2006.12.19 National Central University

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?? ?????????????????? ????
??? ?????????
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

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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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????????????
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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

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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
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/weath
er-cliamte_planning_27Apr06.ppt
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DEFINITION OF WEATHER CLIMATE

• What is WEATHER?
• Instantaneous atmospheric and related conditions,
  and their
• Effects on people over short (up to couple of
  days) periods of time
• What is CLIMATE?
• Statistics of weather over expanded (longer than
  a month) periods
• Are there SEPARATE 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
• FORECASTING weather 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

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
Commerce Energy
Ecosystem Health
Hydropower Agriculture
Sensitivity to Ocean / Land Initital Conditions
Reservoir control Recreation
Transportation Fire weather
Sensitivity to Atmospheric Initial Conditions
Flood mitigation Navigation
WEATHER-CLIMATE FORECASTING LINKAGE
Protection of Life/Property
Weeks
Minutes
Days
Hours
Years
Seasons
Months
NOAA THORPEX WEATHERCLIMATE LINK SCIENCE
PLANNING MEETING, Apr.27,2006
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?????????,?????????????!
http//www.emc.ncep.noaa.gov/gmb/ens/ScienceMtg_04
-27-06.html
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NOAA ???????? ?THORPEX??
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
???
http//www.emc.ncep.noaa.gov/gmb/ens/ScienceMtg_04
-27-06.html
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? ? ? ????????????????
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The CWB Climate Information System Framework
Users
Climate Information Dissemination System
Climate Forecast and Monitoring Decision
Supporting System
Climate Monitoring System
Climate Analysis System
Dynamical-Statistical Climate Prediction System
Statistical Prediction System
Climate Data Process and Display System (in CWB
Virtual Data Center)
Climate Data Base
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Dynamical-Statistical Prediction System

1. Global Model Improvement
2. Dynamical Climate Forecast Models
3. Multi-Model Ensemble and Downscaling
4. Operational Forecast System Management

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Dynamical-Statistic Prediction System1. Global
Model Improvement

• Model CWB/GFS
• Team ???,???

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

• Improvement schedule

(1)
(2)
(3)
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Dynamical-Statistic Prediction SystemCWB/GFS
????(1)

• Shallow convection ????
• 1.??????????(15-30N)??????????(10-20N)?????????
• 2.?????????????(ITCZ)????????

???? ???,???,2002???????????????????,?????
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Dynamical-Statistic Prediction SystemCWB/GFS
????(2)

• Soil model ????
• 1.???????????????
• 2.??????????????,??
• ??????????,????????

???? ???,???,2004?????????????????????,?????
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Dynamical-Statistic Prediction SystemCWB/GFS
????(3)

• Grid Scale Precipitation
• ???????????????
• ????????????????,???pcw???????,????????
• ?????prognostic cloud scheme? level2 ???
  level3(??rain/snow??)

???? ???,???,???,2006??????????????????????
?????,???????????
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Two-Tier Global Dynamical Forecast System
GFS
ECHAM5
????
Step2
OPGSST
NCEP/CFS
????
Step1
Ensemble
Step 3
Step 4
Bias Correction
??? ???? ??????
Step 5
Downscaling
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Dynamical-Statistic Prediction System2.
Dynamical Climate Forecast Models

• 2.1 Intermediate Air-Sea Coupled
  Models(???,???,???)
• 2.2 AGCM GFS and ECHAM5(???,???,???)

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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 (0.25obs0.75forecas
  t) 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/opgs
st.htm
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Dynamical-Statistic Prediction SystemOPGSST
????OPGSST1.1????

• comprises 4 statistical and 2 dynamical modules
• DAMPER SSTA(tl) a(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 (Tungs 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 (???,???,???) 3.2
Bias-Correction and Statistical Downscaling
(???,???,???)
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System Structure -The Backbone of CWB Climate
Forecasts -
Climate Data Base
Predictability Experiments
Initialization
Global SST2 Prediction
Initialization
AGCM12 Ensemble Prediction
Obs. SST
Forecast SST
El Nino Prediction (coupled model)
Global SST1 Prediction
Forecast Climatology
Statistical Downscaling
Seasonal Prediction
Multi-Model Ensemble
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 ????

• 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 SST boundary conditions
• CWB/OPGSST 2.5x2.5
• NCEP/CFS 2X1

• Two AGCMs
• CWB/GFS T42L18
• ECHAM5 T42L19

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Mean Square Skill Score
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Dynamical-Statistic Prediction SystemAGCM
????????

• ??????????????????????
• ????????????????????(APCC)????????
• ??????????????(????)?????????
• ?????????????????

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Dynamical-Statistic Prediction System2007-09
?????

• 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
Taiwans rainfall
Statistical component
Dynamical component
SVD-based projection model
SMIP ensmble hindcasts
Bias correction
Pattern selection
CWB GCM
Couple pattern
Projection Verification
OBS global field
Predictor selection
Yes
Prediction
No
Station rainfall
Large-scale field change
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Dynamical-Statistic Prediction SystemEffectivenes
s 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
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Dynamical-Statistic Prediction SystemEffectivenes
s 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
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Dynamical-Statistic Prediction SystemSkills of
Downscaling System for Taiwan and SE Asia
Verification-period Hit Rate
reasonable skills (gt1/3) 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(???,???)
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Dynamical Downscaling Forecast System
IRI ECHAM4 forecasts
CWB GCM (T42) forecasts
CWB-RSM (60km)
NCEP-RSM (60km)
Ensemble mean forecast
Anomaly forecast
Probability forecast
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?
?
?????????????!
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Dynamical-Statistic Prediction System4.
Operational Forecast System Management(???)

• ??????????
• ???????

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Dynamical-Statistic Prediction System????????????
??
OPGSST
ICM2a ICM2b
GFS ECHAM
CFS Forecast SST
???????
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Dynamical-Statistic Prediction System????????????
?? ?????????

• ????
• vpp300.mic.cwb gt hpc.mic.cwb
• ????
• ???11???8??????
• ??16???????
• ????
• OISSTv2 monthly mean sea surface temperature
• NCEP monthly mean sea level pressure
• NCEP monthly mean 925hpa wind field
• BMRC monthly mean subsurface sea temperature

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Dynamical-Statistic Prediction System???????(???)

• ???????????????????,??????????????????????
• ?????????????????????????
• ????????????????????????????????
• ?????????????????????????????
• ????????????WEB???????

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Statistical Prediction System

• ?????
• ENSO CCA
• ENSO CA
• OCN
• Typhoon Numbers LAD
• WNPTC ? Summer Rain in Taiwan
• ????

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Statistic Prediction System1.1 ENSO-CCA

• ???????,2002??/???(El Niño/La Niña)????????????,4
  4-4,25-39?

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Statistic Prediction System1.2 ENSO-CA

• ???,2004Constructed Analog???????????????????????
  ???,??????????CWB93-1A-02??

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Statistic Prediction System1.3 OCN

• ???????,2006/10?????????????????????95???????????
  ????,3-37?3-41?,?????,??,???

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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???????????????,3-1?3-6?,?????,??,???

TC counts in the vicinity of Taiwan Mean3.67
Std1.56 (1971-2000) 2006 forecast TC counts 4.6
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Statistic Prediction System1.5 WNPTC ? Summer
Rainfall in Taiwan

• ???????,2004????????????????????????????,32-4,407
  -426?
• ????????????????????,?????????????
• ???????????????????,?????????????????

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Statistic Prediction System2. ????

• ??????????(ENSO-CCA?ENSO-CA?OCN?TC-LAD)?????????
  ??/??????,???????,????????????????????
• 96????
• ?? OCN/ENSO-CA ????????,???????????????
• ?? TC-LAD ??????,?????????????????
• ?? ENSO-CCA ?????
• ????????????/??/???????

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Climate Monitoring Systems

• Target Station Monitoring
• ENSO
• East-Asia Monsoon
• Cold Surge/Front Monitoring
• Summer Flow Transition
• Extreme Rainfall Events and Climate

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Target Station Monitoring1. ???????
STID NAME Longitude Latitude
47909 NAZE JAPAN 129.50 28.38
47912 YONAGUNIJIMA JAPAN 120.42 23.50
47918 ISHIGAKIJIMA JAPAN 124.17 24.33
47927 MIYAKOJIMA JAPAN 125.28 24.78
47929 KUMEJIMA JAPAN 114.18 22.33
47936 NAHA JAPAN 127.68 26.20
47945 MINAMIDAITOJIMA JAPAN 131.23 25.83
45007 ROYAL OBS.HONG KONG 114.18 22.33

• 7????? ???? 25??????????
• ????1961/01/012006/10/31(???,5???,???)
• ????????,??,??,????,????,????
• ??,??
• ?????IRI???????
• http//ingrid.cwb.gov.tw/SOURCES/.Taiwan/
  .CWB/.Target_Station

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ENSO1. ????

• ENSO??
• ????
• ENSO??
• ENSO Fast (Biennial) Mode
• ENSO Slow Mode
• ENSO PDO
• ENSO IOD mode Indian Ocean in general

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???ENSO??????
???????(?ENSO)????????
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ENSO2. ??(to be accomplished)

• ?ENSO????????
• ENSO ??????????
• ENSO ??????????
• ENSO ????????????
• ESNO ????????????
• ?ENSO?????????
• ENSO ???????????
• ENSO ???????????
• ENSO ???????????????

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Cold Surge/Front Monitoring1. ????/????

• ??
• ???????
• (?? ? 5mb/day)
• 2. ???????
• (?? ? 4?/day)
• 3. ???????
• (?? ? 3mps/day)
• 4. ?24????????????????????(diurnal
  cycle)?????(semidiurnal cycle)

???12????????????2???????(2),??????????????
??????Chen et al. 2002
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Cold Surge/Front Monitoring2. ????

• Circulation Monitoring
• ????
• ????
• ??????????
• OLR
• Local Area(??????????)
• Y-T
• Z-T

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Summer Flow Transition1. ??????
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Summer Flow Transition2. ????

• 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

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Extreme Rainfall Events and Climate1. ??????
??????????
????
??????
??????????
???????

• ????
• Asia-Australia
• East Asia

????????
??????
????? ?????
????
69
????????
70
http//iri.columbia.edu/climate/cid/Sep2006/
71
?????

• 2004 ??????????????1?,???????????????,??????????
  ???????????????????????3???????????????,?????????
  ???????????
• 2004-06 ????70???????????????,??????????????????
  ?,???????(?????????)??1200?,??????????????????????
  ?????????????????????????????????
• 2004-06?????????????????????????????,?????????20
  0??

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• ? ????(2003-05)
• ???? (?6?)
• 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.

73
B. ???? (?12?) ???????????????????,2002??????????
???? ? ??????????,30,99-116? ???????
,2003 ??????????????????????? ,31,
221-238. ????  ???????????????,2003 CWB
GFS???????????? ????????????????????
,31, 355-374. ???????,2003?????????????(?)1920-1
995????????? ,31,199-220? ???????,2003?????
????????(?)?????????????
???????,31,307-332? ????  ???????????????,2004
CWB GFS??????????? ???????????????????
,32, 367-388. ???????,2004??????????????????????
?????? ,32,407-426.
74
???????,2004 ??????????????????????????
????????,27??1?,57-71? ???????,2005 NCEP
RSM?2001??????????????????? ???????,Vol. 33,
235-254? ???????????,2005 NCEP
RSM?????????????????? ?????Vol.46, No.1,
1-12. ???????,2005???????????????,Vol.46, No.1,
45-60. ???????,2006??????????-
2004?12??2005?3????? ?????(???)
75
C. ????? (2004-05) ????????????Liqiang Sun, 2004
NCEP-RSM???????????????????????????????,351-356?5?
17-20?,?????????? ???????????,2004 NCEP RSM
??????????????????????????????10?5-6?,??????? ???,
2005?????????????????94???????????????,346-349,??
???,??,??? ???????????, 2005 CWB??????????????? 2
004???????????????????????,353-357?10?18-20?,?????
??? 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. ???, 2005
?????????????????????????????????2005?7?20-22?,??,
??? Chen, J.-M. 2005a CWB dynamical model
statistical downscaling. ??????????,
2005?10?18-20?,??,??? Chen, J.-M., 2005b A
dynamical-statistical prediction model for
station rainfall in Taiwan. Climate Workshop in
Taiwan. November 16-17, 2005, Taipei, Taiwan.
76
??????????????????? 2005 ?????????????????00?????
???????????????,?????,94?9?23??(????NSC91-2625-
Z- 052- 008) ???????,2005 ????????????????.??????
????????,?????,94?10?18-20?,409-414? ???????,2005
??????????????????????????,?????,94?10?18-20?,415-
422? Lu, M.-M.,C.-L. Ma and R.-J. May, 2005 The
ENSO impacts on Taiwan climate.
??????????????,?????,94?10?18-20?,358-363? Lu,
M.-M. and C.-J. Chen, 2005 Abrupt changes of the
heavy rainfall frequency in Taiwan. 2005 Climate
Workshop in Taiwa,,????,94?11?16-17?? ???????,2005
Abrupt changes of the heavy rainfall frequency
in Taiwan. ????????????????????,??,94?11?24-25?,13
3-138.
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D. ???? (2004-05) ???,2004??????????????????????
GFS???SMIP???20pp? ???????,2004
1971-2000?NCEP-RSM??????????????????????
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???????,2005 2005?CWB???????????????????????????
??? ???????,2005 CWB????????????????????????????

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79
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80
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Merry Christmas !
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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 Models for
• 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

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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.

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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

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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

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OPERATIONAL PRODUCTS- 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

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OPERATIONAL PRODUCTS- Guidance -

• ? Climate Variation Patterns in Southeast Asia
  a forecasters guide to climate monitoring and
  prediction
• 2008 publish
• ? Scientific Publications (?international
  journals)
• 2003-2009 publish