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Top-Down Constraints on Emissions: Opportunities and Challenges from Satellite Observations of
Atmospheric CompositionRandall Martin
with contributions from
Shailesh Kharol, Gray O’Byrne, Akhila Padmanabhan, Aaron van Donkelaar
2013 China Emissions Workshop, Beijing
28 June 2013
Lok Lamsal (Dalhousie NASA), Chulkyu Lee (Dalhousie KMA)
Jintai Lin (PKU), Daven Henze (CU Boulder),
Guannan Geng, Qiang Zhang, and Yuxuan Wang (Tsinghua)
Satellite-derived PM2.5 for 2001-2006
van Donkelaar et al., EHP, 2010
Evaluation in North America:r=0.77slope = 1.07N=1057
Outside Canada/USN = 244 (84 non-EU)r = 0.83 (0.83)Slope = 0.86 (0.91)Bias = 1.15 (-2.64) μg/m3
PM2.5 Nearly as Sensitive to Emissions of NOx as to SO2
Kharol et al., GRL, 2013
GEOS-Chem Calculation of Annual PM2.5 Response to 10% Change in Emissions
ΔNOx Emissions ΔSO2 Emissions ΔNH3 Emissions
25%41%34%
ΔPM
2.5 (
ug m
-3)
-0.5
0
1
2
But How Accurate are the Emissions Used in this Calculation?How Accurate are Emissions in General?
What Can We Learn about Emissions from Satellite Observations?
Major Nadir-viewing Space-based Measurements of Tropospheric Trace Gases and Aerosols (Not Exhaustive)
Sensor MOPITT MISR MODIS AIRS SCIA-MACHY
TES OMI CALIOP GOME-2
IASI GOSAT VIIRS TROP-OMI
Platform (launch)
Terra Aqua (1999) ( 2002)
Envisat (2002)
Aura (2004)
Calipso(2006)
MetOp(2006)
IBUKI (2009)
NPP (2011)
Sent-5 Precur (2014)
Typical Res (km)
22x22 18x18 10x10 14 x14
60x30 8x5 >24x13
40x40 80x40 12 x12 11x11 6x6 7x7
Aerosol X X X X X X X X
NO2 X X X X
HCHO X X X X
CO XX X X X X X
Ozone X X X X X X
SO2 X X X X X
NH3 X X
CH4 X X X X X
CO2 X X X X
Solar Backscatter & Thermal Infrared
Close Relationship of NOx and SO2 Emissions With Satellite Tropospheric NO2 and SO2 Columns
Emission
NO NO2
HNO3
lifetime hours
Nitrogen Oxides (NOx) Sulfur Dioxide (SO2)
Emission
SO2
OH, cloudSO42-
day
BOUNDARYLAYER
Satellite
NO/NO2
W ALTITUDE
Tropospheric NO2 column ~ ENOx
Tropospheric SO2 column ~ ESO2
Deposition
Top-Down (Mass Balance) Estimates of NOx & SO2 Emissions
SCIAMACHY Tropospheric NO2 (1015 molec cm-2) NOx emissions (1011 atoms N cm-2 s-1)
Lee et al., 2011
2004-2005
SO2 emissions (1011 atoms N cm-2 s-1)OMI SO2 (1016 molec cm-2)
200649.9 Tg S yr-1
Martin et al., 2006
Lamsal et al., GRL, 2011Streets et al., AE, in press
Application of Satellite Observations for Timely Updates to NOx Emission Inventories
Use GEOS-Chem to Calculate Local Sensitivity of Changes in Trace Gas Column to Changes in Emissions
Forecast Inventory for 2010 Based on Bottom-up for 2005 and Monthly OMI NO2 for 2005-2010
2.5% increase in global emissions
27% increase in Asian emissions
23% decrease in North American emissions
Integration of Top-down Information In Bottom-up ApproachExample Evaluation of Spatial Proxies
Guannan Geng (Tsinghua) et al. in prep
Population, Outdated Road Network Industrial GDP, New Road Network
Complications
Satellite Retrievals
Inverse Modeling
Need to Account for Average Kernel in IR Satellite RetrievalsIASI Provides Some Constraint on NH3 Emissions
Kharol et al., GRL, 2013
Using NH3 emissions from Streets et al. (2003) reduced by 30% following Huang et al. (2012)
with Averaging KernelsTotal Column
Need to Account for Vertical Profile and Atmospheric Scattering (Air Mass Factor; AMF) in UV-Vis Retrievals
dt()
IoIB
EARTH SURFACE
Radiative Transfer Model
Scattering weight t
B
e
I1w
ln)(AMF
)(G
Atmospheric Chemistry Model
“a-priori” Shape factor
2
2
OO
( ) ( ) airS
S
S C
1
TdSw )()(AMF
verticalslantAMF G
Calculate w() as function of:• solar and viewing zenith angle• surface albedo, pressure• cloud pressure, aerosol• OMI O3 column
INDIVIDUALOMI SCENES
SO2 mixing ratio CSO2()
() is temperature dependent cross-section
sigm
a (
)
Local Air Mass Factor and Offset Correction Improves Agreement with Aircraft Observations (INTEX-A and B)
Lee et al., JGR, 2009
SCIAMACHY OMI
Orig: slope = 1.3, r=0.78 New: slope = 1.1, r=0.89
Orig: slope = 1.6, r = 0.71 New: slope = 0.95, r = 0.92
SCIAMACHY OMI
Need to Account for Multiple Effects of Aerosols on UV-Vis Trace Gas Retrievals
Accounting for Aerosol Haze Can Increase R2 (0.720.96) of OMI NO2 vs Ground-based DOAS Observations in China
Jintai Lin (PKU) et al., in prep, ACP
Expected OMI NO2 Retrieval Bias for Snow-Covered ScenesDue to Errors in Accounting for Transient Snow & Ice
O’Byrne et al., JGR, 2010
2original correctedRelative NO Bias
corrected
With CloudFractionThreshold (f < 0.3)
-0.5 0 1.0
All CloudFractions
0.5
Aerosol Retrievals Susceptible to Bias over Bright SurfacesAerosol Optical Depth (AOD) from MODIS and MISR over 2001-2006
MODIS1-2 days for global coverage
(w/o clouds)AOD retrievals at 10 km x 10 kmRequires assumptions about
surface reflectivity
MISR6-9 days for global coverage
(w/o clouds)
AOD retrievals at 18 km x 18 km
Simultaneous retrieval of surface reflectance and aerosol optical properties
0 0.1 0.2 0.3AOD [unitless] van Donkelaar et al., EHP, 2010
Can Remove Biased Data Using Sunphotometer Observations Excluded Retrievals for Land Types with Monthly Error vs AERONET >0.1 or 20%
MODISr = 0.39
(vs. in-situ PM2.5)
MISRr = 0.39
(vs. in-situ PM2.5)
CombinedMODIS/MISR
r = 0.61 (vs. in-situ PM2.5)
0.3
0.25
0.2
0.15
0.1
0.05
0
AO
D [u
nitle
ss]
van Donkelaar et al., EHP, 2010
Δ
Adjoint Reduces Inversion Error vs Mass BalanceTest to Recover 30% Increased NOx Emissions in Four Locations Using
a Week of Synthetic Observations of NO2 Columns
November July
Mass Balance
Adjoint
Inversion – Truth (ΔNOx Emissions molec cm-2 s-1)
Padmanabhan et al., in prep
NME=3x10-3 NME=6x10-3
NME=4x10-4 NME=5x10-4
NME = Normalized Mean Error
How Well Do Models Represent SO2 Lifetime in China?Evaluation of GEOS-Chem SO2 Lifetime vs Calculations
from In Situ Measurements in Eastern USU Maryland Research Flights for Eastern U.S.
2( ) 19 7JJA SO hourst
Hains, Dickerson, et al., 2007
June - August
Mon JJAMon JJA
JJA Mon
C HC H
t t
C is SO2 from EPA Network H is GEOS Mixed Layer Depth
Lee et al., JGR, 2011
Inversion Relies on Relative Error in Bottom-up and Top-down Approaches: Embrace Uncertainty
Need information on uncertainty (σ)
2 2a
2 2a
( )( )
σ σE E F E
J E
Observed Trace Gas
a priori emissions
a posteriori emissions
a priori error observational error
Inverse problem seeks emissions E that minimize cost function J
Errorweighting
A posteriori emissionsE
A Priori NOx Emissions (Ea)Observed NO2 Columns (Ω)
Model F(E) σ σa
Uncertainty in SO2 Retrievals Due to Clouds, Surface Reflectance, SO2 Vertical Profile, and Aerosols
Lee et al., JGR, 2009
Cloud-free Fraction of Scene Cloudy Fraction of Scene
Most Satellites Observe at Specific Times of Day
Requires Attention to the Diurnal Profile of Emissions
Conclusions
• Substantial opportunities and challenges
• Integrate top-down and bottom-up methods & communities
• Account for retrieval assumptions in inversion (e.g. trace gas profile)
• Avoid bias (e.g. aerosol, snow) in satellite data products and algorithms
• Quantify uncertainty in both top-down and bottom-up methods
Acknowledgements:NSERC, Environment Canada