Utilizing satellite retrievals in high-resolution particulate matter forecasting
Yongtao Hu, M. Talat Odman andArmistead G. Russell School of Civil & Environmental Engineering, Georgia Institute of Technology
Leigh A. Munchak, Shana Mattoo Science and Exploration Directorate, NASA Goddard Space Flight Center, and Science Systems
and Applications, Inc., Lanham Lorraine A. Remer
Joint Center for Earth Systems Technology, University of Maryland at Baltimore County and helpful discussions and input from Pius Lee and Taifun Chai
AQAST Meeting, June 4th, 2013
Topics • Objective
– Improve regional forecasting systems using satellite and surface observations • Focus on emissions corrections
• Expanded Hi-Res operational forecasting system – Past performance
• Methodology on identifying errors in emissions using observations: – DDM-3D sensitivity analysis – January 2004 emissions adjustments using CSN
measurements • CMAQ derived AOD systematic under-predictions
compared with MODIS and AERONET AODs.
Georgia Institute of Technology
Hi-Res: forecasting ozone and PM2.5 48 hr forecast @ 4-km resolution for Georgia and
@ 12-km for most states of eastern US Hi-Res Modeling Domains
36-km (148x112)
12-km (123x138)4-km (123x123)
36-km (148x112)
12-km (123x138)4-km (123x123)
Hi-Res forecasting products are in use by Georgia EPD assisting their local AQI forecasts for multiple metro areas
Hi-Res forecasting products are potentially useful elsewhere
Georgia Institute of Technology
Overall 2006-2012 Performance (Ozone Season): Atlanta Metro
Ozone PM2.5
MNB 19% MNE 24%
MNB -13% MNE 31%
0
75
150
0 75 150
Obs.
4-km
176 163
640 59
0.0
35.0
70.0
0 35 70
Obs.4-
km
0 0
937
52
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
5/1/2009 5/21/2009 6/10/2009 6/30/2009 7/20/2009 8/9/2009 8/29/2009 9/18/2009
Date
Organi
c Carb
on PM
2.5
forecast obs
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
5/1/2012 5/21/2012 6/10/2012 6/30/2012 7/20/2012 8/9/2012 8/29/2012 9/18/2012
Date
Organi
c Carb
on PM
2.5
forecast obs
0
4
8
12
16
0 4 8 12 16
Observed (ug m-3)
Fore
cast
(ug
m-3
)
2006 2007 2008 2009 2010 2011 2012
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
5/1/2011 5/21/2011 6/10/2011 6/30/2011 7/20/2011 8/9/2011 8/29/2011 9/18/2011
Date
Organi
c Carb
on PM
2.5
forecast obs
Forecast vs. Observed OC at South DeKalb
Ozone Season May-September
2011
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
5/1/2010 5/21/2010 6/10/2010 6/30/2010 7/20/2010 8/9/2010 8/29/2010 9/18/2010
Date
Organi
c Carb
on PM
2.5
forecast obs
2009
2010
• Implemented multigenerational OC formation in 2009 (Baek et al. 2011)
2012
2012
0
2
4
6
8
10
12
14
16
18
20
05/01/12 05/21/12 06/10/12 06/30/12 07/20/12 08/09/12 08/29/12 09/18/12
Date
Daily
Avg
Hum
idity
at 2m
(g/kg
)
2012
290
295
300
305
310
315
05/01/12 05/21/12 06/10/12 06/30/12 07/20/12 08/09/12 08/29/12 09/18/12
Date
Daily
Hi T
emp
at 2
m (K
)
Forecast vs. Observed 2012 Ozone Season Metro Atlanta
MB -1.16g/kg
ME 1.54g/kg
Temperature
Humidity
MB 0.2K
ME 1.35K 020
40
60
80
100
120
Apr-12 May-12 Jun-12 Jul-12 Aug-12 Sep-12
O3 (
ppb)
Obs 4-km
0
5
10
15
20
25
30
35
40
45
May-12 Jun-12 Jul-12 Aug-12 Sep-12
PM2.
5 (u
g/m
3)
Obs 4-km
MNB 27%
MNE 28%
MNB 8%
MNE 26%
PM2.5
O3
Improving Forecasting Performance • Improved meteorological forecasts • CMAQ model improvements
– SOA mechanism, nighttime nitrate dynamics, etc. • MODIS AOD assimilated PM fields as initial conditions.
– Collaborate with NOAA (Pius Lee’s talk) • Emissions forecasting and inventory growth
– Prescribed burn emissions – based on pre-burn permitting information (on-going work)
– Weather forecast, process-based emissions forecasting models ( Tong et al. 2012)
– Accounting for economic trends • Started forecasting ozone high as recession hit
• Emissions correction by inverse modeling – Utilizing near-real-time surface measurements and satellite retrievals.
Georgia Institute of Technology
Adjusting Emissions using Near Real-time Observations for Forecasting
Standard Forecasting
Off-line test
On-line test
Emissions Adjusting Period
DDM-3D
Inverse
WRF-SMOKE-CMAQ
WRF-SMOKE-CMAQ-DDM-3D
CMAQ
Obs
Inverse
CMAQ
Adjtd E
Inverse
Obs
Adjtd E
Inverse Adjtd E
Adjtd E Inverse
Adjtd E Adjtd E
Obs Obs
DDM-3D
Inverse
Evaluate
Standard Forecasting
Off-line test
On-line test
Emissions Adjusting Period
DDM-3D
Inverse
WRF-SMOKE-CMAQ
WRF-SMOKE-CMAQ-DDM-3D
CMAQ
Obs
Inverse
CMAQ
Adjtd EAdjtd E
InverseInverse
Obs
Adjtd EAdjtd E
InverseInverse Adjtd E
Adjtd E Inverse
Adjtd E Adjtd E
Obs Obs
DDM-3D
Inverse
EvaluateEvaluate
Projected Emissions
Inverse Modeling Using CMAQ-DDM-3D
Surface NO, NO2, O3, PM …
Sat. AOD …
“Corrected” Emissions
Georgia Institute of Technology
( )∑∑
∑==
= Γ+
+
−−−
=CTM
jCTMiobsi
CTM
J
j R
jN
i SRc
J
j
basejij
basei
obsi R
SARcc
1
2
2ln1
22
2
1,
2 )(ln1
σσσχ
Prediction errors can be reduced by adjusting emissions by source according to each source’s contribution to concentration (DDM-3D calculated sensitivities)
SAi,j: source j’ contribution to species i’s concentration, Rj: source j’s emissions adjustment factor, to be solved.
y = 0.014x + 606.542R2 = 0.856
10
100
1000
10000
100000
1000000
100 1000 10000 100000
1E+06 1E+07
X2 Initial
X2 R
efin
ed
Improvements after emissions adjustments
Georgia Institute of Technology
Case study: January 2004 CONUS 36-km simulation
PM2.5
y = 0.68x + 10.17R2 = 0.17
y = 0.31x + 5.33R2 = 0.22
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60 70 80Observed (ug m-3)
Mod
eled
(ug
m-3
)
InitialRefined
Uncertainty weighted X2 reduced by over 98%. Reduced bias, slope still low (0.31).
Issues of Emission Corrections Using Surface and AOD Observations for Operational Forecasting
Georgia Institute of Technology
• Speciated/element PM2.5 species measurements are not available. Available observations:
– Hourly TEOM PM2.5 sparsely located, real-time – MODIS AOD L2 products, near real-time, several hours delay.
• Uncertainties in MODIS AOD products – Δτ=±0.05±0.15τ over land, Δτ=±0.03±0.05τ over ocean, τ denotes MODIS
derived monthly AOD (Remer at al. 2005) • Calculate AOD from CMAQ mass concentrations
– Empirical approach, Chameides et al. 2002 – Semiempirical approach, based on IMPROVE formula, Malm et al. 1994 – We use method described in Binkowski and Roselle, 2003, based on Mie
Theory, • Mie extinction efficiency estimated using Heintzenberg and Baker (1976)
• Systematic under-prediction of derived CMAQ-AOD compared with AODs from MODIS and AERONET – Reported by Roy et al. 2007 used Malm et al. 1994 – Zhang et al. 2009 used Chameides et al. 2002
4-km Grid
1-km Grid
Episodic Mean CMAQ-AOD Episodic Mean MODIS-AOD pre-C006
Comparison with Satellite-derived AOD Fields: DISCOVER-AQ June27-August1 2011 4&1 km Domains
0.35
0.35 0.6
0.6
0.2
0.2
0.2
0.2
June 2001 monthly averaged CMAQ derived AOD at 36-km resolution differ
with MODIS AOD (Roy et al. 2007)
Comparison with Satellite-derived AOD: Episodic Average Values for DISCOVER-AQ 1-km Grid Cells
Georgia Institute of Technology
Much larger variation in MODIS AOD than CMAQ-derived
CMAQ-AOD vs. MODIS-AOD pre-C006
y = 0.11x + 0.18R2 = 0.21
0.00
0.20
0.40
0.60
0.80
1.00
0.00 0.20 0.40 0.60 0.80 1.00
MODIS-AOD
CM
AQ
-AO
DCMAQ-AOD vs. MODIS-AOD C051
y = 0.05x + 0.20R2 = 0.03
0.10
0.20
0.30
0.40
0.10 0.20 0.30 0.40
MODIS-AOD
CM
AQ
-AO
D
Georgia Institute of Technology
Comparison with Satellite-derived AOD Fields: G-F Wildfire Period May 8-June 1, 2007 at 12-km Domain
CMAQ AOD (Episodic mean of daily 15-20Z averages)
MODIS AOD (Episodic mean of MOD and MYD L2 pre-C006 ~3-km
MODIS AOD (Episodic mean of MOD and MYD L2 C051 ~10-km
MODIS products: ~3-km vs. ~10-km
0.15
0.50
Georgia Institute of Technology
CMAQ AOD (Annual mean of daily 15-20Z averages)
Comparison with Satellite-derived AOD Fields: Annual 2007 CMAQ5.0.1 SEMAP 12-km Domain
MODIS AOD Annual mean of MOD and MYD L2 C051
MODIS AOD Annual mean of MOD (on Terra) L2 C051
MODIS AOD Annual mean of MYD (on Aqua) L2 C051
0.12
0.3 0.3
0.3
Georgia Institute of Technology
Comparison with AERONET Total AOD 500nm
QQ and Scatter plots. No averaging on AERONET data, compared with hourly CMAQ AOD.
Annual 2007 CMAQ SEMAP 12-km Simulation
y = 0.18x + 0.03R2 = 0.54
0
0.5
1
1.5
2
0 0.5 1 1.5 2AERONET Total AOD 500nm
CM
AQ
AO
D 5
5nm
Annual 2007 CMAQ SEMAP 12-km Simulation
0
0.5
1
1.5
2
0 0.5 1 1.5 2AERONET Total AOD 500nm
CM
AQ
AO
D 5
50nm
CMAQ-AOD underestimates
• PM2.5 not very biased from 2009 on. • Uncertainties in both MODIS AOD products and CMAQ-
AOD derivation – Size distributions in CMAQ model used in AOD derivation
– Parameterization in CMAQ-AOD derivation, mixing state, humidity…
• Underestimation of CMAQ on PM2.5 mass concentration above surface and below 3-4 km (Zhang et al. 2009), – Further evaluation
• Much lower variability in simulated PM2.5 versus observed AOD’s – Similar to PM observations vs. simulated values
Georgia Institute of Technology
Next Steps Quantify the systematic bias between CMAQ derived
AOD and MODIS AOD Correct CMAQ AOD calculation Potentially use other PM2.5-AOD relationships.
Demonstration study of off-line forecasting using emissions adjusted by evaluating with real-time PM2.5 measurements and near real-time MODIS AOD L2 products. • Weekly emissions adjustments
Integrate surface and satellite observations into operational forecasting system • Direct sensitivity analysis and forecasts
Georgia Institute of Technology
Georgia Institute of Technology
Acknowledgements
• NASA • Georgia EPD
– Jim Boylan, Di Tian, Michelle Bergin • 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)
Comparison with CMAQ simulated surface PM2.5 concentration: Episodic Average Values for DISCOVER-AQ
1-km Grid Cells, matched with 3-km MODIS AOD data
Georgia Institute of Technology
Surface CMAQ-PM25 vs. CMAQ-AOD
y = 64.12x + 1.02R2 = 0.52
0
10
20
30
40
0.00 0.10 0.20 0.30 0.40
CMAQ-AOD
CM
AQ
-PM
25 (u
g/m
3)
Surface CMAQ-PM25 vs. MODIS-AOD pre-C006
y = 6.73x + 12.48R2 = 0.09
0
10
20
30
40
0.00 0.30 0.60 0.90 1.20
MODIS-AOD
CM
AQ
-PM
25 (u
g/m
3)
0.4 1.20
Georgia Institute of Technology
Comparison with Satellite-derived AOD and CSN observations: ARCTAS 12 km results at the Del Paso Manor Site
Observed and Modeled at the Del Paso Manor Site: PM2.5 concentrations (left axis) and AOD (right axis)
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
15-Jun-2008 20-Jun-2008 25-Jun-2008 30-Jun-2008 5-Jul-2008 10-Jul-20080.0
1.0
2.0
3.0
4.0
5.0obs_PM2.5cmaq_PM2.5obs_OCcmaq_OCobs_ECcmaq_ECmodis3k_AODmodis_AODcmaq_AOD
Georgia Institute of Technology
Case study: January 2004 CONUS 36-km simulation Emissions adjustments (Rj) made on 33 sources at CSN monitoring locations
0%10%20%30%40%50%60%70%80%90%
100%
0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1
Optimal Rj at all CSN sites in January 2004
COALCMBDIESELCMFUELOILCLPGCMBMEXCMB_MNAGASCMBOTHERCMB
0%10%20%30%40%50%60%70%80%90%
100%
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5
Optimal Rj at all CSN sites in January 2004
AIRCRAFTNRDIESELNRFUELOINRGASOLNRLPGNRNAGASNROTHERSORDIESELORGASOLRAILROAD 0%
10%20%30%40%50%60%70%80%90%
100%
0.1 1 10Optimal Rj at all CSN sites in January 2004
BIOGENICDUSTLIVESTOCKMETALPRMEATCOOKMINERALPROTHERSSEASALTSOLVENT
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
100%
0.1 1 10
Optimal Rj at all CSN sites in January 2004
AGRIBURN LWASTEBU OPENFIRE PRESCRBU WILDFIRE WOODFUEL WOODSTOVE
Utilizing satellite retrievals in high-resolution particulate matter forecastingTopicsSlide Number 3Overall 2006-2012 Performance (Ozone Season): Atlanta MetroSlide Number 5Forecast vs. Observed 2012 Ozone Season�Metro AtlantaImproving Forecasting PerformanceSlide Number 8Slide Number 9Issues of Emission Corrections Using Surface and AOD Observations for Operational ForecastingSlide Number 11Slide Number 12Slide Number 13Slide Number 14Slide Number 15CMAQ-AOD underestimatesNext StepsAcknowledgementsSlide Number 19Slide Number 20Slide Number 21