1. Numerical Weather Prediction Division The usage of the ATOVS data in the Korea Meteorological Administration (KMA) Sang-Won Joo Korea Meteorological Administration Feb. 1999 : TOVS data assimilation in the Global model (1DVAR) Nov. 2001 : AOTVS(HIRS+AMSU-A) assimilation in the Global Model (1DVAR) History of the satellite sounding assimilation in KMA

2. Numerical Weather Prediction Division Introduction 1.1DVAR in KMA - Background error implies geographical variation - Observation error is calculated from the innovation and background error Evaluation of effect on the model performance - Evaluation of the time averaged fields - Typhoon track forecast error

3. Numerical Weather Prediction Division Inhomogeneous background error

4. Numerical Weather Prediction Division Methodology Error variance changes but correlation is fixed Damping area is assigned Eq.20N 20S 90S90N e Error covariance becomes Inverse matrix of error covariance becomes

5. Numerical Weather Prediction Division Observation error Statistical Method for observation error Assumption 1.Tangent linear approximation 2.No correlation between background error and RTM error 3.Biases are well removed Derivation 1 st assumption 2 nd assumption 3 rd assumption RTM error

6. Numerical Weather Prediction Division Meaning of the resulting equation Square of innovation First estimates of Derber and Wu (1999) Background error in radiance space Observation error RTM error and instrument error Innovation is the sum of observation error and background error if there is no correlation The resulting equation says the above statement in radiance space.

7. Numerical Weather Prediction Division Feedback of observation error ROBSERVATIONBACKGROUND B 1DVAR MODEL INNOVATION ANALYSIS Relationship exists between observation error and NWP analysis through B Improvement of background error can readily affect the observation error The error ratio (eigenvlaue) is changed automatically Benefits of our method

8. Numerical Weather Prediction Division MODELBasic EquationPrimitive Equation ResolutionTriangular truncation of 213 in horizontal and 30 levels sigma-p hybrid coordinate from surface to 10hPa Numerical SchemeSemi-implicit time integration, spherical harmonics for horizontal representation and finite difference in the vertical RadiationLacis and Hansen (1974) for short-wave and water vapor, carbon dioxide and ozone for long-wave Convective Parameterization Kuo type(1974) Large Scale CondensationKanamitsu et. al. (1983) Shallow ConvectionTiedke(1985) Gravity Wave DragIwasaki et. al. (1989) PBL scheme2 Layer method from Yamada and Meller (1982) Land Surface ProcessesSiB ANALYSISMethod3 Dimensional Multivariate Optimum Interpolation Resolution0.5625 degrees Update Cycle6 hourly Description of the Global Model

9. Numerical Weather Prediction Division Observation (ATOVS TBB Data=OTB) Background (Profile=BPR)

10. Numerical Weather Prediction Division Others Quality control(Eyre, 1992) Forward operator: RTM(RTTOV version 6) + Vertical interpolation Minimization algorithm: BFGS method (quasi-Newtonian algorithm) Dimension reduction to the TOVS BUFR format Optimum interpolation interface (Lorence 1986, Eyre 1993) Bias correction: Scan angle and air mass bias correction (Joo and Okamoto, 2000)

11. Numerical Weather Prediction Division Flowchart Background(B),Analysis(A) Observation(O), Profile(PR) Brightness Temperature(TB) PREFIX: SURFIX: Departure(D) no ADJOINT MINIMIZATION BIAS C. DTB DTB_B J &J APR BPR APR RTM BIAS C. OTBATB OTB_B - DPR - yes 1st Background Error Observation Error Radiance Space Physical Space BFGS

12. Numerical Weather Prediction Division Flow chart of the 1DVAR with NWP analysis Background Error Bias 1DVAR 3D O.I. Global Model 10 day forecast 6 hour forecast FEP background 24 and 48 hour Forecasts for 1 Month B ATOVS data Synoptic Obs. O-B for 1 Month Observation error Diagnostics R Bias B Tv 1DVAR1DVAR

13. Numerical Weather Prediction Division Analysis verification (September 2001)

14. Numerical Weather Prediction Division Observation verification(Sep. 2001)

15. Numerical Weather Prediction Division Averaged typhoon track forecast error (TY0111-TY0123)

16. Numerical Weather Prediction Division Summary The 1DVAR is developed in KMA to assimilate the ATOVS data The statistics shows positive effect mostly and also in ASIA Typhoon track is well predicted with the 1DVAR and it is mainly caused by the better specification of the Pacific High The 1DVAR is in operation from 1 November, 2001

17. Numerical Weather Prediction Division Future Plans Improvement of the bias correction scheme Utilization of the ATOVS data over the land Improvement of cloud detection scheme Implementation of the 1DVAR in the regional model

18. Numerical Weather Prediction Division Verification with RAOB Poor performance near surface and tropopause Large improvement in the S.H. We need more improvement in the N.H. and Tropics.

19. Numerical Weather Prediction Division ATOVS Information The 2 nd and 4 th quadrants data mislead the analysis. There are many data in the 2 nd quadrant. Observation should be in the same direction as RAOB from background (T-B) X (O-B) > 0 B.G.(B)Obs(O) Anal(A) True Value(T) 12 O-B Right Information T-B Wrong Information