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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.

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Presentation on theme: "Adaptive Estimation and Tuning of Satellite Observation Error in Assimilation Cycle with GRAPES Hua ZHANG, Dehui CHEN, Xueshun SHEN, Jishan XUE, Wei HAN."— Presentation transcript:

1 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)

2 OUTLINE Introduction of GRAPES-3DVar Tuning of obervation error in data assimilation Latest development in the global assimilation/prediction experiment 2008 Summary

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

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 ° forecast 1 °

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)

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

7 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

8 NOAA16,AMSUA diagnosis Obs erroBak. erro

9 ITWG NWP WG list of assumed observation errors

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

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

12 Tuning of observation error improve GRAPES(30km) QPF

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

14 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

15 500hPa ACC against NCEP (0.9,0.3) ( ) (Background Check)

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17 10 500hPa ACC (.vs. NCEP ANA.) ( , 62cases)

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19 31cases(200612), against NCEP ANA. NOAA-15

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

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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

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


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