Meteorologische Zeitschrift, Vol. 21, No. 2, 183-192 (April 2012) c by Gebr¨uder Borntraeger 2012 (published online)
Open Access Article
Numerical simulation study of the effect of buildings and complex terrain on the low-level winds at an airport in typhoon situation L EI L I 1,2 and P.W. C HAN3 ∗ 1 Shenzhen
National Climate Observatory, Shenzhen Meteorological Bureau, Shenzhen, China Key of Laboratory of Atmospheric Boundary Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China 3 Hong Kong Observatory, Hong Kong, China 2 State
(Manuscript received November 4, 2010; in revised form May 5, 2011; accepted May 13, 2011)
Abstract Apart from terrain-induced airf ow disturbances and thunderstorms, buildings and artif cial structures at airports may bring about sudden wind changes to aircraft in certain weather conditions. In the typhoon situation in the morning of 22 August 2008 under a generally crosswind situation, two aircraft landing at the Hong Kong International Airport reported encountering signif cant wind changes, which were considered to affect the operation of the aircraft. At the same time, a wind speed difference in the order of 10–15 knots was observed between the anemometers at the north and the south parallel runways of the airport. The cause of the wind changes experienced by the aircraft is studied in this paper by using numerical simulation, namely, using mesoscale meteorological models to provide the background wind f elds, and nesting them with a computational f uid dynamics (CFD) model to study the effect of buildings and terrain on the airf ow along the glide path of the landing aircraft. It is found that the complete set of simulation (i.e. including both buildings and terrain) successfully captures the wind speed difference between the north and the south runways, and gives the drop of the crosswind along the glide path exceeding the 7-knot criterion as adopted for buildinginduced wind changes affecting the normal operation of the aircraft. The results of the present study suggest that, for the timely warning of wind changes to be encountered by the landing aircraft, it may be necessary to consider examining the low-level wind effects of the buildings on the airf eld by performing numerical simulations by mesoscale meteorological models as nested with a CFD model.
1 Introduction Low-level windshear and turbulence could be hazardous to the departing/arriving aircraft at the airport (HKO, IFALPA and GAPAN, 2010). They may occur in nonrainy weather condition, such as terrain disruption of the prevailing winds and sea breeze fronts on sunny days. They may also be associated with thunderstorms, e.g. microbursts and gust fronts. However, with the construction of more buildings or artif cial structures inside and around the airport, airf ow disturbances may appear along the f ight paths of the aircraft in the form of building-induced windshear and turbulence. They are also known as low-level wind effect, and have been documented in HKO, IFALPA and GAPAN (2010) as well as certain aeronautical information publication (e.g. AIP Hong Kong, GEN3.5, available at www.hkatc.gov.hk/index.html) as reminders to the pilots. A case of low-level wind effect of the hangars inside the airport occurred on 22 August 2008. On that day, strong north to northwesterly winds associated with ∗ Corresponding
author: P.W. Chan, Hong Kong Observatory, 134A Nathan Road, Kowloon, Hong Kong, China, e-mail:
[email protected]
DOI 10.1127/0941-2948/2012/0252
Typhoon Nuri prevailed over the Hong Kong International Airport (HKIA) (Figure 1). A couple of aircraft arriving at the south runway of the airport (which has an orientation of 070/250 degrees, see Figure 1) from the west reported “ dropping out of the sky” during landing, which might arise from sudden changes of the wind. There are two possibilities of the wind changes, namely, “uphill effect” of the airf ow as the winds climbed over the mountains to the south of the airport, or airf ow disturbances arising from the hangars at the western side of the airport. The objectives of this paper include (i) exploring the possibility of reproducing the wind change along the glide path to the west of the airport through the use of mesoscale meteorological models and a computational f uid dynamics (CFD) model, and (ii) identifying the major contributor to the wind change, namely, terrain or building. The eventual aim is to study the possibility of providing timely alert to the pilot in the future through high-resolution numerical simulation of the airf ow. In order to assess the impact of the building on the airf ow, a suitable criterion for building-induced windshear/turbulence that is applicable to aviation meteorology is considered. In this regard, the 7-knot criterion as adopted for Schiphol airport in the Netherlands may be
0941-2948/2012/0252 $ 4.50 c Gebr¨uder Borntraeger, Stuttgart 2012
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Figure 1: Distribution of the 1-minute mean winds near the surface inside and around the airport at 10:33 a.m., 22 August 2008.
used (N IEUWPOORT et al., 2006). This criterion indicates that, for a background crosswind of 25 knots at a height of 10 m above ground, a crosswind change of 7 knots or more along the f ight path of the aircraft may affect the operation of the landing aircraft. It is used in examining the simulated crosswind prof le along the glide path of the landing aircraft, which is def ned as an oblique line with an elevation angle of 3 degrees above the horizon ending at the threshold of the runway, joining with a horizontal line with a height of 10 m above the centreline of the runway. The 7-knot criterion and the above def nition of the landing glide path have been used in previous studies of the effect of the building on the airf ow inside the airport, e.g. L IU et al. (2010). This paper is organized as follows. Section 2 brief y describes the synoptic weather conditions, notably the movement of Typhoon Nuri, and the observations at the airport during the occurrence of the airf ow disturbance. The setup of the numerical models is described in Section 3. The simulation results of the mesoscale meteorological models are discussed in Section 4. The main part of the study is given in Section 5, namely, the CFDsimulated wind f elds in three conf gurations, namely, the complete picture with the natural terrain together with the buildings, the hypothetical case of having the terrain only without the buildings, and another hypothetical case of having the buildings only without the natural terrain. The CFD-simulated wind data are then compared with the anemometer observations on the airport. Finally, the simulated crosswind prof les along the glide path are discussed based on the 7-knot criterion. The conclusions of the study are drawn in Section 6.
2 Synoptic weather condition and observations at the airport An account of the movement of Typhoon Nuri and the observations of the remote-sensing meteorological instruments could be found in W ONG and C HAN (2010). In the present study, we mainly focus on the period just before Nuri making landfall over Hong Kong, i.e. in the morning of 22 August 2008. At that time, Nuri tracked west-northwestwards over the northern part of the South China Sea towards the south China coast. At 00 UTC (8 a.m. Hong Kong time, with HKT = UTC + 8 hours) of 22 August 2008, Nuri was estimated to have maximum wind strength (10-minute mean winds) of 65 knots. North to northwesterly winds prevailed over Hong Kong in association with the typhoon. Nuri made landfall at the eastern part of the territory at about 4:50 p.m. on that day. The two aircraft that reported encountering airf ow disturbances landed at the south runway of the airport at about 10:33 a.m. and 10:49 a.m. Hong Kong time on that day. The 1-minute mean winds inside and around the airport at the former time are shown in Figure 1. The wind distribution at the latter time is very similar. It is seen from Figure 1 that: (i) The crosswind at the north runway was rather large, in the order of 35 to 40 knots. As a result, the aircraft opted to land at the south runway, which generally had crosswinds of 10 to 15 knots smaller. (ii) The wind readings over the waters to the west of the airport were similar in magnitude to those at the north runway, even though they were measured by anemometers with lower altitudes. For the anemometers inside the airport, they had a height of 10 m above the ground, which in turn had a height of about 7
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m above the principal datum. On the other hand, for the anemometers over the waters, they were situated at about 6 m above the sea surface only. For the numerical simulation study to be discussed in the latter part of the paper, the model results are validated by comparison with the surface anemometer observations. In particular, we see if the wind speed difference between the north and the south runways can be reproduced. If such a feature can be successfully reproduced, then we study the major cause of the wind speed difference, namely, whether it is found to arise from the buildings on the airport and/or the complex terrain to the south of the airport. Another useful wind observation in the vicinity of the airport is provided by the Doppler Light Detection And Ranging (LIDAR) system located at the south runway. It gives the radial velocity up to 10 km away from the instrument, covering the airport island and up to about 3 nautical miles away from the runway thresholds. The scanner has an altitude of about 18 m above the ground level of the airport island. The radial velocity imagery at about 10:33 a.m. of 22 August 2008 is given in Figure 2(a). It is obtained by a conical scan of the LIDAR at an elevation angle of 3.2 degrees from the horizon. This elevation angle is chosen so that the laser beam is close to the 3-degree glide path of the landing aircraft and the blockage of the laser beam by the buildings/structures inside the airport is minimized at the same time. The maximum radial velocity as shown in Figure 2(a) reached about 23 m/s. The simulation results as discussed later in the paper are compared with the LIDAR observation as well.
3 Setup of numerical models The background meteorological condition of the event is provided by the simulation results of mesoscale meteorological models. First of all, the Global Spectral Model data at a resolution of about 100 km of Japan Meteorological Agency (JMA) are nested with the Regional Spectral Model (RSM) of the Hong Kong Observatory with simulations down to a resolution of 20 km. More technical details of RSM could be found in Y EUNG et al. (2005). RSM uses primitive hydrostatic equations with sigma-P hybrid vertical co-ordinate up to a pressure level of 10 hPa. It covers an area of 10 to 35 degrees N and 100 to 128 degrees E, namely, east Asia and western north Pacif c. Short-wave and long-wave radiation processes are updated every hour. The surface includes a 4-layer soil model. Cumulus parameterization has been activated. This is necessary because the horizontal resolution of RSM is rather coarse (20 km) and it is not possible to model convection explicitly. The planetary boundary layer process is proposed by T ROEN and M AHRT (1986) in which non-local specif cation of turbulent diffusion and counter-gradient transport in unstable boundary layer is considered.
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The RSM outputs are then nested with Regional Atmospheric Modelling System (RAMS) version 4.4 in a way similar to C HAN (2009), with horizontal resolutions of 4 km, 1.333 km and 0.444 km. Finally, the RAMS outputs at a resolution of 0.444 km are used in performing CFD simulations using FLUENT. RAMS is a three-dimensional, primitive equation and non-hydrostatic numerical prediction model that simulates atmospheric circulations ranging in scale from an entire hemisphere down to large eddy simulations (LES) of the planetary boundary layer (C OTTON et al., 2001). [Note: It is similar to Weather Research and Forecasting (WRF) model, which is also a primitive equation, non-hydrostatic model, though the model physics of WRF may have some options which are developed of late.] Vertical stretching co-ordinates are used, starting from about 20 m near the surface to a maximum vertical extent of 1200 m. Short-wave and long-wave radiation processes are updated every hour. The soil submodel in RAMS is conf gured to have 11 levels down to 0.5 m underground. Cumulus parameterization has been switched off. This is chosen because the horizontal resolution of the model is rather f ne: from 4 km to about 1 km only, in which the convective activities may be modelled explicitly in RAMS. The Land EcosystemAtmosphere Feedback-2 (LEAF-2) model is the surface package included in RAMS. LEAF-2 has the ability to represent multiple land use land classes within a grid cell, by subdividing a cell into so-called “patches”. For each grid cell, the f uxes to the atmosphere are areaweighted by their contributions from each patch. The f uxes are calculated through typical surface layer similarity theory. In all the grids, Mellor-Yamada 2.5-level closure scheme (M ELLOR and YAMADA, 1982) is used. FLUENT is a powerful and f exible general-purpose CFD software and has been used before in the study of the airf ow disturbances associated with a Y-shape building in affecting the operation of the aircraft (L IU et al., 2010). The FLUENT simulation is performed in the RANS (Reynolds averaged Navier-Stokes) framework and the airf ow is considered as incompressible and viscous. The domain of the FLUENT simulation has a size of 10 km x 10 km and the top of the domain reaches a height of 2600 m. The FLUENT simulation domain is divided into 4 parts for performing a discretization with mixed grid system. Three of the four parts are discretized with hexahedral grid and the remaining one which covers the island with airport buildings is discretized with tetrahedron grid. The mixed grid system ensures calculation eff ciency and enough resolution over focus area. Following the method of L I et al (2007, 2010), RAMS and FLUENT are coupled in an off-line way. The simulated wind velocity data of the 3rd grid in the cells near the lateral and the top boundaries of the FLUENT simulation domain are extracted from RAMS output f les. The extracted data and the corresponding Cartesian coordinate parameters (x,
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Figure 2: Radial velocity image obtained by 3.2-degree conical scan of the LIDAR at the south runway (a), and RAMS-simulated radial velocity with respect to the south runway LIDAR at a height of 180 m above the local terrain (b) at 11 a.m., 22 August 2008. The colour scale is given at the bottom of the f gure.
y and z values) are deposited in Boundary Prof le (BP) f les with a format predef ned in FLUENT, which can be read through the BP interface of FLUENT. When driving a FLUENT simulation run, the lateral boundaries and the top boundary are set as velocity-inlet type and the velocity data extracted from the RAMS output are interpolated on each lateral boundary. In the simulation, the bottom boundary is set as a non-slip wall. This kind
of boundary condition setting can provide a very strong forcing for the FLUENT integration. The surface roughness is set to be 0.0005 m for sea, 0.005 m for airport island, and 0.5 m for Lantau Island. Such settings are approximate only, but they are believed to have reasonable representation of the surface roughness.
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Such simulation results are a reliable basis as the background wind f elds for further nesting with RAMS and FLUENT. The simulated winds at a height of 180 m above sea level by RAMS in the innermost domain (horizontal resolution of 0.444 km) are given in Figure 2(b). They are shown as radial velocities with respect to the south runway LIDAR for ease of comparison with Figure 2(a). A height of 180 m is chosen so that such an altitude is reached by the laser beam at about 3 km away from the LIDAR (LIDAR height of 18 m above ground level and the conical scan is performed with an elevation angle of 3.2 degrees above the horizon). It could be seen that, at least in the region around the airport to the north of the mountains of Lantau Island (an island to the south of the airport), the simulated wind f eld looks similar to the actual LIDAR observation. In general, the simulation result looks “smooth” and does not show the small-scale features of the wind distribution in the actual data, such as the irregularities in the isopleths of radial velocities of different colours. This may be due to the use of Mellor-Yamada turbulence parameterization scheme, which tends to produce smoother wind f eld but is computationally more stable. Nonetheless, the background wind speed and wind direction distribution as given by RAMS are a reliable basis as the input to FLUENT.
5 Results of CFD model
Figure 3: RSM simulation results at 11 a.m., 22 August 2010: (a) surface wind f eld and hourly rainfall, and (b) wind and relative humidity at 925 hPa.
4 Results of mesoscale meteorological models Simulation by RSM is performed starting from 21 UTC, 21 August 2008. The 6-hour simulation results are considered, namely, 03 UTC (11 a.m. Hong Kong time) 22 August 2008. In general, the west-northwestward movement of Typhoon Nuri towards the south China coastal area is well reproduced in the RSM simulation. The simulated surface winds and hourly rainfall at 03 UTC are shown in Figure 3(a). It could be seen that RSM successfully gives the strong northwesterly winds over Hong Kong in association with the typhoon. The winds at the usual boundary layer top, namely, 925 hPa, are shown in Figure 3(b). The northwesterly winds are simulated to reach the strength of 40 to 50 knots over Hong Kong.
Three simulations using FLUENT have been performed, namely, buildings and terrain of Lantau Island, buildings only (without terrain) and terrain only (without buildings). The buildings under consideration could be found in Figure 4(a). They include the major buildings on the airport, such as the hangars at the western side, the terminal building at the eastern side, as well as the hotels and exhibition centre at the northeastern part of the airport. The simulation results with buildings and terrain are shown in Figure 4(a), which shows the magnitude of the wind velocity at a height of 15 m above the ground level. It could be seen that there is a gradient of the wind velocity magnitude over the airport island, with higher wind speed along the northern coast (about 20 m/s) and a lower wind speed at the southern coast (about 16 m/s). According to the CFD simulation result, this gradient appears to be related to the wakes associated with the buildings on the airport island in the north to northwesterly f ow. Winds then pick up in strength as they climb over the mountains along the northern coast of Lantau Island. It is also interesting to note that the wind speed is quite high (similar in magnitude to that along the north runway) over the waters to the west of the airport. Such a wind speed distribution is similar to the actual observations of surface anemometers in Figure 1.
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Figure 4: CFD simulation for the “complete set”, i.e. including both the buildings at the airport and terrain of Lantau Island: (a) wind velocity magnitude at a height of 15 m above ground, and (b) crosswind prof le along the glide path for the aircraft landing at the south runway from the west.
To see the effect of the buildings and/or the terrain on the airf ow along the glide path of the aircraft landing at the south runway from the west, the simulated crosswind prof le along this path is considered and depicted in Figure 4(b). It could be seen that, for a distance of about 1 km up to the threshold of the runway, the crosswind is found to drop signif cantly from about 20 m/s to the region of 13–14 m/s. The crosswind drop clearly exceeds the 7-knot criterion as mentioned in Section 1 of this paper. Such a change in crosswind may affect the normal operation of the aircraft, and seems to be consistent with the pilot report that the aircraft was found to drop out of the sky during the event. To determine the major cause of this crosswind change, two more hypothetical simulations are performed. The f rst one includes the buildings only without the Lantau terrain in the CFD simulation. The distribution of wind velocity magnitude is given in Figure 5(a). It could be seen that the velocity gradient from the northern coast to the southern coast of the airport island is apparent. The corresponding crosswind prof le along the glide path is shown in Figure 5(b). The crosswind
change before the touchdown is basically similar to that in the complete set of simulation (buildings plus terrain) in Figure 4(b). It is interesting to note that, just after the touchdown, there is a signif cant rise in the crosswind in the simulation. The simulation with the Lantau terrain only (without the buildings, but with the airport island with the surface roughness setting discussed in section 3) is also performed. The simulated wind velocity magnitude is given in Figure 6(a). It could be seen that the wind velocity is rather uniform over the airport island. The “uphill” effect of the Lantau terrain, namely, drop in wind velocity magnitude when the winds are about to climb up the mountains on Lantau Island, does not seem to extend to the airport island. The corresponding simulated crosswind prof le along the glide path is given in Figure 6(b). There is a very slight drop (∼0.5 m/s) of crosswind only between a distance of 1 km to the west of the runway up to the runway threshold. The use of the limited model height of 2600 m in the CFD simulation is considered to be suff ciently high to resolve the uphill f ow over Lantau Island (with the highest peak of about 1000
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Figure 5: Same as Figure 4 but for the case of having buildings only in the simulation.
m above mean sea level), as shown in the vertical cross section of Figure 6(c) for the model simulation. Based on the above three sets of simulation, it appears that the buildings on the airport island are the major contributor to the crosswind change along the glide path. To establish the accuracy of the simulation, the simulated winds at the locations of the runway anemometers are compared with the actual observations. The wind speed prof les at the anemometer locations are shown in Figure 7. From Figure 7(a), the speed difference between the two anemometers at the western part of the airport, namely, R2W (anemometer near the western end of the north runway) and R1W (anemometer near the western end of the south runway), is successfully reproduced in the complete set of simulation (buildings and Lantau terrain) by examining the speed values at a height of 10 m. The corresponding results for buildings only and terrain only are given in Figures 7(b) and 7(c) respectively. It could be seen that buildings are found to be the major contributor to the wind speed difference between the north and south anemometers. The results of the present study are considered to be more comprehensive than the previous study of cross-
mountain airf ow based on a mesoscale meteorological model alone (C HAN and C HEUNG, 2009) because both the effects of the mountains and buildings have been taken into account with the state-of-the-art technique. The simulated wind speeds are compared with the measurements from all the 6 runway anemometers in Table 1, covering the cases of the complete set of simulation (buildings plus terrain), terrain only, and buildings only respectively. The 6 anemometers include R2W, R1W, R2C (anemometer near the centre of the north runway), R1C (anemometer near the centre of south runway), R2E (anemometer near the eastern end of the north runway) and R1E (anemometer near eastern end of the south runway). It could be seen that the complete set of simulation gives the best results in comparison with the actual observations. On the other hand, for the simulation with terrain only, the winds are rather uniform over the airport island. As a result, the buildings are found to be the major contributor to the wind speed difference between the north and the south runways, based on the present simulation study. There are two observations in the results in Table 1. First of all, for both R1C and R1W, the simulated wind
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Figure 6: Same as Figure 4 but for the case of having Lantau terrain only. The additional f gure (c) is a vertical cross section of the simulated wind speed. The locations of the two runways are marked as black lines in (c). The location of the vertical cross section is shown in (a).
speed is the highest with the use of “terrain only”, followed by “buildings only” and ”terrain and buildings”. This sequence is not followed for R1E. Secondly, from Figure 1, for the pair of anemometer readings at the corresponding relative locations along the runways (viz. R2E comparing with R1E, R2C comparing with R1C, and R2W comparing with R1W), the wind difference
is the largest for R2C comparing with R1C. However, this observation is not reproduced from the results of Table 1 for “terrain only”. Therefore, the present simulation results appear to capture the board feature of the anemometers at the north runway having higher wind speeds than those at the south runway, but the details of the wind distribution over the airport island are not fully
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Figure 7: Wind speed prof les at the anemometers R2W and R1W for the three CFD simulations: (a) buildings and Lantau terrain; (b) buildings only; and (c) terrain of Lantau Island only. The lowest model level is 10 m above ground. The wind prof les look rather straight because they inherit from the prof les of the mesoscale meteorological model (RAMS) instead of assuming a background wind speed prof le shape (e.g. power law or log law) in the CFD modelling. Table 1: Comparison between modeled and observed data for the period of 10:00 to 11:00 a.m. Location Observed values between 10 a.m. and 11 a.m. Wind Wind speed direction R1C 15.8 333.5 R1E 14.3 337.6 R1W 14.1 341.0 R2C 19.2 345.6 R2E 18.2. 345.2 R2W 19.9 340.8
Simulation results (buildings only) Wind Wind speed direction 17.2 339.2 9.9 337.4 14.8 342.8 20.0 322.7 19.0 324.6 19.7 323.0
captured at various instances. Further studies would be carried out to see if the details of the wind distribution could be simulated as well.
6 Conclusion Numerical simulation study is performed to determine the major cause of the wind changes experienced by a couple of aircraft landing at the south runway of the airport from the west in a typhoon situation. Mesoscale meteorological models are used to provide the background wind f eld, which is in turn input into a CFD model. In the simulation with the CFD model, different model setups are used, namely, the complete set of simulation by considering the major buildings on the airport together with the nearby terrain, considering the buildings only, and considering the terrain only. The model simulation results are validated by comparing with the anemometer readings inside the airport. It is found that the complete
Simulation results (terrain only) Wind Wind speed direction 20.3 326.2 20.0 327.1 19.7 326.3 19.2 323.4 17.9 323.0 19.2 323.8
Simulation results (terrain and buildings) Wind Wind speed direction 15.9 330.9 14.1 332.7 14.4 334.3 20.0 322.7 18.5 323.6 20.4 325.1
set of simulation successfully capture the wind speed difference between the north and the south runways. The buildings are determined to be the major contributor to the wind speed difference. The effect of the terrain turns out to be rather small. Based on the above simulation results, the crosswind prof le along the glide path of the aircraft landing at the south runway from the west is determined. With the complete set of simulation, there is crosswind change of about 6 m/s, which, after appropriate scaling, exceeds the 7-knot criterion as adopted in Schiphol airport for building-induced wind change. As a result, the buildings are found to cause signif cant change of the crosswind, which may affect the operation of the aircraft. This result is consistent with the observations reported by the two landing aircraft, both of which indicated that there were sudden changes of the winds during the landing process. The present paper focuses on one particular episode only, in which the buildings appear to have effects on the low-level winds experienced by the landing aircraft.
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More cases will be considered in the future. Nonetheless, based on the present case only, it seems to be necessary to consider nesting a mesoscale meteorological model with a CFD model in forecasting low-level wind disturbances arising from artif cial structures inside and around an airport for the assurance of aviation safety. In the present case, the effect of terrain is rather small and it may be possible to simulate the airf ow along the glide path of the aircraft using CFD modelling incorporating the buildings’ shapes only. But in more realistic simulations, both the effects of the terrain and the buildings have to be included, and the simulations by nesting a mesoscale meteorological model with a CFD model would be more complete. This kind of real-time simulation of the low-level airf ow may become possible in the future with the availability of computers with higher computational power.
Acknowledgments This study was supported by the National Natural Science Foundation of People’s Republic of China (No. 40805004), the Urban Meteorological Science Foundation (No. UMRF200901) and the Open Science Foundation of the State Key of Laboratory of Atmospheric Boundary Physics and Atmospheric Chemistry.
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