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

activity and intensityactivity and intensity

Rapporteur:Rapporteur: Suzana J. Camargo Suzana J. Camargo

International Research Institute for Climate and Society (IRI)International Research Institute for Climate and Society (IRI)The Earth Institute at Columbia UniversityThe Earth Institute at Columbia University

Palisades, NYPalisades, NY

Topic 4.3

Working GroupWorking Group

Maritza Ballester Maritza Ballester (Institute of Meteorology of Cuba, Cuba)(Institute of Meteorology of Cuba, Cuba)

Anthony Barnston Anthony Barnston (IRI, USA)(IRI, USA)

Phil Klotzbach Phil Klotzbach (Colorado State University, USA)(Colorado State University, USA)

Paul Roundy Paul Roundy (State University of New York - SUNY, USA)(State University of New York - SUNY, USA)

Mark Saunders Mark Saunders (University College London, UK)(University College London, UK)

FrFrédéric Vitart édéric Vitart (European Centre for Medium-Range Weather (European Centre for Medium-Range Weather Forecasts - ECMWF, UK)Forecasts - ECMWF, UK)

Matthew Wheeler Matthew Wheeler (Bureau of Meteorology, Australia)(Bureau of Meteorology, Australia)

OutlineOutline

Seasonal tropical cyclone forecastsSeasonal tropical cyclone forecasts Statistical forecastsStatistical forecasts Landfall probability forecastsLandfall probability forecasts Dynamical forecastsDynamical forecasts

Intra-seasonal tropical cyclone forecastsIntra-seasonal tropical cyclone forecasts RecommendationRecommendation

Operational Statistical ForecastsOperational Statistical Forecasts

CenterCenter RegionsRegions SinceSince IssuedIssued

CSUCSU AtlanticAtlantic 19841984 Dec, Apr, Jun, AugDec, Apr, Jun, Aug

NOAA OutlooksNOAA Outlooks Atlantic Atlantic

Eastern PacificEastern Pacific19981998

20032003

May, AugustMay, August

MayMayCity Univ. Hong City Univ. Hong KongKong

Western North Western North PacificPacific

20002000 April, JuneApril, June

Inst. of Meteorol. Inst. of Meteorol. of Cubaof Cuba

Atlantic, CaribbeanAtlantic, Caribbean 19961996 MayMay

Tropical Storm Tropical Storm RiskRisk

AtlanticAtlanticWestern North PacificWestern North Pacific

AustraliaAustralia

19991999

20002000

20002000

Dec. to JulyDec. to July

March to AugMarch to Aug..April to Dec.April to Dec.

Predictants CSU Forecasts (June)Predictants CSU Forecasts (June)

Current ENSO conditionsCurrent ENSO conditions West African rainfallWest African rainfall QBOQBO Caribbean SLP and upper level windsCaribbean SLP and upper level winds Azores SLP anomaliesAzores SLP anomalies Atlantic SST anomaliesAtlantic SST anomalies African Sahel temperature gradientAfrican Sahel temperature gradient

CSU Atlantic ForecastsCSU Atlantic Forecasts Determinist forecastsDeterminist forecasts Adjusted August 2006 forecasts:Adjusted August 2006 forecasts:

VariableVariable ForecastForecast ClimatolClimatol Verif.Verif.

Named Storms - NSNamed Storms - NS 1515 9.69.6 99Named Storm Days - NSDNamed Storm Days - NSD 7575 49.149.1 5050

Hurricanes - HHurricanes - H 77 5.95.9 55

Hurricane Days - HDHurricane Days - HD 3535 24.524.5 2020

Intense Hurricanes - IHIntense Hurricanes - IH 33 2.32.3 22Intense Hurricane Days - IHDIntense Hurricane Days - IHD 88 5.05.0 33Net Tropical Cyclone Activity -NTCNet Tropical Cyclone Activity -NTC 140140 100100 8585

Source: http://hurricane.atmos.colostate.edu/Forecasts

Correlations of CSU ForecastsCorrelations of CSU ForecastsSkill analysis by Phil Klotzbach, CSU

-0.3-0.2-0.1

00.10.20.30.40.50.60.70.8

Dec. Apr. Jun Aug.

NS

NSD

H

HD

IH

IHD

NTC

1992-2005 1995-2005 1984 or 1990 or 1991 to 2005

CSU Forecasts - Mean Square Skill ScoreCSU Forecasts - Mean Square Skill Score

0%

10%

20%

30%

40%

50%

60%

70%

JunClim

Jun 5yr AugClim

Aug 5yr

NS

NSD

H

HD

Skill Analysis by Phil Klotzbach, CSU

Percent of improvement in mean square error over a climatological or persisted forecast.

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

NOAA’s 2005 Seasonal Hurricane OutlooksNOAA’s 2005 Seasonal Hurricane Outlooks Issued 22 May Issued 22 May 20062006

Source: M. Chelliah, NOAA

Source: C. Landsea

-1

0

1

2

3

4

5

6

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

No

rm

alize

d D

ev

iati

on

CT-forecast

CT-updated

CT-real

Comparison: observations and forecasts using normalized standard deviation

-2

-1

0

1

2

3

4

5

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005No

rm

ali

zed

Devia

tio

n

H-forecast

H-updated

H-real

Forecast – 2nd MayUpdated – 1st August

Forecasts Long term mean1996 – 1998: 1966 – 19941999 – 2002: 1966 – 19982000 – 2005: 1965 - 2002

Source: M. Ballester, INSMET

Institute of Meteorology of Cuba Forecasts

Number of Tropical Storms and Hurricanes

Number of Hurricanes

TSR Predictors/MethodologyTSR Predictors/Methodology

Regression with Regression with two predictors:two predictors:

1. Forecast July-Sep 1. Forecast July-Sep trade wind speed trade wind speed (region 7.5°-17.5°N, (region 7.5°-17.5°N, 30°-100°W).30°-100°W).

2.2. Forecast Aug-Sep Forecast Aug-Sep SST for Atlantic SST for Atlantic hurricane main hurricane main development region development region (10°-20°N, 20°-60°W).(10°-20°N, 20°-60°W).

Source: M. Saunders, TSR

Sensitivity to Climate NormSensitivity to Climate Norm

ACE indexACE index

TSR TSR replicated replicated real-time real-time forecasts forecasts 1984-20051984-2005

Source: M. Saunders, TSR

Mean Square Skill Score (Mean Square Skill Score (MSSSMSSS): Percent improvement in ): Percent improvement in MSEMSE (mean square error) over a climatological forecast: (mean square error) over a climatological forecast:MSSSMSSS = (1 – = (1 – MSEFore MSEFore / / MSEClimMSEClim) x 100%) x 100%

City University of Hong Kong City University of Hong Kong Western North Pacific (WNP) Western North Pacific (WNP)

seasonal forecastsseasonal forecasts

ENSO Indices: Nino3.4, Nino4, SOIENSO Indices: Nino3.4, Nino4, SOI Western extent of subtropical high over WNPWestern extent of subtropical high over WNP Strength of the India-Burma trough Strength of the India-Burma trough (15˚-20(15˚-20˚̊N, 80N, 80˚̊-120-120˚̊E)E)

Difference: Equatorial Eastern Pacific and Indonesia SLPDifference: 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, 16 997-479.

Forecasts issued since 2000 in April and June for: • Number of tropical cyclones, • Number of TS and typhoons,• Number of typhoons

CUHK June ForecastsCUHK June Forecasts

Data source: http://aposf02.cityu.edu.hk/~mcg/tc_forecast/index.htm

Australia & Southwest Pacific Australia & Southwest Pacific forecastsforecasts

Issued in September 2003, 2004 and 2005 for Issued in September 2003, 2004 and 2005 for the following November – May season.the following November – May season.

Based on:Based on: SOISOI Potential temperature gradientPotential temperature gradient

Description in:Description in: McDonnell & Holbrook, GRL 2004 McDonnell & Holbrook, GRL 2004 McDonnell & Holbrook, Wea. Forecasting, 2004.McDonnell & Holbrook, Wea. Forecasting, 2004.

Macquarie Univ. Australia.Macquarie Univ. Australia.

Landfall Probability Landfall Probability ForecastsForecasts

FSU Group Landfall Seasonal FSU Group Landfall Seasonal Forecasts MethodologiesForecasts Methodologies

Development of various novel methods for Development of various novel methods for TC seasonal forecasts.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 Methodology used by various private companies for regional forecasts.companies for regional forecasts.**

Source: J. Elsner, personal comm. (2006).

Landfall ForecastsLandfall Forecasts

CSU – Landfall probabilities since 1998. Most CSU – Landfall probabilities since 1998. Most recent development new website with landfall recent development new website with landfall probabilities by counties in the U.S.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: CUHK – South China Sea landfall forecasts:

operational in 2004 & 2005operational 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.

Dynamical Seasonal Tropical Dynamical Seasonal Tropical Cyclone ForecastsCyclone 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).

2222

IRI Tropical Cyclone Activity Experimental Dynamical ForecastsIRI Tropical Cyclone Activity Experimental Dynamical Forecasts

BasinBasin SeasonSeason IssuedIssued TypeType 11stst forecast forecast

Eastern North Eastern North PacificPacific

JJASJJAS March,April, May, March,April, May, JuneJune

NTC, ACENTC, ACE March 2004March 2004

Western North Western North PacificPacific

JASOJASO April, May, June, April, May, June, JulyJuly

NTC, ACE, NTC, ACE, locationlocation

April 2003April 2003

North AtlanticNorth Atlantic ASOASO April, May, June, July, April, May, June, July, AugustAugust

NTC, ACENTC, ACE June 2003June 2003

South PacificSouth Pacific DJFMDJFM September, October, September, October, November, DecemberNovember, December

NTCNTC September September 20032003

Australian Australian basinbasin

JFMJFM September, October, September, October, November,November,

December, JanuaryDecember, January

NTCNTC September September 20032003

NTC=Number of named Tropical CyclonesACE=Accumulated Cyclone Energy , Location= centroid

of all tracks.

How are the forecasts produced?How are the forecasts produced?

1.1. Sea Surface Temperature forecastsSea Surface Temperature forecasts (various (various scenarios) produced.scenarios) produced.

2.2. Atmospheric ModelAtmospheric Model (ECHAM4.5) forced by sea (ECHAM4.5) forced by sea surface temperature forecasts.surface temperature forecasts.

3.3. Tropical Cyclone-like structuresTropical Cyclone-like structures detected and detected and tracked.tracked.

4.4. Statistical correctionsStatistical corrections of the tropical cyclone of the tropical cyclone activity based on the model climatology.activity based on the model climatology.

5.5. Probabilistic forecastsProbabilistic forecasts of tropical cyclone activity. of tropical cyclone activity.6.6. IRI Experimental Seasonal Tropical Cyclone IRI Experimental Seasonal Tropical Cyclone

OutlooksOutlooks released released

IRI SST forecast for ASOIRI SST forecast for ASO

IRI forecasts skill: real-timeIRI forecasts skill: real-timeAustraliaAustralia

Camargo & Barnston, 31st Climate Diagnostic Workshop, Boulder, CO, 2006.

IRI forecasts skill: simulationsIRI forecasts skill: simulationsAtlanticAtlantic

ECMWF Dynamical ForecastsECMWF Dynamical Forecasts

Model tropical cyclones in 3 coupled ocean-Model tropical cyclones in 3 coupled ocean-atmospheric models: multi-model ensemble.atmospheric models: multi-model ensemble.

Produced operationally since April 2002.Produced operationally since April 2002. Forecasts updated monthly for the following 5 Forecasts updated monthly for the following 5

months seasons in the relevant basins.months seasons in the relevant basins. Forecasts are not public, but are available for Forecasts are not public, but are available for

institutions affiliated with ECMWF and by institutions affiliated with ECMWF and by request.request.

Forecasts for 7 ocean basins.Forecasts for 7 ocean basins.

Multi-model ECMWF-UKMO-CNRM: Multi-model ECMWF-UKMO-CNRM: 1959-20011959-2001

ATL ENP WNP NIN SIN AUS SPCBASIN

-0.5

-0.3

-0.1

0.1

0.3

0.5

0.7

0.9

Lin

ea

r co

rre

latio

n

1959-19731973-19871987-2001

Interannual variability: linear correlation with observations

Source: F. Vitart, ECMWF

ECMWF Operational Seasonal ForecastsECMWF Operational Seasonal Forecasts

80°S80°S

70°S 70°S

60°S60°S

50°S 50°S

40°S40°S

30°S 30°S

20°S20°S

10°S 10°S

0°0°

10°N 10°N

20°N20°N

30°N 30°N

40°N40°N

50°N 50°N

60°N60°N

70°N 70°N

80°N80°N

20°E

20°E 40°E

40°E 60°E

60°E 80°E

80°E 100°E

100°E 120°E

120°E 140°E

140°E 160°E

160°E 180°

180° 160°W

160°W 140°W

140°W 120°W

120°W 100°W

100°W 80°W

80°W 60°W

60°W 40°W

40°W 20°W

20°W

14.3 10.39.8 13.325.1 26.22 2.9

No Significance 90% Significance 95% Significance 99% Significance

Ensemble size = 40,climate size = 70Forecast start reference is 01/06/2005Tropical Storm FrequencyECMWF Seasonal Forecast

Significance level is 90%JASON

FORECAST CLIMATE

80°S80°S

70°S 70°S

60°S60°S

50°S 50°S

40°S40°S

30°S 30°S

20°S20°S

10°S 10°S

0°0°

10°N 10°N

20°N20°N

30°N 30°N

40°N40°N

50°N 50°N

60°N60°N

70°N 70°N

80°N80°N

20°E

20°E 40°E

40°E 60°E

60°E 80°E

80°E 100°E

100°E 120°E

120°E 140°E

140°E 160°E

160°E 180°

180° 160°W

160°W 140°W

140°W 120°W

120°W 100°W

100°W 80°W

80°W 60°W

60°W 40°W

40°W 20°W

20°W

15 10.38.8 13.327.4 26.23 2.9

No Significance 90% Significance 95% Significance 99% Significance

Ensemble size = 41,climate size =225Forecast start reference is 01/06/2005Tropical Storm FrequencyMet Office Seasonal Forecast

Significance level is 90%JASON

FORECAST CLIMATE

Forecasts starting on 1st June 2005 JASON

80°S80°S

70°S 70°S

60°S60°S

50°S 50°S

40°S40°S

30°S 30°S

20°S20°S

10°S 10°S

0°0°

10°N 10°N

20°N20°N

30°N 30°N

40°N40°N

50°N 50°N

60°N60°N

70°N 70°N

80°N80°N

20°E

20°E 40°E

40°E 60°E

60°E 80°E

80°E 100°E

100°E 120°E

120°E 140°E

140°E 160°E

160°E 180°

180° 160°W

160°W 140°W

140°W 120°W

120°W 100°W

100°W 80°W

80°W 60°W

60°W 40°W

40°W 20°W

20°W

20.4 11.67.8 12.516.6 21.22.5 2.5

No Significance 90% Significance 95% Significance 99% Significance

Ensemble size = 41,climate size = 55Forecast start reference is 01/06/2005Tropical Storm FrequencyMétéo-France Seasonal Forecast

Significance level is 90%JASON

FORECAST CLIMATE

ECMWF Met Office

Meteo-France

Obs: July- November

AtlW-Pac E-Pac

80°S80°S

70°S 70°S

60°S60°S

50°S 50°S

40°S40°S

30°S 30°S

20°S20°S

10°S 10°S

0°0°

10°N 10°N

20°N20°N

30°N 30°N

40°N40°N

50°N 50°N

60°N60°N

70°N 70°N

80°N80°N

20°E

20°E 40°E

40°E 60°E

60°E 80°E

80°E 100°E

100°E 120°E

120°E 140°E

140°E 160°E

160°E 180°

180° 160°W

160°W 140°W

140°W 120°W

120°W 100°W

100°W 80°W

80°W 60°W

60°W 40°W

40°W 20°W

20°W

17.4 11.68.7 12.520.6 21.22.4 2.5

No Significance Sig at 10% level Sig at 5% level Sig at 1% level

Ensemble size =120,climate size =165Forecast start reference is 01/06/2005Tropical Storm FrequencyEUROSIP multi-model seasonal forecast

Significance level is 10%JASON

ECMWF/Met Office/Météo-France

FORECAST CLIMATE

0

5

10

15

20

25

30

Multi-model

Source: F. Vitart, ECMWF

Landfall in Mozambique:Landfall in Mozambique:Coupled Hindcast (TL159L40)Coupled Hindcast (TL159L40)

Frequency of landfall Obs.

Forecast

JFM 2000

JFM 1998

Source: F. Vitart, ECMWF

Intra-seasonal ForecastsIntra-seasonal Forecasts

BackgroundBackground 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)

MJO PredictionMJO Prediction

Currently: mainly empirical methodsCurrently: mainly empirical methods Dynamical models: difficult in simulating Dynamical models: difficult in simulating

and predicting MJO.and predicting MJO. Progress with high-resolution coupled Progress with high-resolution coupled

models: Vitart (2006)models: Vitart (2006) MJO is monitored on real time: MJO is monitored on real time:

Wheeler & Weickmann (2001).Wheeler & Weickmann (2001).

Modulation of TC activity by MJO phaseModulation 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 IndianOcean SST.•Greatest skill: strong MJO

Waves & Probabilities of TCsWaves & 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

RecommendationsRecommendations Verifications and skills for real-time forecasts Verifications and skills for real-time forecasts

readily available for all forecasts.readily available for all forecasts. Skill analysis (in hindcasts and real time) should Skill analysis (in hindcasts and real time) should

be published in peer review papers, if possible be published in peer review papers, if possible with a common metric for all forecasts.with a common metric for all forecasts.

Improvements could be possible with new Improvements could be possible with new homogeneous datasets for TCs (e.g. new dataset homogeneous datasets for TCs (e.g. new dataset by Jim Kossin).by Jim Kossin).

Combination of statistical and dynamical methods Combination of statistical and dynamical methods should be used for improvement in landfall should be used for improvement in landfall prediction.prediction.

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