Search Results (437)

This single high-impact case study investigates the impact of planetary boundary layer (PBL) representation on long-range transport of Amazon fire smoke that reached the Metropolitan Area of São Paulo (MASP) from 15 to 20 August 2019, using the WRF model to compare three PBL schemes (MYNN 2.5, YSU, and BouLac) and three source-tagged tracers. The simulations are evaluated against MODIS-derived aerosol optical depth (AOD), the Light Detection and Ranging (LiDAR) time–height curtain over MASP, and HYSPLIT forward trajectories. Transport is diagnosed along the source-to-MASP pathway using six-hourly cross-sections and two integrative metrics: the projected mean wind in the 700–600 hPa layer and the vertical moment of tracer mass above the boundary layer. Outflow and downwind impact are strongest when a persistent reservoir between 2 and 4 km coexists with projected winds for several hours. In this episode, MYNN maintains an elevated 2–5 km transport layer and matches the observed arrival time and altitude, YSU yields a denser but delayed column, and BouLac produces discontinuous pulses with reduced coherence over the city. A negatively tilted trough, jet coupling, and a nearly stationary front establish a northwest-to-southeast corridor consistent across model fields, trajectories, and satellite signal. Seasonal robustness should be assessed with multi-event, multi-model analyses. Full article

In the present work, we report daytime latent heat flux profile measurements in the convective boundary layer (CBL) obtained from the combined use of a wind lidar and a thermodynamic Raman lidar. Water vapour flux profiles and, consequently, latent heat flux profiles were obtained as the covariance between the vertical profiles of the water vapour mixing ratio and vertical wind fluctuations. Profile measurements of the water vapour mixing ratio were carried out by the thermodynamic Raman lidar CONCERNING, while simultaneous profile measurements of the vertical wind speed were carried out by a co-located Doppler wind lidar. The considered dataset was collected in the frame of the international field campaign “Water Vapor Lidar Network Assimilation” (WaLiNeAs). Three cloud-free time intervals on 31 October, 28 November, and 8 December 2022 were selected as case studies. Measurements of turbulent kinetic energy (TKE) were also carried out over the same time intervals based on the use of wind lidar data. The three selected case studies were characterised by different atmospheric stability conditions and, consequently, by a different potential for the occurrence of convective activity. More specifically, the atmospheric conditions on 31 October 2022 were very unstable, with intensive convective activity taking place in the area and ultimately leading to relatively intense thunderstorms and rainfall events. The atmospheric conditions on 28 November 2022 were moderately unstable, ultimately leading to light convective activity, with scattered rain episodes observed throughout the day but with no severe thunderstorms taking place. Stratiform precipitations were present on 8 December 2022, with weak embedded convective processes taking place within stratiform clouds and leading to moderate additional precipitation. In all three selected case studies, representative of pre-convective conditions, both latent heat flux and TKE profiles are characterised by values increasing with altitude up to approx. 500 m, while both latent heat flux and TKE are found to decrease, with a steeper negative gradient up to approx. 600 m and more gradually above this altitude, returning to zero just above the top of the CBL. In all three cases, peak values of TKE appear to be strongly correlated with corresponding peak values of the latent heat flux; the higher the maximum values of TKE and latent heat flux, the more intense the following precipitation events. Full article

Hurricane Maria crossed Puerto Rico with winds as high as 250 km/h, resulting in widespread damages and loss of weather station data, thus limiting direct weather measurements of wind variability. Here, we identified more than 155 million trees to estimate the distribution of wind speed over 9000 km² of land from island-wide LiDAR point clouds collected before and after the hurricane. The point clouds were classified and rasterized into the canopy height model to perform individual tree identification and perform change detection analysis. Individual trees’ stem diameter at breast height were estimated using a function between delineated crown and extracted canopy height, validated using the records from Puerto Rico’s Forest Inventory 2003. The results indicate that approximately 35.7% of trees broke at the stem (below the canopy center) and 28.5% above the canopy center. Furthermore, we back-calculated the critical wind speed, or the minimum speed to cause breakage, at individual tree level this was performed by applying a mechanical model using the estimated diameter at breast height, the extrapolated breakage height, and pre-Hurricane Maria canopy height. Individual trees were then aggregated at 115 km² cells to summarize the critical wind speed distribution of each cell, based on the percentage of stem breakage. A vertical wind profile analysis was then applied to derive the hurricane wind distribution using the mean hourly wind speed 10 m above the canopy center. The estimated wind speed ranges from 250 km/h in the southeast at the landfall to 100 km/h in the southwest parts of the islands. Comparison of the modeled wind speed with the wind gust readings at the few remaining NOAA stations support the use of tree breakages to model the distribution of hurricane wind speed when ground readings are sparse. Full article

Low-level wind shear (LLWS) is a critical issue in aviation meteorology, posing serious risks to flight safety—especially at plateau airports with high elevation and complex terrain. This study investigates a convective wind shear event at Xining Airport on 29 May 2021. Multi-source observations—including the Doppler Wind Lidar (DWL), the Doppler weather radar (DWR), reanalysis datasets, and automated weather observation systems (AWOS)—were integrated to examine the event’s fine-scale structure and temporal evolution. High-resolution simulations were conducted using the Large Eddy Simulation (LES) framework within the Weather Research and Forecasting (WRF) model. Results indicate that the formation of this wind shear was jointly triggered by convective downdrafts and the gust front. A northwesterly flow with peak wind speeds of 18 m/s intruded eastward across the runway, generating multiple radial velocity couplets on the eastern side, closely associated with mesoscale convergence and divergence. A vertical shear layer developed around 700 m above ground level, and the critical wind shear during aircraft go-around was linked to two convergence zones east of the runway. The event lasted about 30 min, producing abrupt changes in wind direction and vertical velocity, potentially causing flight path deviation and landing offset. Analysis of horizontal, vertical, and glide-path wind fields reveals the spatiotemporal evolution of the wind shear and its impact on aviation safety. The WRF-LES accurately captured key features such as wind shifts, speed surges, and vertical disturbances, with strong agreement to observations. The integration of multi-source observations with WRF-LES improves the accuracy and timeliness of wind shear detection and warning, providing valuable scientific support for enhancing safety at plateau airports. Full article

The World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) observation site of Lamezia Terme (code: LMT) in Calabria, Italy, has been measuring nitric oxide (NO) and nitrogen dioxide (NO₂) (together referred to as NO_x) for a decade; however, only a limited number of studies have evaluated their variability at the site, accounting for short measurement periods. In this work, nine continuous years (2015–2023) of measurements are analyzed to assess daily, weekly, seasonal, and multi-year tendencies, also accounting for local wind circulation, which is known to have a relevant impact on LMT’s measurements. For the first time, a multi-year evaluation of LMT data also considers the local wind lidar record to integrate conventional measurements with additional information on the transport of NO_x at low altitudes. The study also considers data on local tourism and vehicular traffic to assess correlations with LMT’s measurements, thus providing new insights on NO_x variability at the site. The analysis showed peaks in early morning NO_x concentrations attributable to rush hour traffic, while in the evening NO₂ peaks are present with minor NO counterparts. Weekly cycles have yielded the most statistically significant results of any other similar evaluation at the sites, with all combinations of parameters, seasons, and wind corridors indicating tangible differences between weekday (WD, Monday to Friday) and weekend (WE, Saturday and Sunday) concentrations. The analysis of multi-year variability has shown a slightly declining tendency; however, sporadic bursts in concentrations limit the statistical significance of downward trends. Full article

Excessive tailwind, threatening the safety of aircraft takeoff and landing, is one of the prominent research topics in the field of aviation meteorology. This paper analyzes the causes of tailwinds at Beijing Daxing International Airport (BDIA), based on coherent Doppler wind lidar (CDWL) and ERA5 reanalysis data. CDWL with high spatiotemporal resolution is utilized to detect variations in the low-level wind field in the vicinity of airport areas. ERA5 reanalysis data are employed to investigate the distribution characteristics of meteorological elements such as wind fields, pressure, and temperature in the Beijing surrounding regions. The study of two typical tailwind events reveals that frontal activity, through the combined effects of pressure gradient adjustment and topographic constraints from the Taihang Mountains, drives the development of low-level southerly jets. It serves as the key mechanism triggering excessive tailwind. By integrating CDWL and ERA5 data for local and regional analysis, this study contributes to enhancing understanding of tailwind causal mechanisms and provides critical support for aviation meteorological disaster early warning. Full article

The limited availability of wind profile measurements has long hindered advancements in atmospheric modeling and our understanding of climate systems. The European Space Agency’s (ESA) “Aeolus” mission encountered this challenge with the groundbreaking introduction of space-based Doppler wind lidar technology, capable of providing Near-Real-Time wind profile data. The regional COSMO NWP model was utilized to demonstrate the potential of “Aeolus” in improving NWP model capabilities. The research focuses on analyzing the precipitation of the severe Hephaestion storm, which struck Greece in early January 2020, by comparing the performance of the NWP model with and without the assimilation of “Aeolus” data. Full article

Range height indicator (RHI) scans are performed routinely by the long-range Doppler Light Detection and Ranging (LIDAR) systems at the Hong Kong International Airport (HKIA). This paper presents some novel observations of the airflow in the airport region by RHI scans that have not been reported in the literature in the past—namely, areas of reverse flow and vortex shedding in east to southeasterly winds; severe windshear in the easterly; a converging southerly flow; a descending northeasterly jet; and the undercutting of the southwesterly flow by sea breeze. Many of these flow features are associated with low-level windshear, supported by pilot windshear reports, and thus their observations have practical applications. The technical feasibility of forecasting these airflow features is also studied in this paper, and it is found that large eddy simulations based on a mesoscale meteorology model manage to capture these wind features most of the time, but the simulated headwind change is generally slightly smaller than observed. The results in this paper have application value for windshear alerting and forecasting for an airport situated in an area of complex terrain, and they should be of interest for further studies of mountain meteorology. Full article

Understanding the dispersion of volcanic sulphur dioxide (SO₂) plumes is crucial for assessing their environmental and climatic impacts. This study integrates satellite-based and reanalysis datasets to simulate as well as visualise the dispersion patterns of volcanic SO₂ under diverse atmospheric conditions. By incorporating data from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2), CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), and OMI (Ozone Monitoring Instrument) datasets, we are able to provide comprehensive insights into the vertical and horizontal trajectories of SO₂ plumes. The methodology involves modelling SO₂ dispersion across various atmospheric pressure surfaces, incorporating wind directions, wind speeds, and vertical column mass densities. This approach allows us to trace the evolution of SO₂ plumes from their source through varying meteorological conditions, capturing detailed vertical distributions and plume paths. Combining these datasets allows for a comprehensive analysis of both natural and human-induced factors affecting SO₂ dispersion. Visual and statistical interpretations in the paper reveal overall SO₂ concentrations, first injection dates, and dissipation patterns detected across altitudes of up to ±20 km in the stratosphere. This work highlights the significance of combining satellite-based and global atmospheric reanalysis datasets to validate and enhance the accuracy of plume dispersion models while having a general agreement that OMI daily data and MERRA-2 reanalysis hourly data are capable of accurately accounting for SO₂ plume dispersion patterns under varying meteorological conditions. Full article

The present study aims to explore the technical feasibility of simulating in advance, and thus forecasting, the occurrence of low-level windshear at an airport in the complex terrain of the Qinghai–Tibet Plateau. Three cases of windshear at Xining Airport are investigated. They are related to synoptic scale subsidence flow and downward momentum transfer, terrain modification of a cold front, and wind convergence arising from synoptic- and mesoscale high-pressure areas. The simulation results are compared with actual Doppler LIDAR observations. It is found that the simulations reproduce the windshear features reasonably well. The low-level wind speed and/or wind direction convergence are clearly represented in the simulations. The simulated LIDAR radial velocity has a correlation coefficient of over 0.9 with the actual LIDAR radial velocity. Though the present study involves a limited number of cases, it is found to be feasible to use a high-resolution numerical weather prediction model to simulate low-level windshear at an airport on the plateau. It is hoped that this methodology could be extended from the Hong Kong International Airport to the plateau airports, and then to airports in other parts of the world. Full article

This study used reanalysis and lidar observations to investigate nocturnal downslope wind events in the Taklimakan desert, revealing their vertical structure, influencing factors, climatology, and impacts on boundary layer dynamics and dust emissions. 125 events were detected along the northern slope of the Kunlun Mountains, impacting Minfeng. Due to its weakness after onset, downslope flow is deflected horizontally when it encounters the opposing synoptic winds. The continued radiative cooling, dense air drainage, and adiabatic warming intensify downslope flow as the night progresses, causing it to gradually sink and overcome the opposing synoptic winds. Downslope wind events typically occur between an hour before and two hours after sunset, with the strongest occurring at or before sunset due to the longer period of radiative cooling and the coincidence with early evening instability conditions. Strong events occur under weak stability conditions as a stable atmosphere with a strong inversion layer can inhibit sinking motion. Most events, even the strongest ones, occur under dry conditions due to enhanced radiative cooling. Mechanical turbulence occurs when downslope flow hits the surface, whereas thermal turbulence occurs when warmer, downslope air weakens the lower atmosphere’s temperature inversion. Downslope wind events significantly raise dust emissions in the Taklimakan desert. Full article

Many real-world phenomena corrupt light detection and ranging (lidar) measurements, such as laser energy attenuation, variations in aerosol concentration and composition with height, and hard target returns. Accurate studies of lidar scans using virtual lidar methods should include some realistic model of these corrupting effects to generate more realistic simulations of lidar scans. We present a simple model that characterizes noise caused by energy attenuation and aerosol stratification. The model requires limited inputs and is developed for a Halo Photonics Streamline XR lidar but is readily generalizable for other lidar systems. A critical component of this model is a model of the standard deviation of measured wind speed as a function of the backscattered signal’s signal-to-noise ratio. We derive a general model for this behavior that can be adapted to different scan settings. Full article

Low-level turbulence is a meteorological hazard that disrupts the operation of airports and is particularly pronounced at Hong Kong International Airport (HKIA), which is impacted by various sources of low-level turbulence (e.g., terrain disrupting wind flow, sea breeze, and thunderstorms). The possibility of using root-mean-square vertical acceleration (RMSVA) data from Automatic Dependent Surveillance–Broadcast (ADS-B) for low-level turbulence monitoring is studied in this paper. Comparisons are performed between RMSVA and Light Detection And Ranging (LIDAR)-based Eddy Dissipation Rate (EDR) maps and the aircraft-based EDR. Moreover, the LIDAR-based EDR map, aircraft EDR, and pilot report for turbulence reporting are compared for two typical cases at HKIA. It was found that the various estimates/reports of turbulence are generally consistent with one another, at least based on the limited sample considered in this paper. However, at very low altitudes close to the touchdown of arrival flights, RMSVA may not be available due to a lack of ADS-B data. With effective quality control and further in-depth study, it will be possible to use RMSVA to monitor low-level turbulence and to alert pilots if turbulence is reported by the pilot of the preceding flight based on RMSVA. The technical details of the various comparisons and the assumptions made are described herein. Full article

Low-level wind shear poses a significant hazard to aviation, especially at airports located on high plateaus and surrounded by complex terrain. In this study, we present a comprehensive analysis integrating Doppler Lidar and radiosonde measurements collected at the Xining Caojiapu Airport, situated on the northeastern Tibetan Plateau, during June 2022. The results indicate a remarkably high frequency of severe wind shear events (|Δv| ≥ 6 m/s), with an overall occurrence rate of 34% during the observation period. These events are predominantly confined to two distinct atmospheric layers: just above the surface and near the top of the convective boundary layer. The diurnal cycle of wind shear is closely associated with boundary-layer dynamics, exhibiting sharp increases after sunrise and pronounced peaks around midday, coinciding with enhanced turbulent mixing and surface heating. Case analyses further reveal that the most intense shear episodes occur at strong thermal inversions, where momentum decoupling produces thin, critical interfaces conducive to turbulence generation. In contrast, well-mixed convective conditions result in more distributed but persistent shear throughout the lower atmosphere. Diagnostic profiles of atmospheric stratification and dynamic instability, characterized by the Brunt–Väisälä frequency and Richardson number, elucidate the intricate interplay between thermal structure and vertical wind gradients. Collectively, these findings provide a robust quantitative basis for improving wind shear risk assessments and early warning systems at airports in mountainous regions, while offering new insights into the complex interactions between turbulence and atmospheric stratification. Full article

This study investigates the use of a Random Forest (RF), an artificial intelligence (AI) model, to estimate the planetary boundary layer height (PBLH) over Central Amazonia from climatic elements data collected during the GoAmazon experiment, held in 2014 and 2015, as it is a key metric for air quality, weather forecasting, and climate modeling. The novelty of this study lies in estimating PBLH using only surface-based meteorological observations. This approach is validated against remote sensing measurements (e.g., LIDAR, ceilometer, and wind profilers), which are seldom available in the Amazon region. The dataset includes various meteorological features, though substantial missing data for the latent heat flux (LE) and net radiation (Rn) measurements posed challenges. We addressed these gaps through different data-cleaning strategies, such as feature exclusion, row removal, and imputation techniques, assessing their impact on model performance using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and $r^2$ metrics. The best-performing strategy achieved an RMSE of 375.9 m. In addition to the RF model, we benchmarked its performance against Linear Regression, Support Vector Regression, LightGBM, XGBoost, and a Deep Neural Network. While all models showed moderate correlation with observed PBLH, the RF model outperformed all others with statistically significant differences confirmed by paired t-tests. SHAP (SHapley Additive exPlanations) values were used to enhance model interpretability, revealing hour of the day, air temperature, and relative humidity as the most influential predictors for PBLH, underscoring their critical role in atmospheric dynamics in Central Amazonia. Despite these optimizations, the model underestimates the PBLH values—by an average of 197 m, particularly in the spring and early summer austral seasons when atmospheric conditions are more variable. These findings emphasize the importance of robust data preprocessing and higtextight the potential of ML models for improving PBLH estimation in data-scarce tropical environments. Full article