Aims and Requirements for Ensemble Forecasting By T.N.Palmer ECMWF

1. Aims and Requirements for Ensemble Forecasting By T.N.Palmer ECMWF

2. A Brief History of Ensemble Prediction for Weather/Climate Monthly forecasting 1980s Seasonal-to-Interannual 1990s Medium Range Short range Climate Change 2000s “Roots of ensemble forecasting” J.M Lewis. Mon Wea Rev, 133, 1865 (2005)

3. First operational probabilistic ensemble forecast? Used in Met Office commercial operations.

4. Lorenz (1963): prototype model of chaos Scientific Basis for Ensemble Prediction In a nonlinear dynamical system, the finite-time growth of initial uncertainties is flow dependent. October 1987!

5. In a nonlinear system, finite-time predictability is a function of initial state

7. EPS spread/Error

8. 26 Aug 0z 26 Aug 12z 27 Aug 0z 27 Aug 12z ECMWF Ensemble Forecasts of Katrina

9. Pthreshold Who is coming? Queen 1% Decision: Rent marquee if P>20% 10% Mayor Mother-in-law 30% Mates from the pub 70%

10. From ECMWF web site. Queen Mayor Mother-in-law Mates from pub

11. Collaboration with L.Smith, LSE Weather Roulette • London-Heathrow, 2m temperature • 2002: training data for dressing • 2003: test data • odds: set by dressed T511 forecast • bets: placed by best member dressed EPS • start capital: £1 (re-invest all money, unlimited stakes) odds(bin) = 1 / prob_hr(bin) bets(bin) = prob_eps(bin) * capital(t-1) Daily winnings: win(t) = odds(bin_v) * bets(bin_v) – capital(t-1) = (prob_eps(bin_v)/prob_hr(bin_v) – 1) * capital(t-1)

12. Collaboration with L.Smith, LSE Weather Roulette 168h lead time 40 Winnings [log_10 £] 20 0 0 50 100 150 200 250 300 350 Days in 2003

13. Weather Roulette Collaboration with L.Smith, LSE Bootstrapping Results 50 Winnings [log_10 £] 25 0 1 2 3 4 5 6 7 8 9 10 Lead time [days]

14. DEMETER-based PDFs of malaria incidence for Botswana (forecasts made 5 months in advance of epidemic; Thomson et al 2005) 5 years with highest observed malaria incidence 5 years with lowest observed malaria incidence

15. Why No Ensembles on the TV Weather Forecasts?

18. MLSP 66-hour forecasts, VT: 16-Oct-1987, 6 UTC TL399 EPS with TL95, moist SVs

19. Probability of Beaufort force 12 winds 6-12am October 16th 1987

21. “Weather forecasts are inevitably uncertain, sometimes more so than others. We now run our forecast models many times with slightly different starting conditions to assess the uncertainty in the forecasts. Press the red button on your remote control to see an estimate of the expected accuracy of the forecast for some of the main cities in the UK.”

22. Aims of Ensemble Forecasting • To enhance (substantially) the value of numerical weather and climate forecasts by quantifying the flow-dependent uncertainty in the forecast • To enhance the credibility of weather and climate forecasts, thereby allowing our profession to gain the respect of the public

23. Requirements for Ensemble Forecasting • A better theoretical understanding of the role of error made in truncating/parametrizing the underlying PDEs of climate, on both initial uncertainty and forecast model uncertainty • Much much much bigger computers (resolution, ensemble size and model complexity are all important) • A recognition amongst media forecasters that uncertainty is an intrinsic part of the science of weather and climate prediction..and that the public will respect us more if we are more open about uncertainty • A recognition amongst BBC TV producers that use of ensemble forecast information on weather forecasts can inform, educate and entertain the viewing public and is something worth giving more effort to.

24. “He believed in the primacy of doubt; not as a blemish upon our ability to know, but as the essence of knowing” Gleick (1992) on Richard Feynman’s philosophy of science.