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C.Y. Lam Hong Kong Observatory Hong Kong, China 28 March 2007

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. Tropical Cyclone (TC) Warning System. Maximising effectiveness of TC warning Design of warning system

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C.Y. Lam Hong Kong Observatory Hong Kong, China 28 March 2007

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  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

  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. 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

  8. Asymmetric wind distribution (Stronger to the right, weaker to the left) 3 km TREC wind of a heavy rainstorm (>30mm/hr) 23 UTC 9 August 2002 SW’lies with embedded waves TREC (Tracking Radar Echoes by Correlation) 3 km TREC wind of Typhoon Maria 31 August 2000

  9. Searching radius Dynamic Z-R relation Z = aRb radar reflectivity around 140 rain gauges

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

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

  12. highly Running 24-hr rainfall No. of reported landslides correlated Starting 2000 21-hr actual rainfall from raingauges 3-hr SWIRLS rainfall forecast SWIRLS Landslip Forecast If forecast >= 15 landslips -> issue Landslip Warning

  13. Verification of SWIRLS Landslip Forecast Landslip warning threshold reached (2001-2006 data) 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 %

  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 Extrapolation - effective in advective cases Coping with curved streamlines and intensity changes High resolution NWP Nowcast 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. NHM DMO F/C NHM F/C (rigid transformed) SWIRLS SLA F/C + Radar observation SWIRLS – good intensity F/C NHM – good storm development F/C RAPIDS – the best F/C RAPIDS F/C

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

  20. Ensemble TC track forecast 1999 Poor man's ensemble equally-weighted average 1999 2002 1999

  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, 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)

  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. LOW (0 - 33 %) • MEDIUM (34-66 %) • HIGH (67-100 %) Probability assessment + 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. Meteorological observations More accurate & reliable forecasts Remote-sensing technology NMHS Improvement in effectiveness of warning system Numerical Weather Prediction Nowcasting techniques Improvement in products & services to meet evolving needs & expectations Human expertise Communication technology Advancement in science & technology -> sustained improvement in TC warning

  32. Thank you !

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