1 Using satellite data in joint CO 2 -CO inversion to improve CO 2 flux estimates Helen Wang 1,4 , Daniel J. Jacob 1 , Monika Kopacz 1 , Dylan B.A. Jones 2 , Parvadha Suntharalingam 3 , Jenny A. Fisher 1 1 1. Harvard University 2. University of Toronto 3. University of East Anglia 4. Harvard-Smithsonian Center For Astrophysics Submitted to ACP
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1 Using satellite data in joint CO 2 -CO inversion to improve CO 2 flux estimates Helen Wang 1,4, Daniel J. Jacob 1, Monika Kopacz 1, Dylan B.A. Jones.
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1
Using satellite data in joint CO2-CO inversion to
improve CO2 flux estimates
Helen Wang1,4,
Daniel J. Jacob1, Monika Kopacz1,
Dylan B.A. Jones2, Parvadha Suntharalingam3,
Jenny A. Fisher1
1
1. Harvard University2. University of Toronto3. University of East Anglia4. Harvard-Smithsonian Center For Astrophysics
Submitted to ACP
[Palmer et al., 2006]
Kg/m2
Jan 2006 model column CO2 Jan 2006 model column CO
g/m2
•CO and CO2 share common combustion sources and transport,indicating cross correlation in concentration and error.
•CO has stronger gradient, is more sensitive to transport error, can provide additional constraint.
Why CO2 : CO ?
•CO is relatively easy to measure from space; Multiple validated data sets have been used for CO source inversion.
•Joint CO – CO2 inversion using aircraft data in Asian outflow showed substantial improvement over CO2 – only inversion.
33
Joint CO2 – CO inversion
Coupling between CO2 and CO occurs through off-diagonal elements in error covariance matrices S
2
22
CO CO CO
COco co
S cov( , )S
cov( , ) S
2 2co co co cocov( , ) var( ) var( )r
correlation coefficient•Off diagonal elements increase information content•Negligible correlation in Sa due to CO emission factor uncertainty
Observation Vector: Column
concentration 2
CO
CO
xx
x
State Vector:
Sources
1 1( ) ( ) ( ) ( ) ( )T Ta a aJ x y Kx S y Kx x x S x x