In-Sik KangClimate and Environment System Research Center

Seoul National University, Korea

Chung-Kyu Park and Dong-Il LeeKorea Meteorological Administration

• Current Status of Global Climate Models• Multi-model ensemble prediction system• Computation and network environments • SNU-NASA multi-model prediction• Cyber Institute for Pacific-Asian Climate System

Multi-model Climate Prediction

Numerical Simulation of Earth Climate Atmospheric General Circulation Models (AGCMs)• Widely-used tools for Numerical Reproduction of Weather and Climate• Adapted to Seasonal Prediction Problem with the advance of High-performance Super Computing• Dynamic Equation Set

• Numerical Representation

• Super Computing

Run-type

Ensemble Members Integration Period Initial Conditions Boundary Conditions

SMIP 10May1979~Nov1999

7 months integrations for every year

00Z~12Z of 26Apr~30Apr for

every year

OISST(NCEP) and AMIP II climatological cycl

e Sea ice.

SNU AGCM Modeling and Climate Prediction

Model Resolution Dynamics Physics

SNUAGCM

T63L21 hybrid

vertical coordinate

Spectral model using semi-implicit

method

• 2-stream k-distribution radiation scheme (Nakajima and Tanaka 1986)• Simplified Arakawa-Schubert cumulus convection scheme based on RAS scheme (Moorthi and Suarez 1992)• Orographic gravity-wave drag (McFarlane 1987)• Dry adiabatic adjustment• Bona’s land surface model (Bonan 1996)• Mon-local PBL/vertical diffusion (Holtslag and Boville 1993)• Diffusion-type shallow convection• Modified CCM3 slab ocean/sea-ice.model

Experimental design for Seasonal Ensemble Prediction

SNU (Seoul National University ) AGCM description

Current Status of Global Climate ModelsSNUGCM Model Climatology (Summer)

(a) ObservationRainfall

(c) ObservationSea Level Pressure

(b) Model (d) Model

Climatology of Summer Rainfall (Various Models)

Super-Ensemble Prediction

- Superiority of a multi-model ensemble prediction compared to any of single prediction

- Applicability of superensemble technique to climate prediction

Training ForecastConventional Superensemble SVD

SVD Mean RMSE

Conventional Superensemble

Simple Ensemble

Superensemble Precipitation RMSE (Global)

Yun, Stefanovar and Krishnamurti (2002)

)(1

,

n

iitiit FFaOS

Asia-Pacific Climate Network (APCN)

To develop and maintain an infrastructure of a well-validated multi-model ensemble system (MMES) to produce the seasonal climate Prediction for Asian Pacific Economic Cooperation (APEC) member countries and to use it as an economic tool to effectively manage future weather and climate risks

The APCN-MMES will produce real-time seasonal forecasts and disseminated the forecast products to member countries.

Model Institute Resolution Experiment Type

NCEP NCEP T63L17 SMIP (10 member)

GDAPS KMA T106L21 SMIP (10 member)

GCPS SNU T63L21 SMIP (10 member)

NSIPP NASA 2ox2.5o L43 AMIP (9 member)

CWB Taiwan T42L18 AMIP (1 member)

Participated Model

Target of prediction

: Summer (JJA) mean precipitation

APCN Multi-Model Climate Prediction System

Dynamical

prediction

Dynamical

prediction

Dynamical

prediction

Dynamical

Prediction

Dynamical

Prediction

Correctedprediction

Corrected prediction

Corrected prediction

Corrected prediction

Corrected prediction

Statistical Downscaling (Post-processing)

Specio-Ensemble prediction

Multi Model Ensemble prediction

Multi Model Ensemble procedure

Statistical Prediction

Multi-Model Dynamical-Statistical Ensemble prediction

Conventional Multi-Model Ensemble prediction

Prediction skill – before downscaling / JJA Precipitation

Prediction skill – after downscaling

Summer Mean Precipitation (30S~60N)

Model Comp.

Superensemblewith MLRM

Superensemblewith SVD

(b) RMSE

(a) Pattern Correlation

Specio-ensemble prediction

Comparison of prediction skill for individual summer

Computational Resources (based on NEC/SX4)

Required CPU Time (Best guess, single node)

- Seasonal Hindcast experiments• AGCM 1 month integration (user time) = 1.6 hours • 7 months forecasts for 1 member = 1.6 hrs/month * 7 months = 11.2 hours• 10 member ensemble integrations = 11.2 hours/member * 10 member = 112 hours • 21 years hindcasts = 112 hours/1year * 21 years = 2352 hours• 4 seasons * 2352 hours = 9408 hours

Required Disks and Network Exchange - AGCM Integration 1 month = 0.7 GB) * 7 months * 10 members *21 years = ~ 1.03 TB * 4 seasons = ~ 4.12 TB

9,408 hours (~ 13 months ) CPU Time Needed

4.12 TB Disks Needed

Needed For One Prediction Center

Development of the SNU-NASA Multi-Model Ensemble Prediction System

SNU

NCEP

KISTI

KMAGPCPTRMM

NASA

Validation data

Forecast output

Model input

Model input

Forecast output

Forecast output

Forecast output

Model input

Tokyo. U

NCEP

COLA KMA

SNU NASA

Supported by National Computerization Agency

Network structure between SNU and NASA

CES

45Mbps

SeoulBackbone

node

USA

WebServer

DataServer

AnalysisServer

KMA

TaejonBackbone

nodeKISTI

Super Computer

Super Computer

NCEP NASA

AnalysisServer

DBServer

2.5Gbps

155Mbps

155Mbps

SNU

DBServer

FTPServer

Direct Conn.

KOREN

StarTap(APII-Test bed)

SNU Network

상용인터넷

국내의 KISTI, KMA, 국외의 NASA, NCEP ( 미국 ) 과

국제공동 기후 네트웍 확장

1Gbps

Network Traffic

• 2003. 01. 01 ~ 2003. 06. 30• Traffic amount : 112.13 TB

• Average Input : 5.28 Mbits• Average Output : 1.93 Mbits

2003 1 1 ~ 2003 6 30 5 년 월 일 년 월 일 분 평균네트웍 트래픽

0

10

20

30

40

50

60

70

80

2003- 01- 01 2003- 01- 21 2003-02-10 2003-03-02 2003-03-22 2003- 04- 11 2003-05-01 2003-05-21 2003-06-10 2003-06- 30날짜

(Mbi

t/sec

)트

래픽

Input output

초고속 선도망 및 APII-Testbed 활용도

0102030405060708090

100

데이터서버 모델서버 SNU-KISTI SNU-KMA SNU-NASA SNU-NCEP

네트

웍 속

도(M

bps) 개선전(2002) 개선후(2002말) 개선후(2003.6)

96 Mbps (학내 )60 Mbps (국내 )1.2 Mbps (미국 )

네트웍 Speed 개선

8 Mbps (학내 )5 Mbps (국내 )0.9 Mbps (미국 )

학내는 네트웍 대역폭 확대와 경로단축으로 큰 개선 효과 국외는 네트웍 경로문제로 속도개선이 크게 향상이 안됨

Network Speed after some attempt

SNU-NASA Joint Forecast for Washington D.C.Issued at Oct2002

• Site URL- http://147.46.56.215/cps/index.html• Provide real-time prediction for global and regional domains

Main PageGlobal Prediction

Regional Prediction

Web-based Operational Display System

⊙ Cyber Institute for Pacific-Asian Climate System Network

⊙CIPACS Main Page

About CIPACS Members Online Journal Forum Data News Links

Member`s Institute

The End