Download
slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
簡介 中央氣象局氣候監測預報與分析作業系統 發展現況 PowerPoint Presentation
Download Presentation
簡介 中央氣象局氣候監測預報與分析作業系統 發展現況

簡介 中央氣象局氣候監測預報與分析作業系統 發展現況

264 Views Download Presentation
Download Presentation

簡介 中央氣象局氣候監測預報與分析作業系統 發展現況

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. 簡介 中央氣象局氣候監測預報與分析作業系統 發展現況 盧孟明 中央氣象局科技中心 2006.12.19 National Central University

  2. CWB Climate Projectof Climate Variations and Severe Weather Monitoring / Forecasting System Development Program 2002-2009

  3. GOALS To develop an adaptable climate prediction, monitoring and analysis integrated system for end users with the aims of: • mitigating climate-related disasters • contributing to the national sustainable development • improving the understanding of regional climate variations

  4. 氣象預報監測作業發展情勢

  5. Tropical Ocean Global Atmosphere (TOGA) 1985-1994 The Tropical Ocean Global Atmosphere program is a major component of the World Climate Research Program (WCRP) aimed specifically at the prediction of climate phenomena on time scales of months to years. The philosophy upon which TOGA is based purposefully emphasizes the tropical oceans and their relationship to the global atmosphere. The TOGA program accomplished its objectives by showing:  that certain levels of predictability of SST in the Tropical Pacific exist that skillful predictions of SST could be made  that SST predictions indicate some skill for temperature and precipitation in selected other parts of the world  that these predictions in selected parts of the world could be usefully applied for the amelioration of adverse climatic conditions and for the exploitation of beneficial climatic conditions.

  6. http://www.clivar.org/science/overview.php

  7. THe Observing system Research and Predictability EXperiment http://www.wmo.int/thorpex/

  8. NEW THORPEX NUMERICAL WEATHER PREDICTION PARADIGM WEATHER-CLIMATE LINK ADAPTIVE COLLECTION & USE OF OBSERVATIONS USER CONTROLLABLE PROBABILISTIC FORECASTS INTEGRATED DATA ASSIMILATION & FORECASTING GLOBAL OPERATIONAL TEST CENTER GLOBAL INTERACTIVE FORECAST SYSTEM (GIFS) Days 15-60 NWS OPERATIONS CLIMATE FORECASTING / CTB GLOBAL OPERATIONAL SOCIOECON. SYSTEM TEST CENTER MODEL ERRORS & HIGH IMPACT MODELING http://www.emc.ncep.noaa.gov/gmb/ens/THORPEX/weather-cliamte_planning_27Apr06.ppt

  9. DEFINITION OF WEATHER & CLIMATE • What isWEATHER? • Instantaneous atmospheric and related conditions, and their • Effects on people over short (up to couple of days) periods of time • What isCLIMATE? • Statistics of weather over expanded (longer than a month) periods • Are thereSEPARATE “WEATHER” & “CLIMATE” REALITIES? • No, there is one natural process, with • Variability on multiple spatial and temporal scales • Both weather & climate are concepts about this natural process, • Emphasizing different aspects of nature; • Weather more concrete – you can directly experience at the moment • Climate more abstract – one needs to intellectually comprehend effect • FORECASTINGweather & climate • Predicting the same reality, “weather process” • Sharing the same basic procedures • Priorities differ according to focus (on weather or climate) http://www.emc.ncep.noaa.gov/gmb/ens/ScienceMtg_04-27-06.html

  10. OBSERVING SYSTEM - SYNERGY BETWEEN WEATHER & CLIMATE COMPONENTS • What is important for weather & climate prediction? • Set performance measures for both applications • For assessing impact of observations • What are the observational needs of weather & climate forecasting? • Evaluate in common framework • Observing System Experiments (OSE) • Observing System Simulation Experiments (OSSE) • Assess priorities for both applications • Design future observing system that takes advantage of synergies, eg: • Adaptive observational strategy may be useful for both • Weather– optimized for short-range forecasting • Climate– optimized for detection of extreme events http://www.emc.ncep.noaa.gov/gmb/ens/ScienceMtg_04-27-06.html

  11. DATA ASSIMILATION - SYNERGY BETWEEN WEATHER & CLIMATE COMPONENTS • Real-time data access • Critical for atmospheric data • Ocean data must be made available similarly in real time • Initialization of coupled system • Current practice – treat atmosphere and ocean separately • Challenge related to coupling of atmospheric and ocean models • Technical issue, instabilities related to coupling procedure… • Ensemble perturbation techniques • “Coupled” initial perturbations needed • Model perturbations for describing model-related forecast errors http://www.emc.ncep.noaa.gov/gmb/ens/ScienceMtg_04-27-06.html

  12. NUMERICAL MODELING - SYNERGY BETWEEN WEATHER & CLIMATE COMPONENTS • Test use of ensemble with cascadingly lower resolution models • Start with very high resolution, expensive model for details at short range • Truncate after some time, continue with lower resolution, cheaper model • Need reforecast data set for statistical bias correction • Use of Limited Area Models (LAM) for downscaling? • Originates from weather forecast practice • “Forecast” information is from coupled ocean-atmosphere-land model • LAM specifies regional conditions consistent with global forecast • Test use of mixed-layer ocean model as intermediate solution • Avoid problems with full coupling • Improve extended-range weather forecasts • Study models’ ability to simulate/forecast intra-seasonal variability • Unified approach potentially most beneficial for 10-60 day range http://www.emc.ncep.noaa.gov/gmb/ens/ScienceMtg_04-27-06.html

  13. SEAMLESS APPLICATIONS - SYNERGY BETWEEN WEATHER & CLIMATE COMPONENTS • Study and compare weather and climate forecast applications • Shorter lead times (1-14 days) • Intermediate lead times (10-90 days) • Longer lead times (60+ days) • Exploit experience/knowledge accumulated in climate applications (eg, at IRI) for shorter ranges • Compare economic value of weather & climate forecasts in common framework • Develop application methods viable at all lead times • Common forecast format – Probabilistic information • Seamless suite of products - Digital database • Spatio-temporal variations differ: • High at short, • Low at longer lead times • Yet ensemble offers flexible filtering (no need for additional general smoothing/filtering) • One-stop shopping for weather and climate information is needed as • Society becomes more sensitive to atmospheric, hydrologic, and oceanic conditions • Demonstrate joint weather-climate forecast applications • Joint Demonstration projects • How weather/climate forecast can be used in everyday decision making process • Different sectors of society • Different regions of the globe • Positive results should be distributed among potential users • THORPEX research program (out to 14 days) • Global Interactive Forecast System (GIFS) • Link with climate research http://www.emc.ncep.noaa.gov/gmb/ens/ScienceMtg_04-27-06.html

  14. SOCIO-ECONOMIC BENEFITS OFSEAMLESS WEATHER/CLIMATE FORECAST SUITE Forecast Uncertainty Outlook Guidance Threat Assessments Type of Guidance Forecasts Watches Warnings & Alert Coordination Lead Time Commerce Energy Ecosystem Health Hydropower Agriculture Sensitivity to Ocean / LandInitital Conditions Reservoir control Recreation Transportation Fire weather Sensitivity to AtmosphericInitial Conditions Flood mitigation Navigation WEATHER-CLIMATE FORECASTING LINKAGE Protection of Life/Property Minutes Hours Days Weeks Months Seasons Years NOAA THORPEX WEATHER–CLIMATE LINK SCIENCE PLANNING MEETING, Apr.27,2006

  15. 一週以上的天氣預報,信賴度取決於預報因子的效用!一週以上的天氣預報,信賴度取決於預報因子的效用! http://www.emc.ncep.noaa.gov/gmb/ens/ScienceMtg_04-27-06.html

  16. NOAA 作業系統發展過程 –以THORPEX為例 PATH FROM THORPEX RESEARCH TO NOAA OPERATIONS BASIC RESEARCH APPLIED RESEARCH TRANSITION TO OPERATIONS NOAA OPERATIONS PHASE Answer Science Questions Develop Methods Prepare for Implementation Generate Products What? External investigators NOAA Laboratories Global Test Center / NCEP NCEP Central Operations Who? NSF, DOD, NASA Financial Support? NOAA THORPEX PROGRAM NOAA NWS …我國? http://www.emc.ncep.noaa.gov/gmb/ens/ScienceMtg_04-27-06.html

  17. 國 內  氣象局氣候監測預報與分析作業系統

  18. The CWB Climate Information System Framework Users Climate Information Dissemination System Climate Forecast and Monitoring Decision Supporting System Statistical Prediction System Dynamical-Statistical Climate Prediction System Climate Monitoring System Climate Analysis System Climate Data Process and Display System (in CWB Virtual Data Center) Climate Data Base

  19. Dynamical-Statistical Prediction System • Global Model Improvement • Dynamical Climate Forecast Models • Multi-Model Ensemble and Downscaling • Operational Forecast System Management

  20. Dynamical-Statistic Prediction System1. Global Model Improvement : Model: CWB/GFS Team:汪鳳如,馮欽賜

  21. Dynamical-Statistical Prediction System- Model (CWB/GFS) Improvement • Improvement schedule (1) (2) (3)

  22. Dynamical-Statistic Prediction SystemCWB/GFS – 物理改進(1) • Shallow convection更新效益 1.修正西太平洋副熱帶區(15-30N)及東太平洋靠近中美洲(10-20N)之過強網格尺度降水 2.修正東太平洋的間熱帶輻合帶(ITCZ)對積雲降水之低估 發表論文 汪鳳如,李瑞麟,2002:中央氣象局全球模式之淺積雲參數法的改進,氣象學報。

  23. Dynamical-Statistic Prediction SystemCWB/GFS – 物理改進(2) • Soil model更新效益 1.改進沙漠乾熱區對地表蒸發之高估 2.基於對土壤濕度初始場的敏感度,預期 隨觀測分析技術之進展,將有連帶進步空間 發表論文 汪鳳如,馮欽賜,2004:中央氣象局全球預報系統之地表過程的評估測試,氣象學報。

  24. Dynamical-Statistic Prediction SystemCWB/GFS – 物理改進(3) • Grid Scale Precipitation • 收支分析顯示輻射有主導性的影響 • 將雲水預報量與輻射過程作適當連結,是引進pcw的重要伴隨工作,是現階段研究重點 • 下一階段將prognostic cloud scheme從 level2 更新為 level3(增加rain/snow預報) 發表論文 汪鳳如,馮欽賜,李瑞麟,2006:中央氣象局全球預報系統更新網格尺度降水參數化 的評估測試,天氣分析與預報研討會。

  25. Two-Tier Global Dynamical Forecast System GFS ECHAM5 大氣預報 Step:2 OPGSST NCEP/CFS 海溫預報 Step:1 Ensemble Step: 3 Step: 4 Bias Correction 氣象局 統計動力 氣候預報系統 Step: 5 Downscaling

  26. Dynamical-Statistic Prediction System2.Dynamical Climate Forecast Models: 2.1 Intermediate Air-Sea Coupled Models(童雅卿,胡志文,黃文豪) 2.2 AGCM – GFS and ECHAM5(胡志文,任俊儒,鄭凱傑)

  27. Dynamical-Statistic Prediction System2.1. Intermediate Air-Sea Coupled Models : • Intermediate Atmosphere Model: Gill: (Gill, 1980) Statistical: (Kang and Kug, 2000) • Intermediate Ocean Model: Original CZ: (Zebiak and Cane, 1987) Modified CZ: (Kang and Kug, 2000) UH 2 ½: (Fu and Wand , 2004) • Intermediate Coupled Model (ICM): • ICM1: Original Cane-Zebiak type Model, with Gill atmosphere model DATA: Observed SST and Wind Stress Anomaly • ICM2a: Modified Cane-Zebiak type Model with statistical atmosphere model DATA: Observed SST and Wind Stress Anomaly • ICM2b: Modified Cane-Zebiak type Model DATA: Observed SST and (0.25*obs+0.75*forecast) Wind Stress Anomaly El Niño Prediction

  28. CWB/OPGSST Prediction System Atmos/Ocean data in previous months Construct I.C. to drive intermediate coupled models Dynamic modules (ICM) Forecasting from other centers (NCEP, APCC) Statistical modules Historical data ‧‧‧ ‧‧‧ … Multi-Model Ensemble (MME) CWB/GFS AGCM Ensemble Integration (10 members) Cross Validation OPGSST Seasonal SST prediction (6 months) Seasonal climate prediction (6 months) Assessment

  29. http://rdc03.cwb.gov.tw/exp_rest/sst_forecast/opgsst.htm

  30. Dynamical-Statistic Prediction SystemOPGSST –作業版本OPGSST1.1使用現況 • comprises 4 statistical and 2 dynamical modules • DAMPER – SSTA(tl) = α(tl) * SSTA(t0) • NINO34 – SSTA (170W-120W; 5S-5N) (OISST_v2) • PSSLP – SLPA (110E-170E; Eq-20N) (NCEP R1) • TPOHC – Tsfc-300m anomaly (120E-80W; 10S-10N) (BMRC) • ICM2a – 129E-to-84W; 19S-to-19N • ICM2b – 129E-to-84W; 19S-to-19N • statistically • include the dynamic feedback of winds from the far western Pacific • construct a more realistic relationship between the observed subsurface ocean temperature and thermocline depth anomalies The system has been migrated to IBM/HPC machine after Aug. 2006 and can be initiated in the beginning of every month (Tung’s credit)

  31. Dynamical-Statistic Prediction System2.2. AGCM – GFS and ECHAM5 : • AGCM: • CWBGFS: T42L18 (Hwu et al., 2002) • ECHAM5: T42L19 (Roeckner et al., 2003)

  32. Dynamical-Statistic Prediction System3. Multi-Model Ensemble and Statistical Downscaling: 3.1 Multi-Model Ensemble (胡志文,任俊儒,鄭凱傑) 3.2 Bias-Correction and Statistical Downscaling (陳昭銘,施景峰,謝坤章)

  33. System Structure-The Backbone of CWB Climate Forecasts- Climate Data Base Predictability Experiments Initialization Global SST2 Prediction Initialization Obs. SST AGCM1&2 Ensemble Prediction Forecast SST Global SST1 Prediction El Nino Prediction (coupled model) Forecast Climatology Statistical Downscaling Multi-Model Ensemble Seasonal Prediction Statistical Prediction AGCM1: CWB/GFS AGCM2: ECHAM5 SST1: CWB/OPGSST SST2: NCEP/CFS-SST Prediction from Other Institutes

  34. Dynamical-Statistic Prediction SystemAGCM –模式現況 • Forecast twice each month with four modules: • CWB/GFS – CWB/OPGSST • CWB/GFS – NCEP/CFS • ECHAM5 – CWB/OPGSST • ECHAM5 – NCPE/CFS • Each module has 10 members. Each member integrates 7 months. • 40 members ensemble will be used as forecast. • Two AGCMs: • CWB/GFS : T42L18 • ECHAM5 : T42L19 • Two SST boundary conditions: • CWB/OPGSST : 2.5°x2.5° • NCEP/CFS : 2°X1°

  35. Mean Square Skill Score

  36. Dynamical-Statistic Prediction SystemAGCM –模式結果使用狀況 • 提供每月預報結果給預報中心長期課作為預報參考 • 每季提供預報資料給「亞太經合會氣候中心」(APCC)做多模式氣候預報 • 提供模式資料給師大陳正達教授(防災計畫)發展多模式氣候預報 • 為動力與統計降尺度預報提供背景資料

  37. Dynamical-Statistic Prediction System2007-09年發展重點 • Potential and practical predictability skills of the regional one-tier dynamic prediction systems. • Construct a full coupled atmosphere-ocean GCM. • Completion of the GFS and ECHAM model two-tier hindcast and assessment.

  38. Dynamical-Statistic Prediction System3.2 Bias-Correction and Statistical Downscaling: • Effectiveness of Bias-Correction System • Skills of Downscaling System for Taiwan and SE Asia • Current Progresses and Plan • Model: CWB/GFS • Experiment: 10-member ensemble hindcast for the period of 1979-2003 • SST: CWB/OPGSST 1.1

  39. Dynamical-statistical prediction model for Taiwan’s rainfall Dynamical component Statistical component SVD-based projection model SMIP ensmble hindcasts Pattern selection Bias correction CWB GCM Couple pattern Projection & Verification OBS global field Predictor selection Yes Prediction No Station rainfall Large-scale field change

  40. Dynamical-Statistic Prediction SystemEffectiveness of Bias-Correction System • JJA Bias correction scheme: removes about 40-80% of error intensity for summer S850 field, 75-80% for P. Better performance for P than S850 Double cross validation: with practical capability, no over-fitting in statistical schemes

  41. Dynamical-Statistic Prediction SystemEffectiveness of Bias-Correction System • DJF Bias correction scheme: removes about 50% of error intensity for S850 field, 85-90% in winter. Better performance for P than S850 Double cross validation: With practical capability

  42. Winter (b)T2m (a)P (c)P (d)T 9-station (e)P (f)T 3-station Dynamical-Statistic Prediction SystemSkills of Downscaling System for Taiwan and SE Asia Verification-period Hit Rate reasonable skills (>1/3) for predicting regional climate over Taiwan and SEA

  43. Dynamical-Statistic Prediction System3. Multi-Model Ensemble and Dynamical Downscaling: 3.3 Dynamical Downscaling Forecast System(蕭志惠,莊穎叡)

  44. Dynamical Downscaling Forecast System CWB GCM (T42) forecasts IRI ECHAM4 forecasts CWB-RSM (60km) NCEP-RSM (60km) Ensemble mean forecast Anomaly forecast Probability forecast

  45.  十月的溫度預報參考價值最高!

  46. Dynamical-Statistic Prediction System4. Operational Forecast System Management(童雅卿): • 動力統計預報作業系統 • 自動化管理系統

  47. Dynamical-Statistic Prediction System動力統計預報作業系統管理範圍 OPGSST ICM2a ICM2b GFS ECHAM CFS Forecast SST 統計降尺度系統

  48. Dynamical-Statistic Prediction System動力統計預報作業系統管理雛型 –海溫預報準作業系統 • 作業平台 • vpp300.mic.cwb => hpc.mic.cwb • 預報時間 • 每個月11日進行8個月海溫預報 • 約需16個小時完成預報 • 觀測資料 • OISSTv2 monthly mean sea surface temperature • NCEP monthly mean sea level pressure • NCEP monthly mean 925hpa wind field • BMRC monthly mean subsurface sea temperature