continued improvements of air quality forecasting through emission adjustments using surface and...
TRANSCRIPT
Continued improvements of air quality forecasting through emission adjustments
using surface and satellite data &Estimating fire emissions: satellite vs.
bottom-up
Georgia Institute of Technology
Talat Odman, Yongtao Hu and Ted Russell School of Civil & Environmental Engineering, Georgia Institute of Technology
With thanks to Pius Lee and the NOAA ARL Forecasting Team
AQAST Meeting, January 15th, 2014
ObjectiveImprove air quality forecasting accuracy using earth science products through dynamic adjustments of emissions inventories and simulation of wildland fire impacts
– Air quality forecasting is an integral part of air quality management.
– Current forecasting accuracy calls for improvement.– Forecasting with 3-D models relies on accuracy of
emissions.– Emission inventories are typically at least 4 years
behind and “growth factors” are outdated.– Wildland fires are becoming an increasingly important
contributor to PM and ozone.– Fire is one of the most uncertain emission categories
as multi-year averages of past fires do not represent future fires.
Georgia Institute of Technology
Hi-Res Forecasting System
• Based on SMOKE, WRF and CMAQ models
• Forecasting ozone and PM2.5 since 2006
• 48-hour forecast at 4-km resolution for Georgia and 12-km for most of Eastern US
• Used by GA EPD assisting their AQI forecasts for Atlanta, Columbus and Macon
• Potentially useful for other states
Georgia Institute of Technology
36-km (148x112)
12-km (123x138)
4-km (123x123)
36-km (148x112)
12-km (123x138)
4-km (123x123)
Hi-Res Modeling Domains
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Hi-Res performance during 2006-2013 ozone seasons for Metro
AtlantaOzone PM2.5
MNB 20%
MNE 25%
MNB -10%
MNE 32%
0
75
150
0 75 150
Obs.
4-km
185 165
74960
0.0
35.0
70.0
0 35 70
Obs.4-
km
0 0
1068
52
Inverse Modeling Approach for Adjusting Emissions
An emissions and air quality auto-correction system utilizing near real-time satellite and surface observations
• Minimizes the differences between forecasted and observed concentrations (or AOD)
• With minimum adjustment to source emissions
• Using contributions of emission sources calculated by CMAQ-DDM-3D – Source contributions can be
used for dynamic air quality management.(e.g., fires)
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• Solve for Rj that minimizes 2
J
j R
jN
i C
J
jjji
simi
obsi
jobsi
RRScc
1
2
2ln1
2
2
1,
2 )(ln)1(
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uncertainties
total number of obs
total number of sources
DDM-3D calculated sensitivity of concentration i to source j emissions
emission adjustment ratio
weigh for the amount of change in source strengths
Inverse Model Formulation
Off-line tests using “real-time” PM2.5 observations
• Surface PM2.5 data from six sites in Atlanta– Direct use of satellite
data (AOD) was problematic because of much larger uncertainties compared to surface data.
– AOD will be “fused” to PM2.5 concentration fields to provide “real-time” spatial patterns.
Georgia Institute of Technology
Confederate AveAtlanta
Kennesaw
DouglasvilleConyers
Newnan
South DeKalb
McDonoughFayetteville
Walton
Peachtree City
Yorkville
Gwinnet
Atlanta
NE Atlanta
West Atlanta
NWS MetSLAMS PM2.5
SLAMS O3
Confederate AveAtlanta
Kennesaw
DouglasvilleConyers
Newnan
South DeKalb
McDonoughFayetteville
Walton
Peachtree City
Yorkville
Gwinnet
Atlanta
NE Atlanta
West Atlanta
NWS MetSLAMS PM2.5
SLAMS O3
DDM-3D sensitivities calculated for week1: Dec. 1-7, 2013
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Source Area On-road
Non-road
Point
Dec. 1-7,2013
0.17 0.83 0.85 0.97
Obtained emissions adjustments ratios (Rj)
Shown for select day Dec. 2, 2013
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PM2.5 Forecasting Performance for week 2: Dec. 08-14, 2013
Obs (ug/m3)
Sim (ug/m3)
NFE NFB
Dec. 11, 2013
8.57 16.57 65% 65%
Emis adjusted
8.45 24% 2%
Dec. 8-14, 2013
4.64 10.04 86% 85%
Emis adjusted
5.62 54% 39%
without emissions adjustmentsDec. 11, 2013 PM2.5 Concentration
with emissions adjustmentsDec.11, 2013 PM2.5 Concentration
Comparison of Fire Emission Estimates: Satellite vs. Bottom-up
• Both have roles in improving accuracy of fire impact forecasts: Satellite for wildfires and bottom-up for prescribed burns.
• Global Biomass Burning Emissions Product (GBBEP) is currently using Fire Radiative Power from GOES
• Buttom-up estimates use fuel-loads, consumption and emission factors.
• GBBEP and buttom-up emissions compared for Williams fire, a 200 acre chaparrel fire in California on November 11, 2009
Georgia Institute of Technology
Akagi et al., ACP, 2012
Comparison of Emission Estimates: Williams Fire
• Buttom-up PM2.5 emission estimates are ~50% larger than GBBEP emissions
• Aircraft measured aerosol light scattering, converted to PM2.5 and compared to modeled PM2.5 concentrations
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Comparison of Modeled PM2.5 to Aircraft Measurements
• Uncertainties in dispersion modeling (WS, WD, plume height, etc.) must be reduced to better evaluate emission estimates.
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Conclusions
• Dynamic emissions inventory adjustment dramatically improving PM forecast accuracy in off-line testing. On-line testing and implementation underway– Large bias in dust emissions in winter corrected– Improved approach to assimilating AOD and PM
measurements underway
• Bottom-up and satellite-based fire emission estimates being improved with airborne smoke measurements – Fire emission contribution forecasts underway for
dynamic prescribed-burn management
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Poster
• Davis et al., Nitrogen Deposition (Tiger Team Project)
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Acknowledgements
• NASA• Georgia EPD• Georgia Forestry Commission• US Forest Service
– Scott Goodrick, Yongqiang Liu, Gary Achtemeier
• Strategic Environmental Research and Development Program
• Joint Fire Science Program (JFSP)• Environmental Protection Agency (EPA)
Georgia Emission Totals (tons/yr)
Georgia Totals (2013 Hi-Res) VOC NOx CO SO2 PM10 PM25 NH3area-dust 241150 39240area-others 366497 41790 118093 64613 32450 26965 80896egu 1439 174136 11689 648564 11863 5977 5non-egu 32843 49791 76059 60353 15059 10909 3613non-road 69803 101653 786873 9403 9685 9242 49on-road (NEI2011) 101360 241964 1084877 1133 10943 8144 4382
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DDM-3D sensitivities calculated for week1: Jul. 6-12, 2011
Georgia Institute of Technology
Emission adjustments ratios (Rj)
Shown for Jul. 11, 2011
Source Area On-road
Non-road
Point
Jul. 6-12,2011
3.34 1.09 1.46 1.10
Georgia Institute of Technology
PM2.5 Forecasting Performance of week2: Jul. 13-19, 2011
Obs (ug/m3)
Sim (ug/m3)
NFE NFB
Jul. 15, 2011 11.35 3.85 94% -94%
Emis adjusted
7.23 50% -40%
Jul. 13-19, 2011
14.39 8.67 54% -44%
Emis adjusted
14.92 44% 7%
without emissions adjustmentsJul. 15, 2011 PM2.5 Concentration
with emissions adjustmentsJul.15, 2011 PM2.5 Concentration