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In-Sik Kang Climate and Environment System Research Center Seoul National University, Korea

Multi-model Climate Prediction. In-Sik Kang Climate and Environment System Research Center Seoul National University, Korea Chung-Kyu Park and Dong-Il Lee Korea Meteorological Administration. Current Status of Global Climate Models Multi-model ensemble prediction system

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In-Sik Kang Climate and Environment System Research Center Seoul National University, Korea

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  1. Multi-model Climate Prediction In-Sik Kang Climate and Environment System Research Center Seoul National University, Korea Chung-Kyu Park and Dong-Il Lee Korea 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

  2. 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

  3. SNU AGCM Modeling and Climate Prediction • SNU (Seoul National University ) AGCM description • Experimental design for Seasonal Ensemble Prediction

  4. Current Status of Global Climate Models SNUGCM Model Climatology (Summer) Rainfall Sea Level Pressure (c) Observation (a) Observation (d) Model (b) Model

  5. Climatology of Summer Rainfall (Various Models)

  6. SVD Mean RMSE Conventional Superensemble Simple Ensemble Super-Ensemble Prediction • Superiority of a multi-model ensemble prediction compared to any of single prediction • Applicability of superensemble technique to climate prediction Superensemble Precipitation RMSE (Global) Conventional Superensemble SVD Training Forecast Yun, Stefanovar and Krishnamurti (2002)

  7.  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.

  8. APCN Multi-Model Climate Prediction System • Participated Model • Target of prediction : Summer (JJA) mean precipitation

  9.  Multi Model Ensemble prediction  Multi Model Ensemble procedure Conventional Multi-Model Ensemble prediction Dynamical prediction Dynamical prediction Dynamical prediction Dynamical Prediction Dynamical Prediction Statistical Downscaling (Post-processing) Corrected prediction Corrected prediction Corrected prediction Corrected prediction Corrected prediction Statistical Prediction Specio-Ensemble prediction Multi-Model Dynamical-Statistical Ensemble prediction

  10. Prediction skill – before downscaling / JJA Precipitation

  11. Prediction skill – after downscaling

  12. Comparison of prediction skill for individual summer Summer Mean Precipitation (30S~60N) (a) Pattern Correlation (b) RMSE Model Comp. Superensemble with MLRM Superensemble with SVD Specio-ensemble prediction

  13. Computational Resources (based on NEC/SX4) Needed For One Prediction Center • 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 • 9,408 hours (~ 13 months ) CPU Time Needed • 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 • 4.12 TB Disks Needed

  14. Development of the SNU-NASA Multi-Model Ensemble Prediction System Supported by National Computerization Agency NCEP SNU NASA KMA COLA Tokyo. U

  15. Network structure between SNU and NASA • 국내의 KISTI, KMA, 국외의 NASA, NCEP (미국)과 국제공동 기후 네트웍 확장 KOREN StarTap(APII-Test bed) SNU Network 상용인터넷 Super Computer Web Server 155Mbps FTP Server DB Server Analysis Server DB Server Direct Conn. Seoul Backbone node Data Server SNU KMA CES Analysis Server NASA NCEP 1Gbps 2.5Gbps 155Mbps Taejon Backbone node USA 45Mbps Super Computer KISTI

  16. 초고속 선도망 및 APII-Testbed 활용도 Network Traffic • 2003. 01. 01 ~ 2003. 06. 30 • Traffic amount : 112.13 TB • Average Input : 5.28 Mbits • Average Output : 1.93 Mbits

  17. Network Speed after some attempt 네트웍 Speed 개선 8 Mbps (학내) 5 Mbps (국내) 0.9 Mbps (미국) 96 Mbps (학내) 60 Mbps (국내) 1.2 Mbps (미국) • 학내는 네트웍 대역폭 확대와 경로단축으로 큰 개선 효과 • 국외는 네트웍 경로문제로 속도개선이 크게 향상이 안됨

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

  19. Web-based Operational Display System • Site URL- http://147.46.56.215/cps/index.html • Provide real-time prediction for global and regional domains Main Page Global Prediction Regional Prediction

  20. ⊙ Cyber Institute for Pacific-Asian Climate System Network

  21. ⊙CIPACS Main Page About CIPACS Members Online Journal Forum Data News Links Member`s Institute

  22. The End

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