The Operational Subseasonal to Seasonal Climate Forecast System and - PowerPoint PPT Presentation

The Operational Subseasonal to Seasonal Climate Forecast System and Development at CWB 胡志文 胡志文 胡志文 胡志文 蕭志惠 蕭志惠 蕭志惠 蕭志惠 盧孟明 盧孟明 盧孟明 盧孟明 童雅卿 童雅卿 童雅卿 童雅卿 Mong-Ming Lu Jhy-Wen Hwu Chih-Hui Hsiao Yea-Chin Tung & CWB CFS Team Research and Development Center Central Weather Bureau, Taiwan 5 th Conference on East Asia and Western Pacific Meteorology and Climate cum Hong Kong Meteorological Society 25 th Anniversary 11:00 Sunday, Nov 03, 2013

Long-range Weather Forecast at CWB History Period I (1978 – 1994) � 1978 - Issue the Monthly Weather Outlook � 1994 - Issue the Seasonal Weather Outlook � Prediction methods: pure statistical � Focus area: Taiwan Period II (1995-2001) � Conduct systematic tropical and global climate monitoring � Access the dynamical model prediction products of IRI, ECPC, NCEP, JMA, ECMWF on web � Continuously improve the pure statistical prediction methods � Focus Area: Taiwan

Long-range Weather Forecast Monthly-to-Seasonal Climate Forecast (short-tem climate forecast) Period III (2002-present) A Modernization Breakthrough � Goal: Objective Probabilistic Prediction � � � � Prediction Strategy: Multi-type (statistical, dynamical, dynamical-statistical), Multi-model Multi-member � Prediction Lead Time: extend to 4-6 months � Focus Area: Taiwan and Southeast Asia � � � � Improve the understanding of regional climate variations � � � � Science-based representation of prediction uncertainties

The CWB Climate Information System Framework The CWB Climate Information System Framework The CWB Climate Information System Framework The CWB Climate Information System Framework Climate Users Forum Climate Information Dissemination System Climate Forecast and Monitoring Decision Supporting System 2002 ~ 2009 Statistical Climate Climate Dynamical-Statistical Prediction Monitoring Analysis Climate Prediction System System System System Prediction Climate Data Process and Display System (in CWB Virtual Data Center) Climate Data Base

The Backbone of Climate Service

Prediction Procedure

Prediction Schedule

Product Global SST forecast for NDJ 2013 the first forecast month: Nov 2013 ensemble mean anomaly probability forecast No skill forecasts are masked.

����������������������������������������� Product 850 wind monthly forecast ensemble mean anomaly Nov 2013 ����������������������������������������� Dec 2013 ����������������������������������������� Jan 2013 Above normal winter monsoon in Taiwan area

Product ����������������������������������������� Precipitation Forecast for NDJ 2013 ensemble mean anomaly ����������������������������������������� probability forecast No skill forecasts are masked.

Product Probabilistic forecasts of precip. and temp. for NDJ 2013 derived from the statistical downscaling methods Precipitation Temperature

Product Product Example - Dynamical Downscaling IRI/ECHAM4 + (CWB-RSM,NCEP-RSM) Prediction for NDJ 2013 ensemble mean anomaly 850 wind T2m Precip.

Product Probabilistic forecasts of precip. and temp. for NDJ 2013 derived from the dynamical downscaling methods

On Going Developments

Correlation of T2m Monthly Forecast and Reanalysis Retrospective forecast : 1983-2012 Reanalysis: NCEP CFSR Forecast: CWB 2-tiCWB AGCM/CWB OPGSSTv2) Forecast initial time: Dec. Forecast lead: 3 months CWB 2-T CFSv2 – CWB AGCM/CWB OPGSSTv2 CWB 2-T CFSv1 – CWB AGCM/CWB OPGSSTv1

Correlation of T2m Monthly Forecast and Reanalysis Retrospective forecast : 1983-2012 Reanalysis: NCEP CFSR Forecast: CWB 2-tiCWB AGCM/CWB OPGSSTv2) Forecast initial time: Dec. Forecast lead: 5 months CWB 2-T CFSv2 – CWB AGCM/CWB OPGSSTv2 CWB 2-T CFSv1 – CWB AGCM/CWB OPGSSTv1

Summary • CWB is capable of producing weather and climate forecast information up to 2 seasons. • Since the main source of predictability of the long-term predictability of the climate around Taiwan is ENSO, we expect better forecast skill of the CWB 2-tier CFSv2 than v1. • The retrospective forecast with new MME strategy, make 4-member runs of 7 months everyday, generates a rich data set that can be used to study predictions from subseasonal to seasonal scales.

THE ELEMENTS THE ELEMENTS THE ELEMENTS THE ELEMENTS Interannual Climate Dynamical-Statistical Local Climate Monitoring Local Climate Monitoring Local Climate Monitoring Local Climate Monitoring Variations Prediction System AA Monsoon Monitoring AA Monsoon Monitoring AA Monsoon Monitoring AA Monsoon Monitoring (ENSO, AA Monsoon, Ind Ocean Dipole, TBO, Typh Tracks, Pacific Sub-High) Tropical Convective Tropical Convective Tropical Convective Tropical Convective Systems Monitoring Systems Monitoring Systems Monitoring Systems Monitoring Extreme Weather a (MJO, Easterly Waves, Climate Events Typhoons ) Statistical (Heavy Rainfall, Drought, Prediction System Typhoons, Temperature Midlatitude Purterbation Midlatitude Purterbation Midlatitude Purterbation Midlatitude Purterbation Extremes, Weather Hazar Systems Monitoring Systems Monitoring Systems Monitoring Systems Monitoring (Blockings, Waves, Upper-level Decadal-scale Clim Jet Streams, Storm Tracks) Variations SST/Subsurface Ocean SST/Subsurface Ocean SST/Subsurface Ocean SST/Subsurface Ocean (AO, NAO, PDO, ENSO) Supplemental Temperature & ENSO Temperature & ENSO Temperature & ENSO Temperature & ENSO Conceptual Models Gl G lo ob ba al l C Cl li im ma at te e C Ch ha an n G G l l o o b b a a l l C C l l i i m m a a t t e e C C h h a a n n Monitoring Monitoring Monitoring Monitoring PREDICTION MONITORING ANALYSIS ASSESSMENT ASSESSMENT ASSESSMENT ASSESSMENT

Prediction – probabilistic nature of the forecast beyond 7 days � Monthly and seasonal outlooks are prepared for mean temperature and accumulated precipitation in three categories: Below-, Near- and Above-normal (median). � The likelihoods of three categories are assesed. Monitoring – climate modes on the subseasonal to seasonal time scales � Watch the status of major climate modes (ENSO, NAO, AO, Blockings, MJO, BSISO, Tropical cyclones, … etc.) � Project possible development and the influence on local weather Analysis – climate extremes attribution, understand climate variations on the interannual to interdecadal and longer time scales � Enable CWB to deliver science based climate services