2. Weekly TC forecasts in the Southern Hemisphere Anne Leroy (Météo France) Matthew Wheeler (CAWCR/BOM) John McBride (CAWCR/BOM) funded by the Indian Ocean Climate Initiative May 2009

3. Introduction Potential for TC genesis and occurrence predictions at intraseasonal leadtime based on the MJO state From Leroy and Wheeler 2008

4. Plan Previous method : description and weakness Modifications to the method Description of the new models Exemple of forecats : 1984-1985 Evaluation : Brier skill score, reliability diagram and economic value Conclusion

5. Former version of the method Use of logistic regression to forecast both TC genesis and occurrence probability Predictors : -Daily climatological probability of genesis/occurrence -Interannual predictors : SST1/SST2 to take into account the ENSO state and possibility the interannual variability in the Indian Ocean -Intraseasonnal predictors : RMM1/RMM2 to take into account the MJO state Described in Leroy and Wheeler 2008 Operational forecast from 2006/07 to 2008/2009 on http://www.meteo.nc/espro/previcycl/cyclA.php http://www.meteo.nc/espro/previcycl/cyclA.php Genesis probability forecasted in 2008/2009 in z3

6. Logistique regression Probability P(x) G(x)

7. Total number of click during TC season : 3500 web page in english more visited than the one in french users seems to become faithful (more click at the end of the season than at the beginning) Forecasts of all the 4 areas are regurlarly visited Nb of click to zoom on the curves Nb of click on the 3 more visited pages Web statistic in 2008/2009 Statistiques du web FR page ENG page Doc

8. Weakness of this product Regions too large for most users : forecasts over smaller regions may have less skill but more usefullness Boundary in the Pacific not optimized for ENSO From Camargo et al. 2007

9. Weakness of this product Trends in predictors appeared in recent years => trend in predicted TC probability Artificial skill

10. Changes to the method Changes in areas Use of other interannual predictors Take into account the seasonality of the relationship between ENSO and TC (finally not implemented)

11. Changes in areas Reduce the size of areas : a compromise between size and TC number Number of TC genesis Number of TC day (occurrence) Numbers of TC in overlapping boxes 20 º 15 º Southward tracks

12. Seasonal cycle FFT filtering to calculate the probability of TC occurrence during a week Late season Long season

13. Changes in areas there are more occurrence events than genesis => the statistical model should perform well over more areas to predict occurrence Occurrence is really linked to the impact of TC for users => Focus on occurrence forecast

14. Changes in interannual predictors Like SST1 and SST2, we are looking for : –uncorrelated indices, –Indices taking into account the Pacific and Indian Ocean variability, –Indices available in real-time, –Indices without trend.

15. New interannual predictors Nino3.4 trans-Nino Index: TNI = Nino1+2-Nino4. This index is complementary to Nino3.4 to characterize ENSO (Trenberth and Stepaniak 2000) Indian Ocean Dipole index : DMI= SST in tropical W Indian Ocean – SST in SE Indian Ocean (defined by Saji et al 2000). Chan and Liu showed the DMI is related to seasonal TC numbers. Area used to define the DMI

16. New interannual predictors Use of 2-month average of indices One month lag introduced to simulate real-time Uncorrelated ? Correlation between monthly indices September to MarchSeptember to December

17. NINO3.4 and TC occurrence All season Early season Nb of TC day per 20 * 15 degree boxes Late season No evidence that the impact of NINO3.4 on TC depends on the time within the season El Nino – La Nina

18. TNI and TC occurrence All season Early season Late season

19. DMI and TC occurrence All season Early season Late season Weak signal Looks like an ENSO pattern A signal different from the signal of ENSO ?

20. Predictor selection ascending predictor selection procedure But include predictors with a more strict criterion (pe=0.001) than in Leroy and Wheeler (2008)

21. Predictors selection W1 (first week of forecasts) : selection order

22. Predictors selection W2 (second week of forecasts) : selection order

23. Predictors selection W3 (third week of forecasts) selection order

24. Predictors selection W4 (fourth week of forecasts): selection order

25. Coefficients of the model W1 Predictors have been standardized so that coefficients of different predictors can be compared The method to fit the logistic model to the data does not always converge in region where very few TC occurs

26. Coefficients of the model W2

27. Coefficients of the model W3

28. Coefficients of the model W4

29. Example of forecasts W1 during 1984- 1985 Strong MJO : in the Indian Ocean in early Feb moving to Western Pacific by mid-march From http://www.bom.gov.au/bmrc/clfor/cfstaff/matw/maproom/RMM/phasediag.list.htm

30. Example of forecasts W1 during 1984-1985 Strong MJO : in the Indian Ocean in early Feb moving to Western Pacific by mid-march W1 forecasted probability Anomaly against daily climatological probability

31. Score Brier Skill score Cross validation : the statistical model is validated over a period different from its learning period => no artificial skill Brier Score : Expressed as a percentage of improvement over a reference strategy Brier Skill score : Here, the reference strategy is the one that forecasts the seasonal mean climatology over each area.

32. Cross-validated Brier skill score W1 The reference forecast used to calculate Brier skill score is the mean seasonal climatology. The skill comes mainly from the daily climatology

33. Cross-validated Brier skill score W1 The skill comes mainly from the daily climatology, then from the MJO and ENSO. Weak impact of TNI and DMI.

34. Cross-validated Brier skill score W2 The improvement brought by the MJO is not as important as the one at W1. Impact of Nino3.4 maintains.

35. Cross-validated Brier skill score W3 The MJO still brings an improvement.

36. Cross-validated Brier skill score W4 No skill comes from the MJO (except ?)

37. For operational products Over regions where our model shows little skills, our forecasts are replaced by the daily climatological forecast. little skills at a given lead time = if the Brier score of our forecast is higher than the Brier score of the daily clim at this lead time and the next one.

38. For operational products Areas where our forecasts is replaced by the daily climatology. Hatched areas become larger with lead time as the predictability decreases.

39. Reliability diagram W1 1/10 of the data in each dot The pairs (forecast, observation) are ordered by increasing value of the forecast and then grouped into 10 groups The average of observed probability (1, 0, 0, 1…) for each group is then calculated Perfect forecast

40. Reliability diagrams W1 W2 W3

41. Reliability diagrams W1 W2 W3

42. Reliability diagrams W1 W2 W3

43. Economic Value Cost/loss model : a particular user will take actions when the forecated probability is over a threshold value (Paction) Cost of the action Cost of the loss Yes No Event Yes No

44. Economic Value Expense (E) generated by different strategies and economic value : Perfect forecast => V=100 % Forecast as good as climatology => V=0 Paction E climate - E forecast E climate - E perfect

45. Economic Value W1 W2 W3

46. Economic Value W1 W2 W3

47. Economic value W1 W2 W3

48. Conclusion SST1/SST2 have been removed from the scheme Large impact of Nino3.4, low impact of TNI and DMI Smaller regions with reasonably good skill, probably more useful Plans to use the new scheme for real-time forecasts during 2009/2010 TC season