High-resolution Numerical Weather Prediction Research Articles

A Comparative Verification of High-Resolution Precipitation Forecasts Using Model Output Statistics

Verification of localized events such as precipitation has become even more challenging with the advent of high-resolution mesoscale numerical weather prediction (NWP). The realism of a forecast suggests that it should compare well against precipitation radar imagery with similar resolution, both spatially and temporally. Spatial verification methods solve some of the representativity issues that point verification gives rise to. In this paper, a verification strategy based on model output statistics (MOS) is applied that aims to address both double-penalty and resolution effects that are inherent to comparisons of NWP models with different resolutions. Using predictors based on spatial precipitation patterns around a set of stations, an extended logistic regression (ELR) equation is deduced, leading to a probability forecast distribution of precipitation for each NWP model, analysis, and lead time. The ELR equations are derived for predictands based on areal-calibrated radar precipitation and SYNOP observations. The aim is to extract maximum information from a series of precipitation forecasts, like a trained forecaster would. The method is applied to the nonhydrostatic model Harmonie-AROME (2.5-km resolution), HIRLAM (11-km resolution), and the ECMWF model (16-km resolution), overall yielding similar Brier skill scores for the three postprocessed models, but somewhat larger differences for individual lead times. In addition, the fractions skill score is computed using the three deterministic forecasts, showing slightly higher skill for the Harmonie-AROME model. In other words, despite the realism of Harmonie-AROME precipitation forecasts, they only perform similarly or somewhat better than precipitation forecasts from the two lower-resolution models, at least in the Netherlands.

On forecasting wet-snow avalanche activity using simulated snow cover data

Wet-snow avalanches are relatively poorly understood and difficult to forecast. By definition, water is required in the snow cover, thus assessing the liquid water content of the snow cover is of paramount importance for wet-snow avalanche forecasting. While evaluating wet-snow instability through field measurements is difficult, physically based snow cover models can be used to estimate the amount of liquid water within the snow cover using meteorological input. Recently, an index based on the liquid water content of the snow cover was suggested showing high potential to predict the onset of wet-snow avalanche activity. However, as the snow cover model was forced with data from automated weather stations (AWS) only a now-cast was possible. As snow cover conditions quickly change during snow melt, a forecast would be useful. For this study, we therefore force the 1-D physically based snow cover model SNOWPACK with data from the high-resolution numerical weather prediction model COSMO and investigate whether forecasting regional patterns of the onset of wet-snow avalanche activity is feasible. To validate the index, we compared simulations performed at the location of numerous AWS in the Swiss Alps with wet-snow avalanche observations from the corresponding region. Only by forcing SNOWPACK with data from automated weather stations up to the actual day and then adding the forecasted input data to produce a forecast led to results comparable to the simulations with station data only. While using this setup, the index was able to predict the onset of wet-snow avalanching with a probability of detection of >80% for three winters between 2013 and 2016 and for two different climate regions in Switzerland. However, the false alarm ratio was high (up to 80%), suggesting that further refinements of the classification method are needed.

A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data

A boundary layer scaling (BLS) method for predicting long-term average near-surface wind speeds and power densities was developed in this work. The method was based on the scaling of reference climatological data either from long-term average wind maps or from hourly wind speeds obtained from high-resolution Numerical Weather Prediction (NWP) models, with case study applications from Great Britain. It incorporated a more detailed parameterisation of surface aerodynamics than previous studies and the predicted wind speeds and power densities were validated against observational wind speeds from 124 sites across Great Britain. The BLS model could offer long-term average wind speed predictions using wind map data derived from long-term observational data, with a mean percentage error of 1.5% which provided an improvement on the commonly used NOABL (Numerical Objective Analysis of Boundary Layer) wind map. The boundary layer scaling of NWP data was not, however, able to improve upon the use of raw NWP data for near surface wind speed predictions. However, the use of NWP data scaled by the BLS model could offer improved power density predictions compared to the use of the reference data sets. Using a vertical scaling of the shape factor of a Weibull distribution fitted to the BLS NWP data, power density predictions with a 1% mean percentage error were achieved. This provided a significant improvement on the use of a fixed shape factor which must be utilised when only long-term average wind speeds are available from reference wind maps. The work therefore highlights the advantages that use of a BLS model for wind speed and NWP data for power density predictions can offer for small to medium scale wind energy resource assessments, potentially facilitating more robust annual energy production and financial assessments of prospective small and medium scale wind turbine installations.

Verification of pre-monsoon temperature forecasts over India during 2016 with a focus on heatwave prediction

Abstract. The operational medium-range weather forecasting based on numerical weather prediction (NWP) models are complemented by the forecast products based on ensemble prediction systems (EPSs). This change has been recognised as an essentially useful tool for medium-range forecasting and is now finding its place in forecasting the extreme events. Here we investigate extreme events (heatwaves) using a high-resolution NWP model and its ensemble models in union with the classical statistical scores to serve verification purposes. With the advent of climate-change-related studies in the recent past, the rising number of extreme events and their plausible socio-economic effects have encouraged the need for forecasting and verification of extremes. Applying the traditional verification scores and associated methods to both the deterministic and the ensemble forecast, we attempted to examine the performance of the ensemble-based approach in comparison to the traditional deterministic method. The results indicate an appreciable competence of the ensemble forecast at detecting extreme events compared to the deterministic forecast. Locations of the events are also better captured by the ensemble forecast. Further, it is found that the EPS smoothes down the unexpectedly increasing signals, thereby reducing the false alarms and thus proving to be more reliable than the deterministic forecast.

Skill of Predicting Heavy Rainfall Over India: Improvement in Recent Years Using UKMO Global Model

The quantitative precipitation forecast (QPF) performance for heavy rains is still a challenge, even for the most advanced state-of-art high-resolution Numerical Weather Prediction (NWP) modeling systems. This study aims to evaluate the performance of UK Met Office Unified Model (UKMO) over India for prediction of high rainfall amounts (>2 and >5 cm/day) during the monsoon period (JJAS) from 2007 to 2015 in short range forecast up to Day 3. Among the various modeling upgrades and improvements in the parameterizations during this period, the model horizontal resolution has seen an improvement from 40 km in 2007 to 17 km in 2015. Skill of short range rainfall forecast has improved in UKMO model in recent years mainly due to increased horizontal and vertical resolution along with improved physics schemes. Categorical verification carried out using the four verification metrics, namely, probability of detection (POD), false alarm ratio (FAR), frequency bias (Bias) and Critical Success Index, indicates that QPF has improved by >29 and >24% in case of POD and FAR. Additionally, verification scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and SEDI (Symmetric EDI) are used with special emphasis on verification of extreme and rare rainfall events. These scores also show an improvement by 60% (EDS) and >34% (EDI and SEDI) during the period of study, suggesting an improved skill of predicting heavy rains.

Collaborative Efforts between the United States and United Kingdom to Advance Prediction of High-Impact Weather

AbstractIn recent years, a growing partnership has emerged between the Met Office and the designated U.S. national centers for expertise in severe weather research and forecasting, that is, the National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory (NSSL) and the NOAA Storm Prediction Center (SPC). The driving force behind this partnership is a compelling set of mutual interests related to predicting and understanding high-impact weather and using high-resolution numerical weather prediction models as foundational tools to explore these interests.The forum for this collaborative activity is the NOAA Hazardous Weather Testbed, where annual Spring Forecasting Experiments (SFEs) are conducted by NSSL and SPC. For the last decade, NSSL and SPC have used these experiments to find ways that high-resolution models can help achieve greater success in the prediction of tornadoes, large hail, and damaging winds. Beginning in 2012, the Met Office became a contributing partner in annual SFEs, bringing complementary expertise in the use of convection-allowing models, derived in their case from a parallel decadelong effort to use these models to advance prediction of flash floods associated with heavy thunderstorms.The collaboration between NSSL, SPC, and the Met Office has been enthusiastic and productive, driven by strong mutual interests at a grassroots level and generous institutional support from the parent government agencies. In this article, a historical background is provided, motivations for collaborative activities are emphasized, and preliminary results are highlighted.

A Diagnosis of Excessive Mixing in Smagorinsky Subfilter-Scale Turbulent Kinetic Energy Models

Abstract Large-eddy simulation (LES) models are widely used to study atmospheric turbulence. The effects of small-scale motions that cannot be resolved need to be modeled by a subfilter-scale (SFS) model. The SFS contribution to the turbulent fluxes is typically significant in the surface layer. This study presents analytical solutions of the classical Smagorinsky SFS turbulent kinetic energy (TKE) model including a buoyancy flux contribution. Both a constant length scale and a stability-dependent one as proposed by Deardorff are considered. Analytical expressions for the mixing functions are derived and Monin–Obukhov similarity relations that are implicitly imposed by the SFS TKE model are diagnosed. For neutral and weakly stable conditions, observations indicate that the turbulent Prandtl number (PrT) is close to unity. However, based on observations in the convective boundary layer, a lower value for PrT is often applied in LES models. As a lower Prandtl number promotes a stronger mixing of heat, this may cause excessive mixing, which is quantified from a direct comparison of the mixing function as imposed by the SFS TKE model with empirical fits from field observations. For a strong stability, the diagnosed mixing functions for both momentum and heat are larger than observed. The problem of excessive mixing will be enhanced for anisotropic grids. The findings are also relevant for high-resolution numerical weather prediction models that use a Smagorinsky-type TKE closure.

A Comparison of Methods Used to Populate Neighborhood-Based Contingency Tables for High-Resolution Forecast Verification

Abstract As high-resolution numerical weather prediction models are now commonplace, “neighborhood” verification metrics are regularly employed to evaluate forecast quality. These neighborhood approaches relax the requirement that perfect forecasts must match observations at the grid scale, contrasting traditional point-by-point verification methods. One recently proposed metric, the neighborhood equitable threat score, is calculated from 2 × 2 contingency tables that are populated within a neighborhood framework. However, the literature suggests three subtly different methods of populating neighborhood-based contingency tables. Thus, this work compares and contrasts these three variants and shows they yield statistically significantly different conclusions regarding forecast performance, illustrating that neighborhood-based contingency tables should be constructed carefully and transparently. Furthermore, this paper shows how two of the methods use inconsistent event definitions and suggests a “neighborhood maximum” approach be used to fill neighborhood-based contingency tables.

Real-time precise point positioning augmented with high-resolution numerical weather prediction model

The tropospheric delay is one of the major error sources in precise point positioning (PPP), affecting the accuracy and precision of estimated coordinates and convergence time, which raises demand for a reliable tropospheric model, suitable to support PPP. In this study, we investigate the impact of three tropospheric models and mapping functions regarding position accuracy and convergence time. We propose a routine to constrain the tropospheric estimates, which we implemented in the in-house developed real-time PPP software. We take advantage of the high spatial resolution (4 × 4 km2) numerical weather prediction Weather Research and Forecasting (WRF) model and near real-time GNSS data combined by the least-squares collocation estimation to reconstruct the tropospheric delays. We also present mapping functions calculated from the WRF model using the ray-tracing technique. The performance tests are conducted on 14 Polish EUREF Permanent Network (EPN) stations during 3 weeks of different tropospheric conditions: calm, standard and severe. We consider six GNSS data processing variants, including two commonly used variants using a priori ZTD and mapping functions from UNB3m and VMF1-FC models, one with a priori ZTD and mapping functions calculated directly from WRF model and three variants using the aforementioned mapping functions but with ZTD model based on GNSS and WRF data used as a priori troposphere and to constrain tropospheric estimates. The application of a high-resolution GNSS/WRF-based ZTD model and mapping functions results in the best agreement with the official EPN coordinates. In both static and kinematic modes, this approach results in an average reduction of 3D bias by 20 and 10 mm, respectively, but an increase of 3D SDs by 1.5 and 4 mm, respectively. The application of high-resolution tropospheric model also shortens the convergence time, for example, for a 10 cm convergence level, from 67 to 58 min for the horizontal components and from 79 to 63 min for the vertical component.

The Tropical Air–Sea Propagation Study (TAPS)

Abstract The purpose of the Tropical Air–Sea Propagation Study (TAPS), which was conducted during November–December 2013, was to gather coordinated atmospheric and radio frequency (RF) data, offshore of northeastern Australia, in order to address the question of how well radio wave propagation can be predicted in a clear-air, tropical, littoral maritime environment. Spatiotemporal variations in vertical gradients of the conserved thermodynamic variables found in surface layers, mixing layers, and entrainment layers have the potential to bend or refract RF energy in directions that can either enhance or limit the intended function of an RF system. TAPS facilitated the collaboration of scientists and technologists from the United Kingdom, the United States, France, New Zealand, and Australia, bringing together expertise in boundary layer meteorology, mesoscale numerical weather prediction (NWP), and RF propagation. The focus of the study was on investigating for the first time in a tropical, littoral environment the i) refractivity structure in the marine and coastal inland boundary layers; ii) the spatial and temporal behavior of momentum, heat, and moisture fluxes; and iii) the ability of propagation models seeded with refractive index functions derived from blended NWP and surface-layer models to predict the propagation of radio wave signals of ultrahigh frequency (UHF; 300 MHz–3 GHz), super-high frequency (SHF; 3–30 GHz), and extremely high frequency (EHF; 30–300 GHz). Coordinated atmospheric and RF measurements were made using a small research aircraft, slow-ascent radiosondes, lidar, flux towers, a kitesonde, and land-based transmitters. The use of a ship as an RF-receiving platform facilitated variable-range RF links extending to distances of 80 km from the mainland. Four high-resolution NWP forecasting systems were employed to characterize environmental variability. This paper provides an overview of the TAPS experimental design and field campaign, including a description of the unique data that were collected, preliminary findings, and the envisaged interpretation of the results.

WSR-88D Radar Data Processing at NCEP

Abstract Real-time access to level II radar data became available in May 2005 at the National Centers for Environmental Prediction (NCEP) Central Operations (NCO). Using these real-time data in operational data assimilation requires the data be processed reliably and efficiently through rigorous data quality controls. To this end, advanced radar data quality control techniques developed at the National Severe Storms Laboratory (NSSL) are combined into a comprehensive radar data processing system at NCEP. Techniques designed to create a high-resolution reflectivity mosaic developed at the NSSL are also adopted and installed within the NCEP radar data processing system to generate hourly 3D reflectivity mosaics and 2D-derived products. The processed radar radial velocity and 3D reflectivity mosaics are ingested into NCEP’s data assimilation systems to improve operational numerical weather predictions. The 3D reflectivity mosaics and 2D-derived products are also used for verification of high-resolution numerical weather prediction. The NCEP radar data processing system is described.

Predicting ocean surface currents using numerical weather prediction model and Kohonen neural network: a northern Adriatic study

The paper documents a concept of ocean forecasting system for ocean surface currents based on self-organizing map (SOM) trained by high-resolution numerical weather prediction (NWP) model and high-frequency (HF) radar data. Wind and surface currents data from the northern Adriatic coastal area were used in a 6-month long training phase to obtain SOM patterns. Very high correlation between current and joined current and wind SOM patterns indicated the strong relationship between winds and currents and allowed for creation of a prediction system. Increasing SOM dimensions did not increase reliability of the forecasting system, being limited by the amount of the data used for training and achieving the lowest errors for 4 × 4 SOM matrix. As the HF radars and high-resolution NWP models are strongly expanding in coastal oceans, providing reliable and long-term datasets, the applicability of the proposed SOM-based forecasting system is expected to be high.

The Convective Precipitation Experiment (COPE): Investigating the Origins of Heavy Precipitation in the Southwestern United Kingdom

Abstract The Convective Precipitation Experiment (COPE) was a joint U.K.–U.S. field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly as a result of the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the United States. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve numerical weather prediction (NWP) model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the U.K. BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360° volume scans over 10 elevation angles approximately every 5 min and was augmented by two Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper i) provides an overview of the COPE field campaign and the resulting dataset, ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone, and iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.

Modelling hazardous surface hoar layers across western Canada with a coupled weather and snow cover model

Destructive snow avalanches in western Canada are often caused by failure in buried surface hoar layers. Numerical snow cover models can simulate the formation and evolution of these layers, which could help avalanche forecasters assess the location and timing of avalanches. To investigate this application, we compared modelled surface hoar layers with snow and avalanche observations from the Coast, Columbia, and Rocky Mountains of western Canada. Surface hoar formation and evolution was modelled by forcing the snow cover model SNOWPACK with data from a high-resolution numerical weather prediction model. Surface hoar formation was verified with daily snow surface observations at 88 observation sites over two winters, and the evolution of buried layers was verified with avalanche observations and persistent weak layer assessments from 67 avalanche forecast regions. The frequency of surface hoar formation was over-predicted by 40%, although since modelled crystal sizes were moderately correlated with observed sizes, the more hazardous layers were often distinguished. A structural stability index in SNOWPACK often identified surface hoar layers during storm slab avalanche activity, but identifying layers during persistent slab avalanche activity was more difficult. Model limitations included uncertain meteorological inputs, errors in SNOWPACK's snow surface energy balance, and representing the necessary spatial scales. Despite these limitations, the coupled model resolved differences between major Canadian mountain ranges and could improve avalanche forecasts in data-sparse regions.

Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities

Abstract Rapid advancements of computer technologies in recent years made the real-time transferring and integration of high-volume, multisource data at a centralized location a possibility. The Multi-Radar Multi-Sensor (MRMS) system recently implemented at the National Centers for Environmental Prediction demonstrates such capabilities by integrating about 180 operational weather radars from the conterminous United States and Canada into a seamless national 3D radar mosaic with very high spatial (1 km) and temporal (2 min) resolution. The radar data can be integrated with high-resolution numerical weather prediction model data, satellite data, and lightning and rain gauge observations to generate a suite of severe weather and quantitative precipitation estimation (QPE) products. This paper provides an overview of the initial operating capabilities of MRMS QPE products.

Intrinsic Predictability of the 20 May 2013 Tornadic Thunderstorm Event in Oklahoma at Storm Scales

AbstractUsing a high-resolution convection-allowing numerical weather prediction model, this study seeks to explore the intrinsic predictability of the severe tornadic thunderstorm event on 20 May 2013 in Oklahoma from its preinitiation environment to initiation, upscale organization, and interaction with other convective storms. This is accomplished through ensemble forecasts perturbed with minute initial condition uncertainties that were beyond detection capabilities of any current observational platforms. It was found that these small perturbations, too small to modify the initial mesoscale environmental instability and moisture fields, will be propagated and evolved via turbulence within the PBL and rapidly amplified in moist convective processes through positive feedbacks associated with updrafts, phase transitions of water species, and cold pools, thus greatly affecting the appearance, organization, and development of thunderstorms. The forecast errors remain nearly unchanged even when the initial perturbations (errors) were reduced by as much as 90%, which strongly suggests an inherently limited predictability for this thunderstorm event for lead times as short as 3–6 h. Further scale decomposition reveals rapid error growth and saturation in meso-γ scales (regardless of the magnitude of initial errors) and subsequent upscale growth into meso-β scales.

Hydrologic Evaluation of NWP-Adjusted CMORPH Estimates of Hurricane-Induced Precipitation in the Southern Appalachians

AbstractSatellite-retrieved precipitation has the potential to support flood modeling in mountainous areas. However, to reach this potential satellite estimates need to be corrected for the severe underestimation exhibited in orography-induced heavy precipitation events (HPEs). This paper assesses an existing satellite precipitation error correction technique driven by high-resolution numerical weather prediction (NWP) simulations of HPEs in complex terrain. The study is based on NOAA Climate Prediction Center morphing technique (CMORPH) high-resolution precipitation estimates of six such events induced by hurricane landfalls in the southern Appalachian mountainous region. A distributed hydrological model (Coupled Routing and Excess Storage model) is applied to evaluate the impact of the proposed satellite precipitation error correction on flood simulations for 20 basins of various sizes in this mountainous region. The results demonstrate significant improvements due to the NWP-based adjustment technique in terms of both the precipitation error characteristics and corresponding runoff simulations. These improvements are shown to be comparable to those from the postprocessed gauge-adjusted CMORPH precipitation product, which is promising for advancing hydrologic uses of satellite rainfall in mountainous areas lacking ground observations.

Probabilistic Forecasts of Mesoscale Convective System Initiation Using the Random Forest Data Mining Technique

Abstract A data mining and statistical learning method known as a random forest (RF) is employed to generate 2-h forecasts of the likelihood for initiation of mesoscale convective systems (MCS-I). The RF technique uses an ensemble of decision trees to relate a set of predictors [in this case radar reflectivity, satellite imagery, and numerical weather prediction (NWP) model diagnostics] to a predictand (in this case MCS-I). The RF showed a remarkable ability to detect MCS-I events. Over 99% of the 550 observed MCS-I events were detected to within 50 km. However, this high detection rate came with a tendency to issue false alarms either because of premature warning of an MCS-I event or in the continued elevation of RF forecast likelihoods well after an MCS-I event occurred. The skill of the RF forecasts was found to increase with the number of trees and the fraction of positive events used in the training set. The skill of the RF was also highly dependent on the types of predictor fields included in the training set and was notably better when a more recent training period was used. The RF offers advantages over high-resolution NWP because it can be run in a fraction of the time and can account for nonlinearly varying biases in the model data. In addition, as part of the training process, the RF ranks the importance of each predictor, which can be used to assess the utility of new datasets in the prediction of MCS-I.

Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

Quantitative precipitation estimation and forecasting (QPE and QPF) are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF) model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale band-like precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data.

Performance of super high resolution numerical weather prediction model in forecasting terrain‐disrupted airflow at the Hong Kong International Airport: case studies

ABSTRACTHong Kong International Airport (HKIA) is situated in an area of complex terrain. Mountains near the airport can lead to terrain‐disrupted airflow disturbances, which may bring about hazardous weather phenomena for aviation, such as low‐level windshear and turbulence. Timely forecasting of such airflow disturbances would be useful in providing early warnings to the pilots. The Hong Kong Observatory has been running a super high resolution numerical weather prediction model suite, the aviation model (AVM), since late 2013, with spatial resolution up to 200 m immediately around HKIA. After testing and tuning over the past year, the performance of the AVM in forecasting terrain‐disrupted airflow at the airport area is investigated in this study. Two typical cases of such complex airflow are considered, Foehn wind and mountain wave. The AVM is found to capture successfully some salient features of the airflow as observed around HKIA through comparison with light detection and ranging (LIDAR) and weather buoy observations. The model also helps in shedding new light on the airflow from the vertical cross sections of the winds across the mountains. Limitations of the AVM are also discussed.