Ongoing research project |
TIGGE – global ensemble forecast data
The TIGGE dataset consists of ensemble forecast data from 10 global NWP centres, starting from October 2006, which has been made available for scientific research, via data archive portals at ECMWF and CMA. TIGGE has become a focal point for a range of research projects, including research on ensemble forecasting, predictability and the development of products to improve the prediction of severe weather.
TIGGE was established as a key component of THORPEX: a World Weather Research Programme to accelerate the improvements in the accuracy of 1-day to 2-week high-impact weather forecasts for the benefit of humanity. Although the decade-long THORPEX programme finishes at the end of 2014, TIGGE will continue for a further 5 years, when its future will be reviewed.
The name TIGGE originally stood for “THORPEX Interactive Grand Global Ensemble”. With the completion of THORPEX, the name will officially be “The International Grand Global Ensemble”, but it is recommended to simply refer to “TIGGE”.
Find out more at the TIGGE website.
TIGGE-LAM for regional ensemble forecasts
TIGGE-LAM is an extension of TIGGE archive to include weather forecasts from limited-area model (LAM) ensembles. These forecasts are produced on grids between 12 and 2 km resolution and provide detailed information for the short range, up to a few days ahead.
TIGGE-LAM will enable users to compare models and improve the methodologies for the generation and application of regional ensemble forecasts. It will also provide valuable feedback to global ensemble developments as the resolution of these systems is planned to increase significantly in the coming years.
Links with GEO (Group on Earth Observations)
Both TIGGE and TIGGE-LAM form part of the weather contribution to the Global Earth Observation System-of-Systems (GEOSS) and are accessible through the GEOSS Common Infrastructure (GCI).
The TIGGE-LAM archive has been developed as part of the EU-funded GEOWOW project to improve Earth observation data discovery, accessibility and exploitability.