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This dataset contains derived data from the profiling lidar (Windcube v2.1) deployed at WFIP3's BARG. The derived data files herein are based on the lidar's STA files (i.e., the 10-min average data files). Note: this version of the data has been corrected to account for the motion of the barge.
This dataset contains reviewed data from the profiling lidar (Windcube v2.1) deployed on WFIP3's barge. The reviewed files herein are based on the lidar's RTD files (i.e., the real-time raw data files at near 1 Hz resolution). The data have been corrected for the motion of the ship.
This dataset contains reviewed data from the profiling lidar (Windcube v2.1) deployed on WFIP3's NOAA SHIP. The reviewed files herein are based on the lidar's RTD files (i.e., the real-time raw data files at near 1 Hz resolution). The data have been corrected for the motion of the ship.
The field profiling lidar #ZX300-1945 was deployed on the ground in an open field 880 m south of the NEON tower and tower profiling lidar (ZX300-1381). The zip files contain high frequency data. The excel files are 10-min averages.
The tower profiling lidar #ZX300-1381 was deployed on top of the 30 m tall MLBS NEON tower in an Appalachian forest. Prior to being deployed at MLBS, this lidar was on top of the tower at WREF in Washington. The zip files contain high frequency data. The Excel files are 10-min averages.
The field profiling lidar #ZX300-1945 was deployed in a small open clearing at the Mountain Lake Biological Station and is co-located with the University of Virginia scanning lidar. The zip files contain high frequency data. The Excel files are 10-min averages.
This dataset contains derived data from the profiling lidar (Windcube v2.1) deployed at WFIP3's NOAA SHIP site. The derived data files herein are based on the lidar's STA files (i.e., the 10-min average data files). The data have been corrected for the motion of the ship.
This dataset contains vertical profiling lidar data from AWAKEN site A1. This is the ZephIR300 #773 lidar system. The .csv files are monthly data files with 10-min average data. The .zip files are daily high frequency data and zipped by month.
The field profiling lidar #ZX300-773 was deployed on the ground in an open field 880 m south of the Wind River NEON tower in summer 2025. It was co-located with LLNL's Halo XR+ scanning lidar (z05) and Argonne's Waggle Node to capture mountain-valley flows during the warm summer season. The zip files contain high frequency data. The excel files are 10-min averages.
These data are from a deployment of the LLNL Halo Streamline scanning lidar in Summer 2025. The scanning lidar was deployed in a clearing area 880 m south of the NEON tower.
These data are from a deployment of the LLNL Halo Streamline scanning lidar in Summer 2025. The scanning lidar was deployed in a clearing area 880 m south of the NEON tower.
Aerosol vertical distributions impact both the direct and indirect radiative effects of aerosols. High Spectra Resolution Lidar (HSRL) separates between atmospheric molecular signals and aerosol particle signals and therefore can provide reliable measurements of aerosol properties. Six years of HSRL measurements between 2014 and 2019 from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) atmospheric observatory at Utqiaqvik are used to statistically analyze Arctic aerosol vertical distributions. The annual cycle of aerosol vertical distributions in terms of aerosol particulate backscatter coefficient ($β_p$), lidar scattering ratio (SR), and aerosol particulate depolarization ratio ($δ_p$) profiles at the wavelength of 532 nm shows that Arctic Haze events are prevalent in later winter and spring at the NSA site. Mineral dust is frequently presented in strong aerosol layers in the spring, fall, and winter seasons. Over the summer season, the NSA site has large aerosol loadings that are dominated by small spherical aerosol particles.
The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost–benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.
The number of intense wildfires may increase further in upcoming years as a consequence of climate change. It is therefore necessary to improve our knowledge about the role of smoke in the climate system, with emphasis on the impact of smoke particles on the evolution of clouds, precipitation, and cloud radiative properties. Presently, one key aspect of research is whether or not wildfire smoke particles can initiate cirrus formation. In this study, we present lidar observations over Limassol, Cyprus, from 27 October to 3 November 2020, when extended wildfire smoke fields crossed the Mediterranean Basin from Portugal to Cyprus. We found strong evidence that aged smoke (organic aerosol particles) originating from wildfires in North America triggered significant ice nucleation at temperatures from -47 to -53 °C and caused the formation of extended cirrus layers. The observations suggest that the ice crystals were nucleated just below the tropopause in the presence of smoke particles serving as ice-nucleating particles (INPs). The main part of the 2–3 km thick smoke layer was, however, in the lower stratosphere just above the tropopause. With actual radiosonde observations of temperature and relative humidity and lidar-derived smoke particle surface area concentrations used as starting values, gravity wave simulations show that the lofting of air by 100–200 m is sufficient to initiate significant ice nucleation on the smoke particles, leading to ice crystal number concentrations of 1–100 L -1 .
Cloud cover estimates of single-layer shallow cumuli obtained from narrow field-of-view (FOV) lidar–radar and wide-FOV total sky imager (TSI) data are compared over an extended period (2000–2017 summers) at the established United States Atmospheric Radiation Measurement mid-continental Southern Great Plains site. We quantify the impacts of two factors on hourly and sub-hourly cloud cover estimates: (1) instrument-dependent cloud detection and data merging criteria and (2) FOV configuration. Enhanced observations at this site combine the advantages of the ceilometer, micropulse lidar (MPL) and cloud radar in merged data products. Data collected by these three instruments are used to calculate narrow-FOV cloud fraction (CF) as a temporal fraction of cloudy returns within a given period. Sky images provided by TSI are used to calculate the wide-FOV fractional sky cover (FSC) as a fraction of cloudy pixels within a given image. To assess the impact of the first factor on CF obtained from the merged data products, we consider two additional subperiods (2000–2010 and 2011–2017 summers) that mark significant instrumentation and algorithmic advances in the cloud detection and data merging. We demonstrate that CF obtained from ceilometer data alone and FSC obtained from sky images provide the most similar and consistent cloud cover estimates; hourly bias and root-mean-square difference (RMSD) are within 0.04 and 0.12, respectively. However, CF from merged MPL–ceilometer data provides the largest estimates of the multiyear mean cloud cover, about 0.12 (35 %) and 0.08 (24 %) greater than FSC for the first and second subperiods, respectively. CF from merged ceilometer–MPL–radar data has the strongest subperiod dependence with a bias of 0.08 (24 %) compared to FSC for the first subperiod and shows no bias for the second subperiod. The strong period dependence of CF obtained from the combined ceilometer–MPL–radar data is likely results from a change in what sensors are relied on to detect clouds below 3 km. After 2011, the MPL stopped being used for cloud top height detection below 3 km, leaving the radar as the only sensor used in cloud top height retrievals. To quantify the FOV impact, a narrow-FOV FSC is derived from the TSI images. We demonstrate that FOV configuration does not modify the bias but impacts the RMSD (0.1 hourly, 0.15 sub-hourly). In particular, the FOV impact is significant for sub-hourly observations, where 41 % of narrow- and wide-FOV FSC differ by more than 0.1. A new “quick-look” tool is introduced to visualize impacts of these two factors through integration of CF and FSC data with novel TSI-based images of the spatial variability in cloud cover. The influence of cloud field organization, such cloud streets parallel to the wind direction, on narrow- and wide-FOV cloud cover estimates can be visually assessed.
Abstract. Ground-based lidar data have proven extremely useful for profiling the convective boundary layer (CBL). Many groups have derived higher-order moments (e.g., variance, skewness, fluxes) from high-temporal-resolution lidar data using an autocovariance approach. However, these analyses are highly uncertain near the CBL top when the depth of the CBL (zi) is changing during the analysis period. This is because the autocovariance approach is usually applied to constant height levels and the character of the eddies is changing on either side of the changing CBL top. Here, a new approach is presented wherein the autocovariance analysis is performed on a normalized height grid, with a temporally smoothed zi. Output from a large eddy simulation model demonstrates that deriving higher-order moments from time series on a normalized height grid has better agreement with the slab-averaged quantities than the moments derived from the original height grid.
Accurate building footprint extraction is critical for applications ranging from population estimation to disaster management. Although optical imagery provides detailed spectral information, it often struggles with shadows, occlusions, and background clutter in dense urban environments. Lidar data, by contrast, offer precise elevation and structural attributes but face challenges such as variable point density and noise. This study integrates multispectral imagery from the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) with lidar-derived feature height and intensity from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) to improve footprint extraction using a U-Net–based deep learning model. A six-band input stack (RGB, near-infrared, height, intensity) was developed, normalized, and tiled for training and evaluation against Microsoft Global Building Footprints (GBF). Results from the Houston, TX test site show that the six-band model achieved a precision of 0.86, recall of 0.88, F1 score of 0.87, and Intersection-over-Union (IoU) of 0.76, consistently outperforming four-band baselines by reducing false positives while maintaining sensitivity. Predictions on withheld Houston tiles confirmed strong within-region generalization, yielded a precision of 0.78, recall of 0.81, F1 score of 0.79, and IoU of 0.66. Qualitative analysis further revealed limitations stemming from both training label quality and vegetation–building confusion. These findings demonstrate the complementary value of integrating spectral and structural information for robust building footprint extraction and how domain adaptation strategies can be used to enhance cross-regional transferability.
These files contain 24 hour periods of data collected from the CLAMPS1 Halo Streamline Doppler lidar. While not conducting other scans, the lidar directs the beam to zenith, allowing for the measurement of vertical velocity. These data were collected during the TRACER project.