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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 !