Some Strengths and Weaknesses of ECMWF Forecasts for the UK

1. Some Strengths and Weaknesses of ECMWF Forecasts for the UK Tim Hewson 15th June 2006 Contributors include: Eleanor Crompton, Tim Legg, Helen Watkin

2. Contents • Synoptic Scale performance around the UK • Subjectively-verified EC OP and ensemble forecasts • Focus on adverse/severe weather • 1. Snow • 2. Strong Winds • 3. New products – Cyclonic feature tracking • Summary of recommendations

3. Synoptic Scale performance around the UK 12Z Forecasts subjectively verified during 2005 DAY 3 DAY 4 DAY 5 DAY 6/7 DAY 8-10

4. Comparison with previous years • Some signs of year on year improvement at all leads, though always a noisy signal

5. Multi-model comparison • Within the ‘basket’ of international operational models, NCEP appear to have suddenly become very competitive. • Especially true in winter. Similar results seen for other lead times.

6. Snow in the UK • ECMWF forecasts provide key input to early warning issue • Primarily this is through the Met Office’s calibrated, automated ‘First Guess Early Warning System’ (FGEW) derived from ensemble output. One parameter - snow ppn total. • The utility of FGEW has been revisited for the 2005/6 winter, using a number of high profile UK snow events (‘hits’ and ‘misses’ only)

7. UK Snow Cases - 2005/6 winter • Nov 25th SW England - 7% * • Nov 28th W/SW Midlands - 4% • Dec 27th E England - 4% * • Dec 30th E England - 30% • Mar 3rd+ NE Scotland - 1% • Mar 3rd N England - 4% • Mar 12th NW of UK - 70% * • Apr 10th SE England - 1% Values show FGEW probabilities at 3/4 day lead times. Greater than 10cm snow believed to have occurred at populated altitudes in each of the above cases. All were highlighted by the media. For the convective cases highlighted in red there was little or no increase in probabilities as the event approached.

8. Mar 12th 2006 – NW of UK

9. Mar 12th 2006 – NW of UK

10. Snow Events in the UK • Handling of frontal snow cases by the EPS and FGEW looks good, and has proved very useful to forecasters, in this and previous winters • These constitute approximately 50% (?) of all winter snow cases • Unfortunately the handling of convective cases is much worse, with EPS and FGEW generally misleading (though broadscale often useful)

11. Dec 27th 2005 – E England

12. Dec 27th 2005 – E England

13. Nov 25th 2005 – SW England

14. Nov 25th 2005 – SW England EPS member gridbox

15. MODELS Up to ~100km Precipitation Drift

16. Precipitation totals for Cornwall / Bodmin Snow Event – Nov 2005 Key weakness of current operational (12km) model formulation well illustrated – snow focussed over seas. Same is true of EC model – due to parametrisation used Little or no propogation inland. Reality (≈radar) very different, partly due to slow fall speed of snow. contours show orography

17. Options: Met Office • Parametrisation could incorporate ‘snow-drift’ effect. More appropriate for higher resolution. • The first Met Office Mesoscale model did this (lost during unification). • This aspect is very high priority for forecasting in the Met Office. • Special field modification tools being built in the short term. Inputs – convective cloud depth, wind profile, freezing level. • Higher resolution models will be used longer term

18. Verification of Chief Forecaster’s Output Forecaster’s greatest contribution in short term forecasts is in reducing errors associated with cold air convection In winter snow is often involved

19. Options: ECMWF • For ECMWF data, scope for a post processing stage, to smear out convective ppn totals inland, according to wind strength and freezing level • Worth considering, though boundary layer temperature variations add complexity • In some cases, such as the Cornwall event, this would not work – complexity of mesoscale flow patterns is also too great • Incorporation into parametrisation may be the best strategy? • Important consideration: • affects other parts of Europe with coasts • information content of model runs is being wasted

20. L Cornwall Snow event 12Z 25/11/05 Mesoscale structure H 0C +5C

21. Strong Winds • A strong wind representivity problem exists for fast moving systems • This arises because of diagnostic types used around the world • It is more acute because faster moving systems have a greater potential to facilitate strong winds developing inland (trajectory curvature on S flank is reduced) • Affects Met Office models, EC model, EFI (?)

22. This winter’s ‘one storm’! -12Z 10/1/06

23. 18Z =Strong Wind Zone

24. 00Z

25. 06Z

26. 12Z X No strong winds expected here??!

27. Solution • New but simple lower tropospheric wind diagnostics are required • Interrogation of every model timestep should be used • Analogous to rainfall accumulations • Name of resulting plan view field would be (eg) ‘Max 10m wind in 6 hours up to VT’ • This would emphasise damage swathes • Similar ideas should be used for interrogating temperatures, etc – why not correlate (MOS) with model max temperature, rather than model 12Z temperature (as in talk yesterday!) ?

28. Cyclonic windstorms • Even with improved diagnostic selection, models still fail to fully represent details and strength of damaging windstorms • Resolution and boundary layer issues.. 4+kts rms 10m wind speed error in EC over Europe. • For the more extreme events recalibration is unreliable and ill-specified • Windstorms are however related to synoptic features - commonly a cyclone, which often evolves from a frontal wave – and which models can represent • A feature-based approach is used widely within operational forecasting • Therefore use feature-tracking within model forecasts as a conduit for understanding and forecasting windstorms

29. Diminutive Wave (weak) Barotropic Low Frontal Wave (weak) Frontal Wave Diminutive Wave Objective Cyclonic Features - Snapshot

30. 4 2 3 5 6 1 0 Diminutive Frontal wave Frontal 2 - d Front Frontal wave T - bone Mature cyclone frontal wave cyclone fracture Conceptual Cyclone Life-Cycle After Shapiro and Keyser (1990) – stages 3-6

31. Example: Windstorm Damage,19 Nov 04, Slovakia

32. Storm track, 12h interval 00Z, 18th Nov 04 to 12Z, 22nd 6 5 4 3 1 2 4 3 2 5 6 1

33. Application to ECMWF ensemble data • As part of THORPEX / TIGGE • Code used out to 15 days • Different post-processing strategies required for different lead times • Under development - 2 examples presented • Thanks to Helen Watkin • Processing code will soon be running at ECMWF • Also being used in Met Office MOGREPS ensemble

34. Example Storm in EC Ensemble forecasts Click on feature To follow evolution

35. Feature-Specific plumes MSLP Feature tracks Max 1km Wind Within 300km Radius of feature Vorticity

36. Longer Ranges - use feature track density T+120

37. T+216

38. Summary of Requests/Recommendations • New diagnostics required that utilise multi time-step interrogation – especially for near surface winds • Improved re-derivation of EFI, SOT, SPS based on the above? • Strategy for addressing snow-drift? – views of other member states? • Archive of forecasts of severe events, or hindcasts of these from more recent models, valuable for testing new approaches, such as cyclonic feature tracking • More web-based diagnostics – eg from Operational runs - would help Met Office forecasting effort at all lead times - see last year’s wish list – this still applies!

39. Supplementary slides follow

40. Nov 28th 2005 – W/SW Midlands

41. Nov 28th 2005 – W/SW Midlands

42. Dec 30th 2005 – E England

43. Dec 30th 2005 – E England

44. Mar 3rd 2006 – NE Scotland / N England

45. Mar 3rd 2006 – NE Scotland / N England

46. Apr 10th 2006 – SE England

47. Apr 10th 2006 – SE England

48. Scope for Improvement • Forecasters are usually able to improve upon raw (Met Office) model output, using different models, knowledge of systematic errors, comparison with current trends • Degree of improvement could potentially be increased by making more use of the high quality ECMWF operational run, which at present is under-utilised • WISH LIST! – • 3 hourly data, T+0 to T+48 • Instantaneous total ppn rates, plus cloud cover and mslp (same format?!) • Separate plots showing dynamic /convective rain and snow components • 10m mean wind and likely gust strength • Sub areas – parts of Europe ? • Timely appearance on ECMWF web site is crucial (probably the most expedient route for making this data available)

49. Extras for ‘Wish List’ • Meteograms to include overlapping24-hour rainfall totals (but still in 6 hour blocks) • Total cloud cover – is this ‘altitude weighted’, or is 8 oktas cirrus considered ‘cloudy’? Weighting would be preferable • Postage stamps showing estimated surface gusts, with colour-shading for high values • Cluster ensemble means for mslp, thickness, annotated with percentages of members

50. WAFC World Area Forecast Centre Accreditation