Applications of Satellite Measurements
and Modeling for Air Quality

Changsub Shim

Korea Adaptation Center for Climate Change
Korea Environment InstituteAtmospheric O3 (Why O3?)OZONE: “GOOD UP HIGH, BAD NEARBY”

2/18/2010

Prediction of O3 is challengeO3: Air Quality and Climate impact
CH4, O3 are important greenhouse gases
OH is the most important oxidizing agent

Stratospheric O3

Free Tropospheregreenhouse gas

hnNO2NOOH

HO2VOCs, CH4hn

Global Background O3(Hemispheric Pollution)

Direct Intercontinental Transport

Boundary layer(0-3 km)

CONOx
VOCs

E. AsiaO3air pollution

O3NOxVOCs

air pollution
PacificN. America

O3

Quantitative Understanding of Air Pollution Chemistry
Accurate attribution of the factors is essential to air pollution policy!
 Background from Natural emissions (VOCs, NOx, …)
 Industrial/urban pollutants emissions
 Meteorological Impact (e.g., Stratospheric O3 influence, rainouts) Atmospheric Chemical Kinetics (any missing mechanism?)

Chemical Transport Model

Observations
Understand Obs.
Constrain Satellite Data

Inputs: AssimilatedMeteorology + Emissions+ diverse State (e.g.,
Initial Pollutant Conc.

2/18/2010

Solving continuity
equation for Individual grid
Constrain Most of Modelbox
Variables (Inputs):
Increasing
our knowledge
Outputs: Final Stateof Pollutant Conc.

Global tropospheric O3
distribution by TES in 2006

AURA Satellite (since 2004)

GOME

1997 (Shim2005)
Shim et al.,1997

OMI

Satellite
Instruments

Footprintscale

GOME(1995)

320 x
80 km

Continental ~
globalTOMS(1996)

280 x
100 kmContinental ~
globalSCIAMACHY(2002)

60 x 30km

Regional ~continentalOMI (2004)

13 x 24km

regional

2005 (Kurusou et al., 2006)

Changsub Shim

AIM workshop

Finer scale study

Improving resolution in satellite data (GOME vs OMI HCHO)Utilizing Satellite Measurements: East Asian pollution(Nitrogen Dioxide from OMI in 2006)•Seasonal energy use•NO2 lifetime•Monsoon effectChangsub Shim

AIM workshop

Improving horizontal resolution of CTMNested grid GEOS-Chem simulation (left)
CO

CO in 2005 (Chen et al., 2009)

 Progress on the remote sensing technique and modeling capability
is going to cover regional ~ global scale study
Changsub Shim

AIM workshop

Utilizing CTM + Satellite Obs.
: Analysis of East Asian Pollution w/ TES CO (Seoul)
Apr. 2006

China CO (BL)GEOS-Chem Model

GEOS-ChemModel
China CO (3 – 8 km)

Daily CO over Seoul, 2006 (Satellite vs Model)
KR-JP CO (BL)

Grays: TES Satellite Instr.
Daily TES avg CO
Model CO (all sources)
China industry/urbane
China Biofuel Burning
KR-JP anth
BiosphereBB

Topic 2: Improving emissions estimation
2 approaches to emissions estimation“Bottom up”
emissions estimates“Top-down”
emissions estimates

Creating detailed
emissions inventories
at a model resolution

From observations +Inverse modeling w/atmospheric observations

need for accurate emission estimates for regulatory purposes

Changsub Shim

AIM workshop

Forward (CTM) vs Inverse model• Infer a numerically optimized model variables in the given system derived
from true states“Causes (x)”Problem: Model variable
amounts (more uncertain)“bottom-up” a priori

Solution: Optimized model
variables“Top-down” a posteriori

Changsub Shim

“Effects (y)”Forward model

Prediction (e.g., modeled
chemical species.)

Inverse model

True State of Data(e.g., observed data)(ex. CTM)
y = Fx + e(x = x1 + x2 + .. )LinearizationOptimization
K = dy/dx

AIM WorkhshopAdjoint Inverse Modeling
Objective: calculate model parameters by minimizing the mismatches
between observation and model prediction (cost function J(x))
T 1T 1J(x)
(F(x)
y)
S(F(x)y)(xx)S a (x  x a )minimizeacompute x J (x) = 2(x F(x))T S1 (F(x)  y) + 2 Sa1 (x  xa )adjoint offorward model

Rodgers, 2000

Adjoint method-Compute gradients  x J (x) wrt model parameters byestimating adjoint (backward) forcing at the receptor,
propagating backward in time and space to the initialcondition (e.g. surface emissions). Advantage?
-Use optimization (steepest-descent) algorithm iteratively
until the gradient reaches the minimum  find xˆ

Changsub ShimAIM Workshop

Adjoint Sensitivity (Case I: E. US)•72 hrs adjoint
sensitivity ofPBL O3 over
NYC ~ D.C wrt
PBL NOX
concentration(2005 11/03)

Receptor-oriented (Adj)y 0 y Ny N y 0

Source-orientedSource
oriented (Fwd)Adjoint Sensitivity (Case II: N. Pacific)•

5 days adjoint sensitivityof PAN over Alaska at 0 ~5km height (2005 11/05)

Low T, high Km

PANPANy 0

VOCs NOx

NOxSource region

remote region y Ny N y 0

Source-orientedSource
oriented (Fwd)

Receptor-oriented (Adj)Adjoint Inverse Modeling (iterative way)
Inverse Model

Improved
Estimate

Optimization

Parameter Estimatet0tf
Predictions
Observations

whereGradients(sensitivities)

Adjoint Model

Forward Model
tft0

Cost function

Adjoint Forcing

Henze et al.,2007
Adjoint of CTM

and y is obs. x is a state vector

During the reverse integration for each iteration, the adjoint model calculates the gradient of cost function
to seek the minimum of cost function initiated by the “adjoint forcing” (error weighted difference b/w model predictions andobservations by steepest-descent algorithm based on successive calculations

gradient
Adjoint forcing

Inverse modeling for Constraining Global
NOx emissions
 Critically affect the capacity to produce O3 via photochemical processes- controlling oxidizing power of atmosphere- influence on the lifetime of other GHG (e.g., CH4)
 Limits in our understanding of NOx emission budget(1) A wide variety of sources(industry/urban > biomass burning/soil > lightning > etc.)(2) Large temporal & spatial variability(3) Less understanding in upper tropospheric features (convection,lightning/aircrafts, transport etc..)

O2

OH

NO2

O2

NOx
very low

NOx mid-highHOx
Changsub Shim

Troposphere(day time)

HO2

NO

O3
AIM Workshop

Global NOx emissions (GEOS-Chem a priori,2005 Nov)
NOx emissions

2005 (a priori)2001 (a)2005 (b)%Industry/urban

23.6 (1998)
GEIA

24

27.9 (2000)
EDGAR

56 ~ 62

Biofuel2.02 (1995)

2.2

2.03 (2000)

4.5 ~ 5

Soil/fertilizer

5.06 (c)

5.77

5.5

12 ~13

Biomass Burning

6.7

6.5 (d)5.41(GFEDV2)

10 ~16(Climatological)

Lightning/aircraft

4.2 (f)

4.7

4.5~10Total (Tg/yr)

42 (v6)4345 (v7)100(a): adapted from Park et al., (2004)(b): GEOS-Chem v7-1-3.
(1998): GEIA anthropogenic emission inventory for year 1985 scaled to 1998 by CO2 emission trends[Bey et al., 2001; Marland et al., 1999].
(2000): EDGAR anthropogenic emission inventory based on 2000.
(c): Based on Yienger and Levy et al., (1995).
(d): Climatological monthly biomass burning data (Duncan et al., 2003).
(e): Monthly GFEDv2 biomass burning data.
(f): Based on Wang et al., (1998)

Retrieval of SCIAMACHY NO2 Columns to map NOx emissions(from Dalhousie Univ.)

Spectral Fit (429-452 nm)

SCIAMACHY(since 2002
30 x 60 km
10:00LT)

Total Slant Column

Remove Stratosphere
Tropospheric SlantColumn

Tropospheric NO2columnClouds

Calculate AMF(geometry + NO2 verticalprofile + signal sensitivity +aerosol & cloud effect)

NO/Tropospheric NO2 Column

NO2  W
ALTITUDE

NO

NO2lifetime~hoursEmission

HNO3BOUNDARYLAYERNITROGEN OXIDES (NOx)
SCIAMACHY

Global NO2 columns

Data retrieval
SCIAMACHY NO2

Adjoint Inversion
Objective••Advantage••Inversion of NOx emissions with consideration of physiochemical feedbacks withdirect computing of parameter’s sensitivity
Comparison with “top-down” emissions estimates (or mass balance approach)derived from satellite observations (e.g., Martin et al., 2003; 2006)

Can consider the chemical and physical feedbacks during optimization  quantifying the parameter’s sensitivity w.r.t. model predictions
Optimization controlDisadvantage•

Still computationally expensive Ex) 64 Intel Itanium2 processors (SGI architecture with LINUX)• 1.5 GHz clock speed with 1MB Cache + 1GB RAM• With parallel computing (8 CPUs)
 Each iteration for one month time window (2°x2.5°, globally) takes 44 hours.

Changsub Shim

AIM Workshop

Data (Nov. 2005)






SCIAMACHY NO2 from Dalhousie Univ (reprocessed data), filtered cloudfraction > 40%.
CTM, GEOS-Chem is developed by Harvard Univ. and NASA.
Adjoint of GEOS-Chem v6-2-5 & GEOS-4 & full chemistry (by D. Henze)with 2°x2.5° horizontal resolution
Time window: one month (Nov. 2005) a week x 4Emissions (NOx)• GEIA anthropogenic NOx emission (scaled to 1998) 2005• Climatological Biomass Burning (Duncan et al., 2003)• Biofuel emissions (Yevich et al., 2003)• Soil NOx (Yienger and Levy(1995) & Wang(1998))• Lightning NOx (Cloud Top Height; Price and Rind(1998) & Pickering(1998)): only consider the total emissions for opt.
• Do not optimize the emission scheme  total amount of each type

Changsub Shim

AIM Workshop

Error Specification
State vector errors (%)

Relative instrument errors (N/S)

N.Am

E.U.

Asia

S.Am. Africa

R.W

Ind1

50

50

100

100

100

100

Ind2

50

50

100

100

100

100

Light

200

200

200

200

200

300

Soil

150

150

150

150

150

150

BB

150

150

150

150

150

150

BF

100

100

100

100

100

100

Observation error = e1 + e2 + e3
* e1: retrieval error from instrument (SCIAMACHY)* e2: representation error : ~0.7 of e1 (~4.0x1013 molec/cm2)* e3: model transport error (from Jones et al., 2003)
 ~0.8 of e1 (~4.5x1013 molec/cm2)
total obs. error is about factor of ~2.5 of instrumental (retrieval) error Same quantity of errors were applied to mass-balance approach

Inversion Results
Iteration

Norm of grad.

Cost func.
ratio

1

3.405D+03

12

1.857D+03

0.90

3

7.530D+02

0.73

43.180D+02

0.62

51.485D+02

0.52

6

1.112D+03

0.46

7

1.948D+02

0.403

88.180D+01

0.397

Cost FunctionGradient

Cost function from obs. Vs from a priori = ~10: 1
Now cost function reached ~ 0.40 of initial value after 7th iteration
Changsub Shim

AIM Workshop

NO2 columns: a priori vsA posteriori (Unit: 1015 molecules/cm2) Nov. 2005SCIASCIASCIAMACHY

CTM

CTM-SCIA
Initial (a priori)

8th iteration (a posteriori)

Inversion Results (cost func. Reduction)

Cost function ratio (Jn/J1)

10.8
0.6
0.4
0.2
005

10
Iteration

Changsub Shim

AIM Workshop

1520

Inversion Results : NOx emissions ratio( a posteriori / a priori)Changsub Shim

AIM Workshop

Total NOx emissions (by adjoint method,Nov 2005)•Large reductions in N. Ame,
Europe, and India•Moderate reductions inAfrica and S. Ame• China shows mixedfeatures (central China:10% higher)

Unit: Gg N/ month
Changsub Shim

AIM Workshop

A priori / A posteriori (unit: Gg N/ month; Nov 2005)
N.Am

E.U.

E.Asia

India

S.Ame

Africa

Aus.

R.W.

Total

IND1

430
(209)

220(154)

384
(342)

95.4
(50.)

74.3
(58)

103
(64)

12.6
(13.6)

165
(123)

1484
(1013)

IND2

176
(85)

202
(136)

7.53.600

14.5(8.)9.7(10.2)

32(18)

442(261)Light.

21.8

10

5.3

1.6

62.3

53.4

12.3

23.3

190

Soil

33.4(30)

15.4(15.3)

11.8(11.6)

34.1(25.2)

98.8(85)

142(115)38.7(39.2)

24.8(24)399(345)BB

5.8(5.6)

5.1(5.1)

22.3(19.2)

3.7(3.)

126(77)276(173)94.2(89.4)

45(42)

578(413)BF

7.5(6.9)

18.5(18.2)

51.8(50.0)

33.3
(25)

14.6(13.4)

31.5(27.7)

0.66(0.65)

24(23)

182(164)Total

682(364)

490
(357)

535(481)202(130)391(286)652(451)

169(166)338(273)3456(2507)

 Total global NOx emissions are lower by ~28% (Nov 2005): Annual proj. (42 vs 31 Tg N/yr)
 N.Ame (~48%) and Europe (~30%) have significant reduction in industrial NOx emissions(2005 vs 1998)
 A posteriori BB emissions are well matched with GFEDv2 (global total)A priori / Mass-Balance / Adjoint(unit: Gg N/ month; Nov 2005)
a Priori

MassBalance

Adjoint

N. Ame.

682424 (-38%)

364 (-47%)

Europe490410 (-16%)357 (-27%)

E. Asia535

687 (+28%)

481 (-10%)

India

202

126 (-38%)

130 (-36%)

S. Ame.

391

249 (-36%)

286 (-27%)

Africa652

458 (-30%)

451 (-31%)

Aus.

169

237 (+40%)

166 (-2%)

Global3456

2877 (-19%)2507 (-28%)

Changsub Shim

AIM Workshop

Conclusions

Satellite measurements and CTM can study regional ~ global scale
air quality and inverse modeling technique can better estimate theinitial condition of model inputs from the observations
According to the adjoint inversion, the N.Ame and European
anthropogenic NOx emissions are greatly reduced by 48% and 30%(1998 vs 2005)
Significant increase in Chinese industrial NOx emissions in 21st isevident by SCIAMACHY, but the adjoint inversion does mixedfeatures
Natural a priori NOx emissions overestimated (Nov. 2005) and aposteriori biomass burning emission is closer to a newer GFEDv2
inventory in global total

More ..
Validation of a posteriori emissions by comparison with recentemissions inventory for specific regions (EPA, EMEP, Streets, etc..)
will be continued.

Acknowledgement

All members of KACCC at KEIDr. Qinbin Li at UCLA
Dr. Daven Henze at U of Colorado
Dr. Randall Martin’s group at Dalhousie Univ.(Cananda)
TES group at JPL-NASA
GEOS-Chem group at Harvard Univ.