Evaluating the impact of spatial resolution on tropospheric NO<sub>2</sub> column comparisons within urban areas using high-resolution airborne data

Abstract

NASA deployed the GeoTASO airborne UVvisible spectrometer in May-June 2017 to produce highresolution (approximately 250 m × 250 m) gapless NO 2 datasets over the western shore of Lake Michigan and over the Los Angeles Basin. The results collected show that the airborne tropospheric vertical column retrievals compare well with ground-based Pandora spectrometer column NO 2 observations (r 2 = 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 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 (Tropospheric Emissions: Monitoring Pollution), TROPOMI (TRO-POspheric Monitoring Instrument), and OMI (Ozone Monitoring Instrument), the agreement with Pandora measurements degraded, particularly for the most polluted columns as localized large pollution enhancements observed by Pandora and GeoTASO are spatially averaged with nearby less-polluted locations within the larger area representative of the satellite spatial resolutions (aircraft-to-Pandora slope: TEMPO scale = 0.88; TROPOMI scale = 0.77; OMI scale = 0.57). In these two regions, Pandora and TEMPO or TROPOMI have the potential to compare well at least up to pollution scales of 30 × 10 15 molecules cm −2 . Two publicly available OMI tropospheric NO 2 retrievals are found to be biased low with respect to these Pandora observations. However, the agreement improves when higher-resolution a priori inputs are used for the tropospheric air mass factor calculation (NASA V3 standard product slope = 0.18 and Berkeley High Resolution product slope = 0.30). Overall, this work explores best practices for satellite validation strategies with Pandora direct-sun observations by showing the sensitivity to product spatial resolution and demonstrating how the high-spatial-resolution NO 2 data retrieved from airborne spectrometers, such as GeoTASO, can be used with high-temporal-resolution ground-based column observationsPublished by Copernicus Publications on behalf of the European Geosciences Union. 6092 L. M. Judd et al.: Impact of spatial resolution on tropospheric NO 2 column comparisons to evaluate the influence of spatial heterogeneity on validation results.

Validation of TROPOMI tropospheric NO<sub>2</sub> columns using dual-scan MAX-DOAS measurements in Uccle, Brussels

“…In conclusion, the change of the a-priori profile in the TROPOMI retrieval has a significant impact on the agreement between the satellite and MAX-DOAS measurements, leading to a satisfying closure of the validation study. Although based on a different approach, these results are in agreement with the recent studies of Ialongo et al (2019) and Judd et al (2019).…”

OMI/Aura Nitrogen Dioxide Standard Product with Improved Surface and Cloud Treatments

“…The use of observed NO2 profiles as a priori information reduces the bias from ~50% to 23%, on average. The Multiple-Axis Differential Optical Absorption Spectrometer (MAX-DOAS) (e.g., Chan et al, 2019) or high spatial resolution measurements from aircraft (e.g., Nowlan et al, 2016;Judd et al, 2019) would provide a more comprehensive validation by mapping the NO2 distributions over the complete areas of aircraft spirals and the satellite FOVs.…”

Evaluating Sentinel-5P TROPOMI tropospheric NO<sub>2</sub> column densities with airborne and Pandora spectrometers near New York City and Long Island Sound

“…Finally, the subpixel airborne data within each TROPOMI pixel are gridded to a 250 m matrix to account for overlapping data from adjacent swaths, and then the area-weighted averages of the airborne TrVCs are computed to create values that are spatially and temporally consistent with the TROPOMI TrVC observations (bottom row in Fig. 5; gridding methodology from Kim et al, 2016).…”