comparison of airs and gosat co2 retrievals to geos5 ...co. 2. flux estimation approach using gosat...
TRANSCRIPT
![Page 1: Comparison of AIRS and GOSAT CO2 retrievals to GEOS5 ...CO. 2. flux estimation approach using GOSAT X. CO2 • Variational carbon data assimilation system • Optimize weekly CO. 2](https://reader033.vdocument.in/reader033/viewer/2022052804/6054dba5dba3dc726c4d3fb6/html5/thumbnails/1.jpg)
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4 days
7 days
1 day
Typical coverage for a sun-synchronous satellite
~25º
spacing in longitude ~3.5º
spacing in longitude
NADIR
GLINT
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Outline•
Satellite data promise a new view: a move from the continental scale to the “regional”
scale
•
Things needed, first:–
Efficient numerical methods for the flux inversion
–
Understanding of spatial and temporal correlations of fluxes and column concentrations along orbit
–
Way to remove systematic errors from the satellite retrievals
•
Here: an attempt at removing systematic errors from satellite retrievals
Is it real or a bias in the satellite retrieval?
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GOSAT comparison to CO2
forward models
•
Compare satellite data to a suite of forward model runs:
–
CT fluxes TM5 Standard CT release
–
CT fluxes PCTM
½°x ⅔°
resolution (lat/lon)
–
CSU fluxes PCTM
SiB + Doney ocean
–
CSU fluxes TM5
Just now being run
•
Sample model at same time/place with same vertical weighting as the actual measurements
•
Take the obs -
model difference
•
If the differences are all similar, blame it on retrieval errors
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Obs versus model(GOSAT vs. CT+PCTM)
Model versus model(CT+TM5 vs. CT+PCTM)
1-to-1 lineLand, medium-gain
Land, high-gain
Ocean, glint
Different forward model XCO2
values are closer to each other than any are to the GOSAT-retrieved values
Blame GOSAT-model differences on GOSAT retrieval errors (mostly)
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Figure courtesy of Chris O’Dell, CSU
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Figure courtesy of Chris O’Dell, CSU
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Figures courtesy of Chris O’Dell, CSU
Fraction of GOSAT shots
passingChris’
filters
Number of shots remaining,2009-2010:
Ocean: 76 KM-Land: 48 KH-land: 73 K
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Figure courtesy of Chris O’Dell, CSU
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Obs –
model difference (1σ) after fit:
1.55 ppm
1.4 ppm
1.0 ppm
Slide courtesy of Chris O’Dell, CSU
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Systematic differences (errors?) left after bias correction
Signal in O2
band
LatitudeAerosol optical depth
“Airmass”
= atmospheric path length
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M
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123
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H
Time-dependence of concentrations on fluxes
fluxesconcentrationsTransport basis functions
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4D-Var: NWP vs. carbon flux estimation
NWPSolve for I.C.s over multiple
short windows (6 hours): driven by the need to
update predictions
Carbon fluxes
Solve for B.C.s (fluxes) and I.C.s over long window (1 year +): retrospective
x0
x0 u0 u1uI-1… …
x0
x0x0
…
…assimilationwindow
prediction
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°
°
°
°
0
2
1
3
x2
x1
x3
x0
AdjointTransport
ForwardTransport
ForwardTransport
MeasurementSampling
MeasurementSampling
“True”Fluxes
EstimatedFluxes
ModeledConcentrations
“True”Concentrations
ModeledMeasurements
“True”Measurements
AssumedMeasurementErrors
WeightedMeasurementResiduals
/(Error)2
AdjointFluxes=
FluxUpdate
4-D Var
Iterative Optimization Procedure
Minimum of cost function J
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CO2
flux estimation approach using GOSAT XCO2
•
Variational
carbon data assimilation system
•
Optimize weekly CO2
fluxes for 2010 at 4½°x6°
(lat/lon)
•
Prior fluxes, a CarbonTracker
“projection”
(Jacobson):–
fossil fuel from preliminary 2010 statistics (CDIAC)
–
“climatological”
fluxes for land biosphere and ocean (average of 2000-2009 values from CT 2010)
–
NOT optimized against in situ data for 2010•
PCTM off-line atmospheric transport model, driven by GEOS5 analyzed meteorology fields–
CT fluxes run thru at ½°x⅔°
(lat/lon) to get prior [CO2]
–
Flux corrections estimated at 4½°x6°
(lat/lon)
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4DVar flux inversion cases
Seven flux inversions cases for 2010 using:•
NOAA in situ: 62 weekly flask sites, 4 continuous sites, 8 tall towers (daily)
•
TCCON columns, 14 sites•
Screened ACOS ver. 2.9 GOSAT XCO2
:–
No bias correction–
a separate 3-parameter bias correction for ocean and high- and medium-gain land data
–
Three bias corrections of Wunch, et al (2011)
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Projected CT Prior Post. w/ GOSAT data Δ
= Post. -
Prior
Apr-Jun2010
Jul-Sep2010
Full year2010
4DVar CO2
Flux Estimates w/ ACOS v.2.9 GOSAT XCO2
10-8
[kgCO2
m-2
s-1]
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DJF
ANN
JJA
MAM
SON
NOAAin situ TCCON
ACOS v2.9 GOSAT
No bias correctionH-Land, M-Land, & Ocean H-Land & Ocean
3-param. bias corr. Wunch
bias corr., H-L only
CO2
flux corrections to the CT-PCTM prior [10-8
kgCO2
m-2
s-1]when assimilating only:
JFM
AMJ
JAS
OND
Ann
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Evaluation of a posteriori CO2
fields against independent data
1σ
error [ppm] between optimized model and TCCON (in 2-hr bins)
Prior
GOSAT, H+M+Ocn, no bias corr.
GOSAT, H+M+Ocn, 3-param. “
GOSAT, H+Ocn, Wunch
#1 “
GOSAT, H+Ocn, Wunch
#2 “
GOSAT, H+Ocn, Wunch
#3 “
NOAA in situTCCON
1.307
1.204
1.172
1.219
1.219
1.213
1.268
1.054
1.30
1.20
1.15
1.10
1.05
1.25
TCCON
in situ
Prior
GOSAT, no corr
GOSAT, 3-param.
[ppm]
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Figure 3 from Chevallier, et al (2011) TCCON inversion paper.
Next: make a similar plot for inversions / data comparisons using:
• ACOS GOSAT XCO2 • NOAA surface in situ data• NOAA routine aircraft profiles• TCCON XCO2• HIPPO, AIRS, TES, AirCore, etc
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EnKF
& 4DVar
EnKF
4DVar
Measurement span
KF sliding flux window with Ntimes
fluxes in it
Computational work:•Ntimes
*Nens
for EnKF
(in parallel) •4*Niter for 4DVar (serial)
Backward propagation of information:For EnKF, depends on time width of window –
shorter spans give poorer constraints at larger time/space scales
Columns in C, where P=CCT:Nens
for EnKF2*Niter
for 4DVar