1. Enrica Bellone, Jessica Turner, Alessandro Bonazzi 2 nd IBTrACS Workshop

2. © 2011 Risk Management Solutions, Inc. 2  Stochastic events: large set of storms covering the range of potential hurricanes (100,000+ years) –Long term view: assume same conditions as in past 100 years –Medium term view: consider trends and oscillations to derive representation of the next 5 years of activity

3. © 2011 Risk Management Solutions, Inc. 3  Low frequency events cannot be modelled based on past loss experience. Katrina 70 $Bn Miami Cat 5, NYC Cat 4 $120 – 250B Loss Probability

4. © 2011 Risk Management Solutions, Inc. 4 Path Intensity Windfield Parameters Genesis Track Steps Rmax Pressure Vmax Extratropical transitioning + Shape, Amax

5. © 2011 Risk Management Solutions, Inc.  Based on smoothing historical data –Require a dense historical record –Degree of smoothing optimized (cross-validation) –Used for:  Genesis  Track Path  Extra-tropical transitioning  Central Pressure over water  Global or regional relationships: –Regressions valid over the basin or over predefined regions –Used for:  Filling rate for pressure over land  Pressure to Vmax relationship  Pressure to Rmax relationship 5

6. © 2011 Risk Management Solutions, Inc.  Genesis is a spatial Poisson Process – The mean field is estimated by smoothing historical genesis data –Years used: from 1950 6

7. © 2011 Risk Management Solutions, Inc.  Model Pc t -Pc t-6h  Lower limit: MPI  Upper limit: Penv  Most important predictors: –Previous change in pressure –Total drop from genesis  Filling Model: –Exponential filling  Upper limit: Penv  Predictors for the filling rate: –landfall parameters –e.g. Rmax, translational speed,... 7 Over Water Over Land Vmax Need reliable dense historical record Need landfall information

8. © 2011 Risk Management Solutions, Inc. Regression of log(Vmax) on: –Penv – Pc –Latitude Errors are autocorrelated Intensity distribution is re-calibrated at landfall

9. © 2011 Risk Management Solutions, Inc. 9 “Merge”  Merges and splits: –Do they represent physical mechanisms? –Do they represent different tracks that are close in time and space? –Impact on landfall rates?

10. © 2011 Risk Management Solutions, Inc. 10  Flag for observed Pc (or Vmax) vs derived from satellite?  Is Pressure (Vmax) always estimated from satellite when there are no flights? –Any measurements “assimilated”, especially around landfall? If Pressure is derived from CI through Vmax, which wind to pressure relationship was used? CI number? How is the Dvorak technique applied to transitioning storms?

11. © 2011 Risk Management Solutions, Inc.  In the west Pacific, the annual frequency of Pacific typhoons is decreasing. Multidecadal variability also exists.  The long-term average may not be the best indicator of risk from typhoon over the next 5-years  Question: To what extent might observations changes be creating the trend + variability Histogram of annual west Pacific typhoons with change points calculated using the Elsner et al. (2000) method

12. © 2011 Risk Management Solutions, Inc.  RMS attempts to make predictions of average annual typhoon frequency over the next five years using predictors.  Global (70S-70N) SST turns out to be the best predictor. Black: observations Red: OOS long-term mean Blue: Prelim OOS forecasts

13. © 2011 Risk Management Solutions, Inc. 13  Simulating a large number of tropical cyclones representing 100,000+ years  Data used: –Track position, intensity, size and shape  Meta-data would be very useful!