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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. CWB Climate Project of Climate Variations and Severe Weather Monitoring / Forecasting System Development Program. 2002-2009. GOALS.

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4412739

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


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CWB Climate Projectof Climate Variations and Severe Weather Monitoring / Forecasting System Development Program

2002-2009


Goals

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


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氣象預報監測作業發展情勢


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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.


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


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THe Observing system Research and Predictability EXperiment

http://www.wmo.int/thorpex/


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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


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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


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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


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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


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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


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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


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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


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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


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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


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國 內 

氣象局氣候監測預報與分析作業系統


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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


Dynamical statistical prediction system

Dynamical-Statistical Prediction System

  • Global Model Improvement

  • Dynamical Climate Forecast Models

  • Multi-Model Ensemble and Downscaling

  • Operational Forecast System Management


Dynamical statistic prediction system 1 global model improvement

Dynamical-Statistic Prediction System1. Global Model Improvement :

Model: CWB/GFS

Team:汪鳳如,馮欽賜


Dynamical statistical prediction system model cwb gfs improvement

Dynamical-Statistical Prediction System- Model (CWB/GFS) Improvement

  • Improvement schedule

(1)

(2)

(3)


Dynamical statistic prediction system cwb gfs 1

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。


Dynamical statistic prediction system cwb gfs 2

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。


Dynamical statistic prediction system cwb gfs 3

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。


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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


Dynamical statistic prediction system 2 dynamical climate forecast models

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)


Dynamical statistic prediction system 2 1 intermediate air sea coupled models

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


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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


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


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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)


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Dynamical-Statistic Prediction System2.2. AGCM – GFS and ECHAM5 :

  • AGCM:

    • CWBGFS: T42L18 (Hwu et al., 2002)

    • ECHAM5: T42L19 (Roeckner et al., 2003)


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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)


System structure the backbone of cwb climate forecasts

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


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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°


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Mean Square Skill Score


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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


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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.


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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


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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


Dynamical statistic prediction system effectiveness of bias correction system

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


Dynamical statistic prediction system effectiveness of bias correction system1

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


Dynamical statistic prediction system skills of downscaling system for taiwan and se asia

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


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Dynamical-Statistic Prediction System3. Multi-Model Ensemble and Dynamical Downscaling:

3.3 Dynamical Downscaling Forecast System(蕭志惠,莊穎叡)


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Dynamical Downscaling Forecast System

CWB GCM (T42) forecasts

IRI ECHAM4 forecasts

CWB-RSM (60km)

NCEP-RSM (60km)

Ensemble mean forecast

Anomaly forecast

Probability forecast


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十月的溫度預報參考價值最高!


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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


Dynamical statistic prediction system

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


Dynamical statistic prediction system1

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


Dynamical statistic prediction system2

Dynamical-Statistic Prediction System自動化管理系統(資策會)

  • 主控及排程控制作業:將相關工作設為一排程,管理人員可設定啟動時間與監控各程序之執行狀態

  • 資料處理作業:資料的儲存、備份、傳送及過期資料的刪除

  • 監控作業:監控硬碟使用量、常駐程式狀態、程式執行狀況、排程執行狀況

  • 備援作業:主機無法正常運作時系統會自動切換到備援主機繼續作業

  • 顯示介面:提供系統維護人員WEB畫介面進行管理


Statistical prediction system

Statistical Prediction System

  • 發展之系統:

    • ENSO – CCA

    • ENSO – CA

    • OCN

    • Typhoon Numbers – LAD

    • WNP#TC  Summer Rain in Taiwan

  • 使用現況


Statistic prediction system 1 1 enso cca

Statistic Prediction System1.1 ENSO-CCA

  • 陳孟詩、盧孟明,2002:聖嬰/反聖嬰(El Niño/La Niña)統計預報之發展。氣象學報,44-4,25-39。


Statistic prediction system 1 2 enso ca

Statistic Prediction System1.2 ENSO-CA

  • 陳孟詩,2004:Constructed Analog統計方法應用於海溫預報之研究。中央氣象局研究發展專題,九十三年度研究報告第CWB93-1A-02號。


Statistic prediction system 1 3 ocn

Statistic Prediction System1.3 OCN

  • 李清縢、盧孟明,2006/10:利用適當氣候平均統計方法預報台灣地區溫度。95年天氣分析與預報研討會論文彙編,3-37至3-41頁,中央氣象局,台灣,台北。


Statistic prediction system 1 4 tc lad

Statistic Prediction System1.4 TC-LAD

  • Chu, P. S., X. Zhao and M. M. Lu, 2006/10: Climate prediction of tropical cyclones activity in the vicinity of Taiwan using the multivariate least absolute deviation regression method. 95年天氣分析與預報研討會論文彙編,3-1至3-6頁,中央氣象局,台灣,台北。

TC counts in the vicinity of Taiwan Mean=3.67; Std=1.56 (1971-2000)

2006 forecast TC counts = 4.6


Statistic prediction system 1 5 wnp tc summer rainfall in taiwan

Statistic Prediction System1.5 WNP#TC  Summer Rainfall in Taiwan

  • 盧孟明、麥如俊,2004:一個以西北太平洋颱風個數預報台灣夏季雨量的方法。大氣科學,32-4,407-426。

    • 五月底統計一至五月西北太平洋生成颱風個數,然後查分類表判斷個數類別。

    • 根據颱風個數類別查表判斷是否有氣候訊號,並針對有訊號的區域做雨量類別預報。


Statistic prediction system 2

Statistic Prediction System2. 使用現況

成果使用狀況:目前模式(ENSO-CCA、ENSO-CA、OCN、TC-LAD)逐漸進入實驗與誤差分析/作業測試階段,並輔以參考指引,定期提供預報中心人員做為預報決策之參考。

96年度工作:

進行 OCN/ENSO-CA 統計預報結果分析,並了解此統計模式的預報能力範圍

進行 TC-LAD 之敏感度實驗,並且分析其預報因子與颱風之間的關係

協助 ENSO-CCA 自動化作業

配合統計模式預報產品監測/查詢/校驗系統之建立


Climate monitoring systems

Climate Monitoring Systems

  • Target Station Monitoring

  • ENSO

  • East-Asia Monsoon

    • Cold Surge/Front Monitoring

    • Summer Flow Transition

  • Extreme Rainfall Events and Climate


Target station monitoring 1

Target Station Monitoring1. 選定的目標測站

  • 7個日本測站 + 香港測站 + 25個中央氣象局局屬測站

    • 資料時間:1961/01/01–2006/10/31(日平均,5日平均,月平均)

    • 資料變數:測站氣壓,溫度,雨量,露點溫度,最高溫度,最低溫度

      風向,風速

    • 資料存放於:IRI資料庫映射網站

      http://ingrid.cwb.gov.tw/SOURCES/.Taiwan/.CWB/.Target_Station


Enso 1

ENSO1. 監測項目

  • ENSO指標

  • 海溫現況

  • ENSO預報

  • ENSO – Fast (Biennial) Mode

  • ENSO – Slow Mode

  • ENSO – PDO

  • ENSO – IOD mode & Indian Ocean in general


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氣象局ENSO統計預報模式

氣象局全球海溫(含ENSO)動力統計預報模式


Enso 2 to be accomplished

ENSO2. 應用(to be accomplished)

  • 從ENSO展望台灣氣候變異

    • ENSO 對台灣溫度變異的影響

    • ENSO 對台灣雨量變異的影響

    • ENSO 對台灣附近颱風活動的影響

    • ESNO 對台灣氣象災害預期的影響

  • 從ENSO展望東南亞氣候變異

    • ENSO 對東南亞溫度變異的影響

    • ENSO 對東南亞雨量變異的影響

    • ENSO 對亞澳季風區熱帶氣旋活動的影響


Cold surge front monitoring 1

Cold Surge/Front Monitoring1. 侵台寒潮/鋒面監測

  • 條件:

  • 地面氣壓的遽升

  • (升幅 ≧ 5mb/day)

  • 2. 地面溫度的遽降

  • (降幅 ≧ 4℃/day)

  • 3. 北風分量的增強

  • (增幅 ≧ 3mps/day)

  • 4. 在24小時內的氣壓和溫度的變化幅度大於其日循環(diurnal cycle)和半日循環(semidiurnal cycle)

若連續12小時之內的觀測資料至少有2個測站滿足條件(2),即判定為有鋒面影響台灣天氣。

主要參考文獻:Chen et al. 2002


Cold surge front monitoring 2

Cold Surge/Front Monitoring2. 監測項目

  • Circulation Monitoring

    • 輻散環流

    • 旋轉環流

    • 緯向平均南北垂直環流

    • OLR

  • Local Area(利用台灣測站分布特性)

    • Y-T

    • Z-T


Summer flow transition 1

Summer Flow Transition1. 監測重點區域


Summer flow transition 2

Summer Flow Transition2. 監測項目

  • Seasonal Cycle

    • 850hPa Vor

    • 850hPa U

    • 850hPa V

    • OLR

    • CMAP

  • Transition Index over

    • ARBS(The Arabia Sea vs. West Indian Ocean)

    • BOB(The Bay of Bengal vs. East Indian Ocean)

    • SCS(The South China Sea)

    • PHS(The Philippine Sea vs. Micronesia)

  • SCSIDX

  • USCS Index


Extreme rainfall events and climate 1

Extreme Rainfall Events and Climate1. 監測系統架構

各測站降雨對應其閾值

降雨資料

箱型計數方法

各測站發生之降雨分級

極端降雨事件曆

  • 環流分析:

  • Asia-Australia

  • East Asia

不同延時降雨事件

降雨分級閾值

台灣雨量圖

測站探空圖

監測項目


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氣候監測產品範本


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http://iri.columbia.edu/climate/cid/Sep2006/


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訓練與推廣

  • 2004: 舉辦短期氣候預報與應用研討會1次,參加的政府單位有水利署、農委會,學界則包括中研院、台大、中大、中興大學等單位;美國國際氣候研究院亦派3位專家自費參加。與會者反應熱烈,對氣象局繼續推動跨領域合作抱以高度期望。

  • 2004-06: 每年舉辦70個小時以上的氣候訓練課程與演講,授課講員來自美、加、德、日、韓、中等國,受訓學員總人次(每次平均人數次數)超過1200人,學員所屬單位除了本局之外還包括空軍氣象中心、海軍氣象中心、中正理工學院、台大、中大、師大、文大、明新科大等等。

  • 2004-06:每年舉辦兩次「月與季氣候預報論壇」作為研究與預報的溝通平台,每年參加總人次超過200人。


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  •  論文發表(2003-05):

  • 國際期刊 (共6篇)

  • Chen, J.-M., C.-P. Chang, and T. Li, 2003: Annual cycle of the South China Sea surface temperature using the NCEP/NCAR reanalysis,  J. Meteor. Soc. Japan, 81, 879-884.

  • Juang, H.-M. , C.-H. Shiao and M.-D. Cheng, 2003:The Central Weather Bureau Regional Spectral Model for Seasonal Prediction: Concept, Multi-Parallel Implementation, and Preliminary Result. Monthly Weather Review. ,131, 1832-1847.

  • Hung, C.-W., H.-H. Hsu, and M.-M. Lu, 2004: Decadal oscillation of Spring rain in Northern Taiwan. Geophy. Rev. Lett.17, 699-710.

  • Wang, B., LinHo, Y. Zhang,a nd M.-M. Lu, 2004: A unified definition of the summer monsoon onset over the South China Sea and East Asia, J. Climate,17, 699-710.

  • Li, T., Y.-C. Tung, and J.-W. Hwu, 2005: Remote and local SST forcing in shaping Asian-Australian monsoon anomalies. J. Meteor. Soc. Japan, 153-167.

  • Chen, J.-M., F.-C. Lu, S.-L. Kuo, and C.-F. Shih, 2005: Summer climate variability in Taiwan and associated large-scale processes, J. Meteor. Soc. Japan,83. 499-516.


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B. 國內期刊 (共12篇)

胡志文、馮欽賜、汪鳳如、陳建河、鄭明典,2002:中央氣象局全球模式之氣候特徵:

東 亞夏季季風。大氣科學,30,99-116。

陳昭銘、范惠菱 ,2003: 南海夏季降雨年際變化與侵台颱風之關係。大氣科學 ,31,

221-238.

陳昭銘、 陳仁曾、呂芳川、郭漱泠、胡志文,2003: CWB GFS模擬亞洲夏季季風環流之可

預報度、準確度與海溫變化之關係。大氣科學 ,31, 355-374.

盧孟明、麥如俊,2003:台灣與全球雨量長期變化研究(一)1920-1995變化趨勢。大氣科學

,31,199-220。

盧孟明、麥如俊,2003:台灣與全球雨量長期變化研究(二)亞澳季風區一至三月雨量年際

變化。大氣科學,31,307-332。

陳昭銘、 施景峰、呂芳川、郭漱泠、胡志文,2004: CWB GFS模擬台灣夏季氣候之準確

性、可預報度與海溫變化之關係。大氣科學 ,32, 367-388.

盧孟明、麥如俊,2004:一個以西北太平洋颱風個數預報台灣夏季雨量的方法。大氣科學

,32,407-426.


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蕭志惠、莊漢明,2004: 中央氣象局區域氣候預報模組對夏季降水年際差異之模擬研

究。環境保護期刊,27卷第1期,57-71。

蕭志惠、莊漢明,2005: NCEP RSM之2001年東亞地區短期氣候模擬研究與平均誤差去

除法。大氣科學,Vol. 33, 235-254。

蕭志惠、莊漢明、莊穎叡,2005: NCEP RSM之東亞地區動力降尺度氣候場的初步分。

氣象學報。Vol.46, No.1, 1-12.

盧孟明、陳佳正,2005:豪大雨之頻率分析方法。氣象學報,Vol.46, No.1, 45-60.

盧孟明、羅英祥,2006:監測冬季台灣鋒面氣候─2004年12月至2005年3月之鋒面。

氣象學報。(印製中)


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C. 研討會論文 (2004-05)

蕭志惠、莊穎叡、莊漢明、Liqiang Sun, 2004: NCEP-RSM之東亞地區區域氣候動力降尺度研究。天氣分析與預報研討會論文彙編,351-356。5月17-20日,桃園龍潭宏碁渴望村。

蕭志惠、莊漢明、莊穎叡,2004: NCEP RSM 降尺度預報結果的分析與討論。第七屆區域氣候模擬研討會研討會。10月5-6日,中壢中央大學。

胡志文,2005:中央氣象局混合海氣偶合模式之研發。94年天氣分析與預報研討會論文彙編,346-349,中央氣象局,台灣,台北。

蕭志惠、莊穎叡、莊漢明, 2005: CWB短期區域氣候動力降尺度預報系統

2004年預報技術之回顧。天氣分析與預報研討會論文彙編,353-357。10月18-20日,台北中央氣象局。

Shiao, C.-H., Ying-Jui Chuang , Hann-Ming Henry Juang, 2004: Dynamical downscaling of an AMIP simulation over East Asia with ECHAM4.5 and NCEP RSM. 5th International RSM Conference. July 12-16, 2004. Seoul, Korea.

陳昭銘,2005: 台灣秋季溫度之年代際振盪與太陽黑子循環。氣候變遷與永續發展研討會。2005年7月20-22日,臺北,臺灣。

Chen,J.-M.2005a: CWB dynamical model statistical downscaling. 天氣分析與預報研討會, 2005年10月18-20日,臺北,臺灣。

Chen,J.-M.,2005b: A dynamical-statistical prediction model for station rainfall in Taiwan. Climate Workshop in Taiwan. November 16-17, 2005, Taipei, Taiwan.


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蕭代基、黃星翔、洪銘堅、盧孟明、羅以倫 2005:淡水河流域洪災損失機率風險分析。二00五年環境資源經濟、管理暨系統分析學術研會,中央研究院,94年9月23日。(計畫編號:NSC91-2625- Z- 052- 008)

盧孟明、羅英祥,2005: 冬季台灣鋒面氣候監測系統雛型介紹.天氣分析與預報研討會論文彙編,中央氣象局,94年10月18-20日,409-414。

盧孟明、陳佳正,2005:台灣降雨頻率遽變之研究。天氣分析與預報研討會論文彙編,中央氣象局,94年10月18-20日,415-422。

Lu, M.-M.,C.-L. Ma and R.-J. May, 2005: The ENSO impacts on Taiwan climate. 天氣分析與預報研討會論文彙編,中央氣象局,94年10月18-20日,358-363。

Lu, M.-M. and C.-J. Chen, 2005: Abrupt changes of the heavy rainfall frequency in Taiwan. 2005 Climate Workshop in Taiwa,,台灣大學,94年11月16-17日。

盧孟明、陳佳正,2005: Abrupt changes of the heavy rainfall frequency in Taiwan. 海峽兩岸災變天氣分析與預報研討會論文彙編,台北,94年11月24-25日,133-138.


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D. 技術報告 (2004-05)

胡志文,2004:中央氣象局混合海氣預報模式診端分析。第一部份:GFS模式之SMIP模擬。20pp。

蕭志惠和莊穎叡,2004: 1971-2000年NCEP-RSM動力降尺度氣候場的分析。中央氣象局技術報告。

蕭志惠和莊穎叡,2004: 中央氣象局區域氣候預報模組嵌套策略的研究。中央氣象局技術報告。

蕭志惠和莊穎叡,2005: 2005年CWB動力降尺度預報系統預報技術之更新與測試。中央氣象局技術報告。

蕭志惠和莊漢明,2005: CWB區域波譜模式雲預報方程之建置與測試。中央氣象局技術報告。


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計畫任務編組:氣候預報模式組

  • 動力統計預報系統發展與建置

    (汪鳳如、馮欽賜、陳建河、蕭志惠、莊穎叡、童雅卿、翁叔平<2005>、黃文豪、胡志文、任俊儒、 鄭凱傑、陳昭銘<2006>、施景峰、謝坤山<2004>、賈愛梅)

     全球大氣氣候模式繼續改進

     區域大氣氣候模式發展

     動力統計氣候預報系統建置

  • 氣候統計與分析

    (盧孟明、郭勉之<2003>、麥如俊<2004>、羅英祥、陳佳正<2006>、馬佳齡<2006>、、陳孟詩、李清滕、李明營)

     台灣氣候變異特徵分析與概念模式之建立

     統計預報方法發展

     全球氣候變遷與氣候極端事件研究

     作業性分析報告製作


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徵才!

需要合作夥伴!


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敬請指教

Merry Christmas !


Development requirements

Development Requirements

  • Dynamic Seasonal Prediction system

    • A Hierarchy of Ocean-Atmosphere Coupled Models

    • Atmospheric Global Climate Models (ECHAM5, CWB/GFS)

    • Global and Regional SST Prediction Models

    • Initialization Schemes for coupled model and AGCMs

    • Downscaling Module

    • Super Ensemble Module

    • Ensemble Hindcast Data Base for determining predictability and forecast climatology

  • Statistical and Conceptual Modelsfor

    • Specific Purposes (Applications) of Seasonal Prediction

      * Spring rain * Mei-yu rain * Typhoon activity

      * Summer Temperature Extremes * Winter Temperature Variations

  • Decision Supporting System

    incorporating CWB and other centers’ information


Seasonal outlook schedule leads

Seasonal Outlook Schedule/Leads

 Each season, near mid-month of February, May, August, October, RDC prepares a set of 5 outlooks for 3-month “seasons” (any set of 3 adjacent months) for lead times ranging from ½ month, 1 ½ months, 2 ½ months, 3 ½ months, 4 ½ months, 5 ½ months.

 Each month, near mid-month RDC prepares a set of 3 outlooks for “months” for lead times ranging from ½ month, 1 ½ months, 2 ½ months.

 The outlook for each successive/prior lead time overlaps the prior/successive one by 2 months. This overlap makes for a smooth variation from one map to the next.


Operational products forecast

OPERATIONAL PRODUCTS- Forecast-

3-6 month mean Temperature and Precipitation Outlooks for Taiwan

2006: operational test

2009: in operation

 1 to 6 month Climate Outlooks for Selected Locations in Southeast Asia

2007: operational test

2009: in operation


Operational products monitoring

OPERATIONAL PRODUCTS- Monitoring-

Monthly Regional Climate Watch (electronic report)

2006: operational test

2009: in operation

 Semi-annual Global Climate Watch (electronic report)

2006: operational test

2009: in operation


Operational products analysis

OPERATIONALPRODUCTS- Analysis -

Seasonal Regional Climate Analysis Report (electronic and paper)

2006: operational test

2009: in operation

 Annual Global Climate Analysis Report (electronic and paper)

2006: operational test

2009: in operation


Operational products guidance

OPERATIONAL PRODUCTS- Guidance -

Climate Variation Patterns in Southeast Asia –a forecaster’s guide to climate monitoring and prediction

2008: publish

 Scientific Publications(international journals)

2003-2009: publish


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