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
1.
2.
3.
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
Background & Motivation
Clouds greatly impact tropospheric chemistry by altering dynamics, radiative forcing, and
atmospheric chemical processes



boundary-layer
development
surface insolation &
temperature
Vertical
mixing/transport
Impacting aqueous
phase chemistry
Regulating
photochemical
reaction rates
Chemistry/biogenic
hydrocarbons and
nitrogen oxide
emissions
Lightning generated
nitrogen oxides
wet removal
Weather Forecasting/Climate
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
Evolution and
recycling of aerosols
Air Quality Community



Both precipitating and nonprecipitating clouds are important
Evaluation: Statistical as well as
episodic (PAIRED IN SPACE AND TIME)
Correct location and timing of model
clouds being important
 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
Satellite-Derived Photosynthetically
Active Radiation (PAR)
Based on Stephens
(1978), Joseph (1976),
Pinker and Laszlo (1992),
Frouin and Pinker (1995)
Insolation/PAR Evaluation (September 2013)
Spatial Distribution of NMB (normalized mean bias) Against Soil Climate
Analysis Network (SCAN)
WRF
WRF
NMB = 22%
NME = 34%
Satellite
Satellite
NMB = 14%
NME = 27%
6
PAR evaluation
against SURFRAD
stations for
August 2006.
UAH product with
bias correction
shows the best
agreement with
surface obs.
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.
1. PAR_cntrl:
2. PAR_analytical:
3. PAR_UMD:
4. PAR_UAH:
Base WRF simulation to provide insolation for M EGAN
Base WRF + cloud assimilation from GOES to provide
insolation for M EGAN
Direct use PAR retrievals from UM D, other met inputs
same as case 'PAR_analytical'
Direct use PAR retrievals from UAH, other met inputs
same as case 'PAR_analytical'
OBS_AVE
SIM_AVE
IA
(W/m2)
(W/m2)
WRF cntrl
248.6
299.8
0.95
WRF analytical
248.6
266.8
UAH satellite
248.6
263.6
R
RMSE
MB
MAGE
NMB
NME
(W/m2)
(W/
m2)
(W/m2
)
(%)
(%)
0.91
142.3
53.9
74.7
22.2
30.7
0.95
0.91
143.9
20.3
74.9
8.9
30.7
0.96
0.96
123.2
17.3
71.8
7.5
29.5
Comparing August, 2006, insolation from control WRF simulation (cntrl), UAH WRF simulation
(analytical), and satellite-based (UAH) against 47 radiation monitoring stations in Texas.
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.
Estimated Emission Difference and Impact on O3 for September 2013
(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%).
Evaluation of model isoprene predictions for three cases over 18 TCEQ
CAMS sites.
Case
OBS_AVE
(ppbV)
0.23
SIM_AVE
(ppbV)
0.59
IA
R
0.37
analytical
0.23
0.61
UAHPAR
0.23
0.47
cntrl
Errors in model
estimation of
PAR cannot
explain the large
over-prediction
of isoprene.
0.36
RMSE
(ppbV)
0.69
MB
(ppbV)
0.39
MAGE
(ppbV)
0.49
NMB
(%)
292
NME
(%)
326
0.37
0.37
0.72
0.42
0.51
311
342
0.41
0.40
0.69
0.29
0.41
225
271
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
(ppbV)
(ppbV)
cntrl
30.6
32.7
analytical
30.6
UAHPAR
30.6
R
RMSE
MB
MAGE
NMB
NME
(ppbV)
(ppbV)
(ppbV)
(%)
(%)
0.75
14.5
2.8
11.9
18.2
42.6
32.4
0.76
14.2
2.4
11.6
17.0
41.7
32.5
0.76
14.2
2.5
11.6
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.
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.
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.