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Where Are We in Seasonal Tropical Cyclone Prediction?

Where Are We in Seasonal Tropical Cyclone Prediction?. Johnny Chan. Guy Carpenter Asia-Pacific Climate Impact Centre School of Energy and Environment City University of Hong Kong. Outline. Statistical methods Statistical dynamical method Dynamical methods Summary. Statistical Method.

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Where Are We in Seasonal Tropical Cyclone Prediction?

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  1. Where Are We in Seasonal Tropical Cyclone Prediction? Johnny Chan Guy Carpenter Asia-Pacific Climate Impact Centre School of Energy and Environment City University of Hong Kong

  2. Outline • Statistical methods • Statistical dynamical method • Dynamical methods • Summary

  3. Statistical Method

  4. Statistical method • Identify a list of variables relating to the atmospheric and oceanographic conditions prior to the season that significantly correlate with seasonal tropical cyclone activity • Perform regressions to derive prediction equations

  5. Examples of Predictors used in the CityU Forecasts large-scale atmospheric conditions • Index of the westward extent of the subtropical high over the western North Pacific • Index of the strength of the India-Burma trough (15-20oN, 80-120oE) • West Pacific index • Sea surface temperature (SST) anomalies in the NINO3.4 region (5oS-5oN,170-120oW) • Sea surface temperature (SST) anomalies in the NINO4 region (5oS-5oN, 160oE-150oW) • Equatorial Southern Oscillation Index (Equatorial SOI) • Equatorial Eastern Pacific SLP - Indonesia SLP (standardized anomalies) ENSO conditions

  6. Typhoons All tropical cyclones 7/11 7/11 Tropical storms and typhoons Forecasts of Annual Tropical Cyclone Activity over the western North Pacific (Deviations from Observations) 8/11

  7. Typhoons All tropical cyclones Tropical storms and typhoons Forecasts of Annual Tropical Cyclone Activity over the western North Pacific (Deviations from Observations)

  8. Typhoons All tropical cyclones Tropical storms and typhoons Forecasts of Annual Tropical Cyclone Activity over the western North Pacific vs. Observations

  9. Annual No. of NWP TS & TY

  10. Statistical Dynamical Method

  11. Statistical vs. Statistical-dynamical Methods Problem with the statistical method Relate the past events and future conditions by statistics Inherent problem assumes the future would behave the same as the past, which may not be correct Statistical-dynamical method partly solves the inherent problem by relating dynamical model predictions with future conditions Dynamical atmospheric model Predicted future conditions Integrate over time statistical prediction Observations statistical prediction # TCs Time several months

  12. Dynamical model data – DEMETER Development of a European multimodel ensemble system for seasonal to interannual prediction (from European Union) 7 models (CERFACS, ECMWF, INGV, LODYC, Météo-France, MPI and UKMO) 9 ensemble members each 6 months forecasts available Base time @ 1 Feb, May, Aug, Nov 1980-2001 (22 years hindcast) 2.5 x 2.5 degree resolution

  13. Dynamical model data – DEMETER

  14. Tracks of EC landfalling TCs 1980 – 2001, Aug – Sept Subtropical High

  15. GC Tracks of FL/GC landfalling TCs 1980 – 2001,Aug – Sept Subtropical High Subtropical High FL

  16. Methodology Compute the 9-member ensemble mean of each model-predicted atmospheric fields (Aug-Sept) geopotential, zonal and meridional winds (850, 500 and 200 hPa) SST, SLP Extract the first 4 EOF modes of each predictor fields 11 fields x 4 modes = 44 potential predictors from each DEMETER model Test the statistical significance of the relationship between the coefficient of each mode and the number of landfalling TCs

  17. Fit a forecast equation for the number of landfalling TCs in each region Poisson regression Cross-validation (Jackknife method) 7 forecast equations, each from an individual model Multimodel equation derived from the 7 equations Simple average Agreement coefficient weighted-average Methodology

  18. Regression Linear regression is used in most previous studies Normality assumption of predictors and predictand Fails in # landfalling TCs (Discrete non-negative integers) Poisson regression Discrete probability distribution Zero probability for negative numbers Stepwise regression

  19. Factors affecting EC landfalling TCs Model CERFACS

  20. Observed vs. PredictedEast Coast Single model: CERFACS Multimodel

  21. Observed vs. PredictedGulf Coast Single model: LODYC Multimodel

  22. Observed vs. PredictedFlorida Single model: LODYC Multimodel

  23. Dynamical Method

  24. Dynamical method (1) • Run a global circulation model (GCM) • Identify and count the number of vortices from the model integrations

  25. IRI forecasts

  26. Dynamical method (2) • Run a global circulation model (GCM) with a relatively coarse resolution • Solutions from the GCM are used as boundary conditions for a regional model with a higher resolution that can “resolve” a tropical cyclone • Integrate the regional model to predict seasonal activity.

  27. Example of 850-hPa flow and relative vorticity

  28. Example of simulation of a 3-month forecast red – simulated blue - observed

  29. The model • Modified version of Regional Climate Model Version 3 (RegCM3) developed at ITCP • Horizontal resolution: 60 km • Emanuel cumulus scheme • Domain: 94oE-172oW, 14oS-41oN • Initial conditions: 8 ensemble members from 00UTC on 1 May and every 6 hr after • Integration till the end of October

  30. The model Initial and boundary conditions: ERA40 SST: weekly OISST from NOAA

  31. Detection of a tropical cyclone Local maximum ζ850hPa ≥ζT (450 x 10-6 s-1) T300hPa at centre – Tenvironment ≥ 1oC lifetime ≥ 2 days Genesis over the ocean

  32. Example of a tropical cyclone in RegCM3

  33. Example of a tropical cyclone in the Regional Model

  34. Example of a tropical cyclone in the Regional Model

  35. Model Climatology (1982-2001, May to Oct)

  36. Model vs. Observed (850-hPa relative vorticity) ERA40 May RegCM3 May ERA40 Jul RegCM3 Jul

  37. Model Climatology (1982-2001, May to Oct) Latitude Distribution Monthly Distribution Longitude Distribution

  38. Model Climatology (1982-2001, May to Oct)

  39. Model vs. Observed (1997, May to Oct)

  40. Model vs. Observed (1998, May to Oct)

  41. Model vs. Observed JTWC cold JTWC warm RegCM3 cold RegCM3 warm

  42. Model vs. Observed (500-hPa geopotential heights) RegCM3 May ERA40 May RegCM3 Jul ERA40 Jul

  43. Summary • Even with a 60-km resolution, RegCM3 is able to generate vortices with structures that resemble those of real tropical cyclones. • The model is capable of reproducing the basic climatology, the interannual variability of tropical cyclones and tracks of individual tropical cyclones in the western North Pacific. • It is therefore possible to use RegCM3 with global model predictions as initial and boundary conditions to produce seasonal forecasts of tropical cyclone activity.

  44. RegCM3 simulation using ECHAM5 20C run

  45. RegCM3 simulation using ECHAM5 20C run

  46. RegCM3 simulation using ECHAM5 20C run

  47. Summary • Statistical methods can provide some clues on tropical cyclone activity but suffers from an inherent problem of predicting future events based only on past conditions • Statistical-dynamical methods can provide predictive information and therefore should give better results, but still suffers from the statistical nature of the method. • Dynamical model forecasts could be the way forward to predict tropical cyclone risks although more research is still necessary on (a) fine-tuning the regional model, and (b) having a better GCM.

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