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Dynamical-statistical seasonal prediction for western North Pacific typhoons based on APCC multi-models
May 24, 2016
Ok-Yeon Kim
Agencies that issue seasonal tropical cyclone forecasts for various tropical cyclone basins
North Atlantic
Eastern North Pacific
Central North Pacific
Western North Pacific
Australia Region
North IndianOcean
South Indian Ocean
South Pacific Ocean
City University of Hong Kong (China)
Statistical
Colorado State University(USA)
Statistical
Cuban Meteorological Institute (Cuba)
Statistical
European Centre for Medium-Range WeatherForecasts (UK)
Dynamical Dynamical Dynamical Dynamical Dynamical Dynamical Dynamical
International Research Institute for Climate and Society (USA)
Dynamical Dynamical Dynamical Dynamical
Macquarie University (Australia)
Statistical Statistical
National Meteorological Service (Mexico)
Statistical
National Climate Centre (China)
Statistical
NOAA Climate Prediction Center (USA)
Statistical Statistical Statistical
Tropical Storm Risk (England)
Statistical Statistical Statistical
Source: WMO Report (Klotzbach et al., 2012)
A statistical approach developed by NOAA for use in their 2008 forecasts (Wang et al., 2009)
• It utilizes regression equations that related coupled ocean-atmosphere dynamical climate model forecasts of key atmospheric and oceanic anomalies to the observed seasonal tropical cyclone activity.
Climate model-based statistical approach for seasonal tropical cyclone activity
Predictor Predictand
APCC models-predicted variability of the atmosphere and oceans (hindcast dataset)
Observed Pacific seasonal tropical cyclone activity
Forecast
empirical relationship(statistical model)
Independent Forecast
Dataset: APCC 6-month forecast models
Country Institute Model name AGCM/resolution OGCM/resolutionEnsemble No.
Hindcast / Forecast
Canada MSC MSC_CANCM3 AGCM3/T63 L31OGCM4/CanOM4
(1.41°lonx0.94°lat L40)10/10
Canada MSC MSC_CANCM4 AGCM4/T6 3L31OGCM4/CanOM4
(1.41°lonx0.94°lat L40)10/10
USA NASA NASA GEOS-5/288x181 L72 MOM4/720x410 L40 11/9
USA NCEP NCEP GFS/T62L64 MOM3/⅓°lat x 1°lon L40 20/20
Korea PNU PNU CCM3/T42L18 MOM3/0.7~2.8lat L29 10/4
APCC 6-month hindcasts (1982-2008, 27yrs)
Dataset: APCC 6-month hindcasts
APR
Active Tropical Cyclone Season
MAY JUN JUL AUG SEP OCT
1M lead(IC06)
2M lead(IC05)
3M lead(IC04)
• Predictor field: SST, VWS, VOR, U850 and U200 in JASO
• 5 models, 57 ensembles (MSC_CANCM3, MSC_CANCM4, NASA, NCEP, PNU)
• Predictand: TS, TY, ITY
Dataset: Observation and Reanalysis
NOAA optimum interpolation SST version 2 (Reynolds et al., 2002) NCEP/DOE reanalysis 2 U850 and U200 (Kanamitsu et al., 2002a) Vertical wind shear (VWS): the difference in zonal wind between 200
and 850 hPa (U200-U850) Relative vorticity (VOR) at 850 hPa
Joint Typhoon Warning Center (JTWC) Western North Pacific Best Track Data
Actual number of Western North Pacific TCs during JASO
Number of Typhoons (TY): Maximum sustained wind ≥ 64 kts Number of Intense Typhoons (ITY): Maximum sustained wind ≥ 85 kts
(RSMC Tokyo’s Tropical Cyclone Intensity Scale)
Prediction skill: anomaly correlation (1M lead, JASO SST)
• MME shows the highest skill compared to individual models
• High skill over the central/eastern equatorial Pacific
• MSC_CANCM3, CM4 and NCEP show higher skills compared to others.
(a)
(c)
(e)
(b)
(d)
(f)
Prediction skill: anomaly correlation (1-3M lead, MME)
SST VWS U850
3M lead
2M lead
1M lead
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
TY ITY
Potential predictors: correlations of APCC MME hindcast JASO SST with interannual variation of TY and ITY
TY ITY
Potential predictors: correlations of APCC MME hindcast JASO VWS with interannual variation of TY and ITY
TY ITY
Potential predictors: correlations of APCC MME hindcast JASO VOR with interannual variation of TY and ITY
TY ITY
Potential predictors: correlations of APCC MME hindcast JASO U850 with interannual variation of TY and ITY
TY ITY
Potential predictors: correlations of APCC MME hindcast JASO U200 with interannual variation of TY and ITY
Step Statistics SST VWS VOR U850 U200
April ICs (3M lead)
1
MSE
R2
F-ratio
6.22
0.29
10.06
5.46
0.37
14.93
6.34
0.27
9.39
5.96
0.32
11.64
5.32
0.39
16.05
2+U200
MSE
R2
F-ratio
5.35
0.39
7.57
5.37
0.38
7.49
5.32
0.39
7.67
5.37
0.38
7.50
–
May ICs (2M lead)
1
MSE
R2
F-ratio
6.04
0.31
11.10
5.25
0.40
16.59
6.32
0.28
9.53
5.87
0.33
12.14
4.99
0.43
18.68
2+U200
MSE
R2
F-ratio
4.98
0.43
9.05
4.90
0.44
9.37
4.98
0. 43
9.02
4.83
0.47
9.85
–
3+U200+U850
MSE
R2
F-ratio
4.96
0.43
5.47
4.95
0.43
5.68
4.96
0.43
5.92
– –
Predictor selection: A selection of a best set of predictors
Goodness-of-fit measures including mean squared errors (MSE), R2 and F-ratio in each stage of forward selection procedure. In LAD regression, dependent variable is observed interannual variability of typhoons (TY) and independent variable(s) is (are) obtained from APCC MME hindcasts with April-June ICs. Bold denotes that the predictor is selected at each stage of forward selection
A good predictor must have a lower MSE, a higher R2 and a larger F-ratio.
Predictor selection: List of predictors for LAD regression selected from forward selection procedure
Predictand ICs Selected predictor(s)
TY
1M lead U200, VOR
2M lead U200, U850
3M lead U200
ITY
1M lead U200
2M lead U200, U850
3M lead U200
Cross-validation of seasonal TCs activity with APCC MME hindcasts
Cross-validation
Retrospective
TY ITY TY ITY TY ITY
1M lead
0.67 0.67 0.74 0.56 0.93 0.98
2M lead
0.64 0.68 0.59 0.68 0.81 0.94
3M lead
0.60 0.63 0.57 0.58 0.93 0.99
* Correlation coefficients
(a)
(b)
Prediction skills: correlation coeff. and RMSE
Cross-validation of seasonal TCs activity with APCC MME hindcasts
(a)
(b)
(c)
(d)
Probabilistic prediction for seasonal TCs activity
Prediction skills: Relative operating characteristic (ROC) curve & score
Summary and conclusions
• This study aims at predicting the seasonal number of typhoons over the western North Pacific (WNP) with an APCC (Asia-Pacific Climate Center) multimodel ensemble (MME)-based dynamical-statistical hybrid model.
• The cross validation result from the MME hybrid model demonstrates high prediction skill, with a correlation of 0.67 between the hindcasts and observation for 1982 to 2008.
Summary and conclusions
• Given large set of ensemble members from multi-models, a relative operating characteristic score reveals an 82% (above-) and 78% (below-normal) improvement for the probabilistic prediction of the number of typhoons (TY).
• Using large set of ensemble members from multi-models, the APCC MME could provide useful deterministic and probabilistic seasonal typhoon forecasts to the end-users in particular, the residents of tropical cyclone-prone areas in the Asia-Pacific region.
Thank you !!
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