Depiction of complex airflow near Hong Kong International Airport using a Doppler LIDAR with a two-dimensional wind retrieval technique

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1 Meteorologische Zeitschrift, Vol. 16, No. 5, (October 2007) (published online 2007) c by Gebrüder Borntraeger 2007 Article Depiction of complex airflow near Hong Kong International Airport using a Doppler LIDAR with a two-dimensional wind retrieval technique PAK WAI CHAN 1 and AI MEI SHAO 2 1 Hong Kong Observatory, Hong Kong, China 2 College of Atmosphere Science, Lanzhou University, Lanzhou, China (Manuscript received October 20, 2006; in revised form February 19, 2007; accepted February 22, 2007) Abstract The Hong Kong Observatory (HKO) operates a Doppler LIght Detection And Ranging (LIDAR) system at the Hong Kong International Airport (HKIA) to monitor the airflow around the airport area. The LIDAR measures the radial component of the wind field. To better visualize the airflow, a variational method which has been successfully applied to Doppler Radar data is adopted in this study to retrieve the two-dimensional wind field based on the LIDAR measurements. The wind field so obtained is found to reveal many salient features of terrain-induced airflow disturbances at HKIA, such as mountain waves and vortices. Its application to the detection of low-level windshear is demonstrated through selected cases. With a simple extension of the variational method, dual-doppler analysis is also carried out using the radial velocity data from both the LIDAR and a Terminal Doppler Weather Radar (TDWR) to retrieve the wind field at the airport area in a gust front event. Zusammenfassung Das Hong Kong Observatorium (HKO) betreibt am Internationalen Flughafen von Hong Kong (HKIA) ein Doppler-LIDAR-System zur Überwachung des Windfeldes im Bereich des Flughafens. Das LIDAR misst nur die radiale Komponente des Windfeldes. Für eine bessere Visualisierung der Strömung ist daher in dieser Studie erfolgreich eine Variationsmethode auf die Doppler-Radar-Daten angewendet worden, um das zweidimensionale Windfeld aus den LIDAR-Messungen abzuleiten. Das so erhaltene Windfeld enthält viele charakteristische Strukturen wie Schwerewellen und Wirbel, die auf Geländestrukturen in der Nähe von HKIA zurückzuführen sind. Die Anwendung der Methode zur Aufspürung von bodennahen Windscherungen wird an einigen Beispielen demonstriert. Über eine einfache Ausweitung der Variationsmethode wird auch eine Dual-Doppler-Analyse der Daten der radialen Windkomponenten aus den LIDAR- und Terminal Doppler- Wetterradar-Messungen ausgeführt, um das Windfeld in Flughafennähe beim Durchzug einer Böenfront zu erhalten. 1 Introduction Optical remote sensing technology becomes more common nowadays in the measurement of wind and turbulence (EMEIS et al., 2007). In 2002, the Hong Kong Observatory (HKO) introduced a Doppler LIDAR (location in Fig. 1) to the Hong Kong International Airport (HKIA), the first LIDAR for aviation weather alerting in the world. It is a coherent LIDAR using a 2-micron laser beam with pulse energy of 2 mj. The measureable range reaches 10 km and the range resolution is about 100 m. The technical specifications of the LIDAR are summarized in Table 1 and described in more detail in SHUN and LAU (2002). Located on the rooftop of a building (about 50 m AMSL) near the centre of HKIA, the LIDAR commands a good view of all the runway corridors. Its primary Corresponding author: P.W. Chan, Hong Kong Observatory, 134A Nathan Road, Kowloon, Hong Kong, China, pwchan@ hko.gov.hk application is to detect and alert low-level windshear. In aviation meteorology, significant windshear (ICAO, 2005) refers to a headwind/tailwind change of 15 knots of more over a distance of several hundred metres up to a few kilometres. Low-level windshear is an aviation hazard because it causes a change of lift of the aircraft, leading to deviation from the intended flight path (HKO and IFALPA, 2006). Since the majority of windshear at HKIA occurs in clear-air weather conditions such as terrain-disrupted airflow (70 % of pilot reports of windshear encounter) and sea breeze (20 % of the reports), LIDAR turns out to be well suited for windshear detection. The remaining windshear events (10 % of the reports) are mostly associated with convective weather like gust front and microburst. They are monitored by the TDWR operated by HKO (location in Figure 1), as described in SHUN and JOHNSON (1995). A network of groundbased anemometers and weather buoys (CHAN and YE- DOI: / /2007/ /2007/0220 $ 6.30 c Gebrüder Borntraeger, Berlin, Stuttgart 2007

2 492 P.W. Chan & A.M. Shao: Depiction of complex airflow Meteorol. Z., 16, 2007 (c) Glide-path scans to focus on the wind conditions along the glide paths for operational windshear alerting The LIDAR measures the headwind profile to be encountered by the aircraft and significant wind changes in the profile are detected automatically (CHAN et al., 2006). Figure 1: Geographical situation in the vicinity of HKIA (height contours: 100 m) and locations of the meteorological instruments operated by HKO (red dots: anemometer/weather buoy, blue dot: LIDAR, green triangle: TDWR). The anemometers/weather buoys that are used for comparison with 2D retrieved wind field from the LIDAR are shown in larger red dots. UNG, 2002) has also been set up inside and around HKIA for windshear detection (Fig. 1). For wind monitoring and windshear alerting, the LIDAR at HKIA has employed a special scan strategy, comprising the following scans: (a) Plan-position Indicator (PPI) scans (or conical scans) to provide the weather forecasters with an overview of the wind condition in the vicinity of HKIA There are three PPI scans, namely, with the elevation angles of 0 degree (i.e. horizontal scan), 1 degree and 4.5 degrees in the current implementation. The former two scans are mainly used for monitoring the wind along the arrival glide paths (which have smaller elevation angles, viz. 3 degrees from the ground), and the last one dedicated to the departure glide paths (which have larger elevation angles, viz. 6 degrees from the ground). The PPI scans are blocked by the Air Traffic Control Tower to the north. Moreover, as a laser safety measure, sector blanking has been applied for the residential area outside HKIA to the southeast of the LIDAR. (b) Range-height Indicator (RHI) scans (or verticalslice scans) to measure the vertical structure of the windshear features, e.g. interaction between sea breeze and the background flow, hydraulic jump in cross-mountain airflow (SHUN et al., 2003), etc. To better visualize the complex airflow around HKIA in the assessment of low-level windshear, the present paper studies the possibility of retrieving the 2D wind field based on the radial velocity data from the PPI scans of the LIDAR. There are two basic approaches for retrieving wind fields from single-doppler data using variational methods. One is the full four-dimensional variational (4DVAR) technique (SUN et al., 1991). The other is parameter identification (PI) technique as proposed in QIU and XU (1992). To study the possibility of real-time monitoring of the wind field based on LIDAR measurements, the computationally more efficient approach of PI is adopted here. This paper applies the two-step variational method in QIU et al. (2006) to the LIDAR velocities. This method is originally developed for Radar data and capable of retrieving three-dimensional wind field. However, due to limited number of PPI scans (three elevation angles only) as currently implemented in the LI- DAR at HKIA, only 2D wind fields are considered here. Doppler Radar and LIDAR are complementary to each other in the monitoring of the wind field. In severe convective events, Radars are best in detecting the winds in the rain cells. For the surrounding areas such as the thunderstorm outflow, the air may be much less humid and the Radars would not give persistently good signals. On the other hand, LIDARs are good at measuring the winds in non-rainy situations but the laser beam would be attenuated rapidly in rain. This paper also examines the combination of Radar and LIDAR data in analyzing the airflow around HKIA in a gust front case using a simple extension of the two-step variational method. 2 2D wind retrieval method The cost function in the variational method is defined as: J(u,v) = J 1 + J 2 + J 3 + J 4 + J 5 + J 6 (2.1) The first term in (2.1) is the background term as given by: J 1 = W 1 [(u u B ) 2 +(v v B ) 2 ] (2.2) i, j The summation is made over the grid points (i, j). (u B, v B ) is the background velocity to be described later, and (u,v) is the velocity to be retrieved. W s are the weighting coefficients.

3 Meteorol. Z., 16, 2007 P.W. Chan & A.M. Shao: Depiction of complex airflow 493 Table 1: Key parameters of the LIDAR at HKIA. Pulse repetition frequency 500 Hz No. of pulses for averaging 50 Data output frequency 10 Hz Pulse energy 2 mj Pulse duration 400 ns System efficiency > 10 % Wavelength micron Aperture diameter 10 cm Range resolution about 100 m Maximum unambiguous velocity +/- 20 m/s (extendable to +/- 40 m/s) Measurement distance Maximum Up to 10 km Minimum 400 m Accuracy of radial velocity measurement < 1 m/s The second term in (2.1) is a measure of the difference between the observed (v obs r ) and the retrieved (v r ) radial velocities as given by: J 2 = W 2 (v r v obs r ) 2 (2.3) i, j The third, fourth and fifth terms in (2.1) are the smoothing terms involving the divergence, vorticity and Laplacian of the retrieved velocity field respectively. They are given by: J 3 + J 4 + J 5 = [W 3 ( x) 2 ( u i, j x + v y )2 +W 4 ( x) 2 ( v x u y )2 +W 5 ( x) 4 ( 2 u+ 2 v) 2 ]. (2.4) Here x = y = 100 m is the grid size in the retrieval domain. The last term in (2.1) is the conservation constraint. In QIU et al. (2006), the conservation of precipitation content based on Radar reflectivity is used. In the present paper, conservation of the observed radial velocity is employed because the LIDAR s backscattered power data appear to have beam-to-beam variability arising from the fluctuations of the output power. The conservation equation is given by: J 6 = i, j n [W 6 ( vobs r t + u vobs r x v vobs + r y )2 ] (2.5) The index n is the time index. It is involved in the time derivative of the observed radial velocity. In the current scanning strategy of the LIDAR, the PPI scans are performed every 6 minutes or so. Three consecutive scans are used in the 2D wind retrieval in this paper. Equation (2.5) is the approximate form of the conservation of momentum for the radial velocity component. It is to ensure that the retrieved velocity field (u, v) could observe the conservation of momentum approximately. The weighting coefficients are taken as: W 1 = 0.1 (1/m 2 s 2 ), W 2 = 1 (1/m 2 s 2 ), W 3 = W 4 = W 5 = 0.1 (1/m 2 s 2 ) and W 6 = 104 (1/m 2 s 2 ). They are chosen empirically in this paper to ensure that the constraints have proper orders of magnitude. Following QIU et al. (2006), the background velocity field is determined by expanding it in terms of secondorder Legendre polynomials: u B (x,y) = v B (x,y) = 2 2 nx=0 ny=0 2 2 nx=0 ny=0 a nx,ny P nx (x)q ny (y), b nx,ny P nx (x)q ny (y). (2.6) P nx (x) and Q ny (y) are the orthonormal functions (Legendre polynomials). The background field is then fully determined by the expansion coefficients a nx,ny and b nx,ny, which are the retrieved variables in this step. The cost function for retrieval is similar to (2.1), except that the first term vanishes (i.e. setting W 1 = 0). Before performing the retrieval, the radial velocity data are quality-controlled to remove the outliers due to, for instance, reflection from clutters (CHAN et al., 2006). The main source of clutter is the moving aircraft in the sky and the clutter does not occur very frequently (in the order of a few per day). Such outliers could be detected by mimicking visual inspection to compare each piece of radial velocity with the data points around, and replaced by a median-filtered value if the difference between them is larger than a pre-defined threshold. The threshold is determined from the frequency distribution of velocity difference between adjacent range/azimuthal gates of the LIDAR over a long period of time. The quality-controlled radial velocity in the range-azimuth coordinate system is then interpolated to a Cartesian grid with resolution of 100 m using Barnes scheme (BARNES, 1964).

4 494 P.W. Chan & A.M. Shao: Depiction of complex airflow Meteorol. Z., 16, 2007 Table 2: Comparison results between the 2-D retrieved wind field based on the LIDAR (x) and the anemometer measurements (y). correlation equation: y = a x, with slope = a and correlation coefficient = R (Note: The correlation coefficient for the linear least-square fit with y-intercept set to 0 is determined using LINEST function of Excel 2003.) bias = y x and summed over all the measurements, r.m.s. difference = (y x) 2 and summed over all the measurements. slope a correlation coefficient bias r.m.s. difference wind speed m/s 1.89 m/s wind direction u component m/s 1.76 m/s (east-west component) v component m/s 2.18 m/s (north-south component) along-radial component m/s 1.52 m/s across-radial component m/s 2.34 m/s the scatter plot of the two datasets reaches at least It could be expected that the LIDAR s wind speed is larger than the anemometer reading because of the higher altitude of the laser beam. (b) The bias is small between the two sets of data. Its magnitude is less than 1 m/s for wind speed/individual component of the wind, and about 1 degree for wind direction. (c) The r.m.s. difference is also small between the two datasets. It is less than 2.4 m/s for wind speed/individual component of the wind and 30 degrees for wind direction. Figure 2: Scatter plot of the u-component of the wind as measured by the anemometer and retrieved from the LIDAR. 3 Comparison with anemometer data The wind data from the ground-based anemometers and weather buoys (shown as bigger red dots in Fig. 1) are used as an independent dataset to examine the quality of the 2D wind field retrieved from the LIDAR s radial velocity measurements. The four cases analyzed in Section 4 are considered. The 1-degree PPI scans of the LIDAR are used. The 0-degree PPI scans are even closer to the ground but they could not be used in this analysis because these scans were not available in the early days of the running of the LIDAR (e.g. in 2002 and 2003). The comparison results are summarized in Table 2, and Fig. 2 shows an example of the comparison graph (for u-component, i.e. east-west component, of the wind). It is observed that: (a) The two datasets are highly correlated, with a correlation coefficient more than 0.9. The slope of Considering the above, the LIDAR-retrieved 2D wind field is found to compare well with the anemometer/weather buoy measurements and have satisfactory quality. It should be noted, of course, that the component of the retrieved wind perpendicular to the measurement radial of the LIDAR (viz. the across-radial component) is not directly measured by the LIDAR and is obtained only through the retrieval. As such, there is inherent uncertainty in this unmeasured component of the wind field. The retrieved 2D wind field should be interpreted in this context. 4 Analysis of terrain-disrupted airflow The 2D wind retrieval method described in Section 2 is applied to four typical cases of terrain-disrupted airflow around HKIA to demonstrate its capability in analyzing the complex wind field in the airport area. The 1-degree PPI scans of the LIDAR are used. The cases are discussed below in detail. (a) Spring-time easterly wind case 20 January 2003 (Fig. 3a) This is a typical case of cooler easterly airstream of continental origin undercut the warmer and more humid

5 Meteorol. Z., 16, 2007 P.W. Chan & A.M. Shao: Depiction of complex airflow 495 maritime airstream along the south China coast, resulting in a stably stratified boundary layer. As described in SHUN et al. (2003), the temperature inversion had a magnitude of 3.6 K occurring at about the height of a hill called Lo Fu Tau on Lantau Island (location in Fig. 1). Due to terrain disruption, a vortex appeared in the flow downstream of this hill, as shown in the 2D wind analysis (Fig. 3a). On the other hand, the southerly airflow higher up in the boundary layer climbed over the mountains on the western side of Lantau Island and descended to the sea west of HKIA, where it converged with the prevailing easterly flow near the surface (Fig. 3a). The northerly flow to the south-southeast of the LI- DAR (going through Tung Chung Gap, location in Fig. 1) near the surface and the southerly flow higher up in the boundary layer together forms a vertical circulation associated with a jump-like feature in the airflow (SHUN et al., 2003). (b) Summer-time southerly wind case 30 August 2004 (Fig. 3b) An area of low pressure located at about 500 km west of Hong Kong brought strong southerly wind to the territory between 28 and 30 August Apart from typhoon days, this is the windshear episode with the largest number of aircraft reports in the summer since the opening of HKIA. The strong wind impinging on the mountains of Lantau Island resulted in many kinds of terrain-induced disturbances, as described in WONG and CHAN (2005). One example is shown in Fig. 3b. The retrieved 2D wind field suggests that there are waves emanating from Yi Tung Shan (location in Fig. 1) and Lo Fu Tau as well as the gap flow in between. At the same time, the strong south-southwesterly flow climbs over the mountains on the western side of Lantau Island and no waves are found in the flow downstream of these mountains. Only accelerated gap flow is analyzed to the west of HKIA. (c) Southeasterly wind case of tropical cyclone 11 September 2002 (Fig. 3c) Severe Tropical Storm Hagupit over the northern part of South China Sea brought gale-force east-southeasterly wind to Hong Kong. In a more or less neutral boundary layer, the east-southeasterly wind climbed over the lower mountains on the eastern side of Lantau Island. As shown in the 2D wind analysis (Fig. 3c), the wind was generally uniform to the east of HKIA. On the other hand, convergence between east-southeasterly and southeasterly winds were analyzed downstream of the higher mountains on the western side of Lantau Island (reaching about 1 km AMSL) as a result of terrain disruption (Fig. 3c). Similar convergent flow is found in the numerical modelling of the wind field around HKIA in another case of gale-force southeasterly wind associated with a typhoon (CHAN, 2006). The convergence of airflow is suggested in the 2D wind field. If only radial velocity data are plotted, the airflow to the southwest of HKIA would appear to have streaks of higher and lower wind speeds (coloured in yellow/orange and brown/grey respectively in Fig. 3c) emerging from Lantau Island. (d) Winter-time northeasterly wind case 30 October 2005 (Fig. 3d) The strong northeast monsoon prevailed over the southern part of China on that day. From the radiosonde measurement at 00 UTC, the boundary layer was neutral up to about 400 m and became stable aloft. Two hills to the northeast of HKIA (labelled A and B in Fig. 3d) have about the same altitude as the capping stable layer, which favours the occurrence of accelerated flow from the valley in between the hills and the shedding of vortices/waves from the hills as found in the simulation results of a shallow water model (CHAN and SHUN, 2005). Both the radial velocity plot of the LIDAR and the retrieved 2D wind field show the existence of northeasterly jet from the valley between hills A and B (Fig. 3d). The radial velocity plot shows regions of flow reversal or possible evidence of wakes downstream of hill B, and from the retrieved 2D wind field, two waves appear to emerge from hill B (Fig. 3d). However, due to blockage of the laser beam by the tower on the airport, there were no LIDAR data downstream of hill A to depict the terrain-disrupted airflow. The 2D wind fields also vividly depict the temporal evolution of the terrain-disrupted airflow in the vicinity of HKIA. The non-stationary wave cases on 30 August 2004 and 30 October 2005 as discussed above are analyzed further. Fig. 4 shows the Hovmüller diagrams of wind speed and direction along the runway corridors concerned (locations given in Fig. 3). It is observed that: (i) For the southerly wind case on 30 August 2004, the retrieved 2D wind field suggests that there are waves travelling across the 25RA runway corridor (viz. the arrival runway corridor on the right hand side in 250 direction) between 5700 and 7000 m from the western end of this runway. The waves are marked as A and B in the Hovmüller diagram (Fig. 4a). The passage of the waves shows up as changes of wind speed and fluctuations in wind direction (between southwesterly and south to southeasterly). (ii) For the northeasterly wind case on 30 October 2005, waves are suggested to travel across the 07RD runway corridor (viz. the departure runway corridor on the right hand side in 070 direction) from 5700 m up to the edge of the analysis domain (about 9600 m from the western end of the

6 496 P.W. Chan & A.M. Shao: Depiction of complex airflow Meteorol. Z., 16, 2007 a) 01:56 UTC, 20 January 2003 b) 01:52 UTC, 30 August 2004 Figure 3a-b: The radial velocity from the LIDAR (colour shading, with the scale to the right/bottom of the figure in m/s and positive/negative values referring to the wind blowing away/towards the LIDAR) and the 2D retrieved wind field (wind barbs and streamlines) in four cases of terrain-disrupted airflow at HKIA. Height contours are in 200 m. The runway corridors along which Hovmüller diagrams are prepared (Fig. 4) are shown as red, dotted lines in (b) and (d). runway). They are marked as A and B in the Hovmüller diagram (Fig. 4b). Again, the passage of the waves is associated with changes of wind speed and fluctuations in wind direction (this time between northeasterly and west to northwesterly).

7 Meteorol. Z., 16, 2007 P.W. Chan & A.M. Shao: Depiction of complex airflow 497 c) 11:02 UTC, 11 September 2002 d) 01:53 UTC, 30 October 2005 Figure 3c-d: The radial velocity from the LIDAR (colour shading, with the scale to the right/bottom of the figure in m/s and positive/negative values referring to the wind blowing away/towards the LIDAR) and the 2D retrieved wind field (wind barbs and streamlines) in four cases of terrain-disrupted airflow at HKIA. Height contours are in 200 m. The runway corridors along which Hovmüller diagrams are prepared (Fig. 4) are shown as red, dotted lines in (b) and (d). As shown in the above cases, the radial velocity field reveals many features of the airflow disruptions around the topography, such as flow reversals and wakes downstream of peaks, and in some cases the retrieved 2D wind field suggests these may be waves and vortices. Due to range resolution of the LIDAR measurements (about 100 m) and data interpolation before the wind retrieval, the features to be analyzed in general should have the horizontal dimensions in the order of at least several hundred metres. Nonetheless, the 2D wind re-

8 498 P.W. Chan & A.M. Shao: Depiction of complex airflow Meteorol. Z., 16, 2007 a) 30 August 2004 b) 30 October 2005 Figure 4: Hovmüller diagrams (distance versus time) of wind speed (m/s) and direction (degrees) along the runway corridors as shown in Fig. 3 for (a) the southerly wind case on 30 August 2004 and (b) the northeasterly wind case on 30 October Labels inside the diagrams indicate the passage of waves (as suggested in the retrieved 2D wind field) across the runway corridors. X-axis is the time in UTC and y-axis is the distance from the western end of the respective runway. trieval method as discussed is a good tool to visualize the complicated wind pattern in the airport area. 5 Application to windshear analysis Since the glide paths of the aircraft have small elevation angles (3 degrees for arrival flights and 6 degrees for departure flights) and the majority of windshear at HKIA is due to terrain-disrupted airflow/shearline within the boundary layer, low-level windshear in Hong Kong mainly refers to the change of horizontal wind (HKO and IFALPA, 2006). The LIDAR only measures the radial component of the wind but not the 2D wind field. As such, in the operational LIDAR-based Windshear Alerting System (LIWAS) at HKIA (CHAN et al., 2006), the radial wind component measured by the LI- DAR is used to represent the headwind to be encountered by the aircraft along the glide path if the angle between the laser beam and the runway orientation is less than a threshold of 30 degrees. If the angle is larger than this threshold, the LIDAR data will not be used to construct the headwind profile for windshear detection. The application of this angle threshold works well for the arrival corridors of HKIA because, located near the centre of the airport, the LIDAR has its laser beam orientated at a relatively small angle with respect to the runway up to the runway threshold and as such the headwind profile of the whole arrival corridor could be obtained from the LIDAR s radial velocity data. However, for departure corridor, the aircraft normally climb up near the centre of the runway at which the laser beam is nearly perpendicular to the runway orientation and thus the radial velocity could not be used to represent the headwind to be encountered by the departing flight. This

9 Meteorol. Z., 16, 2007 P.W. Chan & A.M. Shao: Depiction of complex airflow 499 geometrical limitation leaves a large void of LIDARdeduced headwind data over the runway for the departure corridor, such as 07RD corridor (location depicted in Fig. 5) which is commonly used in the spring when the prevailing wind is easterly. The 2D wind field over 07RD is analyzed based on the 4.5-degree PPI scan of the LIDAR in order to reveal the salient features of low-level windshear over that corridor in the spring. It is noted that the 4.5-degree PPI scan may not reach the same altitude as the actual glide path of 07RD (which also varies with the rotation point and the climb-up angle of the aircraft), but it provides the best available LIDAR data covering that runway corridor for 2D wind retrieval purpose. Three typical cases of low-level windshear over 07RD with pilot reports received in spring 2006 are considered, as described below. (a) 8 March 2006 (Fig. 5a) A total of eight aircraft reported encountering of windshear over 07RD in the morning, in which seven reports referred to a headwind gain up to 15 knots and one report indicated a headwind loss of 15 knots below 1000 feet. The 2D wind field retrieved from the LIDAR shows that (i) there are wind speed irregularities in the easterly flow over the western part of the south runway with the wind speed falling to about 5 knots at location A, (ii) an easterly jet, which appears to descend from the hills on the eastern side of Lantau Island, exists over the eastern part of the south runway with the wind speed rising to about 20 knots at location B, and (iii) an area of weaker wind, which seems to be the wake of Lo Fu Tau, at location C. The resulting wind gain of 15 knots between A and B and wind loss of 17 knots between B and C are generally consistent with the pilot reports. The distances of wind changes are within the length scales of windshear (Section 1). (b) 20 March 2006 (Fig. 5b) There were five aircraft on that day reporting the encounter of headwind gain/loss up to 20 knots over 07RD. From the 2D retrieved wind field, disturbance in the southeasterly flow (location A ) is depicted between the accelerated flow originating from the valley south of Lo Fu Tau and that from Tung Chung Gap. On the eastern part of the south runway, there is again the strong gap flow (location B ), followed by southeasterly wind at 40 degrees off the runway direction (location C ) and south-southeasterly wind nearly perpendicular to the runway (locations further east of C ). The magnitudes and distances of headwind changes (Fig. 5b) are largely consistent with the pilot reports. (c) 8 April 2006 (Fig. 5c) Between 16:00 and 16:30 UTC on that day, five aircraft reported headwind gain up to 20 knots at 100 feet altitude/1 nautical mile location over 07RD corridor. Similar to the case on 20 March 2006 (case (b) above), the 2D retrieved wind field also shows that the eastern part of the south runway is affected by a southeasterly jet (location B ) originating from the valley south of Lo Fu Tau. On the other hand, a south-southeasterly jet from Tung Chung Gap extends all the way up to the middle part of the south runway this time, and airflow disturbance is observed in the jet (location A ). Significant windshear in the form of headwind gain is expected between locations A and B in Fig. 5c. In summary, considering the above three cases, terrain-induced windshear over 07RD in east to southeasterly flow situations in spring-time is shown to have the following features from the 2D retrieved wind field based on the LIDAR: (i) there is relatively small headwind over the western part/near the middle of the south runway as a result of wind speed irregularities or disturbances in the southeasterly flow; (ii) a jet exists over the eastern part of the south runway, either originated from the accelerated gap flow south of Lo Fu Tau or associated with airflow descending from the hills on the eastern side of Lantau Island; (iii) the headwind decreases in moving away from the jet due to, for instance, the wake of the hill (Lo Fu Tau) or a change of wind direction (southsoutheasterly flow nearly perpendicular to the runway orientation). 6 LIDAR-Radar analysis The radial velocity data from the TDWR (location in Fig. 1) are combined with those from the LIDAR to analyze the horizontal wind field in a gust front case at HKIA. The retrieval is performed following the steps as described in Section 2, but with the addition of two more terms based on TDWR s radial velocity measurements, namely, another J 2 term similar to Eq. (2.3) and another J 6 term similar to Eq. (2.5) with the observed velocity ) referring to the TDWR data. The 0.6-degree PPI scan of TDWR is used together with the 1-degree PPI scan of the LIDAR at about the same time (within a couple of minutes). The gust front case on 10 September 2002 is studied. On that day, thunderstorms developed over inland areas of southern China and tracked southeastwards to affect Hong Kong under the influence of the northerlies in the (v obs r

10 500 P.W. Chan & A.M. Shao: Depiction of complex airflow Meteorol. Z., 16, 2007 a) 03:40 UTC, 8 March 2006 b) 04:24 UTC, 20 March 2006 Figure 5a b: Radial velocity of the LIDAR (colour shading, same scale as in Fig. 3) and 2D winds retrieved from the LIDAR (wind barbs and streamlines) for the three windshear cases over 07RD runway corridor (red dotted line) in spring-time of The headwind profiles over 07RD are given in the insets.

11 Meteorol. Z., 16, 2007 P.W. Chan & A.M. Shao: Depiction of complex airflow 501 c) 16:24 UTC, 8 April 2006 Figure 5c: Radial velocity of the LIDAR (colour shading, same scale as in Fig. 3) and 2D winds retrieved from the LIDAR (wind barbs and streamlines) for the three windshear cases over 07RD runway corridor (red dotted line) in spring-time of The headwind profiles over 07RD are given in the insets. outer circulation of Severe Tropical Storm Hagupit. The gust front associated with a thunderstorm appeared at about 10 km to the northeast of HKIA at 08:05 UTC on that day, as measured by both TDWR and LIDAR. It then moved to the southwest and swept across HKIA within the next half an hour or so. With the passage of the gust front, the wind over HKIA changed from moderate southerly to strong northeasterly. The combination of TDWR and LIDAR data in this case clearly depicts the wind field associated with the gust front. The retrieved 2D winds in Fig. 6a show the convergence between the northeasterly outflow from the thunderstorm and the southerly wind near HKIA before the front started to affect the airport. In particular, a tiny vortex appears at about 2 km to the east of HKIA. About 8 minutes later in Fig. 6b, the gust front was moving across HKIA, bringing fresh to strong northeasterly wind to the eastern part of the airport. At the same time, the 2D wind field depicts an area of divergent flow at the rear of the gust front. Though vertical wind velocity is not retrieved in the present algorithm, this divergent airflow is believed to arise from the downdraft of the thunderstorm, as also found in QIU and XU (1992). If only LIDAR data are employed in the retrieval (i.e. without the use of TDWR data), the following limitations would be observed in the retrieved wind field, as shown in Fig. 7 (at the time corresponding to that in Fig. 6a):

12 502 P.W. Chan & A.M. Shao: Depiction of complex airflow Meteorol. Z., 16, 2007 a) 08:14 UTC, 10 September 2003 b) 08:22 UTC, 10 September 2003 Figure 6: Radial velocity from TDWR (colour shading, with the scale to the right of the figure) and 2D retrieved wind field based on LIDAR and TDWR measurements (wind barbs and strealines). Location of the gust front is given by the red, broken curve. (a) The analysis region of the wind field would be much reduced (e.g. the area to the northeast of the LIDAR, behind the gust front) because LIDAR observations are not available in heavy rain; (b) As a result of the limited data availability from the LIDAR, artificial feature would appear in the retrieved wind field. For instance, the easterly flow behind the gust front (Fig. 7) is not materialized

13 Meteorol. Z., 16, 2007 P.W. Chan & A.M. Shao: Depiction of complex airflow 503 Figure 7: Radial velocity from LIDAR (colour shading, with the scale at the bottom of the figure) and 2D retrieved wind field based on LIDAR alone (wind barbs) at the time corresponding to that in Fig. 6a. in reality. Instead, strong northeasterly flow prevailed at the rear of the gust front based on surface wind data and TDWR observations. The artificially created easterly flow is believed to be related to the convergent flow to the northeast of the LIDAR (Fig. 7) under the various constraints in the retrieval method (Section 2) and the limited data availability to the northeast of the LIDAR in rain. In the wind retrieval for the gust front case, the LI- DAR data are useful in revealing the airflow ahead of the gust front and within the part of the gust front where heavy rain has not yet taken place, and the TDWR data are useful for both the clear air and, especially, the rainy region. 7 Conclusions A two-step variational method is employed in this study to retrieve the 2D wind field in the vicinity of HKIA based on the LIDAR s radial velocity measurements. It is similar to that described in QIU et al. (2006), but with the conservation equation applied to the radial velocity observations instead of the backscattered power of the LIDAR because of the more significant beam-to-beam variability of the latter quantity in comparison to the reflectivity of a Radar. The quality of the retrieved 2D wind field is found to be satisfactory by comparing with the wind measurements from surface-based anemometers/weather buoys as an independent dataset. The two sets of data are highly correlated, with a correlation coefficient of about 0.9. The 2D wind retrieval method is applied to study terrain-disrupted airflow over HKIA, which is the main cause of low-level windshear to the arriving/departing flights in Hong Kong. It suggests that there are many kinds of terrain-induced disturbances, such as accelerated gap flow, mountain waves, and vortex downstream of the hill. Due to geometrical limitation, the LIDAR s radial velocity data cannot be used directly to analyze the headwind changes over the runway for 07RD corridor of HKIA. The retrieved 2D wind field fills in the headwind data void over a large part of this runway corridor. Based on the study of 2D wind fields in some typical cases, the windshear is found to be related to fluctuating airflow near the ground and an east to southeasterly jet higher up in the boundary layer. A simple extension of the two-step variational method of wind retrieval is also made to include the radial velocity measurements from both TDWR and LI- DAR, which are complementary in the monitoring of severe convective weather. As a demonstration of the method, it is applied to the analysis of a gust front case. The 2D wind field so obtained clearly depicts the convergence between the outflow from the thunderstorm and the wind over HKIA, and the divergent airflow at the rear of the gust front. The dual Doppler analysis of the gust front is demonstrated to be superior over the retrieval based on the LIDAR data alone because of the limited data availability of the LIDAR in rain. In summary, the 2D wind retrieval method discussed in this paper is simple to implement yet provides wind data of reasonably good quality to analyze terraindisrupted airflow and gust front occurring in the airport

14 504 P.W. Chan & A.M. Shao: Depiction of complex airflow Meteorol. Z., 16, 2007 area. Its extension to 3D wind retrieval would be studied in the future, for instance, with the use of more PPI scans of TDWR as well as the velocity data from the second LIDAR inside HKIA (to be installed at about 800 m north of the existing LIDAR for backup purpose). Acknowledgements The second author is supported by the Grant from National Science Foundation of China. The authors would like to thank the two anonymous reviewers for useful comments to improve the manuscript. References BARNES, L., 1964: A technique for maximizing details in numerical weather map analysis. J. Appl. Meteor. 3, CHAN, P.W., 2006: Super-high-resolution numerical simulation of atmospheric turbulence in an area of complex terrain. 12 th Conf. on Mountain Meteorology, Santa Fe, NM, Amer. Meteor. Soc. CHAN, P.W., K.K. YEUNG, 2002: Experimental use of a weather buoy in windshear monitoring at the Hong Kong International Airport. Eighteenth Session of the WMO/IOC Data Buoy Co-operation Panel and Scientific and Technical Workshop, Martinique, France. CHAN, P.W., C.M. SHUN, 2005: Numerical simulation of vortex shedding observed at the Hong Kong International Airport using a shallow water model. Croatian Meteor. J. 40, CHAN, P.W., C.M. SHUN, K.C. WU, 2006: Operational LIDAR-based system for automatic windshear alerting at the Hong Kong International Airport. 12 th Conf. on Aviation, Range, and Aerospace Meteorology, Atlanta, GA., Amer. Meteor. Soc. EMEIS, S., M. HARRIS, R. BANTA, 2007: Boundary-layer wind and turbulence measurements by optical remote sensing. Meteorol. Z. 16, HKO, IFALPA, 2006: Windshear and Turbulence in Hong Kong information for pilots. Hong Kong Observatory and International Federation of Air Line Pilots Associations, 2 nd Edition, 29 pp. ICAO, 2004: Meteorological Service for International Air Navigation, Annex 3 to the Convention on International Civil Aviation. International Civil Aviation Organization, 15 th Edition. ICAO, 2005: Manual on Low-level Wind Shear and Turbulence. International Civil Aviation Organization, 1 st Edition. QIU, C.J., Q. XU, 1992: A simple adjoint method of wind analysis for single-doppler data. J. Atmos. Oceanic Technol. 9, QIU, C.J., A.M. SHAO, S. LIU, Q. XU, 2006: A two-step variational method for three-dimensional wind retrieval from single Doppler radar. Meteor. Atmos. Phys. 91, 1 8. SHUN, C.M., D.B. JOHNSON, 1995: Implementation of a Terminal Doppler Weather Radar for the new Hong Kong International Airport at Chek Lap Kok. 6 th Conf. on Aviation Weather Systems, Dallas, TX, Amer. Meteor. Soc. SHUN, C.M., S.Y. LAU, 2002: Implementation of a Doppler Light Detection And Ranging (LIDAR) system for the Hong Kong International Airport. 10 th Conf. on Aviation, Range, and Aerospace Meteorology, Portland, OR, Amer. Meteor. Soc. SHUN, C.M., C.M. CHENG, O.S.M. LEE, 2003: LIDAR observations of terrain-induced flow and its application in airport wind shear monitoring. International Conf. on Alpine Meteorology (ICAM) and Mesoscale Alpine Programme (MAP) Meeting, Brig, Switzerland. SUN J., D.W. FLICKER, D.K. LILLY, 1991: Recovery of three dimensional wind and temperature fields from simulated Doppler radar data J. Atmos. Sci. 48, WONG, S.H., P.W. CHAN, 2005: Development of LIDARbased windshear detection algorithm and its performance in the windshear episode on August th Guangdong Hong Kong Macao Technical Seminar on Meteorological Science and Technology (in Chinese with English abstract).