Optimized use of real-time vertical-profile wind data and fast modelling for prediction of airflow over complex terrain

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1 METEOROLOGICAL APPLICATIONS Meteorol. Appl. 23: (216) Published online February 216 in Wiley Online Library (wileyonlinelibrary.com) DOI: 1.12/met.44 Optimized use of real-time vertical-profile wind data and fast modelling for prediction of airflow over complex terrain Jenny Stocker, a * David Carruthers, a Kate Johnson, a Julian Hunt a andp.w.chan b a Cambridge Environmental Research Consultants (CERC), UK b Hong Kong Observatory, Kowloon, Hong Kong ABSTRACT: A novel system for real-time prediction of wind speed and direction in a critical region of complex terrain is developed by combining measurements and analytically based fast computation of the flow field. The new system is applied to the aircraft approach flight paths of Hong Kong International (HKIA), where orographically generated turbulence downwind of a mountain ridge in high-wind conditions causes disruptions to aircraft movements regularly. By using wind profiler and radiosonde measurements to drive the analytically based FLOWSTAR model, good agreement is demonstrated between modelled and observed wind flow data from a network of ground-based monitors and LIDAR scans in the vicinity of the airport for a historical wind event. The methodology forms the basis of a system that can be used to forecast extreme wind events. KEY WORDS airflow; validation; wind shear; wind profiler; LIDAR; airport; FLOWSTAR Received 3 February 2; Revised 24 July 2; Accepted October 2 1. Introduction Although most airports are located in regions of flat terrain, some are by necessity situated near hilly terrain or mountains. In certain meteorological conditions, such terrain leads to complex wind structures that disturb aircraft flight paths, causing disruption to passengers, in addition to increased operator costs and in-flight risk. In order to be able to forecast such hazards to aircraft, a better understanding of these meteorological events is required; this can be gained by analysing measurements and undertaking modelling. Mobbs et al. (2) describe a field campaign where meteorological data were recorded at the Mount Pleasant airfield over East Falkland in the South Atlantic. This airfield is located south of a mountain range which generates highly turbulent flows at the airfield for some northerly flows with a strong elevated inversion. The study, which involved deployment of an array of automatic weather stations for a full year in addition to radiosonde recordings, measured unsteady flow separation and strong temperature inversions, conditions that gave rise to downslope flows and rotors aloft; the study suggests that hydraulic jumps may also be present. In a subsequent paper, Sheridan and Vosper (26) performed high-resolution 3D simulations of flows of the same region in an attempt to replicate the phenomena that were observed during the field campaign. The geographical configuration in East Falkland has some features comparable with those at Hong Kong International (HKIA), where easterly winds over Lantau Island periodically generate significant wind shear that disrupts scheduled landings. Eidsvik et al. (24) calculated the wind and turbulence fields over Værnes airport in Norway. This airport is located * Correspondence: J. Stocker, Cambridge Environmental Research Consultants (CERC), 3 King s Parade, Cambridge CB2 1SJ, UK. jenny.stocker@cerc.co.uk in a mountainous region and turbulent flying conditions are experienced on a regular basis. The study found that adjusting modelled data using local measurements of wind speed produced results that were more consistent with the experimental data, and suggests that a data assimilation approach (for example, as presented in Lorenc, 23) would improve flow predictions. This approach of using observations to adjust modelled flow and turbulence fields in mountainous terrain is developed in this present study, in an attempt to model critical meteorological conditions near HKIA. The complex wind structures observed in such studies tend to have characteristic features, such as upwind blocking, downslope flows, large variations in wind direction with height and regions of high-intensity turbulence. The features are usually present in layers sometimes as thin as 1 m with high gradients in the vertical direction and their structure can vary with time. Such features are difficult to reproduce using numerical modelling (Hunt et al., 1991); although, with recent developments in computing technology, standard finite difference numerical models such as the Weather Research and Forecasting model or the Unified Model (Warrilow and Buckley, 1989) may be configured at sufficient space-time resolution to include the layers explicitly, i.e. some of these complex wind flow structures can be resolved if the required computer capacity is available. However, if flow and turbulence field estimates are required in short time scales, for example to alert aircraft to hazardous flying conditions, it is preferable to use models that are based on analytical descriptions of these features which are able to execute calculations in minimal time. The accuracy of meteorological forecasts is related to the lead time. Golding (1998) discusses an approach where numerical weather prediction (NWP) forecasts are combined with nowcasting techniques for the purpose of predicting precipitation, cloud cover and visibility over the United Kingdom. The NWP model gives moderately accurate weather forecasts; these are robust for a relatively large lead time, O ( days), 216 Royal Meteorological Society

2 Real-time prediction of airflow over complex terrain 183 because the model represents the changing atmospheric state. Conversely, nowcasting is very accurate at the current time when observations are available, but accuracy decreases quickly with time, O (1 day), because the forecast is an extrapolation of observed trends. Golding demonstrates that a forecasting system where the two approaches are combined leads to improved performance compared with either approach taken independently. The present study reports on a novel approach to modelling extreme wind events in the vicinity of the HKIA, with the aim of developing a local-scale, high-resolution forecasting system that will be able to predict such events, and allow controlled evasive action to be undertaken early. A particular wind event, which occurred on 27 December 29, caused a number of landing aircraft to be diverted. This event has been reported previously (Chan, 212); initial modelling of one of the aborted landings was undertaken by Carruthers et al. (214) using the FLOWSTAR model (Carruthers et al., 1988) with input wind data from outside the computational domain. In the present study, the model is developed to take better account of vertical wind shear and also to incorporate the real-time upwind profile data recorded at a small island upwind of Lantau Island. Results of further modelling investigations and model validation are compared against not only the headwind measurements on the aircraft, but also with ground-based wind speed and direction measurements from the Hong Kong Observatory (HKO) network and LIDAR scan measurements taken at the airport. This study first summarizes the meteorological measurements available as input to the model in Section 2 as well as data available for validation of the system. Section 3 presents a review of previous modelling approaches, in addition to suggestions for improvements. The validation of two versions of a new approach is presented in Section 4, and the results are discussed in Section. 2. Meteorological measurements The HKO has an instrumentation network that records meteorological data over the whole of the Hong Kong Special Administrative Region. The majority of the instruments are automatic, recording data at high temporal resolution; full details of the instrument type, location height and parameters measured are given in the HKO metadata report (HKO, 212). In order to gain insight into the flow features that give rise to the turbulent flows that disrupt aircraft landings at HKIA, the range of meteorological measurements taken in the vicinity of the airport was reviewed; the measurements recorded at the HKIA, on Lantau and neighbouring islands and at King s Park (KP) in Kowloon were of interest. Data recorded at times leading up to and, in the case of the radiosonde measurement, 2 h after the aborted landing at 2147 UTC on 26 December 29 (47 LST on 27 December 29) were inspected in detail. Figure 1 shows the locations and types of the ground-based monitors at and to the southeast of HKIA; the terrain elevation is also indicated in this figure. These monitors record near-ground (1 m above the height of the terrain) wind speed and direction and have good exposure with minimal obstruction within 3 m from each station. Figure 1 shows the minimum and maximum 1 min average wind speed values recorded between 21 and 47 LST prior to the aborted landing. From these data, it is straightforward to calculate the speed up over the hill, Δs, defined in Mobbs et al. (2) as: Δs = U max U ref (1) U ref where U ref is the wind speed at the upwind site (taken to be the island of Cheung Chau (CCH)) and U max is the maximum downwind speed; Δs ranges between 1.2 in the lee of the hill and.6 at the airport. Consideration of Figure 1 together with these Δs values indicates that strong downslope winds and highly turbulent wind conditions occur on the lee of the hill and at the airport. The instruments located at CCH not only record data near ground level, but also make wind profiler measurements. The radar wind profiler functions in two modes, each recording at 2 min intervals and at vertical resolutions of 2 and 6 m; for the recording at 6 m resolution only part of the profile can be measured. According to the instrument specification, the precision is 1 m s 1 for wind speed and 1 for wind direction. Figure 2 (d) shows the example wind speed and direction measurements taken by this instrument during the episode of interest. The wind direction measurements are consistent between the two modes (Figure 2 and (d)), whereas the wind speed values recorded at the higher vertical resolution show much greater variation than those at lower resolution. As discussed in Chan (212), LIDAR scans are taken periodically at HKIA. This remote sensing equipment is located on top of a fire station at the airport, with a height above mean sea level (AMSL) of 18 m. The LIDAR, which performs Plan Position Indicator (PPI) scans at resolutions of 3 and 6, is able to detect the spatial variation of wind speed at a fixed angle of elevation, radially to distances of around 1 km from the flight path, at heights of up to 6 m. Figure 8 shows the measured radial wind speed 12 min before the aborted landing; the scan range is slightly <36. A radiosonde balloon was released from a hill-top in KP, 66 m AMSL at UTC on 27 December 29. This corresponds to 8 LST, just over 2 h after the extreme wind event was observed at the HKIA; measurements taken by the instrument included wind speed and direction, temperature and buoyancy frequency. 3. A review of previous modelling approaches The occurrence of extreme wind events at HKIA has been the subject of previous studies. Cheung et al. (28) used the Regional Atmospheric Modelling System (RAMS, Cotton et al., 23) to model the extreme gusts that occur when high winds pass over Lantau Island towards the airport. The main drawback of this modelling approach is that it is not possible to incorporate the RAMS into an operational nowcasting system due to restrictive computation times, and the inability to use local data within the computational domain. A very fast analytical approach, FLOWSTAR, as described in Carruthers et al. (1988) and Carruthers et al. (214) with underlying theory from Hunt et al. (1988), has been used in many complex terrain studies in which the input data used are from locations outside the domain. FLOWSTAR is a linear, steady-state perturbation model. It characterizes the atmosphere using up to three layers, comprising a boundary layer, where the impact of upwind shear and locally generated turbulence on the perturbations in the mean flow is taken account of near the surface; an elevated inversion layer; and a stable layer aloft. The key input parameters of the model that determine the flow structure are the horizontal length scale and height of the complex topography; a velocity scale for the flow perturbation near the surface; and the Froude numbers of 216 Royal Meteorological Society Meteorol. Appl. 23: (216)

3 184 J. Stocker et al. Cheung chau Site type Hilltop Terrain height (m) Terrain extent Flight path Wind speed at 1 m (m s 1 ) 1 Hilltop km Runways Figure 1. Near-ground monitors from the Hong Kong Observatory network in the vicinity of the Hong Kong International categorized by type: monitor locations with the height of terrain above sea level shown and average, minimum and maximum 1 min average wind speeds between 21 and 47 LST. msl (c) msl Wind speed m s msl (d) msl Wind direction ( ) Wind Direction ( ) Figure 2. Wind profiler measurement data from the Hong Kong Observatory site on Cheung Chau island between and 46 LST on 27 December 29 up to 2 m above ground level wind speed at 2 m resolution, wind direction at 2 m resolution, (c) wind speed at 6 m resolution and (d) wind direction at 6 m resolution. the inversion layer and stable layer, which depend on the wind speed in each layer and the temperature step at the inversion layer and buoyancy frequency of the upper stable layer. The Froude numbers determine the structure of the internal gravity waves generated by the hill and consequent downslope motions (Carruthers et al., 214). Owing to the computational speed of FLOWSTAR, solutions can be continually adjusted as the incident flow varies. This study shows how using FLOWSTAR the modelling approach can be modified so as to incorporate online time-varying data within the computational domain Summary of Carruthers et al. s (214) modelling approach Results for FLOWSTAR from three sets of model inputs were discussed by Carruthers et al. (214). The inputs were derived from measurements taken by the radiosonde at KP and the 1 m wind measurements observed at CCH. That study concluded that the model results compared best with the observations of the magnitude of headwind speed taken on the aircraft when: 216 Royal Meteorological Society Meteorol. Appl. 23: (216)

4 Real-time prediction of airflow over complex terrain Modelled total wind, U Measured wind speed, flight D Aircraft Height Wind direction ( ) Modelled wind direction Measured wind direction, flight D Aircraft Height Figure 3. Model predictions from Carruthers et al. (214) decomposed into wind speed and wind direction components. Table 1. Model input parameter values from Carruthers et al. (214). Parameter Value Derived from 7.3 at 1 m Cheung Chau Wind direction ( ) 14 King s Park Surface sensible heat flux (W m 2 ) NA Boundary layer height (m) 4 King s Park Temperature jump (boundary layer top) ( ) 7.19 King s Park Buoyancy frequency (boundary layer top).124 King s Park Table 2. Wind speed measurements from the anemometer and wind profiler at Cheung Chau island used to derive the upwind meteorological profile; values highlighted in bold are used as the upwind profile for the lower solution the wind speed was taken from CCH; the wind direction from within the inversion layer was used and the boundary layer height was estimated as the value recorded at KP relative to ground, not sea level. Table 1 summarizes the full set of optimized input parameters of the model. The boundary layer was assumed to be neutral Comments on the Carruthers et al. (214) modelling approach The previous modelling approach can be refined. Firstly, the terrain data used as input to the FLOWSTAR model were at a relatively low resolution, with a 1 m grid spacing. Data available at a higher resolution (1 m) are used in this modelling study. Secondly, in the version of FLOWSTAR used in the previous work a low value for the damping parameter was taken as input, which is used to restrict the amplitude of trapped lee waves generated at the inversion layer; subsequent modelling uses an increased value as input for this parameter to ensure that the trapped lee waves do not dominate the flow unrealistically. Finally, a straight-line trajectory of model output points was used previously as an approximation for the aircraft flight path, whereas in order to perform direct comparisons with the measurements, the exact aircraft location data are now used. Carruthers et al. (214) demonstrated good qualitative agreement between changes in the modelled and observed headwind values (refer to Figure 12 in that work). However, the magnitude of the headwind speed was overestimated. In order to investigate this, it is helpful to look at not only the headwind speed, which is a single component of the wind speed in the opposite direction of the aircraft trajectory, but also at the total wind speed and the overall wind direction. Figure 3 shows the wind speed and the wind direction from a re-run of the Carruthers et al. (214) case, configured to use terrain data at an increased resolution, with increased damping parameter and output points that coincide with the aircraft location. This figure shows the wind component (speed or direction) on the left-hand axis and the aircraft height on the right-hand axis. The horizontal axis shows the distance to the runway, with the zero indicating the beginning of the runway. Figure 3 is quite illuminating in terms of the observations as well as the model predictions. The observed wind speed varies between and 1 m s 1 for the majority of the approach, with the exception of a reduction in wind speed to <1. m s 1 at 13 m from the runway. This reduction in the wind speed coincides with a sharp change in the wind direction from southerly to easterly. In terms of model output, Figure 3 shows that the magnitude of the wind speed is overestimated significantly by the model. The sharp changes in the wind speed near the runway (close to the ground) are derived from the complex 3D nature of the flow resulting from the complex form of the mountain ridge on Lantau. The highest wind speeds correspond to the strong downslope winds relatively close to the steep mountain slopes; the lighter winds are further downstream from the steep slopes where the streamlines have diverged. Figure 3 indicates that the wind direction predicted by the model does not deviate significantly from the upwind direction of 14. Inspecting the measurements recorded by the aircraft in this way motivated changes to the modelling approach. Details of these changes are given in the next section Improvements to the Carruthers et al. (214) modelling approach In order to improve model predictions of wind direction, a conceptual change in modelling is required. One restriction of the 216 Royal Meteorological Society Meteorol. Appl. 23: (216)

5 186 J. Stocker et al. Table 3. Upper and lower flow solution model input parameter values. Parameter Upper solution Lower solution Value Derived from Value Derived from 1.8 at 4 m HKO 1. at 26.7 m 3 CCH (anemometer) 11.3 at 4 m 3 CCH (wind profiler) Wind direction ( ) 147 HKO CCH (anemometer) Surface sensible heat flux (W m 2 ) NA NA Boundary layer height (m) 4 HKO 4 HKO Temperature jump (boundary layer top) ( ) 7.19 HKO 7.19 HKO Buoyancy frequency (boundary layer top).124 HKO.124 HKO CCH, Cheung Chau; HKO, Hong Kong Observatory. Table 4. Surface roughness parameter values used as input into FLOWSTAR. Region Roughness (m) Sea.. Lantau Island below an altitude of m. (moderately built up) Lantau Island above an altitude of m 2. (to represent the tree cover) FLOWSTAR model is that it does not allow for the variation of wind direction with height in the model input. Clearly, wind direction does change with height; the wind direction measurements given in Figure 2 and (d) show a 18 rotation within 2 m of the ground. However, as FLOWSTAR calculates the solution as a set of linear equations, multiple solutions can be super-imposed to allow for the upwind shear, for example: Model prediction = (1 α (z)) FLOWSTAR lower + α (z) FLOWSTAR upper (2) where FLOWSTAR lower and FLOWSTAR upper are the standard FLOWSTAR solutions driven by lower and upper boundary conditions respectively and the apportionment is applied to the wind speed and direction components separately, allowing for the step change between and 36. The function α(z) is within the range [,1] and varies with height, z, within the boundary layer, taking a value of zero at the surface and unity at the inversion height. The function α(z) could be smooth or have a step change at a certain height, if additional information was available. In the current study, the following balance between the lower and the upper solutions have been proposed: Combined Solution I α (z) = Combined Solution II α (z) = { z H if z < H 1 if z H (3a) { ifz < h s 1 ifz h s (3b) where H is the boundary layer height. The value of the cut off h s in the second solution must be derived from the meteorological data available. It is essential that the upper flow solution is driven solely by data recorded by the radiosonde at KP, whereas the lower flow solution should be driven primarily by the meteorological measurements taken at CCH. From a review of the HKO data, keeping in mind that these observations were taken 2 h after the aborted aircraft landing, it is difficult to specify a single set of meteorological parameters for input into FLOWSTAR that best represent the conditions at 47 LST. Figures 1 and 11 in Carruthers et al. (214) demonstrate that altering the boundary layer height affects the magnitude of the wind speed variation predicted by the model, and changing the input wind direction changes the phase of the solution. These model runs were performed using a very small value for the damping parameter, and the variations seen in Figure 1 in Carruthers et al. (214) are of unrealistic amplitude. The results in the present study were obtained by using a damping parameter value that removes high-amplitude trapped lee waves. For the upper flow solution, taking the wind direction to be the value recorded at the inversion height is a reasonable approach. The wind speed of the upper flow is used to define the boundary layer profile. The radiosonde profile shows maximum wind speed near or just below the base of the inversion layer; using this value as input leads to the best representation of the boundary layer profile as measured by the radiosonde and the best results overall with respect to wind speed and direction comparisons against the measurements recorded by the aircraft. For the lower flow solution, the wind direction is given by the ground-level anemometer. A further refinement of this model configuration is to use, as input to the lower solution for wind speed, profiles that can be derived from the wind profiler data from CCH. The profile values recorded in the lowest m at 3 LST (Figure 2(c)) are given in Table 2; this time approximately corresponds to when the air mass that caused the extreme wind event was passing over the island. In addition to the outer layer velocity scale being determined by the flow in the lower part of the boundary layer, the flow is also sensitive to the wind speed in the upper part of the boundary layer as it is used to define the inversion layer Froude number. Investigations have demonstrated that using the highest recorded value from the measured profile in the lowest m to represent this wind speed provides the best results for the near-ground anemometer network. For this reason, in addition to the near-ground measurement, the wind speed profile for the lower flow solution was defined using, for the wind speed at the inversion layer, the maximum of the wind speed values recorded by the wind profiler within the lowest measured band seen in Figure 2(c) (between approximately 2 and 4 m above ground). For both solutions, boundary layer height, temperature jump and buoyancy frequency at the top of the boundary layer are consistent with the previous modelling results; the boundary layer (layer below the inversion) is again assumed to be neutral. Table 3 summarizes the model input values for the two solutions. A further improvement to the previous modelling approach is to allow for the impact on the flow of the variations in protrusions of the ground-level obstacles. This can be modelled by allowing for spatial variation of the surface roughness parameter, z.in 216 Royal Meteorological Society Meteorol. Appl. 23: (216)

6 Real-time prediction of airflow over complex terrain 187 (c) Upper total wind Lower total wind Measured wind speed Aircraft height Combined I total wind Combined II total wind Measured wind speed Aircraft height Wind direction ( ) Wind direction ( ) (d) Upper wind direction Lower wind direction Measured wind direction Aircraft height 1 Combined I wind direction Combined II wind direction Measured wind direction Aircraft height Figure 4. Meteorological conditions recorded compared to model predictions: upper and lower solution wind speed and wind direction and Combined Solutions I and II (c) wind speed and (d) wind direction. Head wind speed (m/s) Head wind speed (m/s) Modelled headwind Modelled headwind perturbation Measured headwind Aircraft Height Modelled headwind Modelled headwind perturbation Measured headwind Aircraft Height Calculated wind speed (m s 1 ) Calculated wind speed (m s 1 ) Hill top Monitored wind speed (m s 1 ) Hill top Monitored wind speed (m s 1 ) Figure. Measurements of headwind speed compared to the model predictions for Combined Solution I and Combined Solution II. Figure 6. Validation of wind speed against ground-level anemometer network lower solution and upper solution. 216 Royal Meteorological Society Meteorol. Appl. 23: (216)

7 188 J. Stocker et al. 2 Table. Measurement times (LST) used for validation against the near-ground anemometers. Calculated wind speed (m s 1 ) Calculated wind direction ( ) Monitored wind speed (m s 1 ) Hill top Monitored wind direction ( ) Hill top Figure 7. Validation of results against ground-level anemometer network for Combined Solution I wind speed and wind direction. previous modelling, this parameter has taken a constant value, which ignores the significant variation in the surface properties between the sea and the forested region on Lantau Island. Table 4 summarizes the surface roughness parameters used as input for the modelling work done in the present study. 4. Validation results The model predictions have been compared to three sets of measurement data: headwind, total wind and wind direction values recorded from the aborted flight; flight path LIDAR measurements; and near-ground-level anemometer wind speed and direction values. These are discussed in the following sections Measurements from the aborted flight Figure 4 shows the total wind speed values recorded by the aircraft, compared against the lower and upper flow solutions respectively; Figure 4 shows the corresponding wind direction graphs. In terms of the magnitude of the wind speed, the present model gives a much better prediction in these cases than the results in Carruthers et al. (214) shown in Figure 3, although the model is still not able to replicate the sharp decrease in wind speed that the aircraft experiences when it reaches a height of m during its descent. Both solutions show some decrease in wind speed near or upstream of the runway but the magnitude of the decrease is too low and the location either too far downstream (upper solution) or too far upstream (lower solution). This underestimation of the slow-down may be related to non-linear effects such as hydraulic jumps (Mobbs et al., 2). In terms of wind directions, clearly the lower flow solution Site type Measurement period 21 3 Hilltop 31 4 Leeside corresponds to the wind direction experienced by the aircraft when it is close to the ground, and the upper flow solution corresponds to the wind direction which is close to, although not exactly in agreement with, the wind direction experienced by the aircraft earlier during the approach; a more accurate prediction of the wind direction in the upper layer might be achieved if the available HKO data were from a time period that was closer to the time of the descent of the aircraft. The wind direction also impacts on the location at which there is a sharp decrease in the wind speed (Carruthers et al., 214). Figure 4(c) shows that the modelled wind speed is, in general, of the correct order of magnitude for both combined solutions, and essentially does not differ significantly from either the lower or upper solution in terms of accuracy. The wind direction is, however, better represented in the combined solutions, with the Combined Solution II fitting very well the step change in wind direction, although this is to be expected as the model has been fitted to the aircraft measurement data at this point. When the two solutions are combined, either smoothly (Figure ) or with a step change (Figure ), both the modelled headwind (not total wind speed) solutions reproduce the peak in headwind as shown by the measurements Near-ground-level anemometer wind speed and direction measurements The graphs shown in Figures 6 and 7 show the comparison of the modelled results against the 1 min average measurements. The period of the measurement (Table ) has been approximated according to the distance of the anemometer from the airport using the categorisation of the anemometers into:, Hilltop, Leeside and, as shown in Figure 1. Figure 6 and compares the measured wind speed with the lower and upper flow solutions, respectively. As the lower flow solution is driven primarily by the CCH measurement data, the lower solution gives better agreement at the ground-level monitors than the upper solution. In fact, as all the monitors are close to the ground, there is negligible difference between the wind speed values for the lower flow solution (Figure 6) and the Combined Solution II (Figure 7). The comparison between measured and modelled wind directions (Figure 7) shows that all modelled values lie within 4 of the measured values, with the majority of values having much better agreement than this. The statistics presented in Table 6 indicate that the lower solution and both the combined solutions give good predictions for the flow parameters recorded at the near-ground anemometers LIDAR measurements Figure 8 shows the LIDAR measurements at HKIA, zoomed in to show the radial velocity over the airport and the location of the aircraft approach. The dimensions of the hollow blue rectangle are 3km 7 km. Figure 8 and (c) shows the modelled results for the two combined solutions. Qualitatively, 216 Royal Meteorological Society Meteorol. Appl. 23: (216)

8 189 Real-time prediction of airflow over complex terrain Table 6. Statistics relating to wind speed and wind direction, for the lower (LOW) and upper (UPP) solutions, and Combined Solutions I (CI) and II (CII). Mean bias (m s 1 for wind speed and for wind direction) Wind speed Wind direction Proportion within a factor of two of the observed Correlation LOW UPP CI CII LOW UPP CI CII LOW UPP CI CII Key: Outline for model results comparisons Flight path Radar scan measurement height Runway Radial wind speed (m s 1) Terrain height (m) (c) Figure 8. Validation of results against the LIDAR scan data at the airport scan data Combined Solution I and (c) Combined Solution II. the modelled results look encouraging; however, it is important to note that due to the nature of the radial scan, as the modelled prevailing wind is in generally the correct direction, the transition between negative radial wind and positive radial wind measured by the LIDAR is easy to reproduce. It is the finer details of the wind speed that are important to replicate in the modelling system. However, both combined solutions appear to represent the overall features of the LIDAR scan quite reasonably. As expected, some of smaller-scale aspects of the flow are not modelled.. Discussion The present study compares the FLOWSTAR model predictions with measured meteorological data during an extreme wind shear event. These measurements include total wind speed, headwind speed and wind direction values recorded on the aircraft, ground-based wind speed and direction measurements from the HKO network and LIDAR scan measurements taken at the airport. A number of enhancements to the modelling approach discussed in the Carruthers et al. (214) paper have been 216 Royal Meteorological Society implemented; these include the use of upstream real-time vertical-profile wind data and high-resolution spatially varying roughness as inputs to the model, together with linear superposition of model runs to create a solution that accounts for the vertical shear in wind direction and speed. Applying this modified FLOWSTAR approach to the extreme wind event at the HKIA demonstrates improvements in model performance; however, although the variation in headwind speed along the flight path is well modelled, its magnitude is overestimated and the modelled total wind speed does not replicate the sharp decrease observed during the aircraft approach. This may in part be due to the use of measured data obtained from a radiosonde ascent 2 h after the aborted landing, but could also be due to non-linear features not captured by the model, such as hydraulic jumps and complex wind structures. These non-linear features have been suggested as the cause of complex turbulent flows downstream of the mountains in East Falkland as observed by Mobbs et al. (2), when a strong elevated inversion prevailed and a large speed-up in wind speed (Δs > 1) was observed downstream of the mountains. Interestingly, in the study associated with that field campaign (Sheridan and Vosper, 26), high-resolution numerical modelling shows large temporal variations in wind speed and Meteorol. Appl. 23: (216)

9 19 J. Stocker et al. direction similar to those observed at HKIA in the present study. The model improvements discussed in the present study show that FLOWSTAR has the potential to be used for real-time forecasting of extreme events at airports where the terrain generates highly turbulent and variable flows. For this to be achieved, an ensemble modelling approach would need to be developed to determine the probability of such an extreme event; this would use a range of upstream profiles for wind speed, wind direction and temperature as informed by the surface and profile measurements at CCH and the radiosonde ascent at the HKO. For optimum performance, an improved real-time estimate of the boundary layer height would be required and in addition the model should be optimized using LIDAR and aircraft data. Another development would be the inclusion of hydraulic jumps by forcing a rapid transition from supercritical to subcritical flow downstream of the mountains. Prior to being incorporated into a forecasting system, further validation of the modelling approach should be undertaken using data from additional extreme wind events at HKIA, where the required meteorological parameters are being recorded. References Carruthers DJ, Hunt JCR, Weng W-S A computational model of stratified turbulent airflow over hills FLOWSTAR I. In Computer Techniques in Environmental Studies, Proceedings of Envirosoft 88, Zanetti P (ed). Springer Verlag; Carruthers DJ, Ellis A, Hunt JCR, Chan PW Modelling of wind shear downwind of mountain ridges at Hong Kong International. Meteorol. Appl. 21(1): Chan PW A significant wind shear event leading to aircraft diversion at the Hong Kong International. Meteorol. Appl. 19(1): Cheung P, Lam CC, Chan PW. 28. Numerical simulation of wind gusts in terrain-disrupted airflow at the Hong Kong International. In 13th Conference on Mountain Meteorology, 11 August 28, Whistler, BC, Canada. Cotton WR, Pielke RA Sr, Walko RL, Liston GE, Tremback C, Jiang H, et al. 23. RAMS 21: current status and future directions. Meteorol. Atmos. Phys. 82: 29. Eidsvik KJ, Holstad A, Lie I, Utnes T. 24. A prediction system for local wind variations in mountainous terrain. Boundary-Layer Meteorol. 112: Golding BW NIMROD: a system for generating automated very short range forecasts. Meteorol. Appl. (1): Hong Kong Observatory Hong Kong Observatory Metadata 212. Hunt JCR, Richards KJ, Brighton PWM Stably stratified shear flow over low hills. Q. J. R. Meteorol. Soc. 114: Hunt JCR, Tampieri F, Weng W-S, Carruthers DJ Air flow and turbulence over complex terrain: a colloquium and a computational workshop. J. Fluid Mech. 227: Lorenc AC. 23. Weather Prediction: Data Assimilation. Encyclopaedia of Atmospheric Sciences Mobbs SD, Vosper SB, Sheridan PF, Cardoso R, Burton RR, Arnold SJ, et al. 2. Observations of downslope winds and rotors in the Falkland Islands. Q. J. R. Meteorol. Soc. 131: Sheridan PF, Vosper SB. 26. Numerical simulations of rotors, hydraulic jumps and eddy shedding in the Falkland Islands. Atmos. Sci. Let. 6: Warrilow DA, Buckley E The impact of land surface processes on the moisture budget of a climate model. Ann. Geophys. 7: Royal Meteorological Society Meteorol. Appl. 23: (216)