2. The Hong Kong University of Science and Technology Department of Mathematics Presented by HUI Kin Yip Ronald

3. Low Level Wind Field Analysis Around Around an International Airport

4. Contents §Introduction and Background §Methodology §LLWAS model §Ideal Cases l Gust Front model l Microburst model §Real Cases §Conclusions

5. What is Windshear? §Any change of wind speed and/or direction §Can appear suddenly in thunderstorms §Associated with gust fronts and microbursts Why is windshear so dangerous? §Dangerous when an aircraft near the ground §Unbalanced forces appeared suddenly §Difficult to predict §Hard for a pilot to make corrections

6. CRASH!!!

8. Objectives §Study the model of a windshear alerting system and test in real time situation by studying the wind field model §Design a windshear warning system §Detect the occurrence of strong windshear §Real case test of system on the Bai-yun International Airport in Guangzhou, China

9. Methodology §Use Automatic Weather Station (AWS) §It is low-cost, easy to maintain and easy to install at any places

11. Introduction and History of LLWAS §LLWAS = Low Level Windshear Alerting System §Developed in 1970s by the US Government §Developed under the Joint Airport Weather Studies (JAWS) §Started at Denver, Colorado in 1982 §Most commonly used method for detecting windshear in US nowadays §It is not well-tested in Asia-Pacific region

12. Methodology on Ideal Case §Only two phenomena will be focused l Gust Front l Microburst §Calculation of Global Wind Difference (GWD) RUNWAY START

13. §“A leading edge of a mesoscale pressure dome followed by a surge of gusty winds on or near the ground” (Wakimoto, 1982) §Typical length = 12 km (along front) and 0.5 km (across front) §Propagation speed = 5 to 20 m/s (Uyeda and Zrnic, 1985) Gust Front

14. §Assumptions: l Wind speed at head = 0 m/s l Wind speed in the front = 10 m/s l Wind speed is increasing linearly for 400 m l The front is propagating in a constant direction Gust Front Model §Background wind speed is added

15. §Skew angle of a gust front is the angle between the runway and the path of the gust §Different skew angles of the gust fronts are applied §GWD values of different gust locations are calculated in both cases Comparisons Gust head Direction of gust Runway 

16. Gust Front with 0° skew angle

17. Gust Front with 30° skew angle

18. Gust Front with 90° skew angle

19. §Facts: l Errors apeared near the ends of the runway when the skew angle is small l Errors near the centre of the runway increases with the skew angle l Maximum error  3 m/s §Conclusion: l Reasonable estimate for the gust passing through the runway nearly from one end to another Results

20. §“An outward-moving airflow induced by the evaporatively cooled downdraft from a thunderstorm or heavy rain.” Microburst §Typical duration = 10 min §Typical radius = 2 to 3 km

21. §A simplified mathematical microburst model from Wilson and Flueck (1986) is used §Assumptions: l it satisfies the mass continuity equation l it exhibits realistic radial outflow at ground level l it is symmetric about its centre l it is radially symmetric about the origin Microburst Model §Background wind speed is added

22. Microburst Model

24. §8 different locations l Locations of the microbursts are lying on a line perpendicular to the runway §10 different size of the microbursts l Radius of microburst = 1.8 to 3.8 km Comparisons

31. §Facts: l Both 16-AWS and 18-AWS networks give reasonable descirptions to the ideal situation l However, 16-AWS gives a relatively better result Results §Conclusion: l Resonable estimate for idealized microbursts

32. Availability of the Real Data §Operation Period: l 27 th June 1998 to 26 th October 1998 l a total of 122 days §Daily Data Monitoring is applied §Data is collected in different time intervals l BY1,3,5,6,7,8: every ONE second l BY2,4 : every FIVE seconds §Time averaging is used to remove high frequency fluctuations with periods shorter than 1 minute (e.g. jet wash)

33. ONE minute

34. Case Selection §All data was averaged by every minute §GWD is found in each minute §Criteria for case study: l Failure Periods l Testing Periods l Thunderstorm and rainy Days l High wind speed Periods §METAR information is used

36. Case Selection CaseDate METAR infoGWD No. of AWS I0701 TS, RA, 11 8.105 II0712 TS, RA, 7 9.228 III0714 TS, RA, 4 7.748 IV0722 N/A 7.416 V0911 TS, RA, 5 7.217 VI0913 NIL, 5 7.207 VII1007 NIL, 511.397 VIII1013 NIL, 1 9.687

37. Methodology §Divergence (DIV) along the runway is used §Windshear is associated with a pair of convergence and divergence zones §If the pair is moving, it is gust-front like §If the pair is nearly stationary, it is more likely a microburst

40. Case I: 980701 14:4015:20

42. Results §There is a pair of moving divergence and convergence zones from 14:52 to 15:00 §It had gust-front features §Figures about this case l GWD  8.10 m/s l Length of Gust  195.65 m l Duration  10 minutes l Skew angle  10.49°

43. Case VII: 981007 02:3003:00

45. Results §There is a pair of nearly stationary divergence and convergence zones §It had microburst features §Figures about this case l GWD  11.39 m/s l Radius of Microburst  1.5 km l Duration  8 minutes

46. Conclusions §Our mathematical model can describe and detect the occurrence of windshear in both idealized and real cases §It registers a few cases of interesting meteorological phenomena which had similar (but weaker) characteristics §Its setup price is relatively cheap, but it is reliable and easy to install §A denser AWS network (with more AWS) can improve the skill of the system §It is suitable detecting windshear for airports in the Asia-Pacific Region

47. Acknowledgements §Department of Mathematics, HKUST §Civil Aviation Administration of China (CAAC) §Center for Coastal and Atmospheric Research (CCAR), HKUST §'Operational Windshear Warning System (OWWS)' consultancy project §Your participation

48. Thank You