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Adaptive Estimation and Tuning of Satellite Observation Error in Assimilation Cycle with GRAPES. Hua ZHANG, Dehui CHEN, Xueshun SHEN, Jishan XUE, Wei HAN China Meteorological Administration (CMA). OUTLINE. Introduction of GRAPES-3DVar Tuning of obervation error in data assimilation - PowerPoint PPT Presentation
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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
?? ??
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