Remote sensing of natural hazards Remote sensing = satellite imagery and aerial photography
Inferring SO 2 and NO x Emissions from Satellite Remote Sensing
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Transcript of Inferring SO 2 and NO x Emissions from Satellite Remote Sensing
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Inferring SO2 and NOx Emissions from Satellite Remote Sensing
Randall Martin
with contributions from
Akhila Padmanabhan, Gray O’Byrne, Sajeev Philip
Dalhousie U
Environment Canada Seminar
17 Jan 2011
Chulkyu Lee, Dalhouse U NIMR, Korea
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Information about Anthropogenic SO2 Sources?Need Accurate SO2 Retrieval Algorithm
Lee et al., JGR, 2009
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Local Air Mass Factor (AMF) Calculation
dt()
IoIB
EARTH SURFACE
Radiative Transfer Model
Scattering weight t
B
e
I1wln)(
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 (
)
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Local Air Mass Factor Improves Agreement with Aircraft Observations (INTEX-A and B)
Lee et al., JGR, 2009
Uniform AMF: slope = 1.6, r = 0.71 Local AMF: slope = 0.95, r = 0.92
Uniform AMF: slope = 1.3, r = 0.78 Local AMF: slope = 1.1, r = 0.89
SCIAMACHYOMI
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Extend Air Mass Factor Calculation to Longer Time Period
SCIAMACHY OMILaunch 2002 2004
Resolution (km) 30x60 >13x24
Repeat (days) 6 1-2
Equator Crossing Time 10:00 1:45
Provide daily local SO2 AMFs and scattering weights so any model can be used in the analysis
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NO2 & SO2 Retrievals Affected by Errors in Surface Reflectance and Clouds
Winter OMI NO2 over Calgary & Edmonton
6OMI Reported Cloud Fraction
≥ 5cm of snow
0 > snow < 5cm
no snow
Mea
n Tr
op. N
O2 (
mol
ec/c
m2 )
O’Byrne et al., JGR, 2010
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Expected Retrieval Bias OMI NO2 for Snow-Covered ScenesDue to Errors in Accounting for Transient Snow & Ice
7
2original correctedRelative NO Bias
corrected
With CloudFractionThreshold (f < 0.3)
-0.5 0 1.0
O’Byrne et al., JGR, 2010
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Trend in Summer Tropospheric NO2 Column over 2003-2009 from SCIAMACHY
Akhila Padmanabhan & Chris Sioris
Bottom-Up Emission Inventories Take Years to Compile
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Evaluate Hindcast Inventory Versus Bottom-upHindcast for 2003 Based on Bottom-up for 2006 and Monthly
NO2 for 2003-2006
Lamsal et al., GRL, 2011
HindcastBottom-up
Application of Satellite Observations for Timely Updates to NOx Emission Inventories
Use Model to Calculate Local Sensitivity of Changes in Trace Gas Column to Changes in Emissions
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Forecast Inventory for 2009 Based on Bottom-up for 2006 and Monthly SCIAMACHY NO2 for 2006-2009
Temporary Dataset Until Bottom-Up Inventory Available
Lamsal et al., GRL, 2011
9% increase in global emissions
19% increase in Asian emissions
6% decrease in North American emissions
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Top-Down (Mass Balance) Constraints on Emissions
SCIAMACHY Tropospheric NO2 (1015 molec cm-2) NOx emissions (1011 atoms N cm-2 s-1)
Lee et al., 2011
2004-2005
Inverse Modeling
SOx emissions (1011 atoms N cm-2 s-1)SCIAMACHY SO2 (1016 molec cm-2)
200652.4 Tg S yr-1
Martin et al., 2006
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Accuracy of Mass Balance Approach for SO2 and NOx Emissions?
Mass Balance Approach•exploits short lifetimes
•Easily implemented for many forward models
•Infer emissions E from local trace gas column Ω
modeltop-down sat
model
EE
Box A Box B
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Accuracy of Mass Balance Approach for SO2 and NOx Emissions?
Test with Adjoint Approach
Mass Balance Approach•exploits short lifetimes
•Easily implemented for many forward models
•Infer emissions E from trace gas column Ω
Adjoint Approach•Explicitly accounts for spatial smearing
•Minimize Cost Function J~[model(E)-obs(Ω)]2
•Use adjoint model to calculate sensitivities λto produce improve estimate of E
modeltop-down sat
model
EE
EJ