Search

Retrieving wind profiles from Doppler lidar radial velocities requires processing software tools. The heterogeneity of Doppler lidar types and data acquisition settings, as well as scan patterns applied for wind profiling, make wind profile processing challenging. Addressing this challenge, a new modular open-source wind profile retrieval software is presented: the Atmospheric Profile Processing toolKIT (AtmoProKIT). The software calculates quality controlled wind profiles from heterogeneous Doppler lidar data, i.e. independent of the system type, data acquisition settings or the scan pattern applied. Ingestion of heterogeneous data is enabled by the definition of a standardized level 1 data format for the measurements, from which level 2 wind profiles are retrieved. Processing flexibility is enabled through the combination of modular processing steps in module chains. Modifications are possible by individually arranging modules, adding calculation modules or adjusting processing parameters. The documentation of the processing steps in the result's metadata ensures the traceability of the results. A standard module chain is presented, which allows for straightforward wind profile retrieval for common Doppler lidar measurement scenarios without the need for coding. The results provided by the standard module chain are validated against radiosondes for three common Doppler lidar systems in differing atmospheric conditions. AtmoProKIT is provided as open-source Python code and includes demonstration examples, allowing for an easy use and future collaborative modification. [ABSTRACT FROM AUTHOR]

This paper presents an extensive investigation of the signal fringe profile for the Fizeau interferometer used in the first spaceborne wind lidar Aeolus, and considers the fundamental implications for the wind measurement accuracy in Aeolus and future systems. The early Aeolus design phase considered that the basic fringe would be made up of a Fizeau instrumental component of ≈ 100 MHz (FWHM), folded with the laser pulse spectral width of ≈ 50 MHz (FWHM), both of Lorentzian form. Fringe anomalies observed before the mission and related to surface defects in the interferometer plates, triggered the development of wave-optic methods for analysis of the fringe formation. These methods, herein described in an instructional Appendix, were subsequently found to be essential for rigorous modelling of complex fringes for different physical and optical arrangements. Initial signal returns from Aeolus suggested that the Fizeau fringe profile was in fact broadened with a large Gaussian component. The laser pulse was subsequently shown to have a profile close to Gaussian of ≈ 45 MHz (FWHM) and thus provided a partial contribution. However, detailed examination of experimental Aeolus fringes constructed from ground return signals, showed a large Gaussian component up to ≈ 130 MHz (FWHM). Wave-optic modelling established that Fizeau 'aperture broadening', of this form and magnitude, would be generated for the input signal beam of 500 µ rad field of view set at large angles of incidence of 300 µ rad. These findings have strong implications for fringe shift and wind measurement accuracy, as given in the quantum limited Cramer-Rao expression and the paramount importance of minimising line width. Extensive modelling and simulation for the broadened profiles calculated above, shows good agreement with measured Aeolus global wind measurement accuracies, and indicates that loss of signal could be due to beam clipping at the field stop for such large AOI. It is established that optimisation of the present Aeolus Fizeau parameters could lead to a factor of 2.5 improvement in wind measurement precision. Future upgrades of the Fizeau interferometer and the laser within reasonable parameters, suggest the potential for an factor of 7.6 improvement on the present performance. [ABSTRACT FROM AUTHOR]

Doppler wind lidar (DWL) demonstrates significant advantages in wind field detection under clear weather conditions and has been widely applied in airports with complex wind environments. However, its detection performance is highly susceptible to weather conditions and meteorological factors. To address this issue, this study analyzes the detection efficiency of DWL based on data collected at Lhasa Gonggar Airport from August 2023 to April 2024, along with ground-based meteorological observations. The results indicate that when the detection efficiency dropped to 40%, the average detection range for the plan position indicator (PPI) mode and Doppler beam swinging (DBS) mode were 5.3 km and 2.7 km, respectively. The influence of different underlying surface types on detection efficiency was minimal, with detection efficiency at a 270° azimuth slightly better than at a 90° azimuth. A 4° elevation angle performed better than a 6° elevation angle. During the study period, the detection efficiency generally improved, with the lowest detection efficiency being observed in August, suggesting that precipitation significantly impacts performance. In August, the detection efficiency of the PPI mode dropped below 50% at 4 km, while the highest detection efficiency occurred in April, where performance remained above 50% at 7 km. This is associated with enhanced thermal and dynamic activity in the lower atmosphere. Low-cloud activity also affected the detection performance of the DBS mode. The daily variation in the detection range in April was more pronounced than in January, with the detection range generally being larger. The increase in detection range was related to the more active vertical atmospheric mixing. The PPI mode was more sensitive to changes in meteorological factors, with its median detection range being 0.2–0.6 km shorter than that of the DBS mode when the meteorological optical range (MOR) was less than 4 km. Additionally, the PPI mode showed weaker stability than the DBS mode when relative humidity was below 75%. When relative humidity exceeded 80%, both modes showed a linear decrease in detection efficiency. [ABSTRACT FROM AUTHOR]

Severe wind shear events near airport runways pose serious safety risks and are a growing concern in civil aviation. Identification of severe wind shear risk factors may enhance aviation safety. Although rare, severe wind shear impacts safety by affecting the airspeed, lift, and maneuverability of aircraft. This study presents TabNet, a novel deep learning technique coupled with Bayesian optimization (BO) to predict wind shear severity in the runway vicinity using Doppler LiDAR data from Hong Kong International Airport. To address imbalanced wind shear data, it was first processed by resampling techniques and then used as input to TabNet. The analysis demonstrated that Bayesian-tuned TabNet (BO-TabNet) with SVM-SMOTE-processed data led to better performance compared to other strategies. The TabNet architecture employs the attention mechanism to enable model-specific interpretability. Analysis showed that the most important contributing factor was the summer season, followed by the wind shear encounter location (1 nautical miles from the runway at the departure end). Additionally, a more comprehensive model-agnostic LIME method was used to elucidate the model from a local perspective. By predicting severe wind shear and assessing contributing factors, aviation stakeholders can proactively manage and mitigate the associated risks, leading to safer and more efficient operations. [ABSTRACT FROM AUTHOR]

European Space Agency (ESA) launched its first space-based Doppler Wind Lidar (DWL) mission called Atmospheric Dynamic Mission (ADM) – Aeolus. Onboard the Aeolus mission is the Atmospheric LAser Doppler Instrument (ALADIN) which measures the horizontal line-of-sight (HLOS) winds. Aeolus Level-2B wind observations in Rayleigh clear and Mie cloudy channels are evaluated for implementation in the NCMRWF (National Centre for Medium Range Weather Forecasting) Global Forecast System (NGFS). The GSI (grid-point statistical interpolation) analysis scheme has been modified and updated to assimilate the HLOS wind information. Quality control criteria are applied during observation processing and during minimization of cost function for computation of the initial condition. An observation system experiment (OSE) is performed by employing the GSI-3DVar (3-Dimensional Variation) approach and involving HLOS data. In addition to assimilation and forecast diagnostics, two case studies of very severe cyclonic storms are investigated to assess the impact of this new wind information on a severe weather event. Statistically, significant improvement is observed mostly over the Southern Hemisphere, Tropics, and RSMC (Regional Specialized Meteorological Centre, 29°–120°E and 21°S–46°N) region in terms of reduction in wind root mean square error. Assimilation of HLOS winds shows a reduction in direct positional error (DPE) for both cyclonic systems. Improvement in the 6-hourly analysis of minimum sea level pressure and maximum 10 m wind speed is also observed. [ABSTRACT FROM AUTHOR]

This work concerns analysis of turbulence in the atmospheric boundary layer (ABL) shortly before and after sunset. Based on a large set of Doppler lidar measurements at rural and urban sites, we analyze frequency spectra of vertical wind at different heights and show that they increasingly deviate from Kolmogorov's -5/3 prediction in the measured low-wavenumber part of the inertial range. We find that before sunset, the integral length scales tend to decrease with time. These findings contrast with a classical model of equilibrium decay of isotropic turbulence, which predicts that the scaling exponent should remain constant and equal to -5/3 and the integral length scale should increase in time. We explain the observations using recent theories of non-equilibrium turbulence. The presence of non-equilibrium suggests that classical parametrization schemes fail to predict turbulence statistics shortly before sunset. By comparing the classical and the non-equilibrium models, we conclude that the former may underestimate the dissipation rate of turbulence kinetic energy in the initial stages of decay. [ABSTRACT FROM AUTHOR]

This paper documents a windshear case for an airport in the Qinghai-Tibet Plateau and explores, for the first time, the capability for high-resolution numerical weather simulation of the wind shear features. The windshear appears to be associated with pulses of the wind speed in a low-level easterly jet. The features are basically reproduced quite well with the high-resolution numerical model, though some discrepancies are identified, such as the maximum wind speed of the easterly jet and the magnitude of the eddy dissipation rate as compared with the actual Doppler LIDAR observations. Statistical analysis has been performed between the observation and the simulation results. The sensitivity of the modeling result to the choice of turbulence parameterization scheme has also been studied. The study result shows that it is possible to forecast the windshear feature using a high-resolution numerical weather prediction model for an airport in the complex terrain of the Plateau. [ABSTRACT FROM AUTHOR]

The limited understanding of the structure of the urban surface atmospheric boundary layer can be attributed to its inherent complexity, as well as a deficiency in comprehensive measurements. We analyzed one year of meteorological data and Doppler wind lidar measurements in Beijing to explore how atmospheric stability is influenced by wind speed, radiation, turbulence, and pollution levels. Results indicate that the predominant state of the urban boundary layers in Beijing is an active condition (characterized by strong unstable and unstable stability regimes) throughout the day, attributed to the significant heat storage capacity of the urban canopy. Strong stable regimes are more frequently observed during winter and autumn, peaking during transitions from night to day. Furthermore, both strong unstable and strong stable regimes occur under very weak wind conditions (indicating weak dynamic instability), with strong instability associated with high net radiation levels while strong stability correlates with low net radiation conditions (indicative of robust thermal stability). The unstable regime manifests under strong winds (reflecting strong dynamic instability) alongside moderate net radiation environments, characterized by elevated values of turbulence kinetic energy and urban boundary height, highlighting the critical role of mechanical turbulence generation during periods of high wind activity. Additionally, six instances of pronounced stable conditions observed during daytime can be partially attributed to low radiation coupled with high pollutant concentrations near the surface, resulting from prolonged temperature inversions due to intense radiative cooling effects and weak dynamic forcing. Our findings presented herein are expected to have urban boundary layer climate and environment implications for other cities with high pollution and dense urban infrastructure all over the world. [ABSTRACT FROM AUTHOR]

From 21 August to 15 September 2022, a WindCube v2 Infrared coherent Doppler Lidar (DL) supplied by EKO Co. (Japan) was deployed at the Shigaraki MU Observatory (Japan) near the LQ7 UHF (1.357 GHz) wind profiler in routine operation. Horizontal and vertical velocity measurements from DL were reliably obtained in the [40–300] m height range with vertical and temporal resolutions of 20 m and 4 seconds, respectively. The LQ7 wind measurements are collected with range and temporal resolutions of 100 m and 59 s, respectively, and 10-min average profiles are calculated after data quality control. Reliable LQ7 Doppler data are collected from the height of 400 m. Despite the lack of overlap in the height range, we compared the Turbulence Kinetic Energy (TKE) dissipation rate ε in the daytime planetary boundary layer estimated by the two instruments. A method based on the calculation of the one-dimensional transverse line spectrum of the vertical velocity W from mean W time series (TS method) was applied to DL (ε DL). The same method was also applied to 1-min LQ7 data (ε LQ 7TS) to assess its performance with respect to DL despite the poorer time resolution. A more standard method based on the Doppler Spectral width (DS) was also applied to LQ7 (ε LQ 7DS) from the 10-min average profiles. We tested recently proposed models of the form ε=σ3/ L where σ is half the spectral width corrected for non-turbulent effects and L is assumed to be a constant or a fraction of the depth D of the Convective Boundary Layer (CBL). The main results are: (1) For the deepest CBLs (max(D) >~1.0 km) that develop under high atmospheric pressure, the time-height cross-sections of ε LQ 7DS and ε DL show very consistent patterns and do not show any substantial gaps in the transition region of 300–400 m when ε LQ 7DS is evaluated with L ~70 m , which is found to be about one tenth of the average of the CBL depth (L ~0.1 D). (2) Hourly mean ε DL averaged over the [100–300] m height range is on average about twice the hourly mean ε LQ 7TS averaged over the [400–500] m height range when D >~1.0 km. (3) Hourly mean ε DL averaged over the [100–300] m height range and hourly mean ε LQ 7DS averaged over the [400–500] m height range with L ~0.1 D are identical on average. Consistent with the fact that ε is expected to decrease slightly with height in the mixed layer, (2) and (3) imply an uncertainty as to the exact value of the L / D ratio: ~0.1 D < L <~0.2 D. We have also studied in detail the case of a shallow (D <~0.6 km) convective boundary layer that developed under low atmospheric pressure and cloudy conditions. Despite the fact that hourly mean ε DL averaged over the [100–300] m height range and hourly mean ε LQ 7TS averaged over the [400–500] m height range show more significant discrepancies, maybe due to the different properties of the shallow convection, the time-height cross-sections of ε DL and ε LQ 7DS show more consistent patterns and levels. [ABSTRACT FROM AUTHOR]

The American WAKE ExperimeNt (AWAKEN) collaboration is an observational-based field campaign in northern Oklahoma intended to analyze the potential influence of onshore wind farms and their collective wakes on wind power production, turbine structural loads, and on the atmospheric boundary layer (ABL). Focusing on the ABL effects, the University of Oklahoma and the Lawrence Livermore National Laboratory collected continuous high-resolution kinematic and thermodynamic profile measurements during 2022 and Summer 2023. The deployment strategy for these campaigns is detailed first, followed by an initial comparison of data from two sites in the AWAKEN domain: a near-farm site to examine collective wake impacts on the ABL, and a far-field site remaining outside the wind farm-waked region. We summarize the datasets available and demonstrate the benefits of these observations and multiple value-added products (VAPs) for investigation of ABL features observed during AWAKEN. We also highlight examples of preliminary analyses, including ABL height detection and nocturnal low-level jet examination, which are produced using novel VAPs based on optimal estimation to retrieve deeper Doppler lidar wind profiles than previously resolved, along with their uncertainty. By including the near-farm and far-field site in these analyses, we identified a pattern of stronger lower-atmospheric mixing at the near-farm site than the far-field site, motivating deeper investigation into the relationship between wind farms and general ABL characteristics. Future analysis will delve deeper into this relationship by examining other ABL characteristics, such as atmospheric stability and convection. [ABSTRACT FROM AUTHOR]

The atmospheric wind field in the troposphere and stratosphere notably impacts human production and life. The lidar is one of the most effective means of detecting the atmosphere in this region due to its high resolution and sensitivity. To meet this need, aerosol and molecular backscattering signals of different wavelengths were analyzed via lidar technology. The results showed that the 355-nm laser provides an advantage in detecting the atmosphere under clear weather conditions, and the 1064-nm laser achieves superior detection performance under severe haze weather conditions, while the detection performance of the 532-nm laser varies between those of the other lasers. The detection performance of the system was simulated using a 532-nm laser, and the maximum detection height reached 43 km under clear weather conditions and 28 km under polluted weather conditions. This analysis provides support for obtaining atmospheric wind fields in the troposphere and stratosphere under all weather conditions in the future. [ABSTRACT FROM AUTHOR]

This study investigated the influence of sea–land breezes on nocturnal spatial and temporal distribution of ozone (O3) and its potential effects on particulate nitrate formation in Qingdao, a coastal city in northern China. Observation campaigns were conducted to measure surface air pollutants and meteorological factors during a typical sea–land breezes event from 22 to 23 July 2022. A coherent Doppler lidar (CDL) system was employed to continuously detect three-dimensional wind fields. The results revealed that nocturnal ozone levels were enhanced by a conversion of sea–land breezes. Initially, the prevailing northerly land breeze transported high concentrations of O3 and other air pollutants from downtown to the Yellow Sea. As the sea breeze developed in the afternoon, the sea breeze front advanced northward, resulting in a flow of high O3 concentrations back into inland areas. This penetration of the sea breeze front led to a notable spike in O3 concentrations between 16:00 on 22 July and 02:00 on 23 July across downtown areas, with an average increase of over 70 μg/m3 within 10 min. Notably, a time lag in peak O3 concentration was observed with southern downtown areas peaking before northern rural areas. During this period, combined pollution of O3 and PM2.5 was also observed. These findings indicated that the nighttime increase in O3 concentrations, coupled with enhanced atmospheric oxidation, would likely promote the secondary conversion of gaseous precursors into PM2.5. [ABSTRACT FROM AUTHOR]

This paper is devoted to the issue of using laser (lidar) sensing to determine wind speed and direction when solving practical problems during the analysis of meteorological conditions in the area around spaceports. This issue is relevant both for making decisions on the possibility of a safe launch of a launch vehicle and for conducting search and rescue operations using groups of manned and unmanned aerial vehicles. Based on numerical and experimental modeling, it is shown that lidars provide highly accurate measurements of wind speed profiles and allow for the determination of weak wind shear in vertical and horizontal directions. This paper proposes a method for determining the main parameters of lidar sensing (range, resolution, detectability, etc.), which allows for the capabilities of this technology in solving the practical problems of meteorological monitoring to be predicted. Of particular interest in this article are experimental modeling data verifying the proposed calculation methods and the experimental determination of the capabilities of lidar diagnostics. This paper summarizes the data from multi-month experiments measuring wind speed in clear weather conditions when other means of remote diagnostics are ineffective. As a result of the experiments, a statistical distribution of the maximum range of wind speed measurement in normal weather conditions with natural variation in the concentration of scattering particles in the atmosphere was obtained. This article also discusses the possibility of combining lidars and meteorological radars for the meteorological support of flights. [ABSTRACT FROM AUTHOR]

As a new type of wind field detection equipment, coherent Doppler wind lidar (CDWL) still needs more relevant observation experiments to compare and verify whether it can achieve the accuracy and precision of traditional observation equipment in urban areas. In this experiment, a self-developed CDWL provided four months of observations in the southern Beijing area. After the data acquisition time and height match, the wind profile data obtained based on a Doppler beam swinging (DBS) five-beam inversion algorithm were compared with radiosonde data released from the same location. The standard deviation (SD) of wind speed is 0.8 m s−1, and the coefficient of determination R2 is 0.95. The SD of the wind direction is 17.7° with an R2 of 0.96. Below the height of the roughness sublayer (about 400 m), the error in wind speed and wind direction is significantly greater than the error above the height of the boundary layer (about 1500 m). For the case of wind speeds less than 4 m s−1, the error of wind direction is more significant and is affected by the distribution of surrounding buildings. Averaging at different height levels using suitable time windows can effectively reduce the effects of turbulence and thus reduce the error caused by the different measurement methods of the two devices. [ABSTRACT FROM AUTHOR]

Floating offshore wind turbines (FOWTs) are equipped with various sensors that provide valuable data for turbine monitoring and control. Due to technical and operational challenges, load estimations for mooring lines and fairleads can be difficult and expensive to obtain accurately. This research delves into a methodology where simulated floater motion measurements and wind speed measurements, derived from forward-looking nacelle-based lidar, are utilized as inputs for different types of neural networks to estimate fairlead tension time series and damage equivalent loads (DELs). Fairlead tension is intrinsically linked to the dynamics and the position of the floater. Therefore, we systematically analyze the individual contribution of floater dynamics to the prediction quality of fairlead tension time series and DELs. Wind speed measurements obtained via nacelle-based lidar on floating offshore wind turbines are inherently influenced by the platform's dynamics, notably the rotational pitch displacement and surge displacement of the floater. Consequently, the lidar wind speed data indirectly contain the dynamic behavior of the floater, which, in turn, governs the fairlead loads. This study leverages lidar-measured line-of-sight (LOS) wind speeds to estimate fairlead tensions. Training data for the model are generated by the aeroelastic wind turbine simulation tool, openFAST, in conjunction with the numerical lidar simulation framework ViConDAR. The fairlead tension time series are predicted using long short-term memory (LSTM) networks. DEL predictions are made using three different approaches. First, DELs are calculated from predicted time series; second, DELs are predicted using a sequence-to-one LSTM architecture, and third, DELs are predicted using a convolutional neural network architecture. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Further, we found that lidar LOS measurements do not improve time series or DEL predictions if motion measurements are available. However, using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of the planetary boundary layer (PBL) is crucial for comprehending land-atmosphere interactions. This study utilizes three years of Doppler wind lidar measurements from June 2019 to June 2022 to investigate PBL dynamics over Hefei, a city in the Western Yangtze River Delta, China. We focus on the seasonal and diurnal variations in key characteristics, such as wind profiles, shear intensity, turbulent mixing, low-level jets (LLJs), and mixing layer heights (MLH). Results show that horizontal wind speeds accelerated more rapidly above 3 km, with the predominant westerly winds (270°±15°) in all seasons. The vertical depth of high wind zone (> 8 m s-1) during the day is found generally deeper than at night, particularly in winter. In Hefei, LLJs primarily form at sunset and dissipate by noon, typically at altitudes between 0.5 and 0.6 km throughout the year, except in July. LLJ occurrences are most frequent in spring (31.7 %), followed by summer (24.7 %), autumn (22.3 %), and winter (21.3 %). Summer LLJs are most intensified, extending up to 1.5 km. The larger wind gradient below the jets significantly enhances turbulence and shear intensity near the ground at night. The seasonal average MLH peaks between 2:00 p.m. and 3:00 p.m., reaching approximately 1.2 km in spring and summer. Cloud cover raises MLH by about 100 m at night but decreases it by 200 m at the afternoon peak. This study provides insights into lidar-based PBL dynamics and highlights implications for local standards concerning low-altitude economic activities. [ABSTRACT FROM AUTHOR]

It has been proven that assimilating winds from the Aeolus global Doppler Wind Lidar (DWL) would enhance the predictive skill of weather forecast models. In this study, we use a series of observing system experiments to examine how currently assimilated "operational" wind products and Aeolus winds impact environment and climate change Canada's global forecast system over the data‐sparse Arctic region. Aeolus winds improve the tropospheric wind and temperature forecasts by about 0.7%–0.9% of error reduction (a 15%–20% effect compared to the impact of operational wind products) while having little impact on specific humidity. In particular, Aeolus winds have an impact on forecasts of strong wind days on the wind and temperature fields that is double the impact for weaker wind days. On the other hand, the operational wind products do not show as large a discrepancy in impact between forecasts of disturbed and normal days. These findings suggest significant potential for global DWL observations to enhance severe weather prediction in polar regions. Plain Language Summary: State of the art observation systems are necessary to produce accurate weather forecasts. In particular, wind observations are essential in regions where the atmospheric winds cannot be estimated from quantities such as temperature and pressure through geostrophic and thermal wind balance. Aeolus is the first satellite that provides global wind profile measurements and has a proven positive impact on forecasts. In this study, we investigate the impact of a large set of wind observations, including Aeolus winds, on Arctic weather forecasts using Canada's main forecast. We can calculate how these wind observations improve the forecast throughout the atmosphere and find that Aeolus winds further improve the forecast in the Arctic's lower atmosphere. Furthermore, our findings highlight the significance of wind observations in ensuring precise forecasts of intense wind days, which show double the improvement over the forecast of less intense wind days. This suggests that future DWL programs following from Aeolus have the potential to significantly benefit forecast skill in data‐sparse regions such as the Arctic and Antarctic, which are of growing societal, political, and economic interest. Key Points: Operationally assimilated wind products are key to skillful numerical weather prediction in the ArcticAeolus winds improve Arctic wind and temperature fields, contributing 14%–18% of the total gain from all operational wind data setsImprovements from Aeolus are most pronounced on strong wind (high Arctic kinetic energy) days [ABSTRACT FROM AUTHOR]

In atmospheric models, stochastic generation of subgrid‐scale profiles or "subcolumns" has been used for a variety of purposes. Such subcolumns can be generated from subgrid probability density functions (PDFs) at different vertical levels, when such PDFs are available. To do so, the generator needs to decide how strongly points should be correlated in the vertical, that is, how much the values should be overlapped. This is sometimes called "PDF overlap." To assess vertical correlation in a simplified, observable setting, here the vertical correlation of vertical velocity in subcloud layers is examined. Doppler lidar is used to evaluate the vertical profiles of vertical velocity produced by a large‐eddy simulation (LES) model and the Subgrid Importance Latin Hypercube Sampler (SILHS) subcolumn generator. In order to diagnose unrealistic features in subcolumn profiles, various statistical diagnostics are examined here, including the bivariate PDF of vertical velocity at two separated points (i.e., altitudes), the two‐point velocity correlation, the integral correlation length, the PDF of two‐point velocity differences, and the skewness and kurtosis of two‐point velocity differences. The profiles produced by LES match lidar well, except that they are too smooth at small scales. The profiles produced by SILHS exhibit sharp jumps from updraft to downdraft that are not observed in the lidar data. To reduce the generation of these unrealistically sharp jumps, the SILHS sampling method is revised. The diagnostics confirm that the revised sampling method reduces the overprediction of sharp jumps. Plain Language Summary: In numerical models of the atmosphere, it proves useful for a variety of applications to model the vertical coherence of various fields. It is difficult, however, to characterize and quantify the degree of vertical coherence in a simple and general way. Several statistical methods to characterize vertical coherence are assessed here for their ability to capture visual impressions of selected vertical profiles. These statistical methods are used to evaluate and revise a model of vertical coherence. Key Points: Lidar is used to evaluate profiles of vertical velocity from a large‐eddy simulation and a stochastic subcolumn generatorStatistical diagnostics are developed for the purpose of evaluation, and they detect spurious sharp jumps in the subcolumn profilesThe over‐prevalence of sharp jumps can be mitigated by the use of a revised sampling strategy [ABSTRACT FROM AUTHOR]

Sea ports play a major role in the transport of goods worldwide, and knowledge of wind characteristics in these areas is vital to maintaining safety. However, coastal wind flow can be highly complex and turbulent, necessitating additional analysis. A Doppler lidar providing continuous wind profiles is deployed in the Port of Genoa, Italy, to characterize the mean wind velocity and turbulence properties within the coastal surface layer (40–250 m above ground level). Weather conditions, reanalysis data, and transient wind profiles are combined to analyze wind field characteristics on a day which experienced a thunderstorm. We also utilize a method to identify and categorize sources of turbulence through analysis of lidar derived quantities such as wind shear, turbulent kinetic energy, and vertical skewness. Seasonal variations in the wind properties are investigated by selecting data from June (summer) and December (winter). Differences are found in dominant wind direction and the associated frequency of convective mixing, with onshore winds most common in summer and offshore winds in winter. The measured lidar velocity is also compared against Monin–Obukhov similarity theory predictions, showing satisfactory agreement at low heights but struggling to reproduce observations of the stable atmospheric conditions present during winter. Plain Language Summary: Approximately 40% of the world's population lives in the coastal regions. Similarly, seaports host about 80% of the global maritime transport and therefore play a major role in the transportation of goods and services. However, coastal regions and ports are also windy places due in part to strong temperature differences between land and sea. This study uses a remote sensing instrument—Doppler lidar—to measure mean wind velocity and turbulence characteristics in the Port of Genoa, Italy, between 40 and 250 m above the ground. The study first provides a detailed investigation of a case (7 June 2020) that was characterized by variable weather that included rain, thunderstorms, and intermittent sunshine. Seasonal variations in the wind characteristics above the Port of Genoa are investigated by focusing on June (summer) and December (winter) months. Winds that blow from sea to land are most common in summer whereas the winds that blow from land to sea are dominant in winter. The wind measurements are compared against theoretical predictions that are often employed in numerical weather prediction models. A varying degree of similarity between the measured and predicted values is found. Key Points: Analyses of wind variability during a thunderstorm and seasonal wind patterns using Doppler lidar measurements in the Port of Genoa, ItalyClassification of turbulent sources using lidar‐derived quantities and comparison of wind profiles against theoretical predictionsSeasonal analysis of surface heating induced convective‐mixing sources and their frequency [ABSTRACT FROM AUTHOR]

As offshore wind energy development accelerates in the United States, it is important to assess the accuracy of hub‐height wind forecasts from numerical weather prediction models over the ocean. Leveraging approximately 2 years of Doppler lidar observations from buoys in the New York Bight, we evaluate 80‐m wind speed forecasts from two weather models: the High‐Resolution Rapid Refresh (HRRR) atmospheric model and the Global Forecast System (GFS) coupled atmosphere‐ocean model. These models have different horizontal grid spacing, vertical layering, initialization methods, and parameterizations of boundary layer mixing and surface–atmosphere interactions. Despite these differences, the models demonstrate similar and highly skillful short‐term forecasts at three measurement sites. At the Hudson Southwest location that provides a full year of data, their performance is statistically indistinguishable: root mean square error (RMSE) = 2.1 m/s and the Pearson correlation coefficient r=0.89$$ r&amp;#x0003D;0.89 $$ for 24‐h forecasts of both models, and RMSE = 2.6 m/s and r≥$$ r\ge $$ 0.83 for 48‐h forecasts. Twenty‐four‐hour forecasts also exhibit skill in predicting quiescent winds and winds associated with maximum turbine power. By Day 10, GFS forecasts on average have almost no skill. The short‐term forecast skill by the HRRR and GFS does not strongly depend on season or time of day, yet we find some dependence of the models' performance on near‐surface stability. Additionally, 4‐ to 14‐day forecasts by the GFS exhibit lower RMSE during summer relative to other seasons. The high skill of the HRRR and GFS short‐term forecasts establishes confidence in their utility for offshore wind energy maintenance and operation. [ABSTRACT FROM AUTHOR]

Automated guided vehicles (AGVs) play a critical role in indoor environments, where battery endurance and reliable recharging are essential. This study proposes a multi-sensor fusion approach that integrates LiDAR, depth cameras, and infrared sensors to address challenges in autonomous navigation and automatic recharging. The proposed system overcomes the limitations of LiDAR's blind spots in near-field detection and the restricted range of vision-based navigation. By combining LiDAR for precise long-distance measurements, depth cameras for enhanced close-range visual positioning, and infrared sensors for accurate docking, the AGV's ability to locate and autonomously connect to charging stations is significantly improved. Experimental results show a 25% increase in docking success rate (from 70% with LiDAR-only to 95%) and a 70% decrease in docking error (from 10 cm to 3 cm). These improvements demonstrate the effectiveness of the proposed sensor fusion method, ensuring more reliable, efficient, and precise operations for AGVs in complex indoor environments. [ABSTRACT FROM AUTHOR]

The ion temperature is an important parameter for ionospheric detection, yet there is limited research on ion temperatures at altitude range from 80 to 300 km. Currently, a single‐frequency Ca+ ion optical parametric oscillator (OPO) Lidar system can be used to obtain the Ca+ ion density in this region at Yanqing Station, Beijing (40.4°N, 116.0°E). In this study, the ion temperature can be obtained by extending our original lidar to include three‐frequency Ca+ Doppler detection. This development represents a pioneering step on measuring ion temperatures by lidar in the ionospheric E‐F region. Preliminary results in the E region show that lidar temperatures at 90–105 km under the condition of smooth changes in Ca+ ion morphology align reasonably with satellite‐based observations and model‐derived temperatures. Additionally, the exploratory ion temperatures of the F layer peak heights at 200–300 km were obtained. Furthermore, parameter optimization and temperature error analysis in the E‐F region are explored, providing valuable insights for the development of Ca+ Doppler lidar systems. The advent of Ca+ Doppler lidar has greatly expanded the lidar detection altitude range of temperature and the possibilities for in‐depth research on ion temperature and velocity in regions affected by complex electrodynamic effects. These findings lay the foundation for subsequent studies of coupling processes in the ionospheric E‐F region. Key Points: Innovatively modified Ca+ Doppler lidar achieves ion temperature measurements in the challenging E‐F regionThe temperatures detected by lidar in the E region are reasonable, and exploratory temperatures in the F region are also obtainedThe discussion on optimizing the Ca+ Doppler lidars provides valuable guidance for temperature measurements in the E‐F region up to 300 km [ABSTRACT FROM AUTHOR]

Wake vortices generated by aircraft during near-ground operations have a significant impact on airport safety during takeoffs and landings. Identifying wake vortices in complex airspaces assists air traffic controllers in making informed decisions, ensuring the safety of aircraft operations at airports, and enhancing the intelligence level of air traffic control. Unlike traditional image recognition, identifying wake vortices using airborne LiDAR data demands a higher level of accuracy. This study proposes the IRSN-WAKE network by optimizing the Inception-ResNet-v2 network. To improve the model's feature representation capability, we introduce the SE module into the Inception-ResNet-v2 network, which adaptively weights feature channels to enhance the network's focus on key features. Additionally, we design and incorporate a noise suppression module to mitigate noise and enhance the robustness of feature extraction. Ablation experiments demonstrate that the introduction of the noise suppression module and the SE module significantly improves the performance of the IRSN-WAKE network in wake vortex identification tasks, achieving an accuracy rate of 98.60%. Comparative experimental results indicate that the IRSN-WAKE network has higher recognition accuracy and robustness compared to common recognition networks, achieving high-accuracy aircraft wake vortex identification and providing technical support for the safe operation of flights. [ABSTRACT FROM AUTHOR]

The accurate wind field observation of tropical cyclone (TC) boundary layer is of great significance to improve the TC track and intensity forecasting. To investigate the vertical structure of TC boundary layer during the landfall process of Super Typhoon Doksuri, three kinds of instruments including the coherent Doppler lidar (CDL), radar wind profiler (RWP) and automatic weather station (AWS) are deployed at two sites in Xiamen, Fujian province. A data fusion method is developed to obtain the complete wind speed profiles covering the whole Atmospheric Boundary Layer (ABL) based on the above instruments. The wind speeds in the near field blind zones of CDL observation are interpolated by combining the AWS measurements at 10 m. The CDL provides high temporal-spatial resolution wind speed profiles from tens of meters to its highest detection height. The wind speeds above the highest detection height of the CDL would be supplemented with the RWP measurements. The hourly mean wind speed profiles are compared with traditional models. Generally, the wind speed profiles fit well with the power law in the lower part of the ABL, before wind speed changes rapidly. However, it would cause a large error (up to 73 %) to describe the exact wind speed profiles with traditional models during and after the typhoon's passage, especially when the wind speed is almost constant with height or when wind shear exists. Then fine structures and evolutionary processes of the wind field in the ABL during the typhoon landfall are investigated. In addition, the wind field distribution and wind speed variation with distance from the typhoon center are statistical analyzed. The joint wind field measurements of CDL, RWP and AWS have the broad application prospects on the dynamics study of the TC boundary layer and the improvement of the boundary layer parameterization scheme in numerical forecast models. [ABSTRACT FROM AUTHOR]

A vigorous development process of the deep convective boundary layer (CBL) was observed at the southern edge of the Taklimakan Desert on 6 June 2022. Based on coherent Doppler wind lidar and ERA5 data, the formation mechanism of the deep CBL exceeding 5 km was analyzed, which was mainly driven by the low-level jet (LLJ) and thermal effects. During the stage of the LLJ preceding the formation of the deep CBL, the LLJ had adequately prepared the conditions for the development of the deep CBL in terms of momentum, energy, and material. Firstly, the cold downhill airflow from the Tibetan Plateau, which leads to the formation of the LLJ, weakens the height and intensity of the temperature inversion layer, thereby reducing the energy demand for the breakdown of this layer. Secondly, the LLJ not only supplements the material and energy in the residual layer but also suppresses the exchange with the lower atmosphere. In addition, the LLJ provides a driving force for the development of the deep CBL. During the stage of thermal effects, the sensible-heat-driven air pump from the Tibetan Plateau and the passage of a cold front provide additional impetus for the development of the deep CBL. Finally, the formation of the deep CBL was catalyzed by extreme thermal effects of the underlying surface, such as the furnace effect and the atmospheric superadiabatic expansion process. The study of the development of the deep CBL is important for revealing the land–air exchange process of momentum, energy, and material between the Taklimakan Desert and the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

The atmospheric boundary layer (ABL) height plays a key role in many atmospheric processes as one of the dominant flow length scales. However, a systematic quantification of the ABL height over the entire range of scales (i.e., with periods ranging from one minute to one year) is still lacking in literature. In this work, the ABL height is quantified based on high-resolution measurements collected by a scanning pulsed Doppler LiDAR during the recent American WAKE experimeNt (AWAKEN) campaign. The high availability of ABL height estimates (≈ 2200 collected over one year and each of them based on 10-min averaged statistics) allows to robustly assess five different ABL height models, i.e., one for convective thermal conditions and four for stable conditions. Thermal condition is quantified by a stability parameter spanning three orders of magnitude and probed by near-ground 3D sonic anemometry. The free-atmosphere stability, quantified by the Brunt–Väisälä frequency, is both calculated from simultaneous radiosonde measurements and obtained from the best fit of two of the chosen ABL height models. Good agreement is found between the data and three of the chosen models, quantified by mean absolute errors on the ABL height between 281 and 585 m. Furthermore, the seasonal variability of the convective ABL height model parameters (−15% to + 23 % with respect to the year baseline) agrees with the variability of buoyancy-generated turbulence caused by the variation in solar radiation throughout the year. [ABSTRACT FROM AUTHOR]

The Atmospheric Dynamics Mission ADM-Aeolus was successfully launched in August 2018 by the European Space Agency (ESA). The Aeolus mission carried a single instrument, the first-ever Doppler wind lidar (DWL) in space, called Atmospheric LAser Doppler INstrument (ALADIN). Aeolus circled the Earth, providing vertical profiles of horizontal line-of-sight (HLOS) winds on a global scale. The Aeolus satellite's measurements filled critical gaps in existing wind observations, particularly in remote regions such as the Brazilian Amazon. This area, characterized by dense rainforests and rich biodiversity, is essential for global climate dynamics. The weather patterns of the Amazon are influenced by atmospheric circulation driven by Hadley cells and the Intertropical Convergence Zone (ITCZ), which are crucial for the distribution of moisture and heat from the equator to the subtropics. The data provided by Aeolus can significantly enhance our understanding of these complex atmospheric processes. In this long-term validation study, we used radiosonde data collected from three stations in the Brazilian Amazon (Cruzeiro do Sul, Porto Velho, and Rio Branco) as a reference to assess the accuracy of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy wind products. Statistical validation was conducted by comparing Aeolus L2B wind products and radiosonde data covering the period from October 2018 to March 2023 for Cruzeiro do Sul and Porto Velho, and from October 2018 to December 2022 for Rio Branco. Considering all available collocated winds, including all stations, a Pearson's coefficient (r) of 0.73 was observed in Rayleigh-clear and 0.85 in Mie-cloudy wind products, revealing a strong correlation between Aeolus and radiosonde winds, suggesting that Aeolus wind products are reliable for capturing wind profiles in the studied region. The observed biases were −0.14 m/s for Rayleigh-clear and −0.40 m/s for Mie-cloudy, fulfilling the mission requirement of having absolute biases below 0.7 m/s. However, when analyzed annually, in 2022, the bias for Rayleigh-clear was −0.95 m/s, which did not meet the mission requirements. [ABSTRACT FROM AUTHOR]

Wind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production. [ABSTRACT FROM AUTHOR]

Over the past decades, various experimental and numerical model studies have indicated cooling trend in the mesosphere and lower thermosphere (MLT), while the magnitude of the trend varies noticeably. Previous studies using the lidar observations derived the temperature trends and solar responses solely from the traditional nocturnal measurements. While these archived results are more or less in agreement with modeling studies, one of the main uncertainties in these studies is the potential biases induced by the trends of the diurnal tide forced in the lower atmosphere, and that of the in situ exothermal reactions involving the photolysis. In the MLT, the diurnal tide has significant seasonal variations, considerable amplitude and is one of the dominant dynamic sources. However, its potential effects in the trend studies have rarely been discussed. In this paper, we present and compare the long‐term temperature trends in the upper mesosphere utilizing the daily mean and nightly mean temperature profiles measured by a Sodium (Na) Doppler lidar at midlatitude. The system was operating routinely in full diurnal cycles between 2002 and 2017, obtaining a unique multi‐year temperature data set. A customized multi‐linear regression (MLR) model is applied to determine the linear trends and the other fitting parameters, such as ENSO and solar F10.7 responses in the upper mesosphere. This study indicates the daily mean cooling trend between 84 and 98 km is larger than that of nightly mean trend by ∼−1 K/decade, while differences in the solar response are within the fitting uncertainties. Plain Language Summary: The upper atmosphere is sensitive to both the climate change in the lower atmosphere and the solar activity. Studies have demonstrated this region has been experiencing long‐term cooling trend over the past several decades. Most of the previous ground‐based investigations utilized nighttime temperature observations to derive the long‐term changes in the mesosphere and lower thermosphere. However, these nighttime trend results can be biased due to the lack of daytime temperature measurements, leading to the uncertainty in these achieved results. In this study, the daily mean temperatures measured by a lidar at middle latitude between 2002 and 2017 are used to determine the trend and the upper atmosphere response to solar activity. The customized algorithm includes new and important climate parameters. This investigation eliminates such potential bias in the temperature, and the results demonstrated noticeably larger cooling trend in this atmospheric region when compared with those derived from the nightly temperature observations. In addition, the solar response derived from these daily mean lidar temperatures are positive but less than those based on nightly mean observations. The revealed differences will lead to future investigations on the underlying mechanisms and, thus, uncover new aspect of the long‐term studies. Key Points: The temperature in the mesosphere and lower thermosphere is coolingThe cooling trend derived from the daily mean temperatures is noticeably larger than that revealed by the nightly average temperaturesThe WACCM‐X simulations also indicate the differences between the daily mean trend and the nightly mean trend with less magnitude [ABSTRACT FROM AUTHOR]

The long-term atmospheric mixing layer height (MLH) information plays an important role in air quality and weather forecasting. However, it is not sufficient to study the characteristics of MLH using long-term high spatial and temporal resolution data in the desert. In this paper, over the southern edge of the Taklimakan Desert, the diurnal, monthly, and seasonal variations in the daytime MLH (retrieved by coherent Doppler wind lidar) and surface meteorological elements (provided by the local meteorological station) in a two-year period (from July 2021 to July 2023) were statistically analyzed, and the relationship between the two kinds of data was summarized. It was found that the diurnal average MLH exhibits a unimodal distribution, and the decrease rate in the MLH in the afternoon is much higher than the increase rate before noon. From the seasonal and monthly perspective, the most frequent deep mixing layer (>4 km) was formed in June, and the MLH is the highest in spring and summer. Finally, in terms of their mutual relationship, it was observed that the east-pathway wind has a greater impact on the formation of the deep mixing layer than the west-pathway wind; the dust weather with visibility of 1–10 km contributes significantly to the formation of the mixing layer; the temperature and relative humidity also exhibit a clear trend of a concentrated distribution at about the height of 3 km. The statistical analysis of the MLH deepens the understanding of the characteristics of dust pollution in this area, which is of great significance for the treatment of local dust pollution. [ABSTRACT FROM AUTHOR]

The optical, physical, and chemical properties of aerosol particles have been previously studied in the United Arab Emirates (UAE), but there is still a gap in the knowledge of particle sources and in the horizontal and vertical transport of aerosol particles and their precursors in the area. To investigate how aerosol particle and SO2 concentrations at the surface responded to changes in horizontal and vertical transport, we used data from a 1-year measurement campaign at a background site where local sources of SO2 were expected to be minimal. The measurement campaign provided a combination of in situ measurements at the surface and the boundary layer evolution from vertical and horizontal wind profiles measured by a Doppler lidar. The diurnal structure of the boundary layer in the UAE was very similar from day to day, with a deep, well-mixed boundary layer during the day transitioning to a shallow nocturnal layer, with the maximum boundary layer height usually being reached around 14:00 local time. Both SO2 and nucleation-mode aerosol particle concentrations were elevated for surface winds coming from the east or western sectors. We attribute this to oil refineries located on the eastern and western coasts of the UAE. The concentrations of larger cloud condensation nuclei (CCN)-sized particles and their activation fraction did not show any clear dependence on wind direction, but the CCN number concentration showed some dependence on wind speed, with higher concentrations coinciding with the weakest surface winds. Peaks in SO2 concentrations were also observed despite low surface wind speeds and wind directions unfavourable for transport. However, winds aloft were much stronger, with wind speeds of 10ms-1 at 1 km common at night and wind directions favourable for transport; surface-measured concentrations increased rapidly once these particular layers started to be entrained into the growing boundary layer, even if the surface wind direction was from a clean sector. These conditions also displayed higher nucleation-mode aerosol particle concentrations, i.e. new particle formation events occurring due to the increase in the gaseous precursor. [ABSTRACT FROM AUTHOR]

This study develops and verifies two advanced wake models based on super-Gaussian distribution: three-dimensional (3D) super-Gaussian (3DSG) and 3D anisotropic super-Gaussian (3DASG) models. They have a smooth Gaussian–top-hat shape (a combination of Gaussian and top-hat shapes) in the near-wake region that gradually transitions to a Gaussian shape in the far-wake region. These models are based on the law of mass conservation and considers wind shear effect; hence, they can accurately describe asymmetric wind distribution in the vertical direction. Because of this Gaussian–top-hat shape, the model is more accurate in simulating the wake in the near-wake region. The anisotropic model also considers different wake expansion rates in various dimensions, rendering the model more realistic. The accuracy and generality of the two models are verified using four wake data sets obtained from wind tunnel tests and wind field measurements. The validation includes the prediction of the wake profile and relative error of the models. The results show that the two models can well predict the wake distribution of various sizes of turbines at any spatial location in the full-wake region. [ABSTRACT FROM AUTHOR]

Ocean temperature profile information plays a key role in understanding the marine environment. The passive remote-sensing technique can provide sea surface temperature measurements over large areas. However, it is sensitive to the atmospheric environment and cannot provide seawater temperature profile information. The lidar technique is the only way to carry out seawater temperature profile measurements over large areas. However, it is insufficient for measuring speed, the receiving field, stability, spectral integrity, simple system structures, and so on. Therefore, we propose a Brillouin lidar method combining two iodine cells at different temperatures to realize temperature measurements, where one iodine cell is used as a filter to absorb the elastic scattering and the other as an edge detection discriminator to obtain the seawater temperature measurement. The system has a fast measurement speed, a large receiving field, a simple system structure, and high stability. The system feasibility was verified via principle simulation and real iodine absorption curve measurements. For an ocean temperature of [5 °C, 15 °C], a laser wavelength of 532.10495 nm was more appropriate, corresponding to the iodine pool temperature combinations of 50 °C and 78 °C. For an ocean temperature of [15 °C, 32 °C], a laser wavelength of 532.10518 nm was more appropriate, corresponding to the iodine cell temperature combinations of 60 °C and 78 °C. When the laser intensity reached a measurement precision of 1‰, the temperature could be predicted with an accuracy of up to 0.2 K. This work shows promise as a potential solution for seawater temperature profile measurement. [ABSTRACT FROM AUTHOR]

Accurate topographic data acquired at appropriate spatio‐temporal resolution is often the cornerstone of geomorphic research. Recent decades have seen advances in our ability to generate highly accurate topographic data, primarily through the application of remote sensing techniques. Structure from Motion‐Multi View Stereo (SfM‐MVS) and lidar have revolutionised the spatial resolution of surveys across large spatial extents. Technological developments have led to commercialisation of small form factor (SFF) 3D lidar sensors that are suited to deployment on both mobile (e.g., uncrewed aerial systems), and in fixed semi‐permanent installations. Whilst the former has been adopted, the potential for the latter to generate data suitable for geomorphic investigations has yet to be assessed. We address this gap here in the context of a 3‐month deployment where channel change is assessed in an adjusting fluvial system. We find that SFF 3D lidar sensors generate change detection products comparable to those generated using a conventional lidar system. Areas of no geomorphic change are characterised as such (mean 3D change of 0.014 m compared with 0.0014 m for the Riegl VZ‐4000), with differences in median change in eroding sections of between 0.02 and 0.04 m. We illustrate that this data enables: (a) accurate characterisation of river channel adjustments through extraction of bank long‐profiles; (b) the assessment of bank retreat patterns which help elucidate failure mechanics; and (c) the extraction of water surface elevations. The deployment of this technology will enable a better understanding of processes across a variety of geomorphic systems, as data can be captured in 4D with near real‐time processing. Plain Language Summary: This research demonstrates how instruments that were initially developed for the automotive and robotics industry can be used to provide detailed information about the shape and size of landforms, and how they change over time. The instruments that we use are small, low‐cost, lidar sensors (which use laser beams to measure distances). We deployed the sensors over a 3‐month period, where they were used to assess how the banks of a river channel changed (eroded and deposited sediment). The river bank of interest was located approximately 30‐m away from the lidar sensors and we found them to generate results comparable to those obtained from conventional lidar systems. The small sensors accurately identified areas where no changes occurred, with minor differences in estimates for areas experiencing change. The fixed installation allowed for high‐resolution data to be collected every 2‐hr. Using this new technology, we were able to accurately study the change of the river channel, measure the patterns of bank retreat, understand the failure mechanisms and extract elevations of the water surface. This new technology has the potential to enhance our understanding of various environments that are undergoing change by capturing detailed data in three‐dimensional space over time. Key Points: Fixed monitoring platforms using small form lidar sensors offer insight into hydrogeomorphic processes at user defined temporal resolutionChange detection products are within 2–4 cm of those produced using conventional lidar systems at river bank sectionsApplication provides quantified insight into bank retreat processes and river water levels at high spatio‐temporal resolution [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) sensors can precisely determine object distances using the pulsed time of flight (TOF) or amplitude-modulated continuous wave (AMCW) TOF methods and velocity using the frequency-modulated continuous wave (FMCW) approach. In this paper, we focus on modelling and analysing the reflection of vector beams (VBs) and vector vortex beams (VVBs) for optical sensing in LiDAR applications. Unlike traditional TOF and FMCW methods, this novel approach uses VBs and VVBs as detection signals to measure the orientation of reflecting surfaces. A key component of this sensing scheme is understanding the relationship between the characteristics of the reflected optical fields and the orientation of the reflecting surface. To this end, we develop a computational model for the reflection of VBs and VVBs. This model allows us to investigate critical aspects of the reflected field, such as intensity distribution, intensity centroid offset, reflectance, and the variation of the intensity range measured along the azimuthal direction. By thoroughly analysing these characteristics, we aim to enhance the functionality of LiDAR sensors in detecting the orientation of reflecting surfaces. [ABSTRACT FROM AUTHOR]

Recently, the growing demand for autonomous driving in the industry has led to a lot of interest in 3D object detection, resulting in many excellent 3D object detection algorithms. However, most 3D object detectors focus only on a single set of LiDAR points, ignoring their potential ability to improve performance by leveraging the information provided by the consecutive set of LIDAR points. In this paper, we propose a novel 3D object detection method called temporal motion-aware 3D object detection (TM3DOD), which utilizes temporal LiDAR data. In the proposed TM3DOD method, we aggregate LiDAR voxels over time and the current BEV features by generating motion features using consecutive BEV feature maps. First, we present the temporal voxel encoder (TVE), which generates voxel representations by capturing the temporal relationships among the point sets within a voxel. Next, we design a motion-aware feature aggregation network (MFANet), which aims to enhance the current BEV feature representation by quantifying the temporal variation between two consecutive BEV feature maps. By analyzing the differences and changes in the BEV feature maps over time, MFANet captures motion information and integrates it into the current feature representation, enabling more robust and accurate detection of 3D objects. Experimental evaluations on the nuScenes benchmark dataset demonstrate that the proposed TM3DOD method achieved significant improvements in 3D detection performance compared with the baseline methods. Additionally, our method achieved comparable performance to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Semantic scene completion is a crucial outdoor scene understanding task that has direct implications for technologies like autonomous driving and robotics. It compensates for unavoidable occlusions and partial measurements in LiDAR scans, which may otherwise cause catastrophic failures. Due to the inherent complexity of this task, existing methods generally rely on complex and computationally demanding scene completion models, which limits their practicality in downstream applications. Addressing this, we propose a novel integrated network that combines the strengths of 3D and 2D semantic scene completion techniques for efficient LiDAR point cloud scene completion. Our network leverages a newly devised lightweight multi-scale convolutional block (MSB) to efficiently aggregate multi-scale features, thereby improving the identification of small and distant objects. It further utilizes a layout-aware semantic block (LSB), developed to grasp the overall layout of the scene to precisely guide the reconstruction and recognition of features. Moreover, we also develop a feature fusion module (FFM) for effective interaction between the data derived from two disparate streams in our network, ensuring a robust and cohesive scene completion process. Extensive experiments with the popular SemanticKITTI dataset demonstrate that our method achieves highly competitive performance, with an mIoU of 35.7 and an IoU of 51.4. Notably, the proposed method achieves an mIoU improvement of 2.6 % compared to previous methods. [ABSTRACT FROM AUTHOR]

The European Space Agency's Aeolus satellite was launched in August 2018 and began delivering horizontal line‐of‐sight (HLOS) wind observations in early September 2018. In early 2019, the Met Office began assessing the suitability of the HLOS winds for operational assimilation into its global numerical weather prediction system. We performed a number of assimilation experiments to assess the impact of HLOS wind observations on our global forecasts. We have found that assimilating HLOS winds changes the zonal winds in the analysis fields predominantly in the Tropics and Southern Hemisphere, with the largest changes being in the upper troposphere and lower stratosphere. This has a positive impact on the accuracy of the global weather forecasts, with improvements in the root‐mean‐square error seen throughout the troposphere. Assimilation of Aeolus HLOS winds improves the standard deviation of the observation minus background (a 6 hr forecast) of almost all other observation types, suggesting that the numerical weather prediction model analysis is improved, which consequently improves the 6 hr forecast. In a set of short‐period observation denial experiments, we found that assimilating Aeolus has an impact similar in magnitude to assimilating surface winds from scatterometers. Assimilating winds from the Rayleigh channel has approximately three times the impact that assimilating HLOS winds from the Mie channel does. Both channels contribute a measureable improvement to the global forecast, and we therefore started operational assimilation of winds from the Mie channel in December 2020 and the Rayleigh channel operationally in May 2022. [ABSTRACT FROM AUTHOR]

Understanding the boundary-layer height and its dynamics is crucial for a wide array of applications spanning various fields. Accurate identification of the boundary-layer top contributes to improved air quality predictions, pollutant transport assessments, and enhanced numerical weather prediction through parameterization and assimilation techniques. Despite its significance, defining and observing the boundary-layer top remain challenging. Existing methods of estimating the boundary-layer height encompass radiosonde-based methods, radar-based retrievals, and more. As emerging boundary-layer observation platforms emerge, it is useful to reevaluate the efficacy of existing boundary-layer-top detection methods and explore new ones. This study introduces a fuzzy logic algorithm that leverages the synergy of multiple remote sensing boundary-layer profiling instruments: a Doppler lidar, infrared spectrometer, and microwave radiometer. By harnessing the distinct advantages of each sensing platform, the proposed method enables accurate boundary-layer height estimation both during daytime and nocturnal conditions. The algorithm is benchmarked against radiosonde-derived boundary-layer-top estimates obtained from balloon launches across diverse locations in Wisconsin, Oklahoma, and Louisiana during summer and fall. The findings reveal notable similarities between the results produced by the proposed fuzzy logic algorithm and traditional radiosonde-based approaches. However, this study delves into the nuanced differences in their behavior, providing insightful analyses about the underlying causes of the observed discrepancies. While developed with the three instruments mentioned above, the fuzzy logic boundary-layer-top detection algorithm, called BLISS-FL, could be adapted for other wind and thermodynamic profilers. BLISS-FL is released publicly, fostering collaboration and advancement within the research community. [ABSTRACT FROM AUTHOR]

This article gives the overview of the Japan Aerospace Exploration Agency (JAXA) level 2 (L2) Standard and Research algorithms and products by Japanese science teams for EarthCARE Clouds, Aerosols and Radiation Explorer (EarthCARE), which is a JAXA and the European Space Agency (ESA) joint satellite mission. First three single sensor algorithms for 94GHz cloud profiling radar (CPR)-only, 355nm-atmospheric lidar with high spectral resolution function (ATLID)-only, and multi-spectral imager (MSI)-only retrievals, and their products were briefly reviewed. CPR-echo algorithms provide radar reflectivity factor, Doppler velocity, normalized radar cross section and path integral attenuation. CPR-only, CPR-ATLID synergy and CPR-ALTID-MSI synergy algorithms for standard cloud products provide cloud detection, cloud particle type and cloud microphysics, and the research products further provide Doppler velocity, terminal velocity and vertical air motion inside clouds. ATLID standalone algorithms produce aerosol, cloud and clear sky classification products as well as total aerosol extinction and extinction and number concentration of each aerosol types. ATLID-MSI synergy algorithms are developed to retrieve effective radius for each aerosol species in addition to the ATLID-only products. MSI algorithms retrieve cloud effective radius, ice and water content and cloud top pressure. Four sensor algorithms are prepared to produce shortwave and longwave radiative fluxes at the top of atmosphere, those at the surface and also heating rate profiles by using the outputs from CPR, ATLID and their synergy algorithms. The shortwave and longwave fluxes from the four sensor algorithms will then be compared with broad band radiation (BBR) to examine the consistency of the JAXA L2 retrievals. The algorithms are developed and evaluated by using observational data from satellites and ground-based instruments, and simulation data from the Japanese global cloud-resolving model, the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) with Joint simulator. As for space-borne data, existing space-borne satellites data such as CloudSat, CALIPSO, MODIS and CERES datasets are intensively used. For ground-based observations, High-sensitivity Ground-based Super Polarimetric Ice-crystal Detection and Explication Radar (HG-SPIDER) with a minimum sensitivity of -40 dBZ at 15 km and over -60 dBZ at 1 km, Electronic Scanning SPIDER (ES-SPIDER), 355 nm high spectral resolution lidar, multiple-field-of-view multiple scattering lidar and Doppler lidars are installed at EarthCARE super-site in Koganei, Tokyo and offers unique opportunities to evaluate and analyse EarthCARE data. [ABSTRACT FROM AUTHOR]

The most common profiling techniques for the atmospheric boundary layer based on a monostatic Doppler wind lidar rely on the assumption of horizontal homogeneity of the flow. This assumption breaks down in the presence of either natural or human-made obstructions that can generate significant flow distortions. The need to deploy ground-based lidars near operating wind turbines for the American WAKE experimeNt (AWAKEN) spurred a search for novel profiling techniques that could avoid the influence of the flow modifications caused by the wind farms. With this goal in mind, two well-established profiling scanning strategies have been retrofitted to scan in a tilted fashion and steer the beams away from the more severely inhomogeneous region of the flow. Results from a field test at the National Renewable Energy Laboratory's 135-m meteorological tower show that the accuracy of the horizontal mean flow reconstruction is insensitive to the tilt of the scan, although higher-order wind statistics are severely deteriorated at extreme tilts mainly due to geometrical error amplification. A numerical study of the AWAKEN domain based on the Weather Research and Forecasting Model and large-eddy simulation are also conducted to test the effectiveness of tilted profiling. It is shown that a threefold reduction of the error on inflow mean wind speed can be achieved for a lidar placed at the base of the turbine using tilted profiling. [ABSTRACT FROM AUTHOR]

Lidar (Light Detection and Ranging), utilizes laser based remote sensing data to measure distances and properties of objects by analysing reflected light, serving diverse applications. Lidar signals in the middle atmosphere face challenges like noise and atmospheric uncertainties. Lidar signal denoising and temperature retrievals are crucial for accurate measurements, improving data reliability in atmospheric research. Lidar signal denoising is the process of reducing unwanted noise or interference from Lidar data, enhancing accuracy and reliability. To address the issues Shuffle Attention with Encoder and Decoder based Deep Convolutional Neural Network (SAED-DCNN) is proposed. The Lidar signal undergoes preprocessing, including normalization, followed by convolution and pooling encoding in the autoencoding layer of SAED-DCNN. Leveraging Deep Convolutional Neural Network (DCNN) and encoder-decoder Framework, with shuffle attention, enhances spatial interactions for reducing Lidar signal noise. the introduction of enhanced spider wasp optimization addresses computational complexity, optimizing SAED-DCNN parameters, presenting an innovative Lidar signal enhancement approach. The suggested framework exhibits outstanding performance, achieving signal-to-noise ratio and root mean squared error of 28.135 dB and 68.113, respectively. These metrics underscores the mode's efficacy in overcoming denoising challenges, establishing it as a robust denoising solution. [ABSTRACT FROM AUTHOR]

This paper focuses on the effective utilization of data augmentation techniques for 3D lidar point clouds to enhance the performance of neural network models. These point clouds, which represent spatial information through a collection of 3D coordinates, have found wide-ranging applications. Data augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization capabilities. Much of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point clouds. However, there has been a lack of focus on making the most of the numerous existing augmentation techniques. Addressing this deficiency, this research investigates the possibility of combining two fundamental data augmentation strategies. The paper introduces PolarMix and Mix3D, two commonly employed augmentation techniques, and presents a new approach, named RandomFusion. Instead of using a fixed or predetermined combination of augmentation methods, RandomFusion randomly chooses one method from a pool of options for each instance or sample. This innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or Mix3D. The crux of this strategy is the random choice between PolarMix and Mix3D for the augmentation of each point within the point cloud data set. The results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation tasks. This is achieved without compromising computational efficiency. By examining the potential of merging different augmentation techniques, the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point clouds. RandomFusion data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the robustness of models. The insights gained from this research can pave the way for future work aimed at developingmore advanced and efficient data augmentation strategies for 3D lidar point cloud analysis. [ABSTRACT FROM AUTHOR]

Despite recent popularization and widespread use in modern electronic devices, LiDAR technology remains expensive for research purposes, in part due to the very high performance offered by commercially available LiDAR scanners. However, such high performance is not always needed, and the expensive price ends up making LiDAR scanners inaccessible for research projects with reduced budget, such as those in developing countries. Here we designed and built a simple ground-based LiDAR scanner, with performance sufficient to fulfil the requirements for a variety of ecological research projects, while being cheap and easy to build. We managed to assemble a LiDAR scanner under 400 USD (as of 2021), and it is simple enough to be built by personnel with minimal engineering background. We also demonstrated the quality of the resulting point clouds by scanning a test site and producing some common LiDAR products. Although not adequate for mapping large area due to its limited range, our LiDAR design is open, customizable, and can produce adequate results while costing ~1% of "low-cost" scanners available in the market. As such, our LiDAR scanner opens a world of new opportunities, particularly for projects in developing countries. [ABSTRACT FROM AUTHOR]

Challenges have been observed in the traditional circumferential-scanning LiDAR underwater to balance between the detection range and the sealing performance. To tackle these challenges, a new scanning unit is presented in this paper, employing a pyramidal-shaped reflector for enhanced performance. Furthermore, an innovative magneto–electric detection module comprising Hall switches and magnetic rings is introduced. It can facilitate the accurate identification of the reflector's edge, thereby enhancing the precision of the target-orientation detection. A rapid target orientation coding method based on split-frequency clocks is proposed on FPGAs. It can output the target's initial and termination orientation codes immediately after capturing it, exhibiting a significantly low output delay of 20 ns and a high detection resolution of 15°. Finally, a prototype is fabricated to validate the design in this paper. The experimental results demonstrate that the scanning unit enables reliable scanning and orientation recognition of the target. In addition, it is trustworthy in receiving echo signals when the laser passes through glass and then an aqueous medium. [ABSTRACT FROM AUTHOR]

AbstractSwitching modulation involving two or more frequencies exhibits outstanding performance for indirect time-of-flight (i-ToF) LiDAR to address a problem with resolution and ambiguity range. However, the accuracy and precision remain issues when a target is far from the system due to a poorly received signal. This work improves a LiDAR system by introducing adjustable gain on the avalanche photodetector (APD) to enhance the accuracy and precision. The system was experimentally characterized by measuring a target at 5, 25, and 45 meters with gain settings of maximum, middle, and no gain. The middle and maximum gain settings improved the signal-to-noise ratio (SNR), accuracy, and precision. Furthermore, the proposed system may be appropriate for environmental remote-sensing systems, such as mapping and autonomous navigation. [ABSTRACT FROM AUTHOR]

This paper presents a current-mode VCSEL driver (CMVD) implemented using 180 nm CMOS technology for application in short-range LiDAR sensors, in which current-steering logic is suggested to deliver modulation currents from 0.1 to 10 mApp and a bias current of 0.1 mA simultaneously to the VCSEL diode. For the simulations, the VCSEL diode is modeled with a 1.6 V forward-bias voltage and a 50 Ω series resistor. The post-layout simulations of the proposed CMVD clearly demonstrate large output pulses and eye-diagrams. Measurements of the CMVD demonstrate large output pulses, confirming the simulation results. The chip consumes a maximum of 11 mW from a 3.3 V supply, and the core occupies an area of 0.1 mm2. [ABSTRACT FROM AUTHOR]

Knowledge of wind field characteristics and variation principles in complex topographical regions is of great importance for the development of numerical prediction models, aviation safety support, and wind energy utilization. However, there has been limited research focused on the lower-tropospheric wind fields in the Qinghai-Tibet Plateau. This paper aims to study the wind characteristics, vertical distributions, and temporal variations in the northeast of the plateau by analyzing a four-year continuous dataset collected from a Doppler wind LiDAR deployed in Xining, Qinghai Province of China. The results indicate that the prevailing horizontal wind direction in the low levels is primarily influenced by the mountain-valley wind circulation. However, as the altitude increases, the prevailing winds are predominantly affected by the westerlies. From a diurnal perspective, noticeable transition processes between up-valley and down-valley winds can be observed. The west-northwest wind (down-valley wind) dominates from late night to morning, while the east-southeast wind (up-valley wind) prevails from afternoon to early evening. The vertical winds in the low levels exhibit a downward motion during the daytime and an upward motion during the nighttime. In this plateau valley, the wind shear exponent is found to be highest in spring and lowest in winter, and it is generally lower during the daytime compared to the nighttime. [ABSTRACT FROM AUTHOR]