Sharing Experiences in Operational Consensus Track Forecasting

1. Sharing Experiences in Operational Consensus Track Forecasting Rapporteur: Andrew Burton Team members: Philippe Caroff, James Franklin, Ed Fukada, T.C. Lee, Buck Sampson, Todd Smith.

2. Consensus Track Forecasting • Single and multi-model approaches • Weighted and non-weighted methods • Selective and non-selective methods • Optimising consensus track forecasting • Practical considerations • Guidance on guidance and intensity consensus • Discussion • Recommendations

3. Consensus Track Forecasting • Consensus methods now relatively widespread, because: • Clear evidence of improvement (seasonal timescales) over individual guidance • It’s what forecasters naturally do • Improved objectivity in track forecasting • Removes the windscreen wiper effect

4. Consensus Track Forecasting Single model approaches (“EPS”)

5. Consensus Track Forecasting • Single model approaches (“EPS”) • Multiple runs, perturb initial conditions/physics • Degraded resolution • Generally not used operationally for direct input to consensus forecast. Generally used qualitatively. • Little work done on long-term verification of ensemble means • Little work done on ‘statistical calibration’ of EPS probabilities.

6. Consensus Track Forecasting

7. Consensus Track Forecasting • Single vs. multi-model approaches • Disjoint in how these approaches are currently used operationally. • Multi model ensembles – lesser numbers of members, but with greater independence between members (?) and with higher resolution.

8. Consensus Track Forecasting • Multi-model approaches • Combining deterministic forecasts of multiple models (not just NWP), • Fairly widespread use in operations. • Weighted or non-weighted. • Selective or non-selective.

9. Consensus Track Forecasting • Multi-model approaches – ‘simple’ example. • Process: • Acquire tracks • Perform initial position correction • Interpolate tracks • Geographically average

10. Consensus Track Forecasting • Non-selective multi-model consensus • Low maintenance • Low training overhead • Incorporate ‘new’ models ‘on-the-fly’ • Robust performance • If many members, less need for selective approach • Widely adopted as baseline approach

11. Consensus Track Forecasting • Multi-model approaches – weighting • Weighted according to historical performance. • Complex weighting: eg. FSSE – unequal weights to forecast parameters for each model and forecast time. • Can outperform unweighted consensus, providing training is up-to-date (human or computer) • Maintenance overhead

12. Consensus Track Forecasting • Selective vs. non-selective approaches • Subjective selection common place and can add significant value. • Semi-objective selection: SAFA – implementation encountered hurdles. • How to identify those cases where selective approach will add value?

13. Consensus Track Forecasting Selective (SCON) Vs Non-selective(NCON) How to exclude members?

14. Consensus Track Forecasting Selective (SCON) Vs Non-selective(NCON) • SCON – How to exclude members?

15. Consensus Track Forecasting Selective (SCON) vs non-selective (NCON) • SCON – How to exclude members? • Requires knowledge of known model biases (this changes with updates)

16. Consensus Track Forecasting Selective (SCON) Vs Non-selective(NCON) • SCON – How to exclude members? • Requires knowledge of model run eg analyses differs from observed BEWARE

17. Consensus Track Forecasting Recent performance of a model does not guarantee success/failure next time

18. Consensus Track Forecasting Recent performance of a model does not guarantee success/failure next time.

19. Consensus Track Forecasting Position Vs Vector Motion consensus • Combining short and long-term members

20. Consensus Track Forecasting Optimising consensus tracks Accuracy depends on: • Number of models • Accuracy of individual members • Independence of member errors Including advisories in the consensus JTWC, JMA, CMA.

21. A Question of Independence Would you add WBAR to your consensus?

22. 24hrs 48hrs Would you add WBAR to your consensus?

23. Consensus Track Forecasting Practical Considerations • Access to models? • Where to get them from? (JMA eg.?) • Can we organise a central repository of global TC tracks? Standard format and timely!

24. Consensus Track Forecasting Practical Considerations contd. • Access to software? • Access to model fields • Pre-cyclone phase –less tracks • Capture/recurvature/ETT

25. Consensus Track Forecasting

26. Consensus Track Forecasting Discussion • How many operational centres represented here commonly have access to <5 deterministic runs? • Do you have access to tracks for which you don’t have the fields? • How many operational centres represented here use weighted consensus methods as their primary method? • Do forecasters have the skill to be selective? Are the training requirements too great? • Modifications for persistence?

27. Consensus Track Forecasting Discussion • Are weighted methods appropriate for all NMHSs? • Bifurcation situations? Should a forecaster sit on the fence – in zero probability space? • Is statistical calibration of EPS guidance a requirement? • How many operational centres are currently looking to produce probabilistic products for external dissemination?

28. Consensus Track Forecasting Discussion • What modifications should forecasters be allowed to make? • Do you agree that the relevant benchmark for operational centres is the ‘simple’ consensus of available guidance? • What is an appropriate means of combining EPS and deterministic runs in operational consensus forecasting? (Is it sufficient to include the ensemble mean as a member).

29. Consensus Track Forecasting Recommendations?

30. Consensus Track Forecasting