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Sei-Young Park

L. Introduction of KMA data assimilation system with FGAT. Sei-Young Park. KMA/Numerical Weather Prediction Division. Contents. Introduction of KMA NWP system KMA data assimilation system History and status (satellite data assimilation & unified 3dvar)

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Sei-Young Park

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  1. L Introduction of KMA data assimilation system with FGAT Sei-Young Park KMA/Numerical Weather Prediction Division

  2. Contents • Introduction of KMA NWP system • KMA data assimilation system • History and status (satellite data assimilation & unified 3dvar) • First Guess at Appropriate Time (FGAT) • On-going & plan

  3. Operational NWP model

  4. Implementation of global forecasting system

  5. RMSE of 500hPa Z North Hemisphere T106(1day) T106(3day) T106(5day) T213(1day) T213(3day) T213(5day) 120 SATEM TOVS 1dVar ATOVS 1dVar Direct 3dVar SATOB 3doi & T213 3dVar 100 80 RMSE 60 40 20 0 4 6 8 4 6 8 4 6 8 4 6 8 4 6 8 4 6 8 4 6 8 4 6 8 10 12 10 12 10 12 10 12 10 12 10 12 10 12 98. 2 99. 2 00. 2 01. 2 02. 2 03. 2 04. 2 05. 2 Verification of Global model (T213L30 for 1998-2005.9 / RMSE of 500hPa )

  6. Data assimilation system

  7. On-going and Plan of data assimilation

  8. Thank you! Numerical Weather Prediction Division, Korea Meteorological Administration

  9. Direct Assimilation Algorithm for ATOVS Structures RTTOV version 7 ATOVS Radiance QC, bias correction & Thinning 1DVAR 1DVAR Observation error(Joo & Lee 2002) Radiance departure Incremental 3dVar • Observation error • RTM error and • instrument error • Background error • in radiance space • Square of innovation • First estimates of • Derber and Wu (1999)

  10. Results Typhoon Track Forecast Error Typhoon track forecast error is much reduced. Reduction of error is about 200km at 72 hour forecast. G3VR – DG3V

  11. NH 500Z 200412 DG3V BIAS 160 140 120 100 80 60 40 20 0 0 1 2 3 4 5 6 7 8 9 10 TR 500Z 200412 DG3V BIAS 25 20 15 10 5 0 0 1 2 3 4 5 6 7 8 9 10 Results of BIAS correction One month averaged RMSE of 500hPa • Experiments • DG3V : Direct assimilation + 3dVar • BIAS : DG3V + Bias correction is applied in the stratospheric channels depending on the latitude Bias correction is important to improve forecast skill.

  12. Minimization of data number for QuikSCAT (Quality Control)

  13. Change of basic fields due to QuikSCAT

  14. Verification of typhoon track Songa Meari Maon Tokage

  15. Motivation (2004) Common code share for global and regional DA (observations, preconditioning, background error statistics, minimization algorithm, observation operator etc) Man Power 2005 Basic Performance Test for T213L30 and WRF cycling 2006 Background error tuning Improvement of observation data processing ( Burf format, QC … ) Introduction of satellite radiance Test on operational frame Starting 4DVAR on WRF model Unified 3dVar (U3VR)

  16. Comparison of R3VR, G3VR and U3VR

  17. Initial MSLP (00 UTC 18 May 2005) SI (CNTL) 3h-Cycled U3dVR (+2 day) T426 (ANAL) • U3dVar by continuous cycling gives the reasonable initial MSLP pattern. • SI gives too small scaled MSLP by the topography.

  18. FGAT is a method to make an innovation with first guess at appropriate time (observation time). : Normally in 3dvar we’ve considered the observation data within the time window are observed at the same time with first guess. → It can make an error! - introduced by D. Vasiljevic (mid 1980’s) - ECMWF reanalysis ERA-40 - NCEP GSI - WRF FGAT First Guess at Appropriate Time (FGAT)

  19. How to make the first guess at appropriate time WRF 3DVAR FGAT -03 –02 –01 00 +01 +02 +03 GDPS 3DVAR FGAT -03 –02 –01 00 +01 +02 +03 × × y y

  20. Data processing CDA (Comprehensive Database for Assimilation) : routine to process the observational data for 3DVAR • D-value • CTRL : DVAL = OBS-GUES • FGAT : DVAL = OBS-(GUES+GSDT×DELT) • Unify data which are the same position and height •   Priority : maximum priority data •  Time difference : the nearest data to anal time • Observation error : minimum observation error data •    D-value : minimum D-value  data •    QC : good quality data , etc.. • ⇒ FGAT : skip this algorithm except simultaneouslyhappened data

  21. Processing time for 3dvar(20051115-20060109) CNTL FGAT 0:20:23 0:21:24 Daily averaged data number(20051229-20060108) 18%↑

  22. In Time NUMERICAL WEATHER PREDICTION DIVISION/KMA

  23. In Time RADIA TION NUMERICAL WEATHER PREDICTION DIVISION/KMA

  24. IV for ATOVS (2005.10.04.) CTRL FGAT NUMERICAL WEATHER PREDICTION DIVISION/KMA

  25. KIROGI 2005-20 (2005.10.13.12.) NUMERICAL WEATHER PREDICTION DIVISION/KMA

  26. RMSE for Analysis field (20051121-20051231) FGAT gave somewhat positive impact.

  27. BIAS for Analysis field (20051121-20051231)

  28. J & DJ

  29. Current status of YOURS GSM - 2005 DFS YOURS Dry primitive global DFS system with multiple levels (Cheong et al. 2003, Park et al. 2005) Radiation (Kim et al. 2005, Byun and Hong 2005) Land surface (Seol and Hong 2005, Hong 2005) Vertical diffusion and PBL (Noh et al. 2003, Hong et al. 2005) Gravity wave drag (Kim and Arakawa 1995, Chang and Hong 2005) (Chun and Baik 1998, Jun et al. 2005) Cumulus parameterization (Hong and Pan 1998, Byun and Hong 2005) Shallow convection scheme(Kim et al. 2004) Explicit cloud scheme (Hong et al. 2004, Lim 2004, Hong and Byun 2004) 독자모델 구축계획 (전지구모델 1) – 기상청-연세대 R&D 모델 체계 Next-generation KMA GSM

  30. 독자모델 구축계획(전지구모델 2) – 기상청-연세대 R&D 모델 역학체계

  31. 독자모델 구축계획(전지구모델 3) – 기상청-연세대 R&D 모델 구축일정 • 기상청 슈퍼컴퓨터에 이식 : 3월 • 분석 시스템 접목 및 실험 : 4월-6월 • 시험 운영 : 7월 • 검증 및 보완 : 8-10월 • 모델 고정 및 고분해능 전지구 예보시스템과 병행운영: 11월 • 해상도는 현 기상청 전지구 예보모델 (T426) 수준 • 분석 시스템은 3dVar로 시작  4dVar로 개선 • KMA/YSU GDAPS 공식 발표

  32. 독자모델 구축계획 (지역모델) • 배경 • - 차세대 기상청 지역예보모델 WRF가 근간 • - 해외 개발 모델 도입 시에도 독자적인 기술력으로 유지보수가 가능한 • 수치예보모델로의 전환 필요 • - WRF 코드의 분석 해체 작업 필요 • 수행 업무 • - WRF 체계의 병렬화 구조 분석 • - WRF 입출력 자료 구조 분석 • - RSL 기반의 병렬화 구조를 제거한 단일 CPU 용 WRF 코드의 개발 • - 단일 CPU 상에서 최적화된 WRF 코드 작성 • - 4dVar 접목 •  독자기술로서 운영, 개선될 수 있는 차세대 지역예보모델로서 KWRF (KoreanWRF) 구축

  33. Table 2. Physical processes in global and regional models at KMA. Table 2. Physical processes in global and regional models at KMA. 물리과정 (전지구 vs. 지역)

  34. 향후 계획 1. 전지구 예보모델 - 독자 모델의 구축을 위해  연세대에서 개발 중인 R&D 모델 을 차세대 기상청 전구 모델로  R&D 모델의 성능 향상에 주력  완전 구축 시 까지 T426 체계와 병행 운영 2. 지역 예보모델 - RDAPS 에서 KWRF 체계로의 전환  10km 해상도  예보시간 51시간~75시간 시험  예보시간 연장과 함께 영역확장 시험  4dVar 시험  최적 시스템 구축 운영 이와 함께 WRF 코드의 분석 해체 작업을 통해 보다 현업적이고 독자적인 체계 구현

  35. 3. 확률론적인 예보로의 접근  결정론적 예보의 한계 - 앙상블 예측시스템의 성능 개선  해상도, 멤버수 증가 후 현업운영 (T106L30  T213L30  T213L40, 32 멤버) - 앙상블 해석 능력 강화 4. 자료동화의 지속적 개선 - 궁극적으로 4dVar 5. 디지털 예보 지원을 위한 재분석 - 주간 디지털예보 지원을 위한 T426L40의 재분석 - 단기 디지털예보 지원을 위한 10km KWRF의 재분석

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