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

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Presentation transcript:

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 Latest development in the global assimilation/prediction experiment 2008 Summary

1. Introduction of GRAPES-3DVar Main features of GRAPES_GAS Grid analysisA+P with flexible resolution setup incremental x a =x b + x Variable options analysis /T, u, v, rh control,, u, rh preconditioning control space model space x=Uw,U U p U v U h Regional : Recursive filterfor U h Global : Spectral filterfor U h MinimizationLimited memory BFGS method Mass-wind constraint Linear balance equation (now) Nonlinear balance equation (on testing) ProgrammingFortran90, Modular structure, to be paralleled

Preprocessing Raw ATOVS DATA Raw ATOVS DATA Quality Control Preprocessing Conventional DATA Conventional DATA Quality Control 10D Forecast GRAPES GLOBAL 3D-VAR GRAPES GLOBAL 3D-VAR GRAPES GLOBAL MODEL GRAPES GLOBAL MODEL INCREMENTAL SI INCREMENTAL SI DIGITAL FILTER INITIALIZATION DIGITAL FILTER INITIALIZATION 6h Forecast cycle At 00/12Z GRAPES_GFS analysis ° forecast 1 °

?? 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 3DVAR Only RH obs. are assimilated to test the approach, since it is thus a univariate analysis Blue dot: initial obs. error of rh Blue dash dot: initial background error of rh

NOAA16,AMSUA diagnosis Obs erroBak. erro

ITWG NWP WG list of assumed observation errors

Against Radiosonde humididy information of AMSUB has a proper response in GRAPES-3DVAR 58238,Nanjing 59948,Sanya Red : xb Blue : xa(amsub) Black : Sounde

Independent verification: RH[xa(amsub)]-Y(sonde) Before Tuning After Tuning ,500hPa Black:Before Tuning; Red:After tuning 10 cases statistics

Tuning of observation error improve GRAPES(30km) QPF

3.Latest development in the global assimilation/prediction experiment 2008 (Xueshun SHEN et al,2008) Re-estimate the obs. error of sonde and radiances SEMI-Bias Correction in background Modify the QC of satellite radiances Introduce NOAA-15 Improve the surface albedo Introduce the diagnostic cloud ref. ECMWF Introduce the new O 3 data Daily SST

ATOVS microwave (NOAA ) 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)

10 500hPa ACC (.vs. NCEP ANA.) ( , 62cases)

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

Summary It 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? Model Weak subtropical high Excessive precipitation over the maritime continent Large cooling bias at top (~10hPa) Coupling of SISL dynamics & physics Hybrid 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 correction As a regularization term in VarBC