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