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The usage of the ATOVS data in the Korea Meteorological Administration (KMA)

The usage of the ATOVS data in the Korea Meteorological Administration (KMA). Sang-Won Joo. Korea Meteorological Administration. History of the satellite sounding assimilation in KMA. Feb. 1999 : TOVS data assimilation in the Global model (1DVAR)

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The usage of the ATOVS data in the Korea Meteorological Administration (KMA)

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

  2. 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 Introduction

  3. Inhomogeneous background error

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

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

  6. Observation error • RTM error and • instrument error • Square of innovation • First estimates of • Derber and Wu (1999) • Background error in • radiance space Meaning of the resulting equation • 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. OBSERVATION 1DVAR ANALYSIS R INNOVATION MODEL BACKGROUND B Feedback of observation error Benefits of our method • 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

  8. MODEL Basic Equation Primitive Equation Resolution Triangular truncation of 213 in horizontal and 30 levels sigma-p hybrid coordinate from surface to 10hPa Numerical Scheme Semi-implicit time integration, spherical harmonics for horizontal representation and finite difference in the vertical Radiation Lacis and Hansen (1974) for short-wave and water vapor, carbon dioxide and ozone for long-wave Convective Parameterization Kuo type(1974) Large Scale Condensation Kanamitsu et. al. (1983) Shallow Convection Tiedke(1985) Gravity Wave Drag Iwasaki et. al. (1989) PBL scheme 2 Layer method from Yamada and Meller (1982) Land Surface Processes SiB ANALYSIS Method 3 Dimensional Multivariate Optimum Interpolation Resolution 0.5625 degrees Update Cycle 6 hourly Description of the Global Model

  9. Observation (ATOVS TBB Data=OTB) Background (Profile=BPR)

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

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

  13. Analysis verification (September 2001)

  14. Observation verification(Sep. 2001)

  15. Averaged typhoon track forecast error (TY0111-TY0123)

  16. 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 Summary

  17. 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 Future Plans

  18. 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. True Value(T) 1 2 Right Information T-B Wrong Information B.G.(B) Obs(O) Anal(A) O-B (T-B) X (O-B) > 0 ATOVS Information • The 2nd and 4th quadrants data mislead the analysis. • There are many data in the 2nd quadrant. • Observation should be in the same direction as RAOB from background

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