data assimilation and observing systems strategies pierre gauthier data assimilation and satellite...
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Data assimilation and observing systems strategies
Pierre GauthierData Assimilation and Satellite Meteorology DivisionMeteorological Service of CanadaDorval, Québec CANADA
Co-chair of the THORPEX working group on DAOS
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Data assimilation and observing Data assimilation and observing strategiesstrategies
• Optimal use of observations– Adaptive observations (targeted observations)
• Deploy observations over regions where small changes lead to substantial changes in the forecasts
– Better use of existing observations, particularly satellite data
• Satellite data• Data assimilation methodology• A few scientific objectives
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Satellite dataSatellite data
• Relatively low proportion of received data makes its way to the assimilation (<20%)
• Observation error– Biases: assimilation is bias blind and
innovations cannot distinguish between model and observation bias
– Observation error correlation• Characterization of surface emissivity to
assimilate many satellite data types
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Distribution of ATOVS satellite data Distribution of ATOVS satellite data received over a 6-h windowreceived over a 6-h window
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Distribution of ATOVS satellite data Distribution of ATOVS satellite data assimilated assimilated over a 6-h windowover a 6-h window
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Channel selection of IASI radiances in meteorologically Channel selection of IASI radiances in meteorologically sensitive areas (Fourrié and Rabier, 2003)sensitive areas (Fourrié and Rabier, 2003)
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Current and Planned Satellites (1/2)Current and Planned Satellites (1/2)
Source: JCSDA (Joint Center for Satellite Data Assimilation) 13th AMS Conf. 2004
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Current and Planned Satellites (2/2)Current and Planned Satellites (2/2)
Source: JCSDA
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Data assimilation methodsData assimilation methods
• Several NWP centres have now implemented 4D-Var– Significant impact on the forecasts– Better usage of satellite and asynoptic data– Issues on specific aspects of the implementation,
particularly when it comes to humidity analysis• Assimilation with a numerical model
– Leads to model improvements and assimilation methodology
– Attention needs to be paid to the details of the implementation
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3D and 4D data screening3D and 4D data screening
4D-Var
0-h-3h +3h
3D-Var
0-h-3h +3h
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Type 4D-Var 3D-Var Difference
Aircrafts 75707 26147 +189%
Radiosonde 66605 66603 ~0%
Satwind 82160 41604 +97%
ATOVS 71517 46832 +53%
GOES 3612 1979 +83%
Profilers 13040 2196 +494%
Data assimilated Data assimilated 4D-Var 4D-Var vsvs 3D-Var3D-Var (12Z 16 (12Z 16
February 2005)February 2005)
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Anomaly correlation: winter periodAnomaly correlation: winter period4D-Var3D-Var
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Impact of the various components of 4D-Var
Type Outer loops Simplified
physics
Observation
thinning
3D-Var 1 - 3D
3D-Var(FGAT)
1 - 3D
3D-Var(FGAT)
1 - 4D
4D-Var 1 (simpler) 4D
4D-Var 2 (simpler,simpler) 4D
4D-Var 2 (simpler, better) 3D
4D-Var 2 (simpler, better) 4D
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August 2004
RMS error
GZ 500 hPa
Southern Hemisphere
Impact of the various components of 4D-Var
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4D-Var
3D-Var
4D-Var (simpler)
4D-Var (simpler,1 loop)
4D-Var (thinning 3D)
7% (better simplified physics)
3% (Updated trajectory)
35% (thinning 4D)
(TL/AD dynamics) 55%
FGAT (thinning 3D)
FGAT (thinning 4D)
16%
18%
Impact of the various components of 4D-Var
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Information Content (%)
0
5
10
15
20
25
Total influence (%) of satellite and in-situ observations when assimilated by ECMWF 4DVar System. From Cardinali et al. 2004.
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Further developments in data Further developments in data assimilation methodsassimilation methods
• Background term– Up to now: little (but positive) impact– Requirements for the assimilation of fine scale
structures, particularly in the humidity field– Hybrid methods (EnKF +4D-Var?)
• Nonlinearities– Observation and physical parameterizations
• Weak-constraint 4D-Var– Extending the assimilation window
(Fisher, 2004)– Dealing with model error
• Surface analyses, high-resolution analysis for mesoscale models
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Verification of 48-h forecastsagainst radiosondes observations over North America
Regional forecast issued directly from the 4D-Var global analysis
12-h regional assimilation cycle initiated from the 4D-Var global analysis
Impact of 4D-Var analysis on regional (15 km) forecasts (24 winter case)
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Impact of 4D-Var global analysis on regional 3D-Var cycle1 case : 48 hr forecast valid on November 16th 2004, at 12z
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0%
10%
20%
30%
40%
50%
60%
70%
80%
Gz 500 hPa MSL pres QPF
EquivalentReg 3DReg 4D
Subjective Evaluation (Winter 2004-2005)Subjective Evaluation (Winter 2004-2005)% in favor of % in favor of 3D-Var3D-Var or or 4D-Var4D-Var
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A few scientific objectives (1)A few scientific objectives (1)
• THORPEX regional campaigns– Storm Winter Reconnaissance Program (US)
over the North Pacific since 1998– Fall of 2003 in the North Atlantic (A-TReC 2003)– Pacific campaign: 2007-2008
• Seattle meeting 6-7 June 2005• What needs to be observed to improve the
large scale forecasts– Design of TReCs by learning from previous
ones– Recommendations for future campaigns
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A few scientific objectives (2)A few scientific objectives (2)
• Improving the assimilation of existing satellite data– What is not currently well observed (e.g., winds)– Estimation of observation error characteristics– Targeting methods
• Impact of large-scale improvements on local short-term forecasts (downscaling)– Relevant weather elements for socio-economic studies
often need the magnifying glass of a higher resolution model
• Ensemble prediction– Impact of changes in the observation network on the
estimated variability in ensemble prediction systems
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Error variance estimated with a Kalman filterError variance estimated with a Kalman filter(Radiosonde coverage only) (Radiosonde coverage only) (Gauthier (Gauthier et al.et al., 1993), 1993)
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Error variance estimated with a Kalman filterError variance estimated with a Kalman filter(Radiosonde and satellite coverage)(Radiosonde and satellite coverage)
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The EndThe End
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ATReC 026
48-h Singular vector SV1 at initial time (Zadra and Buehner)
Valid time: 5 Dec. 2003 12 UTC
MSC-GEMSimplified physics• Vertical diffusion• orographic blocking and GWD• stratiform condensation• convection
Computed with dry physics
Computed with moist physics