6th TEMPO Science Team Meeting NO2 Activities at GSFC Lok Lamsal with contributions from Nickolay Krotkov, Sergey Marchenko, Alexander Vasilkov, Joanna Joiner, Wenhan Qin, Sunny Choi, Dave Haffner, William Swartz, and other GSFC colleagues lok.lamsal@nasa.gov 6th TEMPO Science Team Meeting Denver, CO June 7, 2018

Summary of NO2 activities at Goddard OMI NO2 standard product (Aura/ACMAP-16, PI: Krotkov) Release of Version 3.0 and 3.1 Ongoing works and future plans (next version) Operational SO2 and NO2 products from OMPS-NM (TASNPP-17, PI: Li) Adapting OMI NO2 algorithm in the UV (OMPS-NM and JPSS instruments) Development of long-term NO2 record (MEaSUREs-17, PI: Lamsal) NO2 products from past and concurrent UV-Vis instruments Assessment of NO2 data (intercomparison, validation) Surface NO2 from satellite measurements (Aura/ACMAP-16, PI: Lamsal) Analyze of NO2 observations during DISCOVER-AQ/KORUS-AQ Develop NO2 algorithm for air-borne sensors (GCAS, GeoTASO) Improve NO2 simulation in regional model Explore column-to-surface relationship, and use the knowledge to infer surface NO2 from remote sensors.

Significant improvement in V3 OMNO2 product V3.0 OMI NO2 Standard Product (OMNO2, SPv3), released in 2016 - New algorithm for slant column retrievals - Use of higher-resolution (1° x 1.25°) GMI monthly a priori NO2 profiles with year specific emissions OMI vs FTIR OMI vs SCIAMACHY and GOME-2 SCIAMACHY and GOME-2 data are photochemically corrected Marchenko et al., 2015; Lamsal et al., 2016; Krotkov et al., 2017

OMNO2 V3 and European retrievals (slant column) agree very well NO2 slant columns over Pacific OMNO2A v1: current operational (used in DOMINO) OMNO2A v2: updated OMNO2A (not operational) OMINO2 QA4ECV: QDOAS-based, consortium approved settings OMINO2 NASA: NASA algorithm (used in OMNO2) Zara et al., AMTD, 2018 Upcoming V3.1 OMNO2 product: finished processing, release in ~week - Improvements in uncertainty estimations - Use of improved cloud products (OMCLDO2, Veefkind et al., 2016) - Improved treatment of terrain pressure - Few other minor updates

% change in AMF for 0.01 change in reflectivity Ongoing activities focus on AMF: surface reflectivity, a-priori profiles, cloud and aerosol treatments Cloud algorithm Surface reflectivity Cloud fraction Cloud pressure NO2 Two ways surface reflectivity can affect NO2 retrievals Polluted Unpolluted % change in AMF for 0.01 change in reflectivity % sza=45, vza=30, raz=45 2-20% error in AMF for 0.01 error in surface reflectivity. Error strong function of profile shape and surface brightness.

Water Leaving Radiance Model Replacing climatological LER with geometry dependent MODIS-based surface reflectivity product (GLER) OMI MODIS (Land only) BRDF Model Geometry VLIDORT (V2.7) Land LER Ocean LER LER R = TOA radiance R0 = Path scattering reflectance of atm T = Atmospheric transmittance S = Spherical albedo of atmosphere Cox-Munk + Water Leaving Radiance Model Vasilkov et al, AMT, 2017 Qin et al. (in preparation)

Climatological (OMLER) vs. geometry-dependent LER (GLER) MODIS-derived LER (GLER) at original resolution (1 km x 1 km) OMI Climatological LER (OMLER) November (2005-2009) MODIS-derived LER (GLER) November 14, 2006 OMI resolution GLER - OMLER

Replacing OMLER by GLER changes retrievals considerably Direct GLER effect GLER + Cloud effect GLER effect GLER + Cloud effect GLER effect GLER + Cloud effect Cloud algorithm Surface reflectivity Cloud fraction Cloud pressure NO2 Vasilkov et al, AMTD, 2018

Accounting for aerosol effects in NO2 retrievals Cloud algorithm Aerosols Cloud fraction Cloud pressure NO2 Affect NO2 retrievals directly and indirectly through cloud parameters Effect complicated due to different types of aerosols Challange is to find accurate & computationally efficient approach Lamsal et al, JGR, 2017

AMF and a-priori NO2 profiles: Spatial resolution Short-lifetime of NO2 lead to steep gradient in NO2 concentration near sources, so resolution matters. GMI, June, 2005 sza=45, vza=30, raz=45 (AMF2x2.5 – AMF1x1.25)/AMF1x1.25 2x2.5° 1x1.25° Our future version will use a-priori profiles from GMI-Replay global simulation (Orbe et al., 2017) at very high-resolution (0.25°x0.25°).

Adapting OMI NO2 algorithm to OMPS-NM (TASNPP-17) NO2 structures in UV is ~half of that in visible Narrow fitting window Coarse spectral resolution of OMPS Reduced sensitivity in the lower troposphere Calibration issues in OMPS L1 data Cannot expect same quality of data from OMPS

Adapting OMI NO2 algorithm to OMPS-NM (TASNPP-17) SAA Preliminary Currently revisiting several aspects, will have improved product in ~year

MINDS: MultI-decadal Nitrogen dioxide and Derived products from Satellite (MEaSUREs-17) L2 and L3 NO2 column products L4 surface NO2 concentration Consistent morning and early-afternoon (diurnal) data potentially useful for TEMPO validation MEaSUREs (5 year project)

Assessment of various NO2 measurements (DISCOVER-AQ) Aircraft (P3B) spiral (~4 km) NCAR LIF (highly accurate within 10%) CMAQ (1.3 km × 1.3 km) NO2 column (Maryland) Pandora Surface monitor - Photolytic - Molybdenum OMI (>13 km × 24 km) Direct comparison often complicated as they vary in sampling domain and time

Assessment of various NO2 measurements

Assessment of various NO2 measurements Maryland Texas

Conclusions Goddard team is developing a retrieval framework that couples NO2-Cloud-GLER algorithms; We will create consistent NO2 data record from past and concurrent UV/Vis satellite instruments (MINDS/MEaSUREs project); Diurnal data from MINDS are potentially useful for TEMPO validation; We are in dire need of high quality long-term validation dataset in appropriate locations for satellite data assessment. Acknowledgment: NASA Earth Science Division for funding