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Forecasting Monthly Tropical Cyclone Activities

Lee Sai MingHong Kong Observatory, Hong Kong, China

ESCAP/WMO Integrated Workshop on Urban Flood Risk Management in a Changing Climate:

Sustainable and Adaptation Challenges6 Sep 2010, Macao, China

The need for long-range TC forecast

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To support decision-makingTo support disaster prevention and

preparedness planning

Availability of seasonal TC forecast

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WMO Bulletin 56 (4) : Seasonal Tropical Cyclone Forecasts – a very comprehensive overview

Examples of forecasts:No. of TC / named storms / ACE index over an

ocean basin [no region-specific info.]

No. of TC landfalls [TC affecting a region/city does not need to make landfall there]

Typhoon Chanthu, July 2010

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Typhoon Hagupit, September 2008

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Heavy rain brought by Typhoon Chanthu

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>= 100 mm of rainfall

(courtesy of TVB)

Flooding in Tai O after Typhoon Hagupit

TC activity affecting a region / city

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Definition: No. of TC within a certain range and a certain period of time

Hong Kong: N500 [within 500 km of HK]

Long term mean of annual N500 ≈ long term mean of annual Nsig [issuance of warning signals]

Monthly N500 of Hong Kong

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HK TC season ~ June to October

July N500 of HK

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Forecast formulation

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N500 is a count parameterCan be modelled by the Poisson distribution

The Poisson dist. belongs to the family of exponential dist.

Canonical form:

!),(

y

eyp

y

,...2,1,0y

)]()()()(exp[);( ydcbyayf

where a(y)=y, b(θ)=log θ, c(θ)= θ, d(y)=-logy!

Generalized Linear Model (GLM)

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Tii xYEg ))((

Yi = response variable [monthly N500]

E = expectation of the dist.

g = link function

xi = explanatory variables or predictors

β = model parameters

i = 1, 2, … N

What are the predictors ?

NCEP CFS

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Climate Prediction Center, NOAA, USA: a WMO designated Global Producing Centre (GPC) of Long Range Forecasts

CPC provides digital long range forecast and hindcast [generated by the NCEP Climate Forecast System]

Hindcast used in this study: 1981-2008Variables: mslp, upper air wind, stream

function, geopotential height, SST, vorticity, divergence … [26 variables]

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Spatial coverage of data used

Atmospheric variables and SST

10S – 50N, 90E – 150W

Eq. Pacific SST:15S – 15N, 150E – 80W

Data compression

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Horizontal resolution of data: 1 lat. x 1 lon. for SST 2.5 lat. x 2.5 lon. for others

No. of data grid points >= 1225 [for each variable]

Compress data by EOF analysis1225 data points 28 principal components

Selection of predictors and combinations

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1. Fit a single predictor GLM, search for skilful single predictor

2. Fit a multiple predictor GLM [predictors from step

1], filter out redundant predictors by stepwise regression [no. of combinations >= 2 x 108, hence randomly select a limited no. of combinations]

Cross-validate the ‘reduced GLM’ [from step 2], search for top performers [a ‘brute force’ approach]

Cross-validation

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1. Hide the observation of 1 year

2. Estimate the GLM parameter from the rest of the observations and the principal components

3. Verify the GLM forecast against the hidden observation

4. Rotate the process through 28 years

Cross-validation result provides skill estimates for real-time forecasts.

A low-cost / no-cost tool

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Performance comparison against the climatological forecastHindcat period: 1981-2008

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Climatology(mode)

1971-2000Top GLM Gain (%)

Jun 14 24 71

Jul 16 23 44

Aug 15 22 47

Sep 14 20 43

Oct 16 25 56

Hindcast Vs Actual July N500 (1981-2008)

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Physical Interpretation

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The 4th EOF of 500 hPa geopotential height of June

Physical Interpretation

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The 1st EOF of 850 hPa zonal wind of October

Multi-GLM combination

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Weigel et al., 2008: Can Multi-model Combination Really Enhance the Prediction Skill of Probabilistic Ensemble Forecasts? Quarterly Journal of the Royal Meteorological Society

A message in respect of deterministic forecasts: combination of similarly skilful models can enhance prediction skill

Further exploitation of the cross-validation result

Performance comparison against the climatological forecastHindcat period: 1981-2008

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Climatology(mode)

1971-2000Top GLM

Multi-GLM (mode of top

20 GLM)

Jun 14 24 27

Jul 16 23 25

Aug 15 22 26

Sep 14 20 23

Oct 16 25 28

Conclusion

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Monthly TC forecast can be formulated in terms of GLM

Dynamical climate model (e.g. NCEP CFS) forecast data contain a lot of predictive information

The ‘brute force’ approach is viable in identifying skilful predictors and combinations

Further skill enhancement is made possible by multi-GLM combination

Remarks

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Too many predictors: inexhaustible combinations.

Not all EOF can be easily interpreted.

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Thank you

Acknowledgement:

The Hong Kong Observatory gratefully acknowledges NOAA/CPC for providing CFS forecast and hindcast data on the web to support research and seasonal forecasting operation conducted by the Observatory.

Table 6a. Top 20 GLM combinations for June. The number k after the underscore indicates the kth PC. Same convention for Table 6b to 6e. Meaning of the variable is given in Table 1.

div850_21 pwat_11 sf200_1 z500_4

mslp_23 vp850_8 z200_19 z500_4

sf200_7 v200850_28 z200700_21 z200_19

tmp2m_7 z200_19 z200_3 z500850_13

csstf_7 v850_16 v850_21

csstf_7 pwat_11 v850_21

pwat_11 u200_28 v200850_4

mslp_2 z200_19 z500850_13

sf200_7 tmp2m_7 v200850_18 v850_21

pacsst_24 sf200_1 z500_4

vp850_8 z200_23 z200_3 z500850_13

div850_28 sf200_28 vp200_7

div850_28 pwat_11 sf850_6

sf200_1 u200_28 z200_19 z500_4

div200_18 div850_21 z200_23 z200_3

tmp2m_26 z200_19 z200_3

div200_18 sf200_28 sf200_7 sf850_28

div850_21 sf850_16 v200_19 z500_4

div850_21 sf850_16 z500_4

csstf_7 u200850_14 z500_4

Table 6b. Top 20 GLM combinations for July.

pwat_11 pwat_9 u200850_16 z200700_24

div850_15 u200850_16 z700_25

div200_17 u850_24 v200850_4

u850_24 vp850_12

div850_15 z500850_8 z700_25

div850_15 pwat_9 v200_6 vp200_19

prate_17 u200850_16 v200850_4 z700_25

u200_25 z200850_17 z500850_8

prate_17 u200850_16 v200_24 vp200_8

pwat_11 pwat_9 z200_22

div200_17 vp200_8 z700_25

prate_17 v200850_4 z200850_24

z200700_17 z700_25

div850_15 z500_24

div200_17 pwat_9 z200700_24

div850_15 pwat_9 vp200_19

sf200_20 vp200_8 z200_27

u850_24 vp200_8

sf850_23 vp850_12

pwat_11 pwat_9 u200850_16 z200850_24

Table 6c. Top 20 GLM combinations for August.

u200850_17 u850_23 vor850_28

tmp2m_1 u850_23 v200_24 z200700_17

sf850_17 u850_23 vor850_28

tmp2m_1 z200700_17 z500_14

vor850_28 z200_13

u200850_17 vor850_28 z500_14

pacsst_15 prate_24 u850_12

u200850_15 u200850_17 vor850_28

tmp2m_1 vp200_26 z850_12

pacsst_4 sf850_13 vp200_26

tmp2m_1 u850_23 z200700_17

csstf_20 u850_12 vor850_28

tmp2m_1 vp200_26 z500_14

sf850_17 vor850_28 vp200_26

prate_24 u200850_17 vor850_28

pwat_26 tmp2m_1 z500_14

pwat_26 vor850_28 z500_14

sf850_17 vor850_28 vp850_18

div850_28 prate_24 vor850_28

csstf_20 sf850_17 vor850_28

Table 6d. Top 20 GLM combinations for September.

sf850_16 u200850_11 vor850_13

div200_11 sf850_16 z700_5

div200_25 z500_16

div200_11 v200850_16 vp200_24

vp850_13 z500_16

div200_25 div850_21 pwat_23

mslp_19 z500_16

prate_12 z500_16

div200_25 u200850_11 z500_16

div200_25 div850_21 vp200_5

prate_17 prate_19 vor850_24

div200_25 u200850_11 vp200_5

prate_12 prate_19 vor850_26

vp200_24 z500_16

prate_12 prate_19 u200850_11

div200_11 div200_25 vor850_24

div200_11 v200_18 z700_5

div200_25 vor850_13 vor850_24

div200_11 div200_25 u200850_11

pwat_17 vp200_24 z500_16

Table 6e. Top 20 GLM combinations for October.

div200_20 prate_10 vor850_2 z200_2

div200_11 sf200_11 z500850_4

csstf_19 div200_11 z500850_4

tmp2m_23 v200850_25 z500850_4 z500_3

csstf_19 vor850_1 z500850_4

pwat_1 sf850_1 u850_1 v850_17 z500_1

u200850_12 v200_24 z500850_4 z500_10

pwat_18 tmp2m_3 z850_3

v200850_13 v850_25 z700_1

div200_20 u850_4 z200_1 z700_3

v200850_13 v850_25 z500_1

v200850_13 v200_15 v200_24 vor850_28

sf850_6 v200850_13 v850_25

mslp_2 u850_4 vor850_4 z200850_2 z200_2

prate_10 pwat_18 u200_3

v200850_13 z700_2 z850_3

mslp_3 pwat_18 z850_2 z850_3

csstf_19 div850_21 z500850_4 z500_3

sf850_28 u200_3 z500_1

prate_21 sf850_28 vp850_18