Translating Scientific Advancement into Sustained Improvement of Tropical Cyclone Warnings

– the Hong Kong Experience

C.Y. LamHong Kong Observatory

Hong Kong, China28 March 2007

Tropical Cyclone (TC) Warning System

Maximising effectiveness of TC warning

Design of warning system Coordination with emergency response units Forecast and warning operation Warning product presentation Communication and dissemination Post-event review Public education and outreach

Factors determining the form of a warning system

The builtenvironment

Expectations ofthe Society

WarningSystem

Meteorological Science Communication

Hazards associated with TCs

High winds and flying debris

Heavy Rain Flooding Landslip Storm surge

Warnings Associated with TCs

TC Signals

Rainstorm Warning

Flood Announcement

Landslip Warning

Translating science and technology into operational forecasting skills

SWIRLSShort-range Warning of Intense Rainstorms in

Localized Systems

high resolution 0-3 hr QPF updated every 6 min prompting associated warnings operational since 1998

Dense raingauge network

3 km TREC wind of a heavyrainstorm (>30mm/hr) 23 UTC 9 August 2002

3 km TREC wind of Typhoon Maria31 August 2000

Asymmetric wind distribution(Stronger to the right, weaker to the left)

SW’lies with embedded waves

TREC (Tracking Radar Echoes by Correlation)

Dynamic Z-R relation Z = aRb

Searching radius

bdBRadBZ log10

20

25

30

35

40

45

50

55

60

5 7 9 11 13 15 17 19 21

dBG

dBZ

radar reflectivity

around 140 rain gauges

Amber Rainstorm ( >30mm/hr )Red Rainstorm ( >50mm/hr )Black Rainstorm ( >70mm/hr )

Operational Mode

Front-end display of SWIRLS

Performance of SWIRLS rainstorm forecast

POD = Probability of DetectionFAR = False Alarm Rate

SWIRLS Landslip Forecast

If forecast >= 15 landslips -> issue Landslip Warning

21-hr actual rainfall from raingauges 3-hr SWIRLS rainfall forecast

Starting 2000

Running 24-hr rainfall No. of reported landslides

highly

correlated

Verification of SWIRLS Landslip Forecast

Performance of SWIRLS

landslip forecast

POD 81 %

FAR 26 %

CSI 63 %

Average lead time (hr) 1.5

Probability of Detection : POD = a / (a+b) *100 %False Alarm Rate : FAR = c / (a+c) *100 %Critical Success Index : CSI = a / (a+b+c) *100 %

Forecast

Yes No

ObservedYes a b

No c NA

SWIRLS forecast

Yes No

ObservedYes 17 4

No 6 -

Landslip warning threshold reached

(2001-2006 data)

0

1

2

3

4

5

6

7

8

9

<0 0-1 1-2 2-3 >3

Lead Time (hour)

Nu

mb

er o

f C

ases

ORSM (Operational Regional Spectral Model)

Physical Initialization (PI) - using radar estimated rainfall to modify model relative humidity field and heating profile

• 20-km resolution• 3-hourly update cycle• forecasts up to 42 hours ahead

SWIRLS and ORSM Combined Warning Panel

Meso-scale NWP in support of Nowcasting

Improving very-short-range QPF 0 – 6 hr Better grasp of growth/decay

Nowcast High resolution NWP

Extrapolation - effective in advective

cases

Coping with curved

streamlines and intensity changes

Rapidly updated very-short-range high-resolution QPF

RAPIDS: 1-6 hours(Rainstorm Analysis and Prediction Integrated Data-processing System)

NOWCASTING component – SWIRLS QPF by linear extrapolation of radar echoes

NWP component – NHM QPF by non-hydrostatic numerical modelling

SWIRLS – good intensity F/CNHM – good storm development F/CRAPIDS – the best F/C

RAPIDS F/C

+

Radar observation

NHM DMO F/C NHM F/C (rigid transformed)

SWIRLS SLA F/C

RAPIDS updated hourly (2 km resolution)Trial–operation since May 2005

Ensemble TC track forecast

JMAminimum mean sea-level pressure

ECMWFminimum mean sea-level pressure

NCEPminimum mean sea-level pressure 1.0°

UKMO850-hPa maximum relative vorticity

HKOensemble TC position

forecast

1999

1999

1999 2002

50

100

150

200

250

300

350

400

450

50019

75

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

Err

or (

km)

72-hr forecast48-hr forecast24-hr forecast

150

250

350

Verification of HKO TC position forecast

Use of NWP Use of model ensemble forecast

Skill of HKO TC position forecast

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.419

75

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

HK

O T

C p

osit

ion

fore

cast

err

or /

(1/2

)(P

+C)

.

24-hr forecast48-hr forecast72-hr forecast

Use of NWP

Use of model ensemble forecast

Objective guidance on TC intensity

Model Output Statistics (MOS)

model forecast intensity change vs observed intensity change

Intensity forecast based on model regression with TC probabilistic categorization

Intensity forecast based on climatology method

Statistical dataset • HKO’s 6-hourly best-track data of TCs

within 0-45 N, 90-180 E from 1980 to 2002

Stratified by• initial TC intensity category

• interaction type

• time change (T+12, T+24, T+48, T+72)

Probability forecast of TC signal change

Purpose :

support TC-related decision making choice of “go” or “no go” risk assessment cost analysis

Trial run with public transport sector starting from 2004

Probability assessment Objective tools

• NWP technique - Track probability• Statistical technique – Strong winds/Gales onset probability

Probability assessment

LOW (0 - 33 %)MEDIUM (34-66 %)HIGH (67-100 %)

+ Professional judgment

Flooding due to Storm Surges

ten tide gauges monitoring tide level

"Sea, Lake, and Overland Surges from Hurricanes (SLOSH)" model to predict storm surge during the approach of TCs

Storm Surge Advice

If predicted storm surge+ predicted astronomical tide > pre-defined threshold

-> HKO issues storm surge advice

in TC bulletins

Advancement in science & technology

-> sustained improvement in TC warning

NMHSNumerical Weather

Prediction

Communication technology

Human expertise

Nowcasting techniques

Meteorological observations

Remote-sensing technology

Improvement in products &

services to meet evolving needs &

expectations

More accurate & reliable forecasts

Improvement in effectiveness of warning system

Thank you !