D10: Recommendations on methodologies for identification of the best predictor variables

for extreme events

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)

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

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

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?

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

6. Conclusions

• Include summary table of variables recommended by each group

• Refer to D13 – need for validation of potential predictors