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Short term (seasonal and intra-seasonal) prediction of tropical cyclone activity and intensity Rapporteur: Suzana J. Camargo International Research Institute.

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Presentation on theme: "Short term (seasonal and intra-seasonal) prediction of tropical cyclone activity and intensity Rapporteur: Suzana J. Camargo International Research Institute."— Presentation transcript:

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

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

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

4 Operational Statistical Forecasts CenterRegionsSinceIssued CSUAtlantic1984 Dec, Apr, Jun, Aug NOAA Outlooks Atlantic Eastern Pacific May, August May City Univ. Hong Kong Western North Pacific 2000 April, June Inst. of Meteorol. of Cuba Atlantic, Caribbean 1996May Tropical Storm Risk Atlantic Western North Pacific Australia Dec. to July March to Aug. April to Dec.

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

6 CSU Atlantic Forecasts Determinist forecasts Determinist forecasts Adjusted August 2006 forecasts: Adjusted August 2006 forecasts: VariableForecastClimatolVerif. Named Storms - NS Named Storm Days - NSD Hurricanes - H Hurricane Days - HD Intense Hurricanes - IH Intense Hurricane Days - IHD Net Tropical Cyclone Activity -NTC Source:

7 Correlations of CSU Forecasts Skill analysis by Phil Klotzbach, CSU 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 NOAAs 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 NOAAs 2005 Seasonal Hurricane Outlooks Issued 22 May 2006 Source: M. Chelliah, NOAA

11 Source: C. Landsea

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

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

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

16 CUHK June Forecasts Data source:

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

18 Landfall Probability Forecasts

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

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

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

22 22 IRI Tropical Cyclone Activity Experimental Dynamical Forecasts BasinSeasonIssuedType 1 st forecast Eastern North Pacific JJAS March,April, May, June NTC, ACE March 2004 Western North Pacific JASO April, May, June, July NTC, ACE, location April 2003 North Atlantic ASO April, May, June, July, August NTC, ACE June 2003 South Pacific DJFM September, October, November, December NTC September 2003 Australian basin JFM September, October, November, December, January NTC September 2003 NTC=Number of named Tropical Cyclones ACE=Accumulated Cyclone Energy, Location= centroid of all tracks.

23 How are the forecasts produced? 1. Sea Surface Temperature forecasts (various scenarios) produced. 2. Atmospheric Model (ECHAM4.5) forced by sea surface temperature forecasts. 3. Tropical Cyclone-like structures detected and tracked. 4. Statistical corrections of the tropical cyclone activity based on the model climatology. 5. Probabilistic forecasts of tropical cyclone activity. 6. IRI Experimental Seasonal Tropical Cyclone Outlooks released

24 IRI SST forecast for ASO

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

26 IRI forecasts skill: simulations Atlantic

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

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

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

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

31 Intra-seasonal Forecasts

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

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

34 Modulation of TC activity by MJO phase Source: Leroy, Wheeler, Timbal (2004) Wheeler & Hendon (2004) 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

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. 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. 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). 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. 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. Intra-seasonal forecasts could be used as guidance for forecasting genesis.


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