1. 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
6th TEMPO Science Team Meeting
Denver, CO
June 7, 2018

2. 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.

3. 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 NO 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

4. 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

5. % 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.

6. 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)

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

8. 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

9. 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

10. 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°).

11. 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

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

13. 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)

14. 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

15. Assessment of various NO2 measurements

16. Assessment of various NO2 measurements
Maryland
Texas

17. 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