ECMWF | Reading | 2-5 April 2019
TIGGE (The International Grand Global Ensemble) is a dataset, established by the World Weather Research Programme in 2006, comprised of operational global ensemble forecast data from ten weather forecasting centres. TIGGE is designed to span the medium-range (out to day 15), but a similar multi-model ensemble, the S2S dataset, a joint World Weather Research Programme (WWRP) and World Climate Research Programme (WCRP) effort, has been created with contributions from 11 centres to extend across the sub-seasonal to seasonal range (up to day 60).
Both TIGGE and S2S data are archived at ECMWF and CMA providing a unique resource for predictability and dynamical processes research. TIGGE has already proved invaluable for the development of products for future operational forecasting. Examples include the forecasting of tropical cyclone tracks, heavy rainfall, strong winds, and flood prediction through coupling hydrological models to ensembles. TIGGE has added to our understanding of the dynamics of tropical cyclones, extra-tropical cyclones and storm tracks. The S2S database extends the time horizon and also the processes that are important to prediction out to a season, opening new avenues of research.
This workshop will provide an opportunity to review the main scientific advances in predictability, dynamical process studies and applications of ensemble forecasts across the medium and S2S forecast ranges. Examples of sectors rapidly developing in ensemble applications include energy, retail and agriculture, as well as disaster risk mitigation worldwide. The emphasis will be on the utilisation of the TIGGE and S2S databases in research and contributions on seamless prediction, multi-model prediction and ensemble post-processing are particularly welcome. One session will be dedicated to the technical development of ensembles, the TIGGE and S2S data bases and proposals for future development.
1. Database technical Development
In this theme, we welcome contributions on various technical aspects related to the TIGGE and S2S data handling. It is crucial for the future to ensure the sustainable long-term minimum effort data processing from initial data production to the final archiving. The contributions related to users' experience with available data interfaces to access the data are also welcome.
2. Predictability and Dynamics
In this theme, we welcome contributions addressing the representation and understanding of dynamical processes and sources of predictability on medium-range and intra-seasonal time scale. We also welcome contributions addressing the design of TIGGE and S2S forecasting systems. This includes the design of data assimilation systems and the forecasting system (initialization, ensemble generation, ensemble size, resolution, complexity of the earth system needed for TIGGE and S2S forecasts ...), as well as studies of systematic errors and process-based diagnostics and metrics.
3. Prediction and Verification
In this theme, we welcome contributions addressing the development of methodologies for calibration, evaluation of the skill of TIGGE and S2S forecasting systems and development of probabilistic forecast products. Contributions on seamless prediction from the short/medium range to the sub-seasonal time scale, and verification are particularly welcome.
4. Multi-model approaches to prediction
In this theme, we welcome contributions addressing the development of methodologies for multi-model combination, and comparison of multi-model versus single-model forecasting systems.
5. Application studies
In this theme, we welcome contributions in the broad area of socio-economic applications of TIGGE and S2S forecasts to meet decision-making needs (e.g. early warning-early action, risk management), such as in agriculture and food security, water resource management, retail, public health, energy, and disaster risk reduction... Contributions related to the seamless use of information from short/medium range to extended range forecasts are particularly welcome.
As part of the meeting, we aim to promote the development of 'user-oriented variables' as a means of improving communication between the Forecasting and User communities. We define user-oriented variables as derived variables of forecast output which represent the metrological aspects that a given user might be sensitive to. A simple example might be V^3 for wind power. A key question is "How reliable and skilful are the ensemble forecasts within the TIGGE/S2S databases when predicting user-oriented variables? "
Registration and abstract submission
Registration and abstract submission is now closed.
Craig Bishop (email@example.com)
Manuel Fuentes (firstname.lastname@example.org)
John Methven (email@example.com)
David Richardson (firstname.lastname@example.org)
Andrew Robertson (email@example.com)
Mark Rodwell (firstname.lastname@example.org)
Frederic Vitart (email@example.com)