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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 74 m tall WREF NEON tower. 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 high-frequency (10Hz) data from the GX5-45 IMU on the Barge Science vans. The data are all raw binary files.
This dataset contains wind profiles retrieved from 6-beam Velocity Azimuth Display (VAD) scans done by a Streamline XR Doppler Lidar operated by Lawrence Livermore National Laboratory and deployed at the Tree Tops site (1.5 km South-West of MLBS site). The wind components (expressed as zonal, meridional and vertical) are retrieved through the algorithm of Paschke et al. (2015). The quality control of the radial wind speed is performed following the algorithm of Foken et al. (2004).
This dataset contains wind profiles retrieved from 6-beam Velocity Azimuth Display (VAD) scans done by a Streamline XR Doppler Lidar operated by the University of Virginia and deployed at the MLBS site. The wind components (expressed as zonal, meridional and vertical) are retrieved through the algorithm of Paschke et al. (2015). The quality control of the radial wind speed is performed following the algorithm of Foken et al. (2004).
This dataset contains Dual-Doppler wind reconstruction from stare lidar scans two rotor diameters south of the King Plains G02 turbine. The line-of-sight velocity data can be found in sa5.lidar.z03.b0 and sa1.lidar.z03.b0. The reconstruction is done neglecting the contribution from vertical velocity.
Rainwater chemistry is determined by atmospheric pollutants and particles which vary spatially and temporally. Industrial and agricultural activities and meteorological events (e.g. sea breezes, severe weather, blowing dust) alter atmospheric particle and trace gas compositions. These gases and particles are scavenged by cloud and rain droplets that drive wet deposition. During an Intensive Operation Period (IOP) from April to October 2021, rainwater was collected at higher frequency intervals, usually daily, after precipitation events at three locations on the Savannah River Site (SRS). The farthest locations were separated by approximately 20 km. The mean concentration (μeq/L) of seven ions followed the Cl⁻ > SO 4 2− > Na⁺ > NO 3 ⁻ > K⁺ > Mg 2+ > Ca 2+ downward trend. Ion concentrations were compared to background ion concentrations from the National Atmospheric Deposition Program (NADP). The high frequency monthly averaged SRS data compared well with the monthly averaged NADP background but demonstrated extensive variability. In some months in 2021, the high frequency data compared better to the NADP site near the coast while in other months inland sites compared better. Strong spatial variability for ion concentrations was observed across SRS which was attributed to localized impacts in rainfall spatial variability. High frequency measurements allowed for comparison to regional weather patterns indicating influences from the Atlantic Ocean, Gulf of Mexico, and cities. This can account for spatial variability in the wet deposition flux. Sea breezes, Saharan dust, and anthropogenic sources were shown to impact wet deposition flux variability. Higher frequency precipitation chemistry sampling at numerous locations better captures ion concentration variability and improves measurement representativeness.
The cycle-skipping problem that plagues full waveform inversion (FWI) can be at least partially mitigated if low frequencies (which encode the kinematics of wave propagation in seismic data) are recorded. However, seismic sources and receivers are band-limited, so seismic data does not generally include signals down to 0 Hz. To improve our ability to solve the seismic inverse problem, one can synthesize this missing low-frequency (LF) content from the recorded high-frequency (HF) data using machine learning (ML) models. Deep learning models such as convolutional neural networks (CNNs) demonstrate impressive ability to perform low frequency extrapolation. However, such models require powerful hardware (GPU machines) and careful training. We assess the extrapolation capabilities of three different ML models that do not require GPU machines, namely, random forest, Gaussian process regression and gradient boosting, on both synthetic and real data. Experimental results on two synthetic data sets (generated from a low velocity lens embedded in a homogeneous medium, and the Marmousi model) demonstrate that FWI applied to the extrapolated data consistently improves inversion accuracy relative to FWI applied to the original data sets that do not contain low frequencies. Application of low-frequency extrapolation to real data from the Northwest Shelf of Australia demonstrates that tree-based ML models such as gradient boosting can outperform CNNs in terms of both accuracy and computational cost on non-GPU architectures.
Fluxes of greenhouse gases are a critical component of the earth's natural climate, but anthropogenic emissions have created an imbalance and resulted in global climate change. Quantifying the emission of these gases is vital to our understanding of their sources and sinks, both natural and anthropogenic. The static chamber method, in which a system of interest is enclosed, and gas concentrations are measured over time, is widely used to estimate fluxes of greenhouse gases. With the development of instruments such as infrared gas analyzers (IRGAs) supporting high-frequency concentration data, there is a growing need for open-source workflows to calculate fluxes. Here we present fluxfinder, an R package designed to support reproducible calculations and processing of greenhouse gas fluxes measured with the static chamber method. The package includes raw data file parsing from widely used IRGAs, metadata matching, unit conversion, flux estimations, and initial quality assurance/quality control (QA/QC). Diagnostic graphical plots provide a transparent way to differentiate between measurement issues and nonlinear behavior. The package is also designed to be easily integrated with the gasfluxes package for further fitting of nonlinear concentration-time models, allowing alternative or additional flux QA/QC. The fluxfinder package offers a flexible workflow that is easily adaptable to promote open and reproducible greenhouse gas flux estimations.
The nation’s electric grid is evolving to supply the electricity needed for economic growth, health, and quality of life. As the DOE has pointed out “Old Tools Won’t Solve New Problems” This project conceived, designed, built, and demonstrated a new tool that promises to make the emerging grid dependable at a lower cost. The tool is a smart sensor with the sensitivity and the bandwidth to measure actionable information about grid health that was unmeasurable.
As global wind capacity expands, reducing operations and maintenance costs is critical to lowering the levelized cost of energy. This paper explores the state of the art in condition monitoring and prognostic strategies for wind turbine drivetrains, which are among the most failure-prone and maintenance-intensive subsystems. Current diagnostic methodologies are evaluated, covering supervisory control and data acquisition (SCADA) data, high-frequency vibration and acoustic analysis, machine learning and digital twin frameworks. Finally, practical challenges are identified that limit wide-scale industrial adoption, in order to guide future research and industrial efforts.
Accurate vehicle movement classification through signalized intersections is of paramount importance to the analysis of intersection performance and the optimization of traffic control strategies. Conventional techniques for tracking vehicle turning movements depend on infrastructure-based strategies like human counts, loop detectors, and video analytics, all of which are costly, prone to errors, and spatially constrained. High-frequency trajectory data can be utilized to determine vehicle movement patterns in a scalable and infrastructure-independent method due to the adoption of connected vehicles (CVs). In recent years, several studies have utilized connected vehicle data to generate performance measures. Most of the trajectory-based performance measures approaches, however, require map matching-i.e., extracting geospatial references from maps to identify the movements that individual vehicles make at a signalized intersection. These approaches are often time-consuming and hinder scalability since geographic features need to be provided for an analysis to be conducted. Map matching methods are prone to errors as different map versions change these geographic features. This research presents a novel automatic classification pipeline that uses CV trajectory data to classify vehicle movements at signalized crossings, specifically pass-through left-turn and right-turn maneuvers. The process starts by filtering trips that cross a spatial bounding box that has been defined at the target intersection. Approach and departure headings for each trajectory crossing the boundary are computed and are clustered together to identify dominant movements. The proposed algorithm is used to classify the movement of vehicles at 10 intersections in the state of California, and the results indicate that the algorithm can classify movements at these intersections with varying traffic volumes and road network configurations, all in a map-less framework with no need for conflation of vehicle trajectories to a digital base map.
Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. In addition to providing curated reference site condition datasets for four U.S. areas, our assessment highlights the need for more publicly available metocean and soil condition data.
Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. Further information on the datasets and how they were created can be found in: Biglu, M., M. Hall, E. Lozon, S. Housner. 2024. Reference Site Conditions for Floating Wind Arrays in the United States. Golden, CO: National Renewable Energy Laboratory (NREL). NREL/TP-5000-89897. The data are also available at: https://github.com/FloatingArrayDesign/SiteConditions The content of each dataset is as follows: _NOW23_wind.txt: Hourly NOW-23 wind data up to a height of 400 meter. _metocean_1hr.txt: Hourly time series including wind, wave, surface current and temperature data. _Summary.xlsx: Metocean data, including extreme values, joint probability distributions and monthly statistics. _usSEABED_soil.csv: Extract of the usSEABED database for this specific site. _bathymetry_200m.txt (and 500m, 1000m): Gridded seabed depth data.
Anomalies in radio-frequency (rf) stations can result in unplanned downtime and performance degradation in linear accelerators such as SLAC’s Linac Coherent Light Source (LCLS). Detecting these anomalies is challenging due to the complexity of accelerator systems, high data volume, and scarcity of labeled fault data. Prior work identified faults using beam-based detection, combining rf amplitude and beam position monitor data. Due to the simplicity of the rf amplitude data, classical methods are sufficient to identify faults, but the recall is constrained by the low-frequency and asynchronous characteristics of the data. In this work, we leverage high-frequency, time-synchronous rf phase data to enhance anomaly detection in the LCLS accelerator. Due to the complexity of phase data, classical methods fail, and we instead train deep neural networks within the Coincident Anomaly Detection (CoAD) framework. We find that applying CoAD to phase data detects nearly 3 times as many anomalies as when applied to amplitude data, while achieving broader coverage across rf stations. Furthermore, the rich structure of phase data enables us to cluster anomalies into distinct physical categories. Through the integration of auxiliary system status bits, we link clusters to specific fault signatures, providing additional granularity for uncovering the root cause of faults. We also investigate interpretability via Shapley values, confirming that the learned models focus on the most informative regions of the data and providing insight for cases where the model makes mistakes. This work demonstrates that phase-based anomaly detection for rf stations improves both diagnostic coverage and root cause analysis in accelerator systems and that deep neural networks are essential for effective analysis.