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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) Slide 2 OUTLINE Introduction of GRAPES-3DVar Tuning of obervation error in data assimilation Latest development in the global assimilation/prediction experiment 2008 Summary Slide 3 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 Slide 4 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 1.875 forecast 1 Slide 5 ?? 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) Slide 6 innovation covariance: Iterative fixed-point method: Desrosies et al.,2005 (1) (2) Slide 7 only Sonde RH observation assimilation in GRAPES regional 3DVAR 20070601-0614 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 Slide 8 NOAA16,AMSUA 20070601-0614 diagnosis Obs erroBak. erro Slide 9 ITWG NWP WG list of assumed observation errors Slide 10 Against Radiosonde humididy information of AMSUB has a proper response in GRAPES-3DVAR 58238,Nanjing 59948,Sanya Red : xb Blue : xa(amsub) Black : Sounde Slide 11 Independent verification: RH[xa(amsub)]-Y(sonde) Before Tuning After Tuning 2007060900,500hPa Black:Before Tuning; Red:After tuning 10 cases statistics Slide 12 Tuning of observation error improve GRAPES(30km) QPF Slide 13 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 Slide 14 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 Slide 15 500hPa ACC against NCEP (0.9,0.3) ( ) (Background Check) Slide 16 Slide 17 10 500hPa ACC (.vs. NCEP ANA.) (2006120112 2007013112, 62cases) Slide 18 Slide 19 31cases(200612), against NCEP ANA. NOAA-15 Slide 20 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 Slide 21 Slide 22 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 Slide 23 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