Presentation on theme: "Sharing Experiences in Operational Consensus Track Forecasting Rapporteur: Andrew Burton Team members: Philippe Caroff, James Franklin, Ed Fukada, T.C."— Presentation transcript:
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.
Sharing Experiences in Operational Ensemble Track Forecasting Rapporteur: Andrew Burton Team members: Philippe Caroff, James Franklin, Ed Fukada, T.C. Lee, Buck Sampson, Todd Smith.
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 DiscussionRecommendations
Consensus Track Forecasting Consensus methods now relatively widespread, because: –Clear evidence of improvement (seasonal timescales) over individual guidance –Its what forecasters naturally do –Improved objectivity in track forecasting –Removes the windscreen wiper effect
Consensus Track Forecasting Single model approaches (EPS)
Consensus Track Forecasting Single model approaches (EPS) 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.
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.
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.
Consensus Track Forecasting Multi-model approaches – simple example. Process: Acquire tracks Perform initial position correction Interpolate tracks Geographically average
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
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
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?
Consensus Track Forecasting Selective (SCON) Vs Non-selective (NCON) How to exclude members? How to exclude members?
Consensus Track Forecasting Selective (SCON) Vs Non-selective (NCON) SCON – How to exclude members?
Consensus Track Forecasting Selective (SCON) vs non-selective (NCON) SCON – How to exclude members? Requires knowledge of known model biases (this changes with updates)
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
Consensus Track Forecasting Recent performance of a model does not guarantee success/failure next time
Consensus Track Forecasting Recent performance of a model does not guarantee success/failure next time.
Consensus Track Forecasting Position Vs Vector Motion consensus Combining short and long-term members
Consensus Track Forecasting Accuracy depends on: 1. Number of models 2. Accuracy of individual members 3. Independence of member errors Including advisories in the consensus JTWC, JMA, CMA. Optimising consensus tracks Optimising consensus tracks
Would you add WBAR to your consensus? A Question of Independence
Would you add WBAR to your consensus? 24hrs 48hrs
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!
Consensus Track Forecasting Practical Considerations contd. Access to software? Access to model fields Pre-cyclone phase –less tracks Capture/recurvature/ETT
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 dont 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?
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?
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).
Consensus Track Forecasting Recommendations?
Confidence Level Small Vs Large spread in models
Consensus Track Forecasting Probabilistic Ensemble System for the Prediction of Tropical Cyclones (PEST)
Consensus Track Forecasting Consensus Intensity forecasting? –Early results promising: will become part of the operational procedure –not as good as for track forecasting.