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Research Results from TIGGE
and a Vision for a New Paradigm for Global Prediction
David Parsons
Chief, World Weather Research Division (WWRD)
On behalf of the THORPEX Working Group on GIFS-TIGGE
and the Research Users of TIGGE
Special thanks to Zoltan Toth (NOAA), Richard Swinbank (Met Office), Philippe Bougeault (Meteo France),
THORPEX Intl Project Office, and TIGGE data providers
Baudouin Raoult, Manuel Fuentes - ECMWF
Steven Worley, Doug Schuster - NCAR
Bian Xiaofeng, Li Xiang - CMA
T. Nakazawa - JMA and Tang Xu -- SMB
1. What is TIGGE (THORPEX Interactive
Grand Global Esemble) and why is it important?
Pre-CAS Technical Conference
Background: the TIGGE archive• Detailed output from global ensemble forecasts to around 14 days generated
routinely at different centres around the world• Three archive centres: CMA, NCAR and ECMWF (user access began May 2006)• Ten data providers:
– ECMWF, JMA (Japan), UK Met Office (UK), CMA (China), NCEP (USA), MSC (Canada), Météo-France (France), BOM (Australia), KMA (Korea), CPTEC (Brazil) --- 1st provider began on Oct 2006 all 10 providers starting on Feb 2008
• Data exchanges using UNIDATA LDM, HTTP and FTP• In Sept 2009, 230 TB, 1.5 billion fields, growing by > 1 TB/week, 1.7 million fields/day
Why is TIGGE important? Accelerate shift of weather research and operational
communities toward ensemble prediction, which is appropriate for a non-linear, partly chaotic atmosphere
Advance ensemble research for high impact weather including enhancing collaboration between the academic and operational communities
Develop theory and practice of multi-model ensembles
Examine the feasibility of interactive ensembles responding dynamically to changing uncertainty
Develop the concept of a Global Interactive Forecasting System (GIFS)
A data base to improve individual ensemble systems including providing a challenging bench mark for the best (better) operational centres
Pre-CAS Technical Conference
User Metrics ECMWF: Over 500 users
Chinese
2. What are the results of TIGGE research?
Research results based on TIGGEMore available from http://tigge.ecmwf.int/references.html
Early research results will be shown for:Upper-air variablesSurface temperatureHeavy rainfallTropical cyclone tracks
Acknowledgments to
Young-Youn Park, KMA; Renate Hagedorn, ECMWF; Florian Pappenberger, ECMWF; Richard Swinbank et al., UK Met Office, Tetsuo Nakazawa, JMA/MRI, Takuya Komuri JMA, T. Krishnamurti FSU and Sharan Majumdar, RSMAS/U Miami
Preliminary conclusions for upper air variables Significant differences in quality between the systems
Up to 3 days differences in probabilistic forecast skill Agreement between spread and skill is the most variable aspect and
has a strong impact on probabilistic skill scores In the Tropics the spread is underestimated by almost all systems
Impact of the verification analysis Relatively little impact in the extra-Tropics (as long as the analysis
comes from one of the best systems) Large impact in the Tropics (and difficult to decide which is the best
analysis) Skill of multi-model system versus single-model systems
Only marginal improvement in the extra-Tropics Significant improvement in the Tropics (subject to significant bias
corrections)
Surface temperature Johnson and Swinbank (and subsequently Hagedorn) found from
comparisons with surface observations that the TIGGE Multi-Model (MM) forecasts of T2m outperform significantly any single model EPS
Interpreted as a proof that the variety of physics (soil, vegetation, PBL) between the models captures better the uncertainty in surface parameters
Results are sensitive to the choice of verifying analysis
Generally speaking, MM superiority comes from ECMWF, and ECMWF alone is better than any MM without ECMWF
Calibration using recent forecasts reduces the superiority of the MM, calibration using a special set of re-forecasts may offset completely the superiority of the MM and the superiority of the MM may also be challenged if uncertainty in soil moisture is added in the single systems
Incidentally: T2m from TIGGE database at Fcst time=0 is NOT an analysis of T2m temperature (it is an “intelligent” vertical interpolation) - do not use it for verification!
Verification of T2magainst observations
Multi-ModelECMWF
Met OfficeNCEP
T-2m, 250 European stations2008060100 – 2008073000 (60 cases)
0 2 4 6 8 10Lead time / days
-0.2
0.0
0.2
0.4
CRPSS
Benefits of re-forecast calibration
0 2 4 6 8 10 12 14fc-step (d)
-0.08
0
0.08
0.16
0.24
0.32
0.4
0.48
0.56
CRPSS (-)
2008060100-2008083100 (92 cases)ContinuousRankedProbabilitySkillScore
2m Temperature, Northern Extra-tropics
ECM WF-DM O
ECMWF-RFcali
TIGGE3-BC
Early Work on Heavy Rainfall
More challenging forecast and verification problem so the results are tentative and it
is difficult to generalize
Promising early results for prediction of Mei-Yu, S. China Sea Monsoon and post
typhoon heavy rainfall by Krishnamurti and colleagues
Used a subset of the models in the TIGGE archive (6 to 7 of the best models)
MM ensemble out performs the best ensemble with 1 to 2 days of lead time
added for a given level of forecast skill in the 2 to 5-day range and over 2-days of
lead time in the 10-day forecast
The study did not compare the MM to bias corrected single ensemble systems
Other work has focused on heavy rainfall and river flow in SE Europe with promising
results
David Burridge talk on general use of ensembles for flood prediction-Italian example
Tropical cyclonesTropical cyclones
Tropical cyclone tracks
Made available in near real-time for beginning in summer 2008 for the T-PARC project from Canada, China (2), ECMWF, Korea, Japan, UK, and USA (normally TIGGE has a 48-h delay)
Data is in CXML format from multiple centres hosted by Bureau of Meteorology/Australia and UCAR/USA at http://www.bom.gov.au/bmrc/projects/THORPEX/TC/index.html
Systematic investigations of performance are underway but the data set is generating significant interest in the tropical cyclone research and forecasting communities
WMO Executive Committee recommended that this real-time availability continue
JMA Ensemble Spread for TC Nargis from a Forecast Starting
Over 7 Days Before Landfall
Courtesyof T.
Nakazawa
Courtesyof T. Nakazawa
3. What is (GIFS) Global Interactive Forecast System
Real-time Operational Extension of the TIGGE Research Concept
GIFS likely emphasis on severe weather -- to advance lead-time, skill, and forecast confidence to mitigate loss of life and property and to contribute to the welfare of all WMO nations with a particular emphasis on least developed and developing countries
Will require real-time ensemble data access, product generation and distribution (common web interface using WIS concepts) --- all major efforts
Goal is have operational transition for the Severe Weather Forecasting Demonstration (SWFDP) Project --
Will require some pilot projects to develop products and then Forecast Demonstration Projects to test implementation
Global Interactive Forecast System (GIFS)
A major consideration for all Members (particularly developing nations) is the requirement to develop efficient severe weather warning systems focusing on national needs without becoming overly dependent on a large provider.
GIFS is ideally a shift of paradigm from bilateral cooperation (with a strong partner and a weak partner who needs to adjust to any change decided by the strong) to multi-lateral cooperation
GIFS has the potential to enable the provision of a variety of products of similar quality in standard format giving all countries both a sense of independence and of ownership/control of their own 'customized‘ severe weather products.
PARADIGM FOR GIFS DEVELOPMENT
A First Step: NW Pacific Tropical Cyclones Ensemble Prediction Experiment
A five-year regional project with both a research and operational component that will feed into national efforts such as for the Shanghai MHEWS Project
Intent is to make GIFS-TIGGE typhoon track data available to Typhoon Committee members including RSMCs in near-real time beginning May 2010 via a password protected web site
Training for operational forecasters and evaluation of the utility of such data sets in a
forecast environment
Research efforts will attempt to develop and extract useful information from TIGGE ensemble data (or a subset of this data since many ensembles are not well designed for the tropics) and develop products where appropriate
Product development to feed into the operational SWFDP in Africa and then in the S. Pacific (already a request from the SWFDP to feed into RSMCs in Africa for such data)
Specific plans yet to be presented to data providers but consistent with EC and THORPEX ICSC decisions
4. Future efforts
a) A Limited Area Modeling Version of TIGGE (TIGGE-LAM)
b) Extension of the concept to monthly and seasonal
prediction?