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Nudged isotope AGCM simulation and its comparison with TES, SCIAMACHY and
ground-based FTS isotope retrievals
Kei YoshimuraScripps Institution of Oceanography
Collaborators: TES (J. Worden, D. Noone)
SCIAMACHY (C. Frankenberg, T. Rockman)Ground based FTS (M. Schneider, F. Hase, J. Notholt)
TES workshop in Boulder, Feb. 23-25, 2009
Contents
• Model – Ground based FTS comparison
• Model – Satellite comparison (UT and PBL)
• Isotope Assimilation (plan)
Issues in GCM vs RS comparison
• Timing (clear sky vs rainy)• Space (vertical and horizontal)
• Significant difficulty: GCM’s time and “real” time is different!
A solution: Spectral Nudging– Poor man’s data assimilation –
IsoGSMSIO/UT Isotopic Global Spectral Model
H18OH
HD16O
( )⎩⎨⎧
≥+×−
Comparison with GNIP Climatology“Rainy sky integration”
Ny Alesund
Kiruna
Bremen
Izana@2370m
-350
-300
-250
-200
-150
-100Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
IsoGSMclim Bremen_FTSIsoGSMclim Kiruna_FTSIsoGSMclim NyAlesund_FTSIsoGSMclim Izana_FTS
Monthly climatology of vapor dD
Comparison with FTIR Climatology“Clear sky integration”
Collocated: Ny Alesund
-500
-450
-400
-350
-300
-250
-200
-150
-10001/11/5 02/7/13 03/3/20 03/11/25 04/8/1 05/4/8 05/12/14 06/8/21
FTSIsoGSM_TPW R=0.68
-450
-400
-350
-300
-250
-200
-150
-10095/10/28 98/7/24 01/4/19 04/1/14 06/10/10
FTSIsoGSM_TPW
Kiruna-350-300
-250
-200
-150
-10002/1/1 02/4/11 02/7/20 02/10/28 03/2/5 03/5/16 03/8/24 03/12/2
FTSIsoGSM_TPW
R=0.77
Bremen FTS
y = 0.5304x - 75.269R2 = 0.4988
-350
-300
-250
-200
-150
-100
-50-350 -250 -150 -50
FTS
IsoG
SM
-350
-300
-250
-200
-150
-100
-50
004/6/12 04/12/29 05/7/17 06/2/2 06/8/21 07/3/9 07/9/25
FTSIsoGSM_TPW
R=0.71
-450
-400
-350
-300
-250
-200
-150
-100
-5098/11/1 00/3/15 01/7/28 02/12/10 04/4/23 05/9/5 07/1/18 08/6/1
FTSIsoGSM_700hPa
Izana-450-400
-350
-300
-250
-200
-150
-100
-5004/1/1 04/4/10 04/7/19 04/10/27 05/2/4 05/5/15 05/8/23 05/12/1
FTSIsoGSM_700hPa
R=0.61
-350
-300
-250
-200
-150
-100Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
IsoGSMclim Bremen_FTSIsoGSMclim Kiruna_FTSIsoGSMclim NyAlesund_FTSIsoGSMclim Izana_FTS
-350
-300
-250
-200
-150
-100Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
IsoGSMclim Bremen_FTS IsoGSMIsoGSMclim Kiruna_FTS IsoGSMIsoGSMclim NyAlesund_FTS IsoGSMIsoGSMclim Izana_FTS IsoGSM
Collocated model vs FTS
y = 1.4331x + 2.5103
R2 = 0.69
0
5
10
15
20
25
0 5 10 15 20 25
y = 0.8714x - 19.949
R2 = 0.301
-350
-300
-250
-200
-150
-100
-50
-350 -300 -250 -200 -150 -100 -50
y = 0.9455x + 15.704
R2 = 0.9415
250
255
260
265
270
275
280
285
250 255 260 265 270 275 280 285
0
5
10
15
20
25
1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199 208 217 226 235 244 253 262 271 280 289 298 307 316 325 334 343 352 361 370 379 388 397 406 415
-350
-300
-250
-200
-150
-100
-50
1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199 208 217 226 235 244 253 262 271 280 289 298 307 316 325 334 343 352 361 370 379 388 397 406 415
250
255
260
265
270
275
280
285
1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199 208 217 226 235 244 253 262 271 280 289 298 307 316 325 334 343 352 361 370 379 388 397 406 415
Temperature
Water Vapor
Vapor HDO
2004/09/20-21
Pink: Simulation, Violet: Observation
Obs
Sim
TES vs collocated-IsoGSM comparison
Correlation b/w TES & IsoGSM(averages of 2004-2007 monthly score)
Accuracy order:• T > Humidity > HDO• For HDO
– N-hemi > S-hemi– Mid > Low > High
00.10.20.30.40.50.60.70.80.9
1
N-hi N-mi N-lo S-hi S-mi S-lo0
0.10.20.30.40.50.60.70.80.9
1
N-hi N-mi N-lo S-hi S-mi S-lo
00.10.20.30.40.50.60.70.80.9
1
N-hi N-mi N-lo S-hi S-mi S-lo
Temperature Humidity
HDO in Water Vapor
Yoshimura et al., in prep
SCIAMACHY HDO retrieval (Frankenberg et al., 2009)It sees the boundary layer!
-190
-170
-150
-130
-110
-90
-70
-50Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
IsoGSMclim Izana_SCIA IsoGSMcol
SCIA/FTS/collocated-IsoGSM comparison at Izana(left: SCIA vs Model, right: FTS vs Model)
-240
-220
-200
-180
-160
-140
-120
-100Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
IsoGSMclim Izana_FTS IsoGSMcol
FTS@2370m, IsoGSM@700hPa
SCIA@clm vs IsoGSM@clm
Yoshimura et al., in prep
Difference in “measurements” for DJF-JJA climatology
GN
IP (r
ain)
SC
IAM
AC
HY
(col
umn
vapo
r)TE
S(5
00-8
25hP
a)
Frankenberg et al., submitted
dD in H2O
Rai
nco
lum
n va
por
500-
825h
Pa
Model (IsoGSM) resultsDifference in “measurements” for DJF-JJA climatologyG
NIP
(rai
n)SC
IAM
ACH
Y(c
olum
n va
por)
TES
(500
-825
hPa)
Science: Assimilation of isotopes– No longer a poor man! –
Observation
Model
Assimilation
IsoGSM(Yoshimura et al., 2008)
What is appropriate method?
TES/Aura HDO(Worden et al., 2007)
SCHIAMACHY(Frankenberg et al., 2009)
FTS
• 1st Purpose: Make dD vapor analysis fields
How about EnKF?
• In comparison with variational methods:– Better than 3D-Var in general. – Adjoint model unnecessary. (needed by 4D-
Var)• Possibility to improve (influence) other
thermo-dynamical fields (q, wind, T, etc.) 2nd Purpose (for better weather analysis/forecast, maybe)
(c.f., wind field improvement by humidity assimilation; Liu et al., 2008)
General form of Kalman Filter
( )( )
( ) fiiiaii
Ti
fii
Ti
fii
fii
oii
fi
ai
Tai
fi
ai
fi
H
M
PHKIP
RHPHHPK
xyKxx
MMPP
xx
−=
+=
−+=
=
=
−
−
+
1
1
1
)(
)(x: state in model’s worldy: state by observationP: model error covarianceR: obs error covarianceM, M: modelH, H: obs operatorK: Kalman gain
Forecast model
Forecast of error covariance
Kalman filter
Kalman gain
Analysis of error covariance
Ensemble Kalman Filter(Square Root Filter; SRF)
[ ]
fT
fT
T
foT
fT
ff
afofa
Tf
Tf
Tf
m
mm
H
H
XHδRXHδIUDUUUD
XyRXHδUUDδXX
δXXyKXX
RXHδUUDδXK
δXδXPXXδX
xxX
1
2/1
11
11
1-
1
)()1( where]1
))(()([
))((
)(
1)-(m
−
−
−−
−−
+−=
−+
−+=
+−+=
=
=
−=
= L Ensemble Matrix (small x means each ensemble member) Ensemble perturbation (anomaly)
Eigenvalue decomposit
Kalman gain
Ensemble analysis
Perturbation Model error covariance
x: state in model’s worldy: state by observationP: model error covarianceR: obs error covarianceM, M: modelH, H: obs operatorK: Kalman gain
SSI
Obs.Data
GSMSSI
Obs.Data
GSMSSI
Obs.Data
Analyzed fields
Forecasted fields Forecasted fields Forecasted fields
Analyzed fields Analyzed fields
Forecasted fields ofEnsemble members
EnKF
Obs.Data
Analyzed fields ofEnsemble members
Model
Forecasted fields ofEnsemble members
EnKF
Obs.Data
Analyzed fields ofEnsemble members
Model
Forecasted fields ofEnsemble members
EnKF
Obs.Data
Analyzed fields ofEnsemble members
Framework of “Isotope Assimilation”
TES HDO obs.data
H18OH
HD16O
IsoGSM
LETKF
[ ]
afofa
Tf
Tf
m
H δXXyKXX
RXHδUUDδXK
XXδXxxX
+−+=
=
−==−−
))((
)(
,11
1 L
Ensemble Forecasting
3rd Purpose: Estimate “required” accuracy and resolution of HDO measurement for “adequate” isotope assimilation
Nudged isotope AGCM simulation and �its comparison with TES, SCIAMACHY and ground-based FTS isotope retrievalsIssues in GCM vs RS comparisonA solution: Spectral Nudging�– Poor man’s data assimilation –Comparison with GNIP Climatology�“Rainy sky integration”Comparison with FTIR Climatology�“Clear sky integration”Collocated: Ny AlesundBremen FTSCollocated model vs FTSTES vs collocated-IsoGSM comparisonCorrelation b/w TES & IsoGSM�(averages of 2004-2007 monthly score)SCIAMACHY HDO retrieval (Frankenberg et al., 2009)�It sees the boundary layer!SCIA/FTS/collocated-IsoGSM comparison at Izana�(left: SCIA vs Model, right: FTS vs Model)Science: Assimilation of isotopes�– No longer a poor man! –How about EnKF?General form of Kalman FilterEnsemble Kalman Filter�(Square Root Filter; SRF)Framework of “Isotope Assimilation”
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