APECAPEC ClimateClimate CenterCenter (APCC)(APCC) ClimateClimate InformationInformation ServiceService Australia AAsiasia Brunei Darussalam Canada Chile People’s Republic of China Hong Kong, China PPacificacific Indonesia Japan EconomicEconomic CooperationCooperation Korea Malaysia Mexico New Zealand CClimatelimate Papua New Guinea Peru Philippines Russia Singapore CCenterenter Chinese Taipei First AMY/MAHASRI Workshop Thailand 8-10th, January, 2007, Tokyo, Japan United States 8-10th, January, 2007, Tokyo, Japan Viet Nam APEC Climate Center VisionVision ofof APCCAPCC RealizingRealizing thethe APECAPEC visionvision ofof regionalregional prosperityprosperity ¾¾ thethe enhancementenhancement ofof economiceconomic opportunitiesopportunities ¾¾ thethe reductionreduction ofof economiceconomic lossloss ¾¾ thethe protectionprotection ofof lifelife andand propertyproperty APEC Climate Center (APCC) ECMWF/EU APCC/APEC Iceland Norway Finland Sweden Denmark Ireland The Netherlands Germany United Czech Republic Belgium Kingdom Austria Luxembourg Hungary France Slovenia Switzerland Croatia Italy Portugal Spain Greece Turkey 1973 2005 GoalsGoals ofof APCCAPCC Facilitating the share of high-cost climate data and information Capacity building in prediction and sustainable social and economic applications of climate information Accelerating and extending socio-economic innovation FunctionsFunctions ofof APCCAPCC Developing a value-added reliable climate prediction system Acting as a center for climate data and related information Coordinating research toward the development of an APEC integrated climate- environment-socio- economic system model APCCAPCC StructureStructure Member Science Working Director Advisory Group Committee Administration Science System division division division Climate Climate Information R&D Prediction Service ClimateClimate Prediction:Prediction: APCCAPCC MultiMulti--ModelModel EnsembleEnsemble APCC MultiMulti--InstitutionalInstitutional CooperationCooperation APEC Climate Center MMEMME PredictionPrediction MethodMethod Training Phase Forecast Phase Observed Analysis t=0 MME N Real-time S = O + ∑ai (Fi − Fi ) Prediction i=1 train 2 The weights are computed at each grid point by minimizing the function: G = ∑()St − Ot t=0 APEC Climate Center APCCAPCC DeterministicDeterministic MMEMME ForecastForecast APEC Climate Center Probabilistic Forecast Method Defining terciles using Normal Fitting Method - For the middle/upper tercile boundary : mean plus 0.43 times the standard deviation Æ μ + 0.43σ - For the lower/middle tercile boundary : mean minus 0.43 times the standard deviation Æ μ -0.43σ Forecast probability - Above normal case (For example) Xc A PP =1− N(μ,σ) ∫−∞ A Probability of Above-normal N Probability of Near-normal -Xc Xc B Probability of Below-normal APEC Climate Center Probabilistic Forecast Method Combine different models Na : num. of above-normal Nn : num. of near-normal Combine : according to each model’s square root of ensemble size Nb : num. of below-normal Merged 3-category distribution χ2 (Chi-square) TEST Above-normal k 2 3-Categorical distribution ∑(Oi −Ei ) 2 i=1 χ = Near-normal Ei O : Forecast frequencies E : Hindcast frequencies Under 0.05% significance level Below-normal APEC Climate Center APCCAPCC ProbabilisticProbabilistic MMEMME ForecastForecast APEC Climate Center MME-basedMME-based statisticalstatistical downscaling:downscaling: precipitationprecipitation inin ThailandThailand Bangkok Correlation coefficient before and after dow nscaling in each station 0.8 0.6 0.4 0.2 Before 0 After Cor. coeff -0.2 -0.4 -0.6 Sattahip Lop buri Nakhon Bangkok Average Donmuag ratchsima Chata buri Prachin buriPrachin Suphan buriSuphan APEC Climate Center ForecastForecast Verification:Verification: DeterministicDeterministic MME MME Taylor Diagram : 1983~2003, JJA Hindcast APEC Climate Center ForecastForecast Verification:Verification: DeterministicDeterministic MME MME Anomaly Correlation Coefficient for hindcast in each region (precipitation, JJA, 1983-2003) 0.6 0.5 0.4 0.3 0.2 ACC 0.1 0 -0.1 -0.2 AUSTRALIA EAST ASIA NORTH RUSSIA SOUTH SOUTH AMERICA AMERICA ASIA individual model multi-model ensemble schemes APEC Climate Center ForecastForecast Verification:Verification: ProbabilisticProbabilistic MMEMME Globe,Globe, AboveAbove--Normal,Normal, PrecipitationPrecipitation Reliability Diagram ROC Curve 1.0 1.0 0.9 CWB 0.9 GCPS 0.8 GDAPS_F 0.8 0.7 GDAPS_O 0.7 HMC 0.6 IRI 0.6 0.5 IRIF 0.5 JMA 0.4 0.4 METRI rate Hit 0.3 0.3 Frequency MGO 0.2 NCC 0.2 NCEP Observed Relative Observed Relative 0.1 PMME 0.1 0.0 계열14 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Forecast Probability False alarm rate APEC Climate Center ClimateClimate InformationInformation ServiceService APEC Climate Center RealReal--timetime ClimateClimate MonitoringMonitoring El Nino Monitoring APEC Climate Center Data Service System MAHASRI Transformation Visualization MODEL Data Data format Quality Data MME Acquisition Unification Check Dissemination openDAP OBS (NCEP, CAMS_OPI, Reduce Data ftp the data size CMAP, JRA-25) Backup SATELLITE APEC Climate Center 기상청 Data Service System 장기예보지원 ¾¾ 2D2D 그래픽그래픽 표출 표출 시스템 시스템 구축 구축 및 및 시험 시험 운영 운영 GDS 기반의 원격 기후 자료 표출 시스템 시범 운영 http://gds.apcc21.net:9090/dods (외부접속) 웹에서 제공되는 http://testbed.apcc21.net:9090/dods (내부접속) 엔트리 정보를 이용한 기후자료 표출 APEC Climate Center ResearchResearch && DevelopmentDevelopment Multi-CumulusMulti-Cumulus convectionconvection schemescheme EnsemblesEnsembles Climatology and zonal mean of precipitation during the boreal summer (JJA) z Convection scheme of the four different individual models and unified model SAS Kuo MCA ZM UNI Heating rate Change rate of (SAS + Kuo + moisture MCA + ZM ) / N Rain fall , (N=4) Snow fall Cloud water in cumulus (SAS + ZM ) / N Areal rate of , (N=2) cumulus Cloud mass flux APEC Climate Center Multi-CumulusMulti-Cumulus convectionconvection schemescheme EnsemblesEnsembles EOF of CISO for Precipitation Unified Scheme best captures the CISO evolution Unified scheme Observation APEC Climate Center TC activity/monsoon in high-resolution GCM (2007~) 1997 2004 •Examine high-resoultion GCM performance •Compare with SNU, NASA results 1999 2005 APEC Climate Center FutureFuture PlanPlan Improving Quality of Service 1st phase : Innovative technology development (~2010) 2nd phase : Socio-economic application technology development (2011~2015) 3rd phase : Providing climate and application information to the end users as growth engine (2016~2020) CollaborationCollaboration withwith MAHASRIMAHASRI Development of RCM schemes and application models: APCC will provide hindcast datasets from participating models and from the CLiPAS project, as well as MME seasonal prediction data for dynamical downscaling efforts of MAHASRI, in order to better understand the predictability of the Monsoon systems and for the development of application models. Data service and management: APCC will serve as one of the data centers for the intensive observational data during the AMY-2008 Datasets will be made available to model providers for verification and ultimately model improvement CollaborationCollaboration withwith AMY/MAHASRIAMY/MAHASRI Expects high resolution (spatial and temporal) station data, LS data, for high-res model verification Statistical/RCM downscaling development application model development Climate monitoring ((intraseasonal,intraseasonal, drought, flood, TCTC,, other extreme events) FocalFocal points/memberspoints/members inin MAHASRIMAHASRI WG:WG: Prediction & application: C.K. Park/Nohara RCM downscaling: Tam/Song Data Management: Tam/Lee Thank you .