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

Abstract

Abstract. Airborne and ground-based Pandora spectrometer NO2 column measurements were collected during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region, which coincided with early observations from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOMI NO2 Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO2. 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 (r2= 0.92 and slope of 1.03), with the largest 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 representativity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representativity 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 (r2=0.96) than Pandora measurements are with TROPOMI (r2=0.84). The largest outliers between TROPOMI and the reference measurements appear to stem from too spatially coarse a priori surface reflectivity (0.5∘) over bright urban scenes. In this work, this results during cloud-free scenes that, at times, are affected by errors in the TROPOMI cloud pressure retrieval impacting the calculation of tropospheric air mass factors. 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 the TM5-MP model; replacing these profiles with those from a 12 km North American Model–Community Multiscale Air Quality (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.

TROPOMI NO2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation With Surface NO2 Concentrations

“…For this work, we rely on the operational TROPOMI NO 2 algorithm, which underestimates tropospheric vertical column NO 2 in urban areas. Previous studies suggest that this underestimate is due to the AMF and ∼5km pixel size which cannot resolve street-level variations in concentrations (Goldberg, Lu, Streets, et al, 2019;Griffin et al, 2019;Judd, Al-Saadi, Szykman, et al, 2020;Judd, Al-Saadi, Janz, et al, 2019;Zhao et al, 2020); investigating the effects of the AMF bias on trends as well as investigating the effects of the pixels sizes will be the subject of future work. Also, there may be a clear-sky bias (Geddes, Murphy, et al, 2012) that NO 2 column measurements are lower and incrementally more spatially homogeneous in the afternoon than during the morning (Chong et al, 2018;Fishman et al, 2008;Herman et al, 2019;Knepp et al, 2015;Penn & Holloway, 2020;Tzortziou et al, 2015); it is likely that data from geostationary platforms such as TEMPO (Zoogman et al, 2017), GEMS (W. J.…”

Assessment of the TROPOMI tropospheric NO<sub>2</sub> product based on airborne APEX observations

“…This is done based on the high-resolution APEX observations and under the assumption that the retrieved VCDs represent the true state of the NO 2 field. We have adopted a downsampling method described in Kim et al (2016) and Judd et al (2019): first, we construct a pseudo-TROPOMI VCD grid (VCD pTROPO ) by aggregating the APEX NO 2 VCDs (VCD APEX ) according to a weighted average technique within grid cells of 5 km × 5 km, 4.4 km × 4.4 km, and 1 km × 1 km. The pixels are square in shape in order to avoid an orientation bias.…”

“…Maximum differences can be up to ∼ 1900 % and are due to the overestimation of retrievals with very low background values in the original APEX data (∼ 0.3 × 10 15 molec cm −2 ). Based on a similar study applied to OMI data (13 km × 24 km) over the contiguous United States (Kim et al, 2016), it was found that under-or overestimation biases are on the order of 5-10 × 10 15 molec cm −2 or 20 %-30 % for major cities like Washington D.C. and New York. Biases are more than 100 % for small-scale cities like Norfolk and Richmond.…”

Meteorological Drivers of Permian Basin Methane Anomalies Derived from TROPOMI

“…Other studies, comparing aircraft-and ground-based spectrometer measurements in urban and non-urban areas. have also found a systematic underestimation in TROPOMI NO 2 measurements, particularly during highly polluted conditions [34,[41][42][43][44][45].…”