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Short term (seasonal and intra-seasonal) prediction of tropical cyclone activity and intensity

Short term (seasonal and intra-seasonal) prediction of tropical cyclone activity and intensity. Topic 4.3. Rapporteur: Suzana J. Camargo International Research Institute for Climate and Society (IRI) The Earth Institute at Columbia University Palisades, NY. Working Group.

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Short term (seasonal and intra-seasonal) prediction of tropical cyclone activity and intensity

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  1. Short term (seasonal and intra-seasonal) prediction of tropical cyclone activity and intensity Topic 4.3 Rapporteur: Suzana J. Camargo International Research Institute for Climate and Society (IRI) The Earth Institute at Columbia University Palisades, NY

  2. Working Group • Maritza Ballester (Institute of Meteorology of Cuba, Cuba) • Anthony Barnston (IRI, USA) • Phil Klotzbach (Colorado State University, USA) • Paul Roundy (State University of New York - SUNY, USA) • Mark Saunders (University College London, UK) • Frédéric Vitart (European Centre for Medium-Range Weather Forecasts - ECMWF, UK) • Matthew Wheeler (Bureau of Meteorology, Australia)

  3. Outline • Seasonal tropical cyclone forecasts • Statistical forecasts • Landfall probability forecasts • Dynamical forecasts • Intra-seasonal tropical cyclone forecasts • Recommendation

  4. Operational Statistical Forecasts

  5. Predictants CSU Forecasts (June) • Current ENSO conditions • West African rainfall • QBO • Caribbean SLP and upper level winds • Azores SLP anomalies • Atlantic SST anomalies • African Sahel temperature gradient

  6. CSU Atlantic Forecasts • Determinist forecasts • Adjusted August 2006 forecasts: Source: http://hurricane.atmos.colostate.edu/Forecasts

  7. Correlations of CSU Forecasts Skill analysis by Phil Klotzbach, CSU 1992-2005 1995-2005 1984 or 1990 or 1991 to 2005

  8. CSU Forecasts - Mean Square Skill Score Skill Analysis by Phil Klotzbach, CSU Percent of improvement in mean square error over a climatological or persisted forecast.

  9. Basis and Procedures for the Seasonal Hurricane Outlooks • NOAA’s makes seasonal hurricane outlooks by first analyzing and predicting these leading recurring patterns of climate variability in the tropics, and then predicting their impacts on hurricane activity. • The two dominant climate factors that influence/control • seasonal hurricane activity in the Atlantic and Eastern Pacific regions are: • El Niño/ Southern Oscillation (ENSO):Gray (1984) • Tropical multi-decadal climate variability:Chelliah and Bell (2004) • Bell and Chelliah (2006) Source: M. Chelliah, NOAA

  10. NOAA’s 2005 Seasonal Hurricane Outlooks Issued 22 May 2006 Source: M. Chelliah, NOAA

  11. Source: C. Landsea

  12. Institute of Meteorology of Cuba Forecasts Comparison: observations and forecasts using normalized standard deviation Forecasts Long term mean 1996 – 1998: 1966 – 1994 1999 – 2002: 1966 – 1998 2000 – 2005: 1965 - 2002 Number of Tropical Storms and Hurricanes Number of Hurricanes Forecast – 2nd May Updated – 1st August Source: M. Ballester, INSMET

  13. TSR Predictors/Methodology Regression with two predictors: 1. Forecast July-Sep trade wind speed (region 7.5°-17.5°N, 30°-100°W). 2.Forecast Aug-Sep SST for Atlantic hurricane main development region (10°-20°N, 20°-60°W). Source: M. Saunders, TSR

  14. Sensitivity to Climate Norm Mean Square Skill Score (MSSS): Percent improvement in MSE (mean square error) over a climatological forecast: MSSS = (1 – MSEFore / MSEClim) x 100% ACE index TSR replicated real-time forecasts 1984-2005 Source: M. Saunders, TSR

  15. City University of Hong Kong Western North Pacific (WNP) seasonal forecasts • ENSO Indices: Nino3.4, Nino4, SOI • Western extent of subtropical high over WNP • Strength of the India-Burma trough (15˚-20˚N, 80˚-120˚E) • Difference: Equatorial Eastern Pacific and Indonesia SLP • Primary mode of low-frequency variability in the WNP. • Forecasts issuedsince 2000in April andJunefor: • Number of tropical cyclones, • Number of TS and typhoons, • Number of typhoons Chan et al. (2001), Wea. Forecasting, 16 997-479.

  16. CUHK June Forecasts Data source: http://aposf02.cityu.edu.hk/~mcg/tc_forecast/index.htm

  17. Australia & Southwest Pacific forecasts • Issued in September 2003, 2004 and 2005 for the following November – May season. • Based on: • SOI • Potential temperature gradient • Description in: • McDonnell & Holbrook, GRL 2004 • McDonnell & Holbrook, Wea. Forecasting, 2004. • Macquarie Univ. Australia.

  18. Landfall Probability Forecasts

  19. FSU Group Landfall Seasonal Forecasts Methodologies • Development of various novel methods for TC seasonal forecasts. • Landfall forecast paper for U.S. forecasts: • Leehmiller, Kimberlain & Elsner, MWR (1997). • Recent improved scheme: • Jagger & Elsner, J. Climate (2006). • Methodology used by various private companies for regional forecasts.* Source: J. Elsner, personal comm. (2006).

  20. Landfall Forecasts • CSU – Landfall probabilities since 1998. Most recent development new website with landfall probabilities by counties in the U.S. • TSR – U.S. ACE index forecasts Saunders & Lea, Nature (2005) • CUHK – South China Sea landfall forecasts: operational in 2004 & 2005 Liu & Chan, MWR (2003) • INSMET – landfall of tropical cyclones in Cuba.

  21. Dynamical Seasonal Tropical Cyclone Forecasts • IRI experimental forecasts • Skill: Camargo, Barnston & Zebiak (2005) • Methodology: Camargo & Zebiak (2002) • ECMWF experimental forecasts: • Skill: Vitart (2006). • Methodology: Vitart et al. (1997,1999).

  22. IRI Tropical Cyclone Activity Experimental Dynamical Forecasts NTC=Number of named Tropical Cyclones ACE=Accumulated Cyclone Energy , Location= centroid of all tracks.

  23. How are the forecasts produced? • Sea Surface Temperature forecasts (various scenarios) produced. • Atmospheric Model (ECHAM4.5) forced by sea surface temperature forecasts. • Tropical Cyclone-like structures detected and tracked. • Statistical corrections of the tropical cyclone activity based on the model climatology. • Probabilistic forecasts of tropical cyclone activity. • IRI Experimental Seasonal Tropical Cyclone Outlooks released

  24. IRI SST forecast for ASO

  25. IRI forecasts skill: real-timeAustralia Camargo & Barnston, 31st Climate Diagnostic Workshop, Boulder, CO, 2006.

  26. IRI forecasts skill: simulationsAtlantic

  27. ECMWF Dynamical Forecasts • Model tropical cyclones in 3 coupled ocean-atmospheric models: multi-model ensemble. • Produced operationally since April 2002. • Forecasts updated monthly for the following 5 months seasons in the relevant basins. • Forecasts are not public, but are available for institutions affiliated with ECMWF and by request. • Forecasts for 7 ocean basins.

  28. Multi-model ECMWF-UKMO-CNRM: 1959-2001 Interannual variability: linear correlation with observations Source: F. Vitart, ECMWF

  29. ECMWF Operational Seasonal Forecasts Forecasts starting on 1st June 2005 JASON ECMWF Met Office Obs: July- November Meteo-France Multi-model W-Pac E-Pac Atl Source: F. Vitart, ECMWF

  30. Landfall in Mozambique:Coupled Hindcast (TL159L40) JFM 2000 Obs. Frequency of landfall JFM 1998 Source: F. Vitart, ECMWF Forecast

  31. Intra-seasonal Forecasts

  32. Background • Relationship of MJO (Madden-Julian Oscillation) & tropical cyclone activity in various regions: • Western North Pacific: • Liebmann, Hendon, Glick (1994); Sobel and Maloney (2000) • Gulf of Mexico & Eastern North Pacific: • Maloney & Hartmann (2000); Molinari & Volaro (2000) • Australian region: • Hall, Matthews & Karoly (2001) • South Indian Ocean: • Bessafi & Wheeler (2006)

  33. MJO Prediction • Currently: mainly empirical methods • Dynamical models: difficult in simulating and predicting MJO. • Progress with high-resolution coupled models: Vitart (2006) • MJO is monitored on real time: Wheeler & Weickmann (2001).

  34. Modulation of TC activity by MJO phase • New statistical forecast method: • Weekly probabilites of TC • Activity within large zones in the • Southern Hemisphere • Predictors: MJO indices, • ENSO SST indices, and Indian • Ocean SST. • Greatest skill: strong MJO Wheeler & Hendon (2004) Source: Leroy, Wheeler, Timbal (2004)

  35. Waves & Probabilities of TCs • Developed by Paul Roundy • Based on relationship of waves and TCs (Roundy & Frank, 2004a,b,c) • Logistic regression between wave modes and TC genesis • Skill of 10-40% (location dependent) over climatology in one-week leads

  36. Recommendations • Verifications and skills for real-time forecasts readily available for all forecasts. • Skill analysis (in hindcasts and real time) should be published in peer review papers, if possible with a common metric for all forecasts. • Improvements could be possible with new homogeneous datasets for TCs (e.g. new dataset by Jim Kossin). • Combination of statistical and dynamical methods should be used for improvement in landfall prediction. • Intra-seasonal forecasts could be used as guidance for forecasting genesis.

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