1. TEMPO NO 2 Validation Ron Cohen, UC Berkeley

2. 1. Precision of 1x10 15 molecules/cm 2 (~0.5 ppb in the PBL) Approach: ~3 Pandoras for 1 month; 4 seasons Contract requirement

3. Most approaches to using the data assume/will work better if the observations have little bias (or a Gaussian distribution of bias). We want the data to be unbiased with respect to viewing and solar zenith angles (time of day), cloudiness, aerosol, albedo (several comments about this yesterday). NO 2 Validation issues

4. Los Angeles: WRF-Chem

5. from Choi et al. 2014 observations modeled fit 1σ variation range Particulate Matter (co-emitted with CO 2, NO x, CO, …)

6. NASA standardBEHR Terrain pressure High-res terrain database, center of OMI footprint High-res terrain database, average over OMI footprint Terrain reflectivity Monthly 1° × 1°MODIS, 8 day 0.05° × 0.05° NO 2 profile shape Annually 2° × 2.5°WRF-Chem, Monthly 4 × 4 km 2 (CA&NV) 12 x 12 km 2 U.S. Clouds OMI cloud productMODIS cloud product Russell et al., Atmos Chem & Phys 11, 8543-8554, 2011 http://behr.cchem.berkeley.edu/ /

7. Terrain Reflectivity (Albedo) NASA Standard Product June 2008 BEHR June 2008 MODIS True Color SP NO 2 June 18, 2008 OMI Monthly Albedo MODIS 8 day Albedo Russell et al., Atmos Chem & Phys, 2011

8. Terrain Reflectivity (Albedo) Russell et al., Atmos Chem & Phys, 2011 Histogram of systematic errors

9. NO 2 profile shape Russell et al., Atmos Chem & Phys, 2011 Histogram of systematic errors

10. The BEHR product is generally higher in urban regions and lower in rural regions than the operational products BEHR % Difference Standard Product Russell et al., Atmos Chem & Phys, 2011

11. Trends in cities are similar while trends at power plants are more variable Russell et al., ACP 2012 47 cities, 23 power plants!

12. Example: look in remote places with uniform (but low) NO 2 columns and make sure observed variation is geophysical sensible—not driven by viewing angle etc. Stare at one location for an hour (at midday) and check that clouds moving across the scene don’t affect the interpretation. Examine repeats at a power plant with near constant emissions and check that there is little variation of NO 2 with time of day. NO 2 Validation Strategies Check all possible avenues for internal consistency

13. OMI Berkeley High-resolution Retrieval (BEHR) 0 1 2 3 4 5 6 7 8 9 10x10 15 NO 2 (molecules cm –2 ) May–October 2005–2006

14. NO 2 Validation Strategies Additional “conventional data” Aircraft/ground based experiments e.g. DISCOVER; KORUS Surface network additional PANDORA’s

15. NO 2 Validation Strategies “unconventional data”

16. CO 2 Emissions in San Francisco bay area at 1km resolution

17. NO NO 2 O 3 CO CO 2 aerosol

18. BEACO 2 N observing network http://beacon.berkeley.edu/

19. Vaisala GMP343 NDIR CO 2 Sensor Shinyei Grove Particulate Sensor Electrochemical O 3, NO, NO 2 & CO Sensors

20. BEACO 2 N CO 2 2013 Sites:LaurelKorematsuHeadRoyce BurckhalterKaiserODowdElCerrito PrescottCollegePrepStLizNOakland

21. WRF-STILT for day bridge was closed Alex Turner 10 km

22. NO 2 Validation Strategies “other unconventional data?” Profiling with small sensors and drones LIDARS Sondes