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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. content.

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  1. Introduction of temperature observation of radio-sonde in place of geopotential height to the global three dimensional variational data assimilation system in JMAA.NaruiJapan 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 pressure00UTC 3rd July2002in N.H.left:temperatureright:height

  9. Anomaly correlation of 500hPa height 2002 7/1~7/21(against initial field)blue:heightred:temperatureGlobal NHTropics SH

  10. Anomaly correlation of 500hPa height2002 12/1~12/21(against initial field)blue:heightred:temperatureGlobal NHTropics SH

  11. RMSE and Bias of 500hPa height 2002 7/1~7/21(against radio-sonde)green:heightred:temperatureleft:rmse right:biasNHTropics SH

  12. vertical profile of temperature2002　12/31(after one month anlalysis cyclemean of Northern Hemisphere)green:heightblack: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: JOQC ＝－l n[（γ＋exp(ｰJON) )／(γ＋ 1)]∇JOQC ＝ ∇JON ×（１－P)JON : normal cost function γ : VarQC parameter for each observationP: gross error probabilityOIQC: oprerational QC in JMAcompare 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 pressureupper:increment left:VarQC right:OIQC Lower: left:difference right:analysed field

  16. The End

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