1 / 23

Research Results from TIGGE and a Vision for a New Paradigm for Global Prediction David Parsons

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.

charleshmartin
Download Presentation

Research Results from TIGGE and a Vision for a New Paradigm for Global Prediction David Parsons

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Research Results from TIGGE and a Vision for a New Paradigm for Global Prediction David Parsons Chief, World Weather Research Division (WWRD)

  2. 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

  3. 1. What is TIGGE (THORPEX Interactive Grand Global Esemble) and why is it important?

  4. 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 Pre-CAS Technical Conference

  5. 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

  6. User Metrics ECMWF: Over 500 users Chinese Pre-CAS Technical Conference

  7. 2. What are the results of TIGGE research?

  8. Research results based on TIGGEMore available from http://tigge.ecmwf.int/references.html Early research results will be shown for: Upper-air variables Surface temperature Heavy rainfall Tropical 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

  9. 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)

  10. 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!

  11. Verification of T2magainst observations T-2m, 250 European stations 2008060100 – 2008073000 (60 cases) Multi-Model ECMWF Met Office NCEP

  12. Benefits of re-forecast calibration

  13. 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

  14. Tropical cyclones

  15. 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

  16. JMA Ensemble Spread for TC Nargis from a Forecast Starting Over 7 Days Before Landfall Courtesy of T. Nakazawa

  17. Courtesy of T. Nakazawa

  18. 3. What is (GIFS) Global Interactive Forecast System Real-time Operational Extension of the TIGGE Research Concept

  19. Global Interactive Forecast System (GIFS) • 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

  20. PARADIGM FOR GIFS DEVELOPMENT • 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. 

  21. 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

  22. 4. Future efforts a) A Limited Area Modeling Version of TIGGE (TIGGE-LAM)b) Extension of the concept to monthly and seasonal prediction?

More Related