed distribution of NO 2 vertical column densities (VCDs) and lower VCDs in urban regions than corresponding 36 and 4 km model simulations, likely reflecting the spatial distribution bias of NO x emissions in the National Emissions Inventory (NEI) 2011., Competing Interests: Competing interests. The authors declare that they have no conflict of interest.