Light Detection And Ranging Observations Research Articles

Aerial Hybrid Adjustment of LiDAR Point Clouds, Frame Images, and Linear Pushbroom Images

In airborne surveying, light detection and ranging (LiDAR) strip adjustment and image bundle adjustment are customarily performed as separate processes. The bundle adjustment is usually conducted from frame images, while using linear pushbroom (LP) images in the bundle adjustment has been historically challenging due to the limited number of observations available to estimate the exterior image orientations. However, data from these three sensors conceptually provide information to estimate the same trajectory corrections, which is favorable for solving the problems of image depth estimation or the planimetric correction of LiDAR point clouds. Thus, our purpose with the presented study is to jointly estimate corrections to the trajectory and interior sensor states in a scalable hybrid adjustment between 3D LiDAR point clouds, 2D frame images, and 1D LP images. Trajectory preprocessing is performed before the low-frequency corrections are estimated for certain time steps in the following adjustment using cubic spline interpolation. Furthermore, the voxelization of the LiDAR data is used to robustly and efficiently form LiDAR observations and hybrid observations between the image tie-points and the LiDAR point cloud to be used in the adjustment. The method is successfully demonstrated with an experiment, showing the joint adjustment of data from the three different sensors using the same trajectory correction model with spline interpolation of the trajectory corrections. The results show that the choice of the trajectory segmentation time step is not critical. Furthermore, photogrammetric sub-pixel planimetric accuracy is achieved, and height accuracy on the order of mm is achieved for the LiDAR point cloud. This is the first time these three types of sensors with fundamentally different acquisition techniques have been integrated. The suggested methodology presents a joint adjustment of all sensor observations and lays the foundation for including additional sensors for kinematic mapping in the future.

Evolution of Secondary Periglacial Environment Induced by Thawing Permafrost near China–Russia Crude Oil Pipeline Based on Airborne LiDAR, Geophysics, and Field Observation

The China–Russia crude oil pipeline (CRCOP) operates at a temperature that continuously thaws the surrounding permafrost, leading to secondary periglacial phenomena along the route. However, the evolution and formation mechanisms of these phenomena are still largely unknown. We used multi-temporal airborne light detection and ranging (LiDAR), geophysical, and field observation data to quantify the scale of ponding and icing, capture their dynamic development process, and reveal their development mechanisms. The results show that the average depth of ponding within 5 m on both sides of the pipeline was about 31 cm. The volumes of three icings (A–C) above the pipeline were 133 m3, 440 m3, and 186 m3, respectively. Icing development can be divided into six stages: pipe trench settlement, water accumulation in the pipe trench, ponding pressure caused by water surface freezing, the formation of ice cracks, water overflow, and icing. This study revealed the advantages of airborne LiDAR in monitoring the evolution of periglacial phenomena and provided a new insight on the development mechanisms of the phenomena by combining LiDAR with geophysics and field observation. The results of our study are of great significance for developing disaster countermeasures and ensuring the safe operation of buried pipelines.

AFLI-Calib: Robust LiDAR-IMU extrinsic self-calibration based on adaptive frame length LiDAR odometry

As an effective complement to common laser scanning systems, the portable laser scanning system can acquire point clouds flexibly and quickly. Calibration between Light detection and ranging (LiDAR) sensors and inertial measurement units (IMU) is the prerequisite for laser scanning systems to obtain high-quality point clouds. Related methods have been proposed in the last two decades, where the global navigation satellite system (GNSS) or high-precision calibration fields are commonly used. However, the extrinsic self-calibration of LiDAR-IMU is challenging, due to the large distortion in single-frame point cloud caused by rapid motion and the position errors of IMU integration which drift quickly. At the same time, the highly dynamic motion patterns of portable devices and the changes in the scanned scene structure are not well considered in existing LiDAR odometry methods. To take better advantage of the characteristics of non-repetitive scanning LiDAR sensor, this paper proposes AFLI-Calib, which utilizes adaptive frame length LiDAR odometry to perform the extrinsic self-calibration of LiDAR-IMU. Unlike LiDAR odometry methods with a fixed frame length, the LiDAR frame length is dynamically adjusted according to the motion state of sensors and the matching stability of scenes. The single-frame point cloud is registered to the map through a linear-based continuous-time model, eliminating the motion distortion correction in advance. For further optimization of trajectory and extrinsic parameters, IMU raw measurements and LiDAR observations are involved in the multi-constraint optimization, through tightly-coupled IMU pre-integration constraints, LiDAR point-to-plane constraints, and prior constraints. The method is fully validated using self-collected calibration data of indoor and outdoor scenes and different motion modes. Experiments show that on the test data, the translation parameter accuracy of the method is 0.041 m, which is 56.3% higher than the state-of-the-art method. The standard deviation is significantly reduced, with translation deviation (0.017 m, 0.024 m, 0.022 m) and rotation deviation (0.17°, 0.25°, 0.15°), which verifies the robustness of our method. The average RMSE of distances to the reference point cloud acquired by the terrestrial laser scanning system (TLS) is 0.042 m, showing a high accuracy calibration result. Comparative experiments with the fixed frame length LiDAR odometry method and classical “correction-then-registration” motion distortion model further verify the superiority and effectiveness of the proposed adaptive frame length LiDAR odometry.

Urban ventilation assessment with improved vertical wind profile in high-density cities – Comparisons between LiDAR and conventional methods

The vertical wind speed profile is crucial to urban ventilation assessment and urban planning/design. This study uses Light Detection and Ranging (LiDAR) observation as benchmark to evaluate accuracy of wind profiles estimated by conventional methods. The conventional methods include Boundary Layer Wind Tunnel (BLWT), Regional Atmospheric Modeling System (RAMS), Power Law (PL), and Weather Research and Forecasting (WRF). The evaluation involves two typical urban sites with different densities under summer weak-wind conditions. Large Eddie Simulations (LES) are conducted to further investigate the sensitivity of urban ventilation assessment results to the deviations of wind profiles. The results indicate significant deviations in LES caused by conventional methods. The largest deviations of wind velocity ratio are found in mesoscale meteorological models (RAMS and WRF (>65%)). Deviations caused by physical and empirical models are smaller but still significant (BLWT (>25%) and PL (>40%)). Consequently, large deviations (>100%) of wind-relevant criterion for outdoor thermal comfort are observed. Finally, to balance accuracy and data availability, we recommend power law method as the optimal method to provide inflow boundary condition for numerical simulations when LiDAR observation is not available. We provide new and valuable understandings to improve urban ventilation assessment in high-density cities.

Influence of UAS Flight Altitude and Speed on Aboveground Biomass Prediction

The management of low-density savannah and woodland forests for carbon storage presents a mechanism to offset the expense of ecologically informed forest management strategies. However, existing carbon monitoring systems draw on vast amounts of either field observations or aerial light detection and ranging (LiDAR) collections, making them financially prohibitive in low productivity systems where forest management focuses on promoting resilience to disturbance and multiple uses. This study evaluates how UAS altitude and flight speed influence area-based aboveground forest biomass model predictions. The imagery was acquired across a range of UAS altitudes and flight speeds that influence the efficiency of data collection. Data were processed using common structures from motion photogrammetry algorithms and then modeled using Random Forest. These results are compared to LiDAR observations collected from fixed-wing manned aircraft and modeled using the same routine. Results show a strong positive relationship between flight altitude and plot-based aboveground biomass modeling accuracy. UAS predictions increasingly outperformed (2–24% increased variance explained) commercial airborne LiDAR strategies as acquisition altitude increased from 80–120 m. The reduced cost of UAS data collection and processing and improved biomass modeling accuracy over airborne LiDAR approaches could make carbon monitoring viable in low productivity forest systems.

Urban ventilation assessment with improved vertical wind profile in high-density cities – Investigations in nighttime extreme heat

Climate change and heatwaves challenge urban dwellers’ thermal comfort and health. In Hong Kong, more frequent and intensive extreme heat has been observed recently. This study uses Light Detection and Ranging (LiDAR) to observe vertical wind profiles and calibrate numerical simulation methods for urban ventilation assessment in nighttime extreme heat (i.e. hot nights (HNs)) at a high-density site. A cross-comparison is conducted to the characteristics between the observed HN-averaged wind profiles and commonly-used 24-h-period-averaged wind profiles in summer. The observation reveals a weaker wind environment in HNs than 24-h-periods, and the weakest condition is found in HNs during prolonged extreme heat where the impacts of weakened advection surpass enhanced thermal buoyancy. Furthermore, CFD simulations are conducted to evaluate the deviations on urban ventilation assessment caused by the lack of consideration of nighttime extreme heat when setting inlet wind profiles and site thermal conditions. In the simulation results, the 24-h-period-averaged wind profiles cause significant deviations on pedestrian-level wind speed and velocity ratio (empirical model (>45%); LiDAR observation (>20%)). Considerable deviations are found when unstable thermal stratification is ignored (>20%). Consistent deviations on vertical turbulent flow structures induced by the inexplicit coupling between thermal buoyancy and advection are found. The findings call for urgent attention to the wind conditions in HNs since they are most needed for releasing heat stress and urban overheating. It is recommended to include explicit buoyancy effects in both measurement and modeling works to optimize urban ventilation assessment for tackling extremely high-temperature and weak-wind conditions.

Fusion of optical, radar and waveform LiDAR observations for land cover classification

Land cover is an integral component for characterizing anthropogenic activity and promoting sustainable land use. Mapping distribution and coverage of land cover at broad spatiotemporal scales largely relies on classification of remotely sensed data. Although recently multi-source data fusion has been playing an increasingly active role in land cover classification, our intensive review of current studies shows that the integration of optical, synthetic aperture radar (SAR) and light detection and ranging (LiDAR) observations has not been thoroughly evaluated. In this research, we bridged this gap by i) summarizing related fusion studies and assessing their reported accuracy improvements, and ii) conducting our own case study where for the first time fusion of optical, radar and waveform LiDAR observations and the associated improvements in classification accuracy are assessed using data collected by spaceborne or appropriately simulated platforms in the LiDAR case. Multitemporal Landsat-5/Thematic Mapper (TM) and Advanced Land Observing Satellite-1/ Phased Array type L-band SAR (ALOS-1/PALSAR) imagery acquired in the Central New York (CNY) region close to the collection of airborne waveform LVIS (Land, Vegetation, and Ice Sensor) data were examined. Classification was conducted using a random forest algorithm and different feature sets in terms of sensor and seasonality as input variables. Results indicate that the combined spectral, scattering and vertical structural information provided the maximum discriminative capability among different land cover types, giving rise to the highest overall accuracy of 83% (2–19% and 9–35% superior to the two-sensor and single-sensor scenarios with overall accuracies of 64–81% and 48–74%, respectively). Greater improvement was achieved when combining multitemporal Landsat images with LVIS-derived canopy height metrics as opposed to PALSAR features, suggesting that LVIS contributed more useful thematic information complementary to spectral data and beneficial to the classification task, especially for vegetation classes. With the Global Ecosystem Dynamics Investigation (GEDI), a recently launched LiDAR instrument of similar properties to the LVIS sensor now operating onboard the International Space Station (ISS), it is our hope that this research will act as a literature summary and offer guidelines for further applications of multi-date and multi-type remotely sensed data fusion for improved land cover classification.

Merging Landsat and airborne LiDAR observations for continuous monitoring of floodplain water extent, depth and volume

The Darling River system in Australia is under pressure from water extraction and climate change. Management interventions such as environmental flow releases require understanding of water storage dynamics and the connectivity of floodplains and wetlands. Such knowledge can be gleaned from the long observational record of the Landsat series of satellite sensors and high (<5 m) resolution digital elevation models derived from airborne light detection and ranging (LiDAR). Here, for the first time, we develop and demonstrate an approach to reconstruct 16-day floodplain water dynamics, including extent, depth, and volume for a long Landsat time series (1987 to present). Time series mapping of surface water extent at 5-m resolution was achieved by topographic downscaling of Landsat-derived surface water data. We propose a simple and effective algorithm to restore missing data in the images caused by, e.g., cloud and shadows, swath edges and the Landsat 7 Scan Line Corrector (SLC) failure, thereby increasing the number of useable images five-fold. The 5-m surface water extent maps clearly delineate the narrow river channel and the boundary of floodplain wetlands. They can capture the development, peak and retreat of flood events. By combining Landsat and airborne LiDAR observations, we produced time series of surface water depth mapping at 5-m resolution, accounting for the degree of hydraulic surface water connectivity. Based on these maps, we derived 16-day floodplain volume dynamics for 1987 to present. The correlation coefficient between upstream river flow records and floodplain volume time series was 0.88, indicating that the estimates were robust. The algorithms developed can be used for ongoing very high-resolution mapping to assist in managing human water use and environmental health in the Murray-Darling Basin.

Forest emissions reduction assessment using airborne LiDAR for biomass estimation

Given its large mitigation potential and predictable cost, forest carbon sequestration is crucial to reducing anthropogenic CO2 emissions to achieve climate mitigation targets. However, forest-based mitigation activities have been constrained owing to the absence of a timely and cost-effective approach to assess emissions reduction, thereby limiting its financial support. This study presented a more economically viable approach to forest emissions reduction assessment using airborne light detection and ranging (LiDAR) technology. Using a forest-based emissions reduction project in China as an on-site experiment subject, we developed species-specific LiDAR-based biomass estimation models to assess emissions reduction. It was observed that LiDAR-derived feature parameters could accurately predict forest biomass measurements estimated by the traditional ground-based method (R2 = 0.93). Interestingly, we found emissions reduction through field measurement (22.2 ktCO2e) or LiDAR observations (22.4 ktCO2e) was about two times higher than that estimated by the project developer before its implementation (10.5 ktCO2e). Furthermore, we found airborne LiDAR technology could significantly reduce monitoring cost ($/tCO2e), which would only account for a small share of the total implementation cost of a forest emissions reduction project. Based on these results, we summarized the advantages of applying airborne LiDAR technologies in developing forest-based emissions reduction projects and provided policy recommendations.

Observation Selection, Total Variation, and L-Curve Methods for LiDAR Data Denoising

In this article, we propose a light detection and ranging (LiDAR) data denoising scheme for wind profile observation as a part of quality control procedure for wind velocity monitoring and windshear detection. The proposed denoising scheme consists of several components. (i) It selects LiDAR observations according to their SNR values so that serious noisy data can be removed. (ii) A polar-based total variation smoothing term is employed to regularize LiDAR observations. (iii) The regularization parameters are automatically determined to balance the data-fitting term and the total variation smoothing term. Numerical results for LiDAR data collected at the Hong Kong International Airport are reported to demonstrate that the denoising performance of the proposed method is better than that of the testing LiDAR data denoising schemes in the literature.

Unlocking the potential of hyperspectral and LiDAR for above-ground biomass (AGB) and tree species classification in tropical forests

Tree species classification using a combination of airborne hyperspectral and Light Detection and Ranging (LiDAR) can provide valuable and effective methods for forest management, such as planning and monitoring purposes. However, only a few studies have applied tree species classification using both combinations in the tropical forest. The research takes a comparative classification approach to examine several classifiers using airborne hyperspectral in the tropical forest. In addition, Object-Based Image Analysis (OBIA) method was applied on hyperspectral data to extract the crown of individual tree species for classification and estimation purposes. Minimum Noise Fraction Transform (MNF) was applied to reduce the data dimensionality and different training samples from the various species used in this study. The result shows that Support Vector Machine (SVM) and Random Forest (RF) achieved the highest overall accuracy above 50% compared to other classifiers in the tropical forest. Besides, LiDAR data was also used to estimate individual trees' height for all species in the study area. The multiple coefficients of determination (R2) test result between LiDAR and field observation data in eight years gaps is 0.754. Therefore, Above-Ground Biomass (AGB) and carbon stock will estimate using a combination of LiDAR, hyperspectral, and field observation data for individual tree species. This method has proven to provide the required information for forest planning and generation in a short time, especially in tree species identification, AGB, and carbon stock estimation in tropical forests.

Quantifying the observational requirements of a space-borne LiDAR snow mission

This study quantifies the level of observational accuracy required from a spaceborne light detection and ranging (LiDAR) snow depth retrieval mission for enabling beneficial impacts for snow estimation. The study is conducted over a region in Western Colorado using a suite of observing system simulation experiments (OSSEs). The Joint UK Land Environment Simulator, version 5.0 (JULES v5.0) is employed to simulate a suite of idealized LiDAR observations, considering a range of LiDAR snow depth retrieval errors, different hypothetical sensor swath widths, and the impact of cloud cover on observability. These simulated observations are then assimilated into the Noah land surface model with multi-parameterization options, version 3.6 (Noah-MP v3.6) model. This data assimilation setup is used to systematically evaluate the potential utility of LiDAR observations for improving modeled snow water equivalent (SWE) estimates and water budget variables such as runoff. Results from the OSSE runs show that, in general, assimilation of synthetic LiDAR observations provide beneficial impacts when the LiDAR snow depth retrieval error standard deviation (σerror) is below 60 cm. Based on comparisons between the realistic (i.e., swath-limited and cloud-attenuated) case and the idealized (i.e., infinite swath width in the absence of cloud cover) case, this study concludes that observations with a conservative error standard deviation threshold of 40 cm (i.e., upper limit of the snow depth retrieval error that adds value to the SWE estimates via assimilation) are needed for improving modeled snow estimates. More than a 33% reduction in SWE root mean square errors and more than a 15% increase in correlation coefficients are achieved when σerror ≤ 40 cm using a 170-km sensor swath width in the presence of cloud attenuation effects. Further, the integrated hydrologic response, as represented by total (surface and subsurface) runoff estimates during the snow ablation season, are also enhanced when assimilating synthetic LiDAR snow depth retrievals with errors below this level.

LIDAR Observations of Free-Surface Time and Length Scales in Hydraulic Jumps

A hydraulic jump is a complex, rapidly varied flow phenomenon occurring in the transition from supercritical to subcritical flows. Hydraulic jumps are characterized by instationary motions including jump toe oscillations and free-surface fluctuations. A light detection and ranging (LIDAR) instrument was used to continuously record the time-varying free-surface features of fully aerated hydraulic jumps with fully developed inflow conditions and high spatial and temporal resolution. The free-surface features were analyzed with two processing methods considering a freely moving hydraulic jump and a case where each instantaneous hydraulic jump profile was shifted to the mean jump toe position. The results provided detailed dimensionless distributions of the mean profiles, as well as free-surface fluctuations and jump toe oscillations that increased with increasing Froude numbers. The high spatial resolution data provided continuous free-surface time and length scales along the hydraulic jumps. Largest free-surface scales were observed close to the jump toe linked with longitudinal oscillations of the hydraulic jumps, and the dimensionless free-surface scales increased with the Froude number. The present study provides new insights into free-surface turbulent structures, highlighting the strong relationship between jump toe movements and free-surface fluctuations in hydraulic jumps.

Regional Atmospheric Aerosol Pollution Detection Based on LiDAR Remote Sensing

Atmospheric aerosol is one of the major factors that cause environmental pollution. Light detection and ranging (LiDAR) is an effective remote sensing tool for aerosol observation. In order to provide a comprehensive understanding of the aerosol pollution from the physical perspective, this study investigated regional atmospheric aerosol pollution through the integration of measurements, including LiDAR, satellite, and ground station observations and combined the backward trajectory tracking model. First, the horizontal distribution of atmospheric aerosol wa obtained by a whole-day working scanning micro-pulse LiDAR placed on a residential building roof. Another micro-pulse LiDAR was arranged at a distance from the scanning LiDAR to provide the vertical distribution information of aerosol. A new method combining the slope and Fernald methods was then proposed for the retrieval of the horizontal aerosol extinction coefficient. Finally, whole-day data, including the LiDAR data, the satellite remote sensing data, meteorological data, and backward trajectory tracking model, were selected to reveal the vertical and horizontal distribution characteristics of aerosol pollution and to provide some evidence of the potential pollution sources in the regional area. Results showed that the aerosol pollutants in the district on this specific day were mainly produced locally and distributed below 2.0 km. Six areas with high aerosol concentration were detected in the scanning area, showing that the aerosol pollution was mainly obtained from local life, transportation, and industrial activities. Correlation analysis with the particulate matter data of the ground air quality national control station verified the accuracy of the LiDAR detection results and revealed the effectiveness of LiDAR detection of atmospheric aerosol pollution.

Investigation of the Fetch Effect Using Onshore and Offshore Vertical LiDAR Devices

An offshore wind measurement campaign using vertical light detection and ranging (LiDAR) devices was conducted at the Hazaki Oceanographic Research Station (HORS) as part of an investigation into determining the optimal distance from the coast for a nearshore wind farm from a meteorological perspective. The research platform was a 427 m long pier located on a rectilinear coastline on the Pacific coast of the central Honshu Island in Japan. The relationship between the ratios of the increase of wind speed near the surface and fetch length within 5 km of the coast was analyzed via LiDAR observations taken at heights from 40 to 200 m. The results showed that the speed of the coastal wind blowing from land to sea gradually increased as the fetch length increased, by approximately 15–20% at 50 m above sea level around a fetch length of 2 km. Moreover, empirical equations were derived by applying the power law to the relationship between the increase of wind speed and fetch lengths at 1–5 km, as obtained from the LiDAR measurements. It was also found that the wind speed increase at a 2 km fetch length was equivalent to the effect of a 50–90 m vertical height increase on the coast in this region.

WHEAT EAR DETECTION IN PLOTS BY SEGMENTING MOBILE LASER SCANNER DATA

Abstract. The use of Light Detection and Ranging (LiDAR) to study agricultural crop traits is becoming popular. Wheat plant traits such as crop height, biomass fractions and plant population are of interest to agronomists and biologists for the assessment of a genotype's performance in the environment. Among these performance indicators, plant population in the field is still widely estimated through manual counting which is a tedious and labour intensive task. The goal of this study is to explore the suitability of LiDAR observations to automate the counting process by the individual detection of wheat ears in the agricultural field. However, this is a challenging task owing to the random cropping pattern and noisy returns present in the point cloud. The goal is achieved by first segmenting the 3D point cloud followed by the classification of segments into ears and non-ears. In this study, two segmentation techniques: a) voxel-based segmentation and b) mean shift segmentation were adapted to suit the segmentation of plant point clouds. An ear classification strategy was developed to distinguish the ear segments from leaves and stems. Finally, the ears extracted by the automatic methods were compared with reference ear segments prepared by manual segmentation. Both the methods had an average detection rate of 85 %, aggregated over different flowering stages. The voxel-based approach performed well for late flowering stages (wheat crops aged 210 days or more) with a mean percentage accuracy of 94 % and takes less than 20 seconds to process 50,000 points with an average point density of 16 points/cm2. Meanwhile, the mean shift approach showed comparatively better counting accuracy of 95% for early flowering stage (crops aged below 225 days) and takes approximately 4 minutes to process 50,000 points.

Effect of Land-Use Change on the Urban Heat Island in the Fukuoka–Kitakyushu Metropolitan Area, Japan

In coastal cities, the effect of the sea breeze in mitigating the urban heat island (UHI) phenomenon has attracted attention. This study targeted the Fukuoka–Kitakyushu metropolitan area, the fourth largest metropolitan area in Japan which is also coastal. Doppler Light Detection And Ranging (LiDAR) observations were conducted in the summer of 2015 to clarify the transition of the wind field over the targeted area. To investigate the effects on the UHI of land-use change related to urbanization, the National Land Numerical Information (NLNI) land-use datasets for Japan in 1976 (NLNI-76) and 2009 (NLNI-09) were used in the Weather Research and Forecasting (WRF) model. The results of the simulation showed that most of the northern part of the Kyushu region became warmer, with an average increase of +0.236 °C for the whole simulation period. Comparing the two simulations and the Doppler LiDAR observations, the simulation results with the NLNI-09 dataset (for the year closest to the study period in 2015) showed closer conformity with the observations. The results of the simulation using NLNI-76 showed faster sea breeze penetration and higher wind velocity than the observations. These results suggest that the land-use change related to urbanization weakened the sea breeze penetration in this area.

On the impact of a refined stochastic model for airborne LiDAR measurements

Abstract Accurate topographic information is critical for a number of applications in science and engineering. In recent years, airborne light detection and ranging (LiDAR) has become a standard tool for acquiring high quality topographic information. The assessment of airborne LiDAR derived DEMs is typically based on (i) independent ground control points and (ii) forward error propagation utilizing the LiDAR geo-referencing equation. The latter approach is dependent on the stochastic model information of the LiDAR observation components. In this paper, the well-known statistical tool of variance component estimation (VCE) is implemented for a dataset in Houston, Texas, in order to refine the initial stochastic information. Simulations demonstrate the impact of stochastic-model refinement for two practical applications, namely coastal inundation mapping and surface displacement estimation. Results highlight scenarios where erroneous stochastic information is detrimental. Furthermore, the refined stochastic information provides insights on the effect of each LiDAR measurement in the airborne LiDAR error budget. The latter is important for targeting future advancements in order to improve point cloud accuracy.

LIDAR observation and numerical simulation of vortex/wave shedding at the Eastern Runway Corridor of the Hong Kong International Airport

ABSTRACTVortex/wave shedding is sometimes observed in the Hong Kong International Airport (HKIA) region by such sophisticated instruments as Doppler LIght Detection And Ranging (LIDAR) systems. During the past three years several vortex/wave shedding cases have been documented, often to the west of HKIA, downwind of the mountains which can block and disturb the prevailing airflow from the southeast. These shedding movements might trigger turbulence and wind shear, threatening aircraft landing from the west. This paper documents, for the first time, that such shedding can also occur at the runway corridor to the east of the airport's south runway, caused by mountains on the eastern side of Lantau Island. It calculates the shedding period and Froude number of the events, comparing these data with results reported in the literature. It also discusses, for the first time, the use of a computational fluid dynamics (CFD) model to simulate these events, based on homogeneous initialization only and using the upper air ascent data closest in time to the events. The simulations were shown to successfully reproduce the main features of the shedding, pointing to the possibility of applying this kind of simulation method in real‐time forecasts to provide early warning services for aircraft taking off from HKIA.

Modelling of Aerosol Vertical Distribution during a Spring Season at Gwangju, Korea

The vertical distributions of aerosol extinction coefficient were estimated using the scaling height retrieved at Gwangju, Korea (35.23°N, 126.84°E) during a spring season (March to May) of 2009. The aerosol scaling heights were calculated on a basis of the aerosol optical depth (AOD) and the surface visibilities. During the observation period, the scaling heights varied between 3.55 km and 0.39 km. The retrieved vertical profiles of extinction coefficient from these scaling heights were compared with extinction profile derived from the Light Detection and Ranging (LIDAR) observation. The retrieve vertical profiles of aerosol extinction coefficient were categorized into three classes according to the values of AODs and the surface visibilities: (Case I) the AODs and the surface visibilities are measured as both high, (Case II) the AODs and the surface visibilities are both lower, and (Others) the others. The averaged scaling heights for the three cases were 3.09±0.46 km, 0.82±0.27 km, and 1.46±0.57 km, respectively. For Case I, differences between the vertical profile retrieved from the scaling height and the LIDAR observation was highest. Because aerosols in Case I are considered as dust-dominant, uplifted dust above planetary boundary layer (PBL) was influenced this discrepancy. However, for the Case II and other cases, the modelled vertical aerosol extinction profiles from the scaling heights are in good agreement with the results from the LIDAR observation. Although limitation in the current modelling of vertical structure of aerosols exists for aerosol layers above PBL, the results are promising to assess aerosol profile without high-cost instruments.