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 !!