The Doppler LIDAR of the Hong Kong Observatory was used to measure eddy dissipation rate (EDR) directly for the first time at the Hong Kong International Airport in an experiment in 2004. EDR is a measure of turbulence intensity adopted by the International Civil Aviation Organization. The laser beam of the LIDAR stared in a direction parallel to the runways and radial velocity data were obtained at a range resolution of 60 m. The velocity structure function was computed based on two different estimates of the velocity fluctuation (viz. temporal and spatial methods) and EDR was then calculated by fitting the structure function with the von Kármán model. The two estimates of velocity fluctuation were found to give comparable EDR values. The LIDAR-derived EDR also turned out to have good correlation with EDR obtained from runway anemometers and a boundary-layer wind profiler. In a case of terrain-disrupted airflow during the experiment, the LIDAR-derived EDR showed that turbulence was present near the centre of a micro-scale vortex to the west of the airport

C.M. Shun

K.K. Lai

P.W. Chan

English

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MOASURAMENT OF TURBULENCT IN TERRAIN.DISRUPTXD AIRFLOW ATTHE HONG KONG INTNRNATIONAL AIRPORT USING A DOPPLER LIDARP.W. Char1K.K. Lai and C.M. ShunHong Kong Observatory, l34A Nathan Road, Kowloon, Hong Kong, ChinaE-mall:. pw c han@h ko. g ov. hkAbstract: The Doppler LIDAR of the Hory Kcng Observatory was used to measure eddy dissipation rate (EDR) directly forthe first time at the Hong Kong International Airport in an experiment in 2004. EDR is a measure of turbulence intensityadopted by the Intemational Civil Avialion Organization. The laser beam of the LIDAR stared in a direction parallel to therunways and radial velocity data were obtained at a range resolution of 60 m. The velocity structure function was computedbased on two different estimates of the velocity fluctuation (viz. temporal and spatial methods) and EDR was then calculatedby fitting the structure funclion with the von K6:m6n model. The two estimtes of velocity fluctuation were found to givecomparable EDR values. The LIDAR-derived EDR also turned out to have good correlation with EDR obtained fromrunway anemometers and a boundary-1ayer wind profiler. ln a case of terrain-disrupted airflow during rhe experiment, theLIDAR-derived EDR showed that turbulence was present near the centre of a micro-scale vofex to the west of the airport.Keywords - turbulence, eddy dissipation rate, Dappler LlDARI.INTRODUCTIONThe Hong Kong International Airport (HKIA)(Figure 1) is located on a reclaimed island to the northof the mountainous Lantau Island with peaks rising tonearly 1000 m separated by gaps as low as 400 m.Turbuleat airflow which may affect landing anddeparting aircraft mostly occurs over the airport whensoutheasterly winds are disrupted by this complexterrain. Measurement of :he eddy dissipalion rate(EDR), a measure of turbulence intensity adopted bylnternatioaal Civil Aviation t)rgznization (ICAO),would facilitate the monitoring and alerting ofturbulence at the airpo*.The Doppler Llght Detection and Ranging(LIDAR) system operated by the Hong KongObservatory G{KO) at the airport (Figure l) has been figure 1. Map of Hong Kong Intemational Airportused before to determine the fluctuation of the wir:d and the adjacent areas (height contours: 100 m).by measuring the standard deviation of the radial Equipment labels are in italic fonts. Black dots are thevelocity and to compare with the aircraft's turbulence airfield anemometers. The grey arrow is the viewingreport in a typhoon case (Chan and Mok 2004). An direction of the staring LIDAR beam.experiment was conducted in 2004 to use the LIDAR to measure EDR directly at the airport for &e first 1ims.The measurement methodology is described in Section 2. The IDR so determined is compared wittr themeasurements fiom the conventional cup and vane anemometers on the airfield and a boundary-layer windprofiler (Figure 1) and the results are presenled in Section 3. An example of the LIDAR's EDR profile in asoutheaslerly wind case is presented in Section 4. Conclusions are given in Section 5.2. CALCULATION OF EDRThe LIDAR is a coherenl pulsed system with a wavelength of 2 microns. It has a pulse repetition freq;ency(PRF) of 500 Hz and outputs radial velocity at l0 Hz. During the experiment, the range resolution wasconfigured to be 60 m, smallc than the 100-m resolution for operational windshear alerting, in order to samplethe smaller-scale turbulence. The measurement range was about 6.3 km. The laser beam was made to starehorizontally at a height of about 50 m AMSL towards the 250 degrees direction (from the North), i.e.parallel to the two runways of the airport (Figwe 1).503To calculate EDR" the velocity structure function based on the LIDAR radial velocity data is first determined.IJ could be obtained from two different estimates of the velocity fluctuatioa associated with turbulence. Oneway is to follow Frehlich et al. (1998) and use the velocity fluctuation at range.R and time I with respect to thetemporal mean wind: i,(R,t)* i(r*,r) - rtnl (l)where i is the radial velocity from the LIDA& i the temporal mean and i' the fluctuation component. In all thecases studied below, LIIAR data collected within a period of 10 - 12 minutes would be used to calculate thetemporal mean. The velocity fluctuation is determined for each range R of the LIDAR. This first method iscalled the "temporal flucfualion method".The second method of determining the velocity fluctuation is to remove the mean wind and the windshearover a certain length I from each radial velocity datum of the LIIAR. For a staring beam, the velocityfluctuation is again expressed as in Eq. (l), but with vrepresenting the sum of the spatial mean wind and linearwind change along the lengtb l, i.e. a de-meaning and linear de-lrending of the LIDAR velocity data (as afunction of range) at aparticular time. As a start, Z is taken to be 1000 m. This second rnelhod is called the"spatial fl uctuation method".Using the velocity fluctuation determined by either one of the above methods, the velocity structure functionis then calculated by (Frehlich et al. 1998):b,1Rr,Rr1=,nr-'5'[;'1R,,a,r; -i,'(Rr,rN))' -a'*1nr,nrS e)t-1where -R1 and .R2 are two range gates and l/ the number of radial velocity data samples. It is calculated over asliding window of 400 m, which is comparable with the window adopted in Frehlich et al. (1998). The last termon the right hand side of Eq. (2) is an estimation of fhe error associated with random fluctuations of the LIDARsignal. Frehlich (2001) describes three differenl techniques for deriving the error and shows that the velocitydifferencing mefhod using "rad'velocity data at the PRF has the best performance. The "raw'? data is howevernot available from the LIDAR system in Hong Kong. So we use the spectral-based estimate for the error term(Frehlich et al. 1998):82*1ar,Rry =NlZ ^I @a"("lV,ftr,Rz).(N l2* jr +t)At j4rThe truncation fiequency jN was choser to beA.2 ,rz in Frehlich et al. (1998) and from 5% of theconstant value in the high-frequency tail ofrbo,11) inFrehlich and Comman (2002). In our data, an exampleof <$o,11; is shown in Figure 2. ln view of thefluctuations in the spectrum, it does aot seem to bepraclical to adopt these approaches. Instead, theinstrumental noise is determined as follows. Firstly, wetake 0.1% of the maximum value of the spectrum as thethreshold value. Then in moving from the highestfrequency to the lowest frequency in the spectrum, thefrequency f at which this threshold is exceeded for thefirst time is taken. All the frequencies below f areconsidered to conlain atmospheric signal and those aboveare associated with instrumenlal loise.The velocity structure function calculated in Eq. (2) isfitted with the theoretical result from the von Kdrm6nmodel (Frehlich et al. 1998; Davies et al. 2004):(3)r1ob1i 0.1<e0.010.0010.00010.0001 o.{tol o.or o.l o'39 l 10Freqreacy f (trr)Figure 2. The spectrum@o"(/)between the rangegates 3 km and 3.3 km from the LIDAR far t 12-minute period starting at 9:57 a.m., 28 August 2004.The frequency indicated by an anow (0.39 llz)corresponds to the point where 0.001 times of themaximum spectral value is first exceeded.D1r, o,, LoJ = 2o3 G(fr , *,%*, (4)where s is the separationbetween the range gates,of,the variance of radial velocity an&Lo the outer scale oflurbulence. Meaning of the other variables and the function G in Eq. (4) could be found in the above tworeferences. The fitting is made by minimizing the weighte d error J2 (Davies et aL 2A04), f:om which of, and Lsare determined. The energy dissipation rate s is then calculated by (Frehlich and Cornman 20A2):s * 0.933668dLoEDR used in aviation community and adopted by ICAO is e/6.504(s)The fiiting to the von Krirm6n model sometimes could not be made within a reasonably small error, e.g. thevelocity structure functiot still rises rapidly with separation distance between the LIDAR range gates due towindshear, even after removal of linear shear. In that situation, 16 becomes very large. The fitting resulting inL6 being larger than 4000 m is not considered. On the other hand, if the resulling 16 is comparable with fherange resolution of lhe LIDAR (60 m), the quality of the:esult is also queslionable as the'LIDAR may not haveresolved such line-scale turbulence. Thus the fitting data with Ie smaller than 70 m are not considered as well.3. ?HE TXPTRIMENT AND THA MSASURtrMENT RESULTSThe objective of the experiment is to measure EDR at the airport area in terrain-disrupted ai:flow using astaring LTDAR beam towards the 250-degree direction. However, to study the quality of the LIDAR-derivedEDR, measurements were also made in other weather conditions as reference, such as westerly sea breeze andlight northerly wind. A total of 12 sets of data were collected on 6 days (in the period 13 July to I 1 September2004), each lasting t0 * 12 minutes.Fint of all, EDR values obtained from the temporal fluctuation method and the spatial fluctuation method(Section 2) are compared. They are found to be highly conelated {coefficiant of nearly 0.98). On the whole, thelatter method gives a slightiy larger value of EDR (by about 10%). The length L over which LIDAR data wasused to calculate the velocity structure functio:r (1000 m in this study) for the spatial fluctuation method could bevaried to see if the :esulting EDR values would get even closer to those determined from the temporalflucJuatiol method. This would be the subject of anolher study. In the ensuing discussions, olly EDRdetersrined from the temporal fluctuation method is considered.The standard deviation of the LIDAR radial velocity in an azimuthal sector scan has-been shown to havecertaiq degree ofcorrelation with aircraft turbulence reports and vertical acceleration fluctuations measured onboard aircraft in terrain-disrupted turbulent flow at the airport in a typhoon caso (Chan and Mok 2004). Sinceonly a staring beam is employed in the present experiment, tbe standard deviation (o,) values of the LIDARvelocity data over time are calculated. They are found to have good correlation with the EDR values obtainedfrom the temporal fluctuation method, with a correlatioa coefficient of about 0.83. The least square linear fitequation is:EDR:#.0.0382. (6)The l-second wind data obtained from the runway anemometers (10 m above ground, or about 15 m AMSL)to the west of the LIDAR (including RlC, R1W, R2C and R2W, see Figure 1) are also used to calculate EDR forcomparison with the LIDAR-derived values. The maximum likelihood method for the wind spectrum wasemployed. If the mean wind directio:r measured by the ansmometer is within 30 degrees from the iunwayorientation Q7Al250 degrees), the EDR determined from the longitudinal spectrurn of the wind data is useddirectly. If it is within 30 degrees from the perpendicular !o the runways, the wind spectra from the anemometerare checked for isotropicity (transverse spectrum : 4/3 limes the longitudinal spectnrm). If this is the case(isotropic within 3}Ya),the EDR determined from the ffansverse spectrum (corrected for the 4/3 factor) is used.Anemometer data not meeting these critieria are not considered. The anemometer-derived EDR and the LIDAR-derived EDR (at the range gates closest to the anemometers) are found to have reasonable correlation, with acorrelation coefficient of about 0.8.Furthermore, the EDR obtained from the Sha Lo Wan wind profiler (Figure 1), a boundary layer windprofiler with tbe operatiag frequency of. 1299 MHz, is also used for comparison. Ils spectral data are processedby the NCAR improved moments algorithm (NIMA) and the lowest msasurement (about 140 m AMSL) isconsidered. Such EDR values are also found to correlate well with the LIDAR measurements at the range gateclosest to the profiler, with a correlation coefficient of 0.88. The profiler-derived EDR values are generallylarger (up to 2 limes in light turbulence), probably because the airflow could be more turbulent at locationscloser to the hills on Lantau Island or the windshear term has not been removed from the profiler EDR estimates.4. EXAM?LE OF EDR PROFILN IN A SOUTIIEASTERLY WIND CASEIn the moming of 28 August 2004, southeasterly winds prevaited over Hong Kong. Shong winds wererecorded by anemometers on the hilltops and valleys of Lantau lsland. At about 10:10 a.x. (Hong Kong time, 8hours ahead of UTC), whilst the winds over the airport were'mostiy moderate east 1o southeasterly, cyclonicflow was depicted by the wind measulements from the three weather buoys to the west of the airporl (Figure 3).The staring LIDAR beam towards the 25O-degree direction {Figure 3) revealed a rapid decrease of the radialvelocity, from 4.3 mls (away from the LIDAR) at 3.5 km (range from the LIDAR) to -1.1 m/s (towards theLIDAR) at 4.6 km (Figure 4).505.a 0'3** o.lsXntFr0.15Er,Ddltidai.d EDRLlhr;,"-.. /\ :ln-l^ I V\/\\/\/ V J f* ""/4?s1l,:'ri0'?-r 'i0.10.050 -l0 1000 2000 3000 1000 5000 6000Range from tie LIDAR (n)7000Figure 3. Surface wind observalions a1 10:10 a.m., Figure 4. The mean radial velocity and the LIDAR-28 August 2004. The grey arrow (in a broken line) 6sriys6 EDR for the 16-minute period starting atindicates the viewing direction of the staring LIDAR 10:10 a.m., 2g August 2004.beam. Height contours are in 100 m.The centre of the vortex was locaied a1 about 4.4 km from the LIDAR (in the 25O-degree direction) asdetermined fiom the surface wind data and the LIDAR's radial velocity data (falling to 0 m/s). The LIDAR-derived EDR increased to 0.3 m2i3s-r (moderate turbulsnce according to ICAO) at this range and conlinued torise to about 0.32 m2l3s-1 at about 4.6 km. The airflow appears to be more turbulent at the vortex centoe and acouple of hundred metres west-southwest of it.5. CONCLUSIONSIn an experimenl i:r 2004, the Doppler LIDAR at HKIA was used to measure EDR directly by computing thevelocity structure functioa from the radial velocity data and fitting the results with ihe von K6rmd'n model. Thelaser beam was made to slare in a direction parallel to the orientation of the runways to provide velocitymeasurements with the spatial resolution of about 60 m. The LIDAR-derived FDR was found 1o have goodcorrelation with EDR obtained from runway anemometers and a boundary-layer wind profiler as wetl as thestandard devialion of the radial velocify &om the LIDAR itself.In a southeasterly wind case, the airflow at the airport area was disrupted by the complex terrain of Lantauisland and a micro-scale cyclonic vortex was depicted by surface wind observatiols over the sea west of theairport. Based on the LIDAR-derived EDR, the airflow was formd to be more turbulent near the vo*ex centre.The experiment demonstrated that the LIDAR is capable of providing very useful turbllence data in terrain-disturbed airflow,During normal operations at the airporl, instead of having the laser beam staring in a particular direction, theLIDAR performs surveillance scans to delect windshear. The next step is to derive "EDR maps" &omsurveillance scans so that the LIDAR could be used for both windshear and turbulence ale*ing at the same time.REF'ERENCESChan, S.T., and C.W. Mok, 2004: Comparison of Doppler LIDAR observations of severe turbllence and aircraftdata. t t'h Conference on Aviation, Range, and Aiiospace Meteorologt, American Meteorological Society,Massachusetts, U.S.A.Davies, F., C.G. Collier, G.N. Pearson and K.E. Bozier, 2004: Doppler Lidar measurements of tffbulentstructure function over an urban area. J. Atmos. Oceanic Technol.,2l,'753-761.Frehlich, R., 2001: Estimation of velocity error for Doppler Lidar measurements. J. Atmos. Oceanic Technol.,18,1628*t639.Frehlich, R., and L. Cornmaa, 2002: Estimating spatial velocity statistics with coherent Doppler Lidar. J. Atmos.Oc eanic Technal., 19, 355-366.Frehlich, R., S.M. Hannon and S.W. Henderson, 1998: Coherent Doppler Lidar measurements of wind fieldstatisti cs. B ound. - L ay er Met e or., 86, 233-25 6.506

MEASUREMENT OF TURBULENCE IN TERRAIN-DISRUPTED AIRFLOW AT THE HONG KONG INTERNATIONAL AIRPORT USING A DOPPLER LIDAR

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