Strategies for Identifying Best Predictor Variables

1. D10: Recommendations on methodologies for identification of the best predictor variables for extreme events

2. 1. Introduction Definition of good/best predictor variables • Strong/robust relationship with predictand • Stationary relationship with predictand • Explain low-frequency variability/trends • Physically meaningful • Appropriate spatial scale (physics/GCM) • Data widely/freely available (obs/GCM) • Well reproduced by GCM (see D13)

3. 2. Identification of potential predictor variables • Constrained by Reanalysis/GCM data • Guided by expert judgement • Two general approaches in STARDEX: • Start with minimum and add more if necessary • Start with (nearly) everything and select/prune

4. 3. Choices • Surface and/or upper air • Continuous vs discrete (CTs) predictors • Circulation only or include atmospheric humidity/stability etc • Spatial domain • Lags – temporal and spatial • Number of predictors

5. 4. Number of predictors What is optimal/desirable number? • Traditionally feel comfortable with “a few” • Physical understanding • Avoid correlated predictors • Also an issue “within” predictors • Few PC/sEOFs or Guy’s clusters (e.g., 3-5) vs CT classifications (e.g., 12-20 classes) • But is it so important to prune?

6. 5. Methods • Correlation, e.g., UEA, USTUTT-IWS • Stepwise multiple regression, e.g., KCL • PCA/CCA, e.g., ARPA-SMR, UEA • Compositing, e.g., KCL • Neural networks, e.g., KCL, UEA(SYS) • Genetic algorithm, e.g., KCL • “Weather typing”, e.g., AUTH, USTUTT-IWS • Trend analysis, e.g., DMI, USTUTT-IWS

7. 6. Conclusions • Include summary table of variables recommended by each group • Refer to D13 – need for validation of potential predictors