Adaptive Estimation and Tuning of Satellite Observation Error in Assimilation Cycle with GRAPESHua ZHANG, Dehui CHEN, Xueshun SHEN, Jishan XUE, Wei HAN

China Meteorological Administration (CMA)

OUTLINEIntroduction of GRAPES-3DVar

Tuning of obervation error in data assimilation

Latest development in the global assimilation/prediction experiment2008

Summary

1. Introduction of GRAPES-3DVarMain features of GRAPES_GAS

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Cost function

Bacground error:Observation error:Basic hypothesis:

Optimality criterion (Bennet 1992;Talagrand,1999)

2. Tuning of background and observation error in data assimilation (Wei HAN and Jishan XUE,2007)

innovation covariance: Iterative fixed-point method: Desrosies et al.,2005(1)(2)

only Sonde RH observation assimilation in GRAPES regional 3DVAR20070601-0614Only RH obs. are assimilated to test the approach, since it is thus a univariate analysisBlue dot: initial obs. error of rhBlue dash dot: initial background error of rh

NOAA16,AMSUA20070601-0614

diagnosisObs erroBak. erro

ITWG NWP WG list of assumed observation errors

Centre

Met Office

ECMWF

MeteoFrance

NCEP

Canada

CMA

NRL

Japan

DWD

AMSU-A

AAPP 1d

AAPP 1d

AAPP 1d

NOAA 1c

NOAA 1c

NOAA 1c

NOAA 1c

NOAA 1c

AAPP 1d

1

2

4.5

2

2

4.5

3

2

1.87

4.5

4

1.265

0.54

0.8

0.276

5

0.25

0.45

0.33

0.203

0.4

0.15

6

0.25

0.35

0.27

0.123

0.4

0.11

7

0.25

0.35

0.26

0.121

0.4

0.1

8

0.25

0.35

0.32

0.340

0.4

0.16

9

0.4

0.35

1.6

0.136

0.4

0.18

10

0.4

0.35

3

0.204

0.4

0.18

11

0.5

0.6

0.48

0.5

0.18

12

0.95

1.2

0.68

1

0.23

13

1.225

1.07

1.5

0.38

14

1.225

3.58

2.1

0.53

15

3

4.5

AMSU-B

AAPP 1d

AAPP 1d

AAPP 1d

NOAA 1c

NOAA 1c

NOAA 1c

NOAA 1c

NOAA 1c

1

8

7

2

5

3.86

1.586

2

3

4

3

3.03

1.149

4

4

4

2.5

2.54

1.240

3

5

4

2

2.13

1.494

2

MHS

AAPP 1d

AAPP 1d

AAPP 1d

NOAA 1c

NOAA 1c

NOAA 1d

NOAA 1c

NOAA 1c

1

8

7

2

5

2

3

4

4

4

4

3

5

4

2

Against Radiosondehumididy information of AMSUB has a proper response in GRAPES-3DVAR58238,Nanjing59948,SanyaRed : xbBlue : xa(amsub)Black : Sounde

Independent verification: RH[xa(amsub)]-Y(sonde)Before TuningAfter Tuning2007060900,500hPaBlack:Before Tuning; Red:After tuning10 cases statistics

Tuning of observation error improve GRAPES(30km) QPF

3.Latest development in the global assimilation/prediction experiment2008 (Xueshun SHEN et al,2008)Re-estimate the obs. error of sonde and radiancesSEMI-Bias Correction in backgroundModify the QC of satellite radiancesIntroduce NOAA-15Improve the surface albedoIntroduce the diagnostic cloud ref. ECMWFIntroduce the new O3 dataDaily SST

ATOVS microwave (NOAA15 16 17) radiances Sondes geop/ humidity / wind Synops geop/ humidity/ wind Ships geop/ humidity/ wind Airep temp/ wind Satob wind

Data application of GRAPES-3DVAR

500hPa ACC against NCEP (0.9,0.3)()(Background Check)

10500hPa ACC(.vs. NCEP ANA.)(20061201122007013112, 62cases)

10500hPa ACC(.vs. NCEP ANA.)(20061201122007013112, 62cases)

31cases(200612), against NCEP ANA.NOAA-15

SummaryIt is promising for the new implementation of the tuning observation error.GRAPES is progressing ,which improve its performance.Sondes are important in southern pole region.more satellite data application

Suggestions?Assimilation: more satellite data application, especially in SH and ocean any possible data (real-time) & experiences?ModelWeak subtropical highExcessive precipitation over the maritime continentLarge cooling bias at top (~10hPa)Coupling of SISL dynamics & physicsHybrid vertical coordinate in non-hydrostatic model

It is obvious that the systematic departure : H(xb)-Yo ,Is due to model bias,So we make a Semi-Bias correctionAs a regularization term in VarBC

Now Basic scheme.The main characteristics of GRAPES 3dvar are listed in this form. Actually, the basic idea and technique scheme are very similar to those recently adopted in like Met office or WRF group, but we have developed most of the details by ourselves. GRAPES