Session 1 Science underpinning meteorological observations, forecasts, advisories and warnings 1.3 Aerodrome throughput 1.3.1 Wake vortex detection and prediction Frequent-output sub-kilometric NWP models supporting enhanced runway throughput and performance-based navigation K.K. Hon, Hong Kong Observatory kkhon@hko.gov.hk -------------------------------co-authors: P.W. Chan -------------------------------speaker: P.W. Li Rapid advances in high-performance computing technology and meteorological modelling capability have made within reach numerical weather prediction (NWP) at unprecedented spatial resolutions even under real-time, operational settings. Indeed, aviation-specific initiatives in fine-resolution NWP have been reported in a number of major airports around the world [1-2], with applications tailored towards respective local service needs. For the Hong Kong International Airport (HKIA) one of the busiest in the world the surrounding terrain as well as complex land-sea distribution presents additional challenges in the form of low-level windshear and turbulence, which are known aviation safety hazards [3]. Fig. 1 Configuration (left; terrain in coloured contours) of the outer 600-m ( AVM-PRD ) and inner 200-m ( AVM-xHKA ) resolution domains of HKO s Aviation Model. Zoom-in image on the right highlights the complex geography surrounding HKIA. To support short-term, fine-scale meteorological forecasts for HKIA, the Hong Kong Observatory (HKO) has commenced operation of the Aviation Model (AVM) [4] since late 2014. The AVM is a sub-kilometric NWP suite based on the Weather Research and Forecast (WRF) model [5] and provides detailed hourly-updated forecasts for the immediate vicinity of HKIA at a horizontal resolution of up to 200 m (Fig. 1). In addition to the conventional elements of winds, temperature and pressure, the AVM also produces specialised forecast guidance for occurrence potential of low-level windshear and turbulence, in the form of simulated LIDAR headwind profiles and eddy dissipation rate (EDR) respectively (Fig. 2).
Fig. 2 Sample low-level windshear forecast guidance based on simulated LIDAR return using the 200-m resolution inner domain of the AVM. Potential areas of windshear occurrence along arrival/departure glide paths are highlighted in red. During the 1-hour interval covered by the above forecast, 4 aircraft reported encountering significant low-level windshear (actual pilot reports shown in inset; aircraft call signs masked). Meanwhile, evolving ATM needs (e.g. under the Aviation System Block Upgrade, or ASBU, methodology) have led to new requirements for specialised meteorological forecasts for the terminal area and beyond (e.g. under the concept of Meteorological Services for the Terminal Area, or MSTA) [6]. This places additional challenges on the provision of weather data and/or products including aviation-specific NWP in support of Air Traffic Management (ATM) at the Nowcast and Short-term Forecast timeframes. One of the areas where the contribution of NWP might not be immediately obvious would be aircraft wake turbulence (or wake vortex) mitigation. With a view to enhancing air traffic efficiency by increased runway throughput, a number of airports in both Europe and the US have taken forward RECAT, or re-categorisation of wake turbulence separation minina, initiatives. At HKIA, the first series of wake turbulence measurements (covering both arrival and departure corridors) have been conducted by HKO between 2014 and 2016 [7] (Fig. 3). An eventual goal of the ASBU work package on Advanced Wake Turbulence Separation includes a proposed move towards full dynamic Weather Dependent Separation (WDS) [8]. In WDS, the wake turbulence separation minima, one of the major factors governing airport arrival rate, would be determined dynamically with explicit consideration of current and anticipated meteorological conditions. This requires, in addition to establishing validated wake turbulence risk models and the corresponding time-based pairwise separation matrix, reliable frequentupdating short-term forecasts of key meteorological parameters governing the decay and transport of aircraft wake vortices (e.g. crosswind and low-level turbulence intensity evolution at minute intervals or so) [9]. For airports which are susceptible to spatial and temporal flow inhomogeneity at the meso-/micro-scales, such as HKIA, conventional limited-area NWP models, with typical spatial resolution of a few kilometres and output frequency in terms of hours, are clearly incapable of meeting such technical demands.
Fig. 3 A unit of short-range LIDAR (SRL) overlooking corridor 25RA (i.e. arriving from the east using the North Runway) of HKIA during the first series of wake turbulence measurements conducted by HKO in 2014 (top panel). Sample sequence of observed wake vortex evolution (coloured pixels representing radial velocities) shown in the bottom panel. Here the capability of HKO s AVM in reproducing detailed wind variations along the arrival/departure glide paths, as well as near-surface turbulence intensity in terms of the eddy dissipation rate (EDR) [10], would be examined through comparison with HKIA LIDAR measurements at up to 1-minute frequencies (Fig. 4). Additionally, the fine-resolution lowertropospheric wind profile nowcasts near the terminal area would be capable of providing meteorological support to continuous climb/descent operations (CCO/CDO) and performancebased navigation (PBN) for improved flexibility and fuel efficiency, contributing to seamless 4-D trajectory-based forecasts in a SWIM (System-Wide Information Management) environment.
Fig. 4 Complex wind variations along the landing glide path of HKIA as revealed by LIDAR and predicted using AVM (left). On the right, LIDAR-derived EDR distribution (top) around HKIA is compared against the concurrent forecast by AVM (bottom). It is expected that rapidly-cycled sub-kilometric NWP models with high data output frequency would be essential in supporting and integrating with the next-generation air navigation systems in fulfilment of ASBU and beyond. References [1] Hagelin, S., Auger, L., Brovelli, P. and Dupont, O., 2014: Nowcasting with the AROME Model: First Results from the High-Resolution AROME Airport. Wea. Forecasting, 29, 773 787. [2] Boutle, I. A., Finnenkoetter, A., Lock, A. P. and Wells, H., 2016: The London Model: forecasting fog at 333 m resolution. Q.J.R. Meteorol. Soc., 142: 360 371. [3] Shun, C. M. and Chan, P. W, 2008: Applications of an infrared Doppler Lidar in detection of wind shear. J. Atmos. Oceanic Technol., 25(5): 637 655. [4] Chan, P. W. and Hon, K. K., 2016: Performance of super high resolution numerical weather prediction model in forecasting terrain-disrupted airflow at the Hong Kong International Airport: case studies. Met. Apps, 23: 101 114. [5] Skamarock W. C., Klemp J. B., 2007: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys. 227: 3465 3485.
[6] Shun, C.M. and Song, M.K., 2010: New Meteorological Services Supporting Air Traffic Management. 47th Conference of Directors General of Civil Aviation Asia and Pacific Regions, Macau, China. [7] Hon, K. K. and Chan P. W., 2017: Aircraft wake vortex observations in Hong Kong. Journal of Radars, (preprint), DOI: 10.12000/JR17072. [8] ICAO, 2012: ASBU Working Document, Edition 2, Version 3. [9] Holzaepfel, F., Gerz, T. and Schwarz, C., 2011: The Wake Vortex Prediction and Monitoring System WSVBS - Design and Performance at Frankfurt and Munich Airport. Ninth USA/Europe Air Traffic Management Research and Development Seminar, Berlin, Germany. [10] Hon, K. K. and Chan, P.W., 2014: Sub-kilometre simulation of terrain-disrupted airflow at the Hong Kong International Airport Aviation applications and inter-comparison with LIDAR observations. 16th Conference on Mountain Meteorology, 18-22 August 2014, San Diego, CA, USA.