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Translating Scientific Advancement into Sustained Improvement of Tropical Cyclone Warnings – the Hong Kong Experience C.Y. Lam Hong Kong Observatory Hong.

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Presentation on theme: "Translating Scientific Advancement into Sustained Improvement of Tropical Cyclone Warnings – the Hong Kong Experience C.Y. Lam Hong Kong Observatory Hong."— Presentation transcript:

1 Translating Scientific Advancement into Sustained Improvement of Tropical Cyclone Warnings – the Hong Kong Experience C.Y. Lam Hong Kong Observatory Hong Kong, China 28 March 2007

2 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

3 Factors determining the form of a warning system The built environment Expectations of the Society Warning System Meteorological ScienceCommunication

4 Hazards associated with TCs High winds and flying debris Heavy Rain Flooding Landslip Storm surge

5 Warnings Associated with TCs TC Signals Rainstorm Warning Flood Announcement Landslip Warning

6 Translating science and technology into operational forecasting skills

7 SWIRLS Short-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

8 3 km TREC wind of a heavy rainstorm (>30mm/hr) 23 UTC 9 August km TREC wind of Typhoon Maria 31 August 2000 Asymmetric wind distribution (Stronger to the right, weaker to the left) SW’lies with embedded waves TREC (Tracking Radar Echoes by Correlation)

9 Dynamic Z-R relation Z = aR b Searching radius radar reflectivity around 140 rain gauges

10 Amber Rainstorm ( >30mm/hr ) Red Rainstorm ( >50mm/hr ) Black Rainstorm ( >70mm/hr ) Operational Mode Front-end display of SWIRLS

11 Performance of SWIRLS rainstorm forecast POD = Probability of Detection FAR = False Alarm Rate

12 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

13 Verification of SWIRLS Landslip Forecast Performance of SWIRLS landslip forecast POD81 % FAR26 % CSI63 % 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 YesNo Observed Yesab NocNA SWIRLS forecast YesNo Observed Yes174 No6- Landslip warning threshold reached ( data)

14 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

15 SWIRLS and ORSM Combined Warning Panel

16 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

17 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

18 SWIRLS – good intensity F/C NHM – good storm development F/C RAPIDS – the best F/C RAPIDS F/C + Radar observation NHM DMO F/C NHM F/C (rigid transformed) SWIRLS SLA F/C

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

20 Ensemble TC track forecast JMA minimum mean sea-level pressure ECMWF minimum mean sea-level pressure NCEP minimum mean sea-level pressure 1.0 ° UKMO 850-hPa maximum relative vorticity HKO ensemble TC position forecast

21 Verification of HKO TC position forecast Use of NWP Use of model ensemble forecast

22 Skill of HKO TC position forecast Use of NWP Use of model ensemble forecast

23 Objective guidance on TC intensity Model Output Statistics (MOS) model forecast intensity change vs observed intensity change

24 Intensity forecast based on model regression with TC probabilistic categorization

25 Intensity forecast based on climatology method Statistical dataset HKO’s 6-hourly best-track data of TCs within 0-45 N, E from 1980 to 2002 Stratified by initial TC intensity category interaction type time change (T+12, T+24, T+48, T+72)

26 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

27 Probability assessment Objective tools NWP technique - Track probability Statistical technique – Strong winds/Gales onset probability

28 Probability assessment LOW ( %) MEDIUM (34-66 %) HIGH ( %) + Professional judgment

29 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

30 Storm Surge Advice If predicted storm surge + predicted astronomical tide > pre-defined threshold -> HKO issues storm surge advice in TC bulletins

31 Advancement in science & technology -> sustained improvement in TC warning NMHS Numerical 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

32 Thank you !


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