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

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?
Background: the TIGGE archive
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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
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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
User Metrics ECMWF: Over 500 users
Chinese
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2. What are the results of
TIGGE research?
Research results based on TIGGE
More 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
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 T2m
against observations
T-2m, 250 European stations
2008060100 – 2008073000 (60 cases)
0.4
Multi-Model
ECMWF
Met Office
NCEP
CRPSS
0.2
0.0
-0.2
0
2
4
6
Lead time / days
8
10
Benefits of re-forecast calibration
2m Temperature, Northern Extra-tropics
ContinuousRankedProbabilitySkillScore
2008060100-2008083100 (92 cases)
0.56
0.48
CRPSS (-)
0.4
0.32
0.24
0.16
ECM WF-DM O
0.08
ECMWF-RFcali
0
TIGGE3-BC
-0.08
0
2
4
6
8
fc-step (d)
10
12
14
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
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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
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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 cyclones
Tropical cyclone tracks
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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/ind
ex.html

Systematic investigations of performance are underway
but the data set is generating significant interest in the
tropical cyclone research and forecasting communities
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WMO Executive Committee recommended that this realtime availability continue
JMA Ensemble Spread for TC
Nargis from a Forecast Starting
Over 7 Days Before Landfall
Courtesy
of T.
Nakazawa
Courtesy
of T. Nakazawa
3. What is (GIFS) Global
Interactive Forecast System
Real-time Operational Extension
of the TIGGE Research Concept
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
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.
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?