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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 American Wake Experiment (AWAKEN) project. The CLAMPS1 facility was deployed at the Rolling Meadows Golf Course east of Enid, OK.
These files contain 24 hour periods of data collected from the CLAMPS2 Halo Streamline XR+ 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.
These files contain 24 hour periods of data collected from the SPARC Halo Streamline XR+ 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.
These files contain 24 hour periods of data collected from the CLAMPS2 Halo Streamline XR+ 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 AWAKEN project.
This dataset contains daily csv files summarizing data from 10-min wind statistics from ground-based Doppler lidar at the NANT site for the WFIP3 event log. See https://a2e.energy.gov/ds/wfip3/nant.lidar.10min.z02.c1/summary
This dataset contains daily csv files summarizing data from 10-min wind statistics from ground-based Doppler lidar at the NANT site for the WFIP3 event log. See https://a2e.energy.gov/ds/wfip3/nant.lidar.10min.z01.c1/summary
Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s -1 and RMSEs of 2-3 m s -1 . Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Finally, measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.
As part of the Perdigão 2017 campaign, vertical RHI (range-height indicator) scans with long-range pulsed Doppler wind lidars were performed aligned with the main wind direction and a wind turbine (WT) located on a mountain ridge. The measurements are used to not only retrieve flow velocities, but also their variance and - by using the turbulent broadening of the Doppler spectrum - also turbulent kinetic energy (TKE) dissipation rate. The study shows that turbulence in the WT wake is dependent on the turbulence of the inflow, but also on atmospheric stability. In stable atmospheric conditions, wakes could be analyzed up to five rotor diameters downstream (D) and showed the maximum turbulence in the wake at 2-3 D, whereas in unstable conditions, the maximum was found at 2 D and the wake could not be detected further than 3 D. A clear dependency of wake turbulence enhancement on inflow turbulence intensity is found, which levels out to no further enhancement at turbulence intensities of 30%.
The spatial structure of turbulence in atmospheric boundary layer flows is highly relevant to wind energy. In particular, wind turbine control strategies based on inflow preview measurements require knowledge of the longitudinal evolution of turbulent flow as it approaches the rotor. These upstream measurements are usually obtained with nacelle-mounted wind lidars. In contrast to traditional in situ anemometry, lidars collect measurements within a probe volume which varies in size depending on the technology of the commercial system being used. Here, we address two issues related to the use of wind lidar to measure the incoming flow to a wind turbine: (i) whether existing longitudinal coherence models can be used to predict flow at the rotor, based on measurements performed at a distance away from the rotor; and (ii) what effect probe-volume averaging has on the inflow predictions. These two questions are critical to the design and implementation of robust wind turbine control strategies. To address these questions, we perform field measurements and large-eddy simulations to determine which incoming flow structures can be readily predicted with existing coherence models, and which require additional corrections to account for lidar volumetric averaging effects. Results reveal that the wind turbine induction zone has a negligible impact on the longitudinal coherence and first-order turbulence quantities, such as the standard deviation of velocity fluctuations. However, the phase of the signal, from which advection time periods of the turbulent structures are derived, is affected by the rotor blockage effect.
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This project will ultimately combine observations from Raman lidar, Doppler lidar, AERI, eddy correlation measurements, and airborne and ground-based high spectral resolution lidar systems with many large eddy simulations (LES) of realistic boundary layer cases to study: The diurnal evolution of the boundary layer thermodynamic structure; The turbulent structure of the CBL and the entrainment zone; The ability of LES models to simulate the thermodynamic and turbulent structure of the CBL; How perturbations in the surface properties and CBL properties influence boundary layer clouds; The similarities and differences in the boundary layer and cloud interaction in the marine vs. continental boundary layer.
Abstract. The planetary boundary layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, which can estimate PBLH by integrating the morning temperature profiles and surface meteorological observations. The DNN model is developed by leveraging a rich dataset of PBLH derived from long-standing radiosonde records augmented with high-resolution micro-pulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden-layer structures, which collectively yield a robust 27-year PBLH dataset over the southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micro-pulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (Green Ocean Amazon; tropical rainforest) and CACTI (Cloud, Aerosol, and Complex Terrain Interactions; middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary layer processes with implications for improving the representation of PBL in weather forecasting and climate modeling.
The planetary boundary-layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, designed to estimate PBLH by integrating morning temperature profiles with surface meteorological observations. The DNN model is developed by leveraging a rich data set of PBLH derived from long-standing radiosonde records and augmented with high-resolution micropulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden layer structures, which collectively yield a robust 27-year PBLH data set over the Southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote-sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micropulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote-sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (tropical rainforest) and CACTI (middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary-layer dynamics with implications for enhancing the representation of PBL in weather forecasting and climate modeling.
METEX21 was an atmospheric tracer release experiment executed at the Department of Energy’s Nevada National Security Site (NNSS) in the southwestern U.S to study terrain-induced wind and thermodynamic conditions that influence local-scale (<5-km) plume transport under varying atmospheric forcing conditions. Meteorological observations were collected using 10-m tall meteorological towers, 2-m tall tripods with 3-d sonic anemometers, a 3-m tall eddy covariance flux tower, Doppler profiling lidars, Doppler scanning lidars, weather-balloon launched radiosondes, and a tethered balloon equipped with wind, temperature, and aerosol sensors at heights up to 800 m. A smoke tracer was released along three transects in the horizontal and vertical directions and observed with video cameras, aerosol sensors and lidars (via aerosol backscatter). The observations showed evidence of large-scale/synoptic transience as well as local-scale upslope and downslope flows, along-axis valley flows, recirculation eddies on leeward slopes, and periods of strong shear and veer aloft. The release days were classified as either synoptically-driven or locally-driven, and a single case day is presented in detail for each. Synoptically-forced days show relatively narrow smoke plumes traveling down the valley from north to south (with the predominant wind direction), with little deviation in transport direction regardless of the elevation or ground locations of the smoke releases, except near the presence of leeside recirculation eddies. Locally-forced days exhibit a wider range of plume behavior due to the combination of thermally-induced valley and slope flows, which are often flowing in different cardinal directions, and wind shear found aloft at higher altitudes and elevations. We saw evidence of smoke lofting on top of the mesas due to strong upslope flows on these days. A major finding of this experiment was the effectiveness of scanning lidars to measure 2-dimensional plume transport out to a 2–3 km distance; much farther than could be visibly observed. METEX21 was the first of three planned tracer experiments at NNSS, and future experiments will incorporate multiple tracers to improve individual plume identification so that finer resolution flow details can be attained from these measurements, as well as deploy a larger suite of meteorological instrumentation, including more temperature profiling data.
WINDPROF provides 10-minute wind and turbulence profiles that integrate a scanning Doppler lidar, a profiling Doppler lidar, and two sonic anemometers across Northeast U.S. coastal and offshore sites during the WFIP3 campaign. Variables include wind speed, wind direction, vertical velocity, turbulence intensity, and turbulent kinetic energy, each with per-instrument quality control and inter-instrument agreement validation. Profiles are mapped to a standardized height grid – dedicated near-surface levels at the sonic measurement heights (4 m and 10 m AGL), 20 m spacing to 100 m, and 30 m spacing above – and carry component and derived uncertainty estimates. Heights are reported above ground level; the site's ground elevation is stored separately. The Cape Cod dataset covers 1 February 2024 – 6 September 2025.
The moisture advection term in the water–vapor budget equation is investigated with a combination of a vertically-staring water–vapor lidar and Doppler lidar systems. These instruments make it possible to get the mean profile of moisture tendency and the latent heat flux (LHF) divergence. We use data of the Land–Atmosphere Feedback Experiment (LAFE) at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) site, Oklahoma, USA, collected on 30 August 2017 between 15 and 24 UTC, which corresponds to 09 to 18 LT. The lidars provide turbulence resolving profiles of moisture and vertical wind fluctuations. The LHF profile is derived from the covariance of these moisture and vertical wind fluctuations. The mean boundary layer height z i is determined from the peak of the moisture variance. The results demonstrate that the combination of two remote sensing instruments can be applied for determining the dominant water–vapor budget terms, namely moisture tendency, latent heat flux divergence and moisture advection.