utilizing satellite retrievals in high- resolution...

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


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.


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


( )∑∑

∑==

= 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


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


• 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


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


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


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


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


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



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


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


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


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


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


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

utilizing satellite retrievals in high- resolution...

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