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

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

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

Distribution of ATOVS satellite data Distribution of ATOVS satellite data received over a 6-h windowreceived over a 6-h window

Distribution of ATOVS satellite data Distribution of ATOVS satellite data assimilated assimilated over a 6-h windowover a 6-h window

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)

Current and Planned Satellites (1/2)Current and Planned Satellites (1/2)

Source: JCSDA (Joint Center for Satellite Data Assimilation) 13th AMS Conf. 2004

Current and Planned Satellites (2/2)Current and Planned Satellites (2/2)

Source: JCSDA

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

3D and 4D data screening3D and 4D data screening

4D-Var

0-h-3h +3h

3D-Var

0-h-3h +3h

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)

Anomaly correlation: winter periodAnomaly correlation: winter period4D-Var3D-Var

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

August 2004

RMS error

GZ 500 hPa

Southern Hemisphere

Impact of the various components of 4D-Var

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

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.

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

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)

Impact of 4D-Var global analysis on regional 3D-Var cycle1 case : 48 hr forecast valid on November 16th 2004, at 12z

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

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

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

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)

Error variance estimated with a Kalman filterError variance estimated with a Kalman filter(Radiosonde and satellite coverage)(Radiosonde and satellite coverage)

The EndThe End

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