Updated assessment of TROPOMI NO2 and HCHO columns using airborne spectrometers during the MOOSE and TRACER-AQ field campaigns
Airborne spectrometer data offers the opportunity to evaluate satellite product performance without the impact of subpixel heterogeneity between the different satellite and ground-based measurement footprints. Previous measurements during the Long Island Sound Tropospheric Ozone Study were used to evaluate TROPOMI’s v1.3 NO2 product and found very strong relationships (r2=0.96) between the airborne spectrometer and TROPOMI with a systematic low bias mostly attributed to the coarse a priori profile assumption within the standard TROPOMI retrieval. This presentation will update that analysis using the most up-to-date version 2 TROPOMI NO2 product as well as expand analysis to the HCHO product. In summer 2021, NASA GeoCAPE Airborne Simulator (GCAS) collected measurements over southeast Michigan/western Ontario for the Michigan-Ontario Ozone Source Experiment (MOOSE) and Houston, Texas during the TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ). Flight strategies for both deployments included repeated systematic sampling over common areas of interest coinciding with TROPOMI. During these flights, GCAS NO2 tropospheric columns are available at 250 m x 560 m resolution. Preliminary evaluation of GCAS NO2 retrievals with Pandora spectrometer data in Houston, Texas (3 sites) shows a median percent difference of 1.4% with an interquartile range of -15.5-14.9% (r2=0.72). Column HCHO was also retrieved at a slightly coarser resolution in Houston, Texas (750 m x 1680 m) showing distinct spatial patterns associated with secondary production through the oxidation of VOCs downwind of industrial facilities. Comparison to Pandora HCHO showed a low bias of ~25% (r2=0.29) with further investigation needed to identify the cause for this bias. This presentation will share how the GCAS/Pandora/TROPOMI NO2 and HCHO intercompare and will also extend analysis toward thinking about how these assets will contribute to the validation of future geostationary observations.