Ensemble Forecasting of High-Impact Weather

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

2. Ensemble forecasting of High-Impact Weather • Challenges of convective-scale ensembles • Ensemble-based warnings & products • Links with other post-THORPEX initiatives

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.

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.

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 <1km grid scale and larger (regional) domain sizes.

6. Example: MOGREPS-UK system • Currently run as a downscaling ensemble, initial and boundary conditions driven by 33km MOGREPS-G (NB. No intermediate regional ensemble). • Challenges: • Time to spin up small scales • Use high-resolution analysis to initialise ensemble?

7. Ensemble Modelling challenges • Representing uncertainties • Initial condition uncertainties - in MOGREPS, currently from MOGREPS-G, but should use ensemble DA. • Model errors – what stochastic physics is appropriate for convective scales? • Surface uncertainties – how to represent uncertainties in soil moisture, surface roughness, sea surface, etc.? • Consistency with lateral boundary conditions – movie from Warrant Tennant

8. Tropical Cyclones • Potential for improved prediction of structure & intensity using high resolution nested ensembles. • High-resolution simulation, by Stu Webster (Met Office)

9. Challenges of convective-scale:post-processing • How to post-process when details are unreliable? • Neighbourhood methods for displaying output at predictable scales observed forecast Threshold exceeded where squares are blue [thanks to Nigel Roberts]

10. Optimising smoothing for skill

11. 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

12. Warnings based on ensembles:EPS-W weather impact matrix Likelihood Example of EPS-W wind gust thresholds used for the “Highlands and Islands” Impact ≥70mph ≥80mph ≥90mph • Likelihoods of low, medium and high impact weather are presented as probability contour maps • These are also combined to form overall warning colour maps… Thanks to Rob Neal, Met Office

13. MOGREPS-UK example – yellow warning for gales in Orkneys & Shetlands 14-15 Dec 2012 36hr forecast 30hr forecast

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.

15. WWRP-THORPEX GIFS-TIGGE working group PDP working group TIGGE-LAM panel TIGGE-LAM dataset TIGGE dataset Users Predictability, dynamics, probabilistic forecasting

16. WWRP WCRP HIW project team P&EF expert team S2S project team Dataset liaison group TIGGE-LAM dataset TIGGE dataset S2S dataset Users Sub-seasonal to seasonal and polar predictability, high-impact weather, probabilistic forecasting, RDPs, FDPs

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

18. Any Questions?