1. Model Evaluation with Satellite Data: NO 2, HCHO, and Beyond Monica Harkey Tracey Holloway Alex Cohan Rob Kaleel

2. Air Quality from Space Witman, Holloway, and Reddy (2014)

3. Past CAMx Evaluation vs. Ground-Based Observations Mean Normalized Bias Past work evaluated hourly ground-based NO 2 from model and measurements May – September 2011 in many locations, modeled NO 2 > observed

4. Satellite Data to Assess Model Perfomance NO 2 evaluation based on Harkey and Holloway (in review) Formaldehyde-to-NO 2 ratio (FNR) evaluation based on Duncan et al. (2010), Witman, Holloway, and Reddy (2014) Jin and Holloway (in press)

5. Comparing Satellite and Model Data Apples and Oranges! satellite views a “swath” model represents data on a fixed grid ≠

6. Comparing Satellite and Model Data Apples and Oranges! satellite views a “swath” model represents data on a fixed grid SOLUTION: apply Wisconsin Horizontal Interpolation Program for Satellites (WHIPS) to satellite data, uses regional intersection to grid data to any specified grid

7. Comparing Satellite and Model Data Apples and Oranges! satellite sees total column but is more sensitive to “signal” from some altitudes than other altitudes sum of model layers = total column, but...

8. Comparing Satellite and Model Data Apples and Oranges! satellite sees total column but is more sensitive to “signal” from some altitudes than other altitudes sum of model layers = total column, but... SOLUTION: apply the satellite sensitivity factors (“averaging kernel” or “weighting function”) to model layers

9. Formaldehyde and NO 2 OMI NO 2 and HCHO averaging kernels applied to model column interpolated to 1 pm local time only use data where satellite data also exists—same sample size CB6R2 chemistry 2011 NEI version 1 and BEIS no lightning or forest fire emissions NO 2 DOMINO product HCHO NASA product Level 2 data gridded through WHIPS 2011 warm season (May – September) average OMICAMx

10. OMI & CAMx NO 2 10 15 molecules/cm 2 OMI CAMx OMI average = 1.52 x 10 15 molecules/cm 2 CAMx average = 2.43 x 10 15 molecules/cm 2 CAMx 60% higher than OMI CAMx captures overall pattern and urban-rural differences

11. OMI & CAMx NO 2 -- how are they correlated? time 10 15 molecules/cm 2 OMI CAMx

12. OMI & CAMx NO 2 -- how are they correlated? negative positive 10 15 molecules/cm 2 OMI CAMx time

13. average temporal correlation 0.18 mean bias0.97 mean error1.20 RMSE1.86 (x 10 15 molecules/cm 2 ) OMI & CAMx NO 2 model * values higher than observed * captures day-to-day variability

14. OMI & CAMx HCHO 10 15 molecules/cm 2 OMI CAMx OMI average = 8.19 x 10 15 molecules/cm 2 CAMx average = 10.2 x 10 15 molecules/cm 2 CAMx 24% higher than OMI CAMx captures overall pattern with highest values in southeastern US

15. OMI & CAMx HCHO average temporal correlation 0.16 mean bias2.6 mean error6.3 RMSE12.3 (x 10 15 molecules/cm 2 ) model * values higher than observed * captures day-to-day variability

16. What controls ozone production? FNR > 2, FNR-stddev > 1 = NOx-limited FNR ~ 1 = transitional FNR < 1, FNR+stddev < 2 = VOC-limited HCHO as a marker for VOCs

17. Compared to OMI, CAMx overestimates HCHO, NO 2 mean bias of HCHO twice that of NO 2 CAMx sees more NO X -limited areas OMI & CAMx FNR OMI CAMx

20. HCHO month-by-month: May CAMx OMI OMI average = 6.71 CAMx average = 6.83 average temporal correlation 0.13 mean bias0.87 mean error4.98 RMSE8.92 (x 10 15 molecules/cm 2 )

21. HCHO month-by-month: June CAMx OMI OMI average = 7.81 CAMx average = 9.82 average temporal correlation 0.13 mean bias2.93 mean error6.45 RMSE11.17 (x 10 15 molecules/cm 2 )

22. HCHO month-by-month: July CAMx OMI OMI average = 9.72 CAMx average = 11.92 average temporal correlation 0.12 mean bias2.60 mean error6.66 RMSE12.28 (x 10 15 molecules/cm 2 )

23. HCHO month-by-month: August OMI average = 8.83 CAMx average = 11.99 average temporal correlation 0.10 mean bias3.63 mean error7.40 RMSE16.29 (x 10 15 molecules/cm 2 ) CAMx OMI

24. HCHO month-by-month: September OMI average = 7.31 CAMx average = 9.56 average temporal correlation 0.15 mean bias2.48 mean error5.83 RMSE10.14 (x 10 15 molecules/cm 2 ) CAMx OMI

25. BEIS configuration 2011 meteorology from WRF - North American Model (NAM) used as initial conditions with Group for High Resolution Sea Surface Temperatures (GHRSST, 1 km horizontal resolution) - National Land Cover Database 2006 - Kain-Fritcsh cumulus parameterization - analysis nudging of T, q, u, v above PBL -> WRF configuration shown to have cool bias (< 1 C) in summer daytime temperatures, slightly overestimate precipitation in southeastern states in July, and overpredict downwelling shortwave (~PAR) in late morning/early afternoon in summer months (up to 100 W/m 2 ) Land-use from Biogenic Emissions Land Use Database version 3 (BELD3) - 1990 US Cenus Data - 1992 Agricultural Census Data -> BEIS shown to overpredict formaldehyde compared to MEGAN