Slide 1

Ensemble Forecasting of High-Impact Weather Richard Swinbank with thanks to various, mainly Met Office, colleagues High-Impact Weather THORPEX follow-on project meeting, Karlsruhe, March 2013 Slide 2 Ensemble forecasting of High-Impact Weather Challenges of convective-scale ensembles Ensemble-based warnings & products Links with other post-THORPEX initiatives Slide 3 Limits of Predictability Following Lorenz (1984), errors grow fastest at smaller scales, eventually affecting largest scales. Leads to challenges in high-resolution forecasting in both making and using the predictions Since the predictability limit is shorter for small scales, ensembles are key to high-resolution prediction. Slide 4 An Ensemble-based future For data assimilation, as we focus on higher resolution (convective scales), we cannot exploit Gaussian assumptions about the behaviour of error statistics, so need an ensemble-based approach. For short-range high resolution forecasting, ensemble methods are needed to predict the risks of severe weather at close to the model grid scale. For longer range global forecasts, ensemble methods are required to estimate the risks of high-impact weather and produce probabilistic forecasts beyond the limits of deterministic predictability. Slide 5 Challenges of convective-scale: modelling Operational centres are now starting to introduce convective-scale ensembles. Gives the potential to produce much more detailed forecasting of storm systems, but Grey zone still cannot afford to truly resolve convective processes, rather use convection permitting km-scale resolutions. Limited to small, (sub?) national-scale domains. During life of the HIW project, look forward to MOGREPS-UK Heavy Rainfall forecast Probability Torrential Rain >16mm/hour CT 2012/06/28 03Z VT 17-18Z Probability Heavy Rain >4mm/hour CT 2012/06/28 03Z VT17-18Z Slide 12 Crown copyright Met Office Warnings based on ensembles: EPS-W weather impact matrix 70mph80mph90mph Example of EPS-W wind gust thresholds used for theHighlands and Islands Likelihoods of low, medium and high impact weather are presented as probability contour maps These are also combined to form overall warning colour maps High 60% Medium 40% Low 20% Very Low 1% Very LowLowMediumHigh Likelihood Impact Thanks to Rob Neal, Met Office Slide 13 Crown copyright Met Office MOGREPS-UK example yellow warning for gales in Orkneys & Shetlands 14-15 Dec 2012 36hr forecast30hr forecast Slide 14 HIW project - links with other ensemble forecasting initiatives A trio of complementary datasets: TIGGE project (global medium-range EPS), since October 2006. TIGGE-LAM project, limited area counterpart to TIGGE, will be an additional resource for HIW project European LAM-EPS data now starting to be archived at ECMWF. Sub-seasonal to Seasonal archive to support S2S project coming soon. All planned to use similar GRIB2 format and conventions. A technical liaison group (representatives from data providers & archive centres) could manage archive. Proposed Predictability and Ensemble Forecasting working group, focusing on science of dynamics & predictability and ensemble forecasting. Slide 15 WWRP-THORPEX TIGGE dataset Users Predictability, dynamics, probabilistic forecasting PDP working group GIFS-TIGGE working group TIGGE-LAM panel TIGGE-LAM dataset Slide 16 WWRP TIGGE dataset Users Sub-seasonal to seasonal and polar predictability, high-impact weather, probabilistic forecasting, RDPs, FDPs P&EF expert team Dataset liaison group TIGGE-LAM dataset HIW project team S2S project team S2S dataset WCRP Slide 17 Summary Convective-scale ensembles give new challenges and opportunities Opportunities More realistic simulation of severe storms More detailed local forecasts Better warnings of severe weather Exploit TIGGE & TIGGE-LAM datasets for HIW research Challenges Resolving convection? Representing uncertainties initial and model error Balance between resolution, domain size & members Presentation of small-scale information Combine short-range detail & longer range warnings Slide 18 Any Questions?