1. Introduction of temperature observation of radio-sonde in place of geopotential height to the global three dimensional variational data assimilation system in JMA A.Narui Japan Meteorological Agency, Japan

2. content Ⅰ. The reason to introduce temperature observation of radio-sonde in place of geopotential height Ⅱ. modification to assimilate temperature Ⅲ. Forecast experiments Ⅳ. summary

3. Ⅰ.The reason to introduce temperature observation of radio-sonde in place of geopotential height  The global three dimensional variational data assimilation system (3D-Var) was implemented in JMA operation in September 2001. _ The direct assimilation of ATOVS radiances was introduced in May 2003.  As the next step for our 3D-Var, we have a plan to introduce variational quality control (VarQC). Because VarQC is natural extension of variational method.  Since it is favorable for VarQC that there is no correlation between observation data, we are going to assimilate temperature of radio-sonde in place of geopotential height, which has strong vertical correlation.

4. Ⅰ.The reason to introduce temperature observation of radio-sonde in place of geopotential height Though the main purpose of this work is to prepare for the introduction of VarQC, the use of temperature data itself showed some good impacts on the forecast score.

5. SYSTEM OF JMA/3D-VAR FORECAST MODEL : GLOBAL MODEL RESOLUTION ： T213L40 （ OUTER MODEL) T106L40 （ INNER MODEL) INCREMENTAL METHOD ANALYSIS VARAIBLE: ζ 、 D 、 T 、 Ps 、 ln q CONTROL VARIABLE: ζ 、 Du 、（ T,Ps)u 、 ln q BACKGROUND ERROR: NMC METHOD for 2000 HOMOGENEOUS MINIMIZATION: LBFGS

6. Ⅱ． modification to assimilate temperature 1. remove the vertical correlation of observational errors for all elements 2. use the significant level data in addition to the standard level data 3. recalculate all observational errors for all elements of radio-sonde from the statistics of observation departure from the background

7. Ⅲ. Forecast experiments Exp.1: assimilation of geopotential height Exp.2: assimilation of temperature Forecast Model:Global Model T213L40 6hourly cycle period 1 period 2 assimilation 27Jun-30July 2002 27Nov-31Dec 2002 initial 12UTC 1-21 July 12UTC 1-21 Dec Fcst range 216 hour 216 hour

8. Analysis increment of sea level pressure 00UTC 3rd July 2002 in N.H. left:temperature right:height

9. Anomaly correlation of 500hPa height 2002 7/1~7/21 (against initial field) blue:height red:temperature Global NH Tropics SH

10. Anomaly correlation of 500hPa height 2002 12/1~12/21 (against initial field) blue:height red:temperature Global NH Tropics SH

11. RMSE and Bias of 500hPa height 2002 7/1~7/21 (against radio-sonde) green:height red:temperature left:rmse right:bias NH Tropics SH

12. vertical profile of temperature 2002 12/31 (after one month anlalysis cycle mean of Northern Hemisphere) green:height black:temperature

13. Ⅳ. Summary 1. We Introduced temperature of radio-sonde in place of geopotential height. 2. There is some good impact on forecast score. 3.Problem: Bias against radio-sonde 4. VarQC is developed now.

14. Example of VarQC Formulation (including gross error probability) Cost function: J O QC ＝－ l n[ （ γ ＋ exp( ｰ J O N ) ) ／ (γ ＋ 1)] ∇ J O QC ＝ ∇ J O N × （１－ P) J O N : normal cost function γ : VarQC parameter for each observation P: gross error probability OIQC: oprerational QC in JMA compare each datum in turn against an analysis based on surrounding data with OI

15. Example of VarQC rejected with VarQC(gross error probability is >75%) Analysed surface pressure upper: increment left:VarQC right:OIQC Lower: left:difference right:analysed field

16. The End