1. The 96th AMS Annual Meeting
Improved Air Quality Simulations during NASA’s DISCOVER-AQ Texas: Incorporating Geostationary Satellite Observations
Arastoo Pour Biazar1, Andrew White1, Richard T. McNider1, Daniel Cohan2, Rui Zhang2, Bright Dornblaser3, Mark Estes3
University of Alabama in Huntsville
Rice University
Texas Commission on Environmental Quality (TCEQ)
Presented at:
The 96th AMS Annual Meeting
New Orleans, LA
10-14 January 2016
Session J13.3: NASA Earth Observation and Climate Change, Seventh Conference on Environment and Health

2. Background & Motivation
Clouds greatly impact tropospheric chemistry by altering dynamics, radiative forcing, and atmospheric chemical processes
Evolution and recycling of aerosols
Regulating photochemical reaction rates
surface insolation & temperature
Impacting aqueous phase chemistry
Lightning generated nitrogen oxides
boundary-layer development
Vertical mixing/transport
wet removal
Chemistry/biogenic hydrocarbons and nitrogen oxide emissions
CLOUDS
Weather Forecasting/Climate Community
Air Quality Community
Precipitation, impact on climate
Evaluation: Statistical performance over large area and longer times
Correct location and timing of model clouds being less important as long as statistical evaluation is satisfactory
Both precipitating and non- precipitating clouds are important
Evaluation: Statistical as well as episodic (PAIRED IN SPACE AND TIME)
Correct location and timing of model clouds being important

3. Biogenic Volatile Organic Compounds (BVOC) Emissions
BVOC estimates depend on the amount of radiation reaching the canopy (i.e. Photosynthetically Active Radiation - PAR) and temperature.
Large uncertainty is caused by the model insolation estimates that can be corrected by using satellite-based PAR in biogenic emission models (Guenther et al. 2012) hv
NOx + VOC + hv O3
Biogenic Volatile Organic Compounds (BVOC) Emissions
BVOC is a function of radiation and temperature
T & R

4. Satellite-Derived Photosynthetically Active Radiation (PAR)
Based on Stephens (1978), Joseph (1976), Pinker and Laszlo (1992), Frouin and Pinker (1995)

6. Insolation/PAR Evaluation (September 2013)
Spatial Distribution of NMB (normalized mean bias) Against Soil Climate Analysis Network (SCAN)
WRF
Satellite
WRF
NMB = 22%
NME = 34%
Satellite
NMB = 14%
NME = 27% 6

7. PAR evaluation against SURFRAD stations for August 2006
PAR evaluation against SURFRAD stations for August UAH product with bias correction shows the best agreement with surface obs.

8. Statistics for 47 TCEQ Sites (for August 2006)
Satellite cloud assimilation reduced mean bias by 63% and NMB by 60% over 47 TCEQ sites.
Due to WRF higher clear sky value, correlation is unchanged.
OBS_AVE
SIM_AVE IA R
RMSE MB
MAGE
NMB
NME
(W/m2)
(%)
WRF cntrl
248.6
299.8
0.95
0.91
142.3
53.9
74.7
22.2
30.7
WRF analytical
266.8
143.9
20.3
74.9
8.9
UAH satellite
263.6
0.96
123.2
17.3
71.8
7.5
29.5

9. Comparing August, 2006, insolation from control WRF simulation (cntrl), UAH WRF simulation (analytical), and satellite-based (UAH) against 47 radiation monitoring stations in Texas.

10. Satellite-derived PAR substantially reduced isoprene emission estimates over Texas (DISCOVER-AQ period)
Domain-wide sum of estimated isoprene (ISOP) and monoterpene (TERP) emission strength over Texas area using different PAR inputs in MEGAN during September 2013.
Comparison of the spatial pattern of estimated average isoprene emission rate in MEGAN using different PAR inputs over Texas domain during September 2013.

11. ISOP Diff in % TERP Diff in %
Estimated Emission Difference and Impact on O3 for September (Satellite - WRF)
ISOP Diff in %
TERP Diff in %
Isoprene emission is more sensitive to PAR input with the highest increase region at Northeast (>30%) and decrease at the Southwest (> 20%). The relative change for monoterpene emission is modest (-10% to 5%).

12. Evaluation of model isoprene predictions for three cases over 18 TCEQ CAMS sites.
OBS_AVE
SIM_AVE IA R
RMSE MB
MAGE
NMB
NME
(ppbV)
(%)
cntrl
0.23
0.59
0.37
0.36
0.69
0.39
0.49
292
326
analytical
0.61
0.72
0.42
0.51
311
342
UAHPAR
0.47
0.41
0.40
0.29
225
271
Errors in model estimation of PAR cannot explain the large over-prediction of isoprene.

13. Maximum daily 8-hr average ozone concentrations (MDA8 O3) for September 1-15, 2013.
Normalized mean bias (NMB) for CMAQ hourly ozone from three simulations at TCEQ sites.
Case
OBS_AVE
SIM_AVE R
RMSE MB
MAGE
NMB
NME
(ppbV)
(%)
cntrl
30.6
32.7
0.75
14.5
2.8
11.9
18.2
42.6
analytical
32.4
0.76
14.2
2.4
11.6
17.0
41.7
UAHPAR
32.5
2.5
17.3
41.8
Most areas impacted by reduction in BVOC are NOx limited, and the reductions are not enough to make considerable improvement in O3 predictions.

14. Recap and Concluding Remarks
A new satellite-based PAR was produced and evaluated for this study.
The impact of using satellite PAR on BVOC emission estimates by MEGAN and consequently on CMAQ simulation during the Texas DISCOVER-AQ Campaign (September 2013) was examined.
Satellite-based PAR is in reasonable agreement with surface observations and is able to correct model errors.
For September 2013, using satellite PAR in MEGAN increased isoprene and monoterpene emission estimates over the east coast but decreased them over the west coast and Texas.
The impact of PAR inputs on ozone prediction depends on the local NOx/VOC ratio and is more pronounced over VOC limited regions. In this study, over the VOC limited regions, the satellite PAR changed surface O3 prediction by 5-8%.
Over east Texas, MEGAN greatly over-estimated isoprene emissions and thereby the reductions caused by the use of satellite PAR did not significantly affected ozone predictions.
The large model isoprene over-prediction over east Texas could not be corrected by the use of satellite PAR.
This study will be repeated using BEIS model.

15. Acknowledgment
The findings presented here were accomplished under partial support from NASA Science Mission Directorate Applied Sciences Program and the Texas Air Quality Research Program (T-AQRP).
Note the results in this study do not necessarily reflect policy or science positions by the funding agencies.