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57 results for “wind lidar”

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zenodo52/100

Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal

<p>These data are supplements for the calculations of the methods from the article &quot;Alignment of scanning lidars in offshore wind farms&quot;.<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>

opencc-by-4.0Nov 2021View details →
zenodo48/100

Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 3/1/2016

<p>Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pach&oacute;n, Chile.&nbsp; It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5 km interval and from 23.8 UT 2/29/2016 to 8.9 UT 3/1/2016 at 0.1 hour interval.&nbsp; Errors of these values are also included.&nbsp; -999 represents missing value.&nbsp;&nbsp;</p>

opencc-by-4.0Jun 2020View details →
zenodo48/100

Vertical profiles of urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland

<p><strong>Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland</strong></p> <p>=================================</p> <p>README version 1.3, 21/07/2022</p> <p>==================================</p> <p>Contact info:</p> <p>Paul Leahy, University College Cork</p> <p>paul.leahy@ucc.ie | +353 21 4902017</p> <p>================================</p> <p>&nbsp;</p> <p><strong>Contents</strong></p> <p><strong>1. Measurement location and time period</strong></p> <p><strong>2. What is measured (brief description)</strong></p> <p><strong>3. Instrumentation</strong></p> <p><strong>4. CSV file detailed descriptions</strong></p> <p>================================</p> <p>&nbsp;</p> <p><strong>1. Measurement location and time period: </strong></p> <p>North roof of Kane Building, University College Cork (UCC), Ireland.</p> <p>Lat 51 d 53 m 34 s N.</p> <p>Long 8 d 29 m 39 s W.</p> <p>Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building).</p> <p>The measurements were taken over a time period of several months in the years 2013 / 2014.</p> <p>=================================</p> <p><strong>2. What is measured (brief description):</strong></p> <p>* LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5 &nbsp;altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level.</p> <p>* Air temperature, atmospheric pressure, relative humidity.</p> <p>* Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level).</p> <p>* 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided.</p> <p>See &#39;CSV file detailed description&#39; below for detailed information.</p> <p>* Diagnostic information.</p> <p>=================================</p> <p><strong>2.1 Surrounding terrain:</strong></p> <p>Surrounding area is urban/suburban. The aspect is northerly.</p> <p>To the West: 2-5 storey buildings, open spaces, suburban.</p> <p>To the South: 2-3 storey buildings, open spaces, trees, river.</p> <p>To the East: 2-3 storey buildings, open spaces.</p> <p>To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban.</p> <p>=================================</p> <p><strong>3. Instrumentation:</strong></p> <p>ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS.</p> <p>=================================</p> <p><strong>4. CSV files detailed description:</strong></p> <p><strong>4.1 Data on 10-minute averages:</strong></p> <p>Filename 05092013-03122013_10min_res.csv contains:</p> <p>10 minute averaged data from 05/09/2013 to 03/12/2013.</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.</p> <p>&nbsp;</p> <p>Filename 03122013-07082014_10min_res.csv contains:</p> <p>10 minute averaged data from:&nbsp; 03/12/2013 to 07/08/2014.</p> <p>Measurement altitudes:&nbsp; 148 m, 90 m,&nbsp; 50 m, 35 m,&nbsp; 15 m above instrument level.</p> <p>Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).</p> <p>&nbsp;</p> <p>The first two rows contain header information.</p> <p>Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds.</p> <p>Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West.</p> <p>Row 2 contains the data column headers including units.</p> <p>&nbsp;</p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= number of scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) &amp; standard deviation [m/s]</p> <p>Vertical wind speed (mean) &amp; standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p>Horizontal min [m/s]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p>Horizontal max [m/s]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p>TI (turbulence intensity) []</p> <p>&nbsp;</p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator&nbsp; [unitless] Higher values indicate more rain during the averaging interval.</p> <p>Wind Speed [m/s] (column &#39;MET Wind Speed&#39; measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column &#39;MET Direction&#39; measured at the top of the instrument by the ultrasonic anemometer).</p> <p>&nbsp;</p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V]&nbsp;</p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p>&nbsp;</p> <p>=====================================================</p> <p>&nbsp;</p> <p><strong>4.2 Data with high time resolution (~23 s):</strong></p> <p>&nbsp;</p> <p>Filename 05092013-11112013_23s_res.csv contains:</p> <p>High resolution data from 05/09/2013 to 11/11/2013</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.</p> <p>&nbsp;</p> <p>Note on time resolution:</p> <p>The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete.</p> <p>The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.</p> <p>&nbsp;</p> <p>Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds.</p> <p>Row 2 contains the data column headers including units.</p> <p>&nbsp;</p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) &amp; standard deviation [m/s]</p> <p>Vertical wind speed (mean) &amp; standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2]&nbsp;&nbsp;&nbsp;&nbsp;not defined as measurement interval is too short.</p> <p>Horizontal min [m/s]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; not defined as measurement interval is too short.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p>Horizontal max [m/s] &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; not defined as measurement interval is too short.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p>TI (turbulence intensity) []&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; not defined as measurement interval is too short.</p> <p>&nbsp;</p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator&nbsp; [unitless] Higher values indicate more rain during the scanning interval.</p> <p>Wind Speed [m/s] (column &#39;MET Wind Speed&#39; measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column &#39;MET Direction&#39; measured at the top of the instrument by the ultrasonic anemometer.</p> <p>&nbsp;</p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V]&nbsp;</p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p>&nbsp;</p> <p>=====================================================</p> <p><strong>4.3 Quality control indicators:</strong></p> <p>&nbsp;</p> <p>9998&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog</p> <p>9999&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path</p> <p>Status Flag&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &#39;Green&#39; =&gt; good</p> <p>=======================================================</p> <p>&nbsp;</p>

opencc-by-4.0Jul 2022View details →
zenodo48/100

Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 10/30/2016

<p>Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pach&oacute;n, Chile.&nbsp; It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5-km intervals and from 23.7&nbsp;UT 10/29/2016 to 8.9 UT 10/30/2016 at 0.1-hour intervals.&nbsp; Errors of these values are also included.&nbsp; -999 represents missing values.&nbsp;&nbsp;</p>

opencc-by-4.0Oct 2016View details →
zenodo48/100

Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer

<p>Dataset of the paper &quot;Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer&quot; published in Remote Sensing [1].</p> <p>[1] Brugger P, Fuertes FC, Vahidzadeh M, Markfort CD, Port&eacute;-Agel F. Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer. <em>Remote Sensing</em>. 2019; 11(19):2247. https://doi.org/10.3390/rs11192247.</p>

opencc-by-4.0Sep 2019View details →
zenodo48/100

Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars

<p>Dataset of the paper &quot; Dataset of the paper &quot;Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer&quot; published in Remote Sensing [1]. &quot; published in Wind Energy Science [1].</p> <p>[1] Brugger, P., Markfort, C., and Port&eacute;-Agel, F.: Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars, Wind Energ. Sci., 7, 185&ndash;199, https://doi.org/10.5194/wes-7-185-2022, 2022.</p>

opencc-by-4.0Jan 2022View details →
zenodo48/100

Costal operating wind farms: two datasets with concurrent SCADA, LiDAR and turbulent fluxes

<p>This data collection consists of two datasets from a micrometeorological experiment conducted in two distinct operating wind farms in a coastal area of the northeast region of Brazil, called Pedra do Sal Wind Farm (UEPS) and Beberibe Wind Farm (UEBB). These wind farms are located on the northeast coast of Brazil where meteorological conditions are strongly influenced by trade winds and sea breeze. Both datasets represent a full-year of measurements from August/2013 to July/2014.</p> <p>On both operating wind farms it was commissioned a fully instrumented IEC-compliant 100m met mast, with five levels of first-class calibrated cup anemometers and one level (100m) with 3D sonic anemometer. Additionally at UEPS there&#39;s an extra 3D sonic at 20m height on the met mast, as well as a VAISALA LEOSPHERE Windcube8 doppler wind lidar with a range up to 500m height and located 2.5D upwind of one of the wind turbines.</p> <p>The Pedra do Sal wind farm (UEPS) has an installed capacity of 18MW, with 20 Enercon E-44 installed at 55m a.g.l. At Beberibe wind farm (UEBB) there are 32 Enercon E-48 wind turbines installed at 75m a.g.l. The dataset includes 10min SCADA data for all wind turbines on both wind farms.</p> <p>This dataset has a high-quality combination of meteorological, SCADA and turbulent flux data of two operating wind farms in Brazil. During a full-year of measurements both datasets had a high data recovery rate (see attached tables). The dataset has already been used to assess the impact of atmospheric stability on the wind farm performance, as well as the effect of mesoscale patterns on the wind profile and wind farm power production. Recirculation of the sea breeze and the development of an internal boundary layer upwind the wind turbines were also characterized.</p> <p>For more details on the experimental layout, wind turbine locations, meso and microcale wind conditions and any other information not stated in the NetCDF4 files, please refer to the reference material or contact one of the authors.</p> <p>&nbsp;</p>

opencc-by-sa-4.0Jun 2017View details →
zenodo44/100

TEAMx-PC22 (TEAMx pre-campaign 2022) - ACINN Doppler wind lidar data sets (SL88, SLXR142)

<p><strong>ABSTRACT</strong></p> <p>The data sets found here were collected with <a href="http://acinn.uibk.ac.at/">ACINN</a>&#39;s Doppler wind lidars SL88 and SLXR142 in Innsbruck, Austria, in summer 2022 in the framework of the TEAMx pre-campaign 2022 (TEAMx-PC22). The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in Serafin et al. (2020) and in Rotach et al. (2022).</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Spatial coverage and locations</strong></p> <p>Measurements with the SL88 and SLXR142 lidar were collected during TEAMx-PC22 in Innsbruck, Austria, at the Campus Innrain of the University of Innsbruck. More specifically, the SLXR142 lidar was located on the rooftop of one of the university buildings (Bruno-Sander-Haus) at Innrain 52f. The SL88 lidar was located in the forecourt of the Campus Innrain, the so-called GEIWI-Forum, next to the Bruno-Sander-Haus. The exact lidar locations are:</p> <ul> <li>SL88: 47.264083&deg;N / 11.384986&deg;E / 575 m MSL</li> <li>SLXR142: 47.26431&deg;N / 11.38529&deg;E / 613 m MSL</li> </ul> <p><strong>2. Temporal coverage</strong></p> <p>The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. However, the SL88 data set contains a shorter period from 11 August to 02 October 2022 (1 Hz data, vertical stares). The SLXR142 data set covers an extended period from 01 May to 31 October 2022 (VAD products, 10-min averages) as this lidar was operated in a semi-permanent mode.</p> <p><strong>3. Instrument details</strong></p> <p><em><strong>General</strong></em></p> <p>Measurements were taken with two scanning Doppler wind lidars, model Stream Line (SL88) and Stream Line XR (SLXR142), manufactured by HALO Photonics. The SL88 and SLXR142 are part of the Innsbruck Atmospheric Observatory (IAO; Karl et al. 2020). Available here are vertical profiles of radial velocity and backscatter data based on vertical stares at 1 Hz for the SL88 lidar and vertical profiles of horizontal winds (10-min averages) derived from plan position indicator (PPI) scans by applying the VAD method for the SLXR142 lidar. PPI scans were performed as continuous motion scans (CSM mode) at an azimuth angle of 70&deg;. For continuous motion scans, the scanner moves continuously (changing its azimuth angle) while data is being acquired.</p> <p><em><strong>Data correction</strong></em></p> <p>No corrections were applied to the data (level0 data).</p> <p><strong>4. Data file structure</strong></p> <p><em><strong>File format</strong></em></p> <p>Provided are data in netCDF format. File names contain date and time information in UTC. The following wildcard characters are used in the file examples below: yyyy - year; mm - month, dd - day; HH - hour, MM - minute, `SS` - second. NetCDF data files are zipped together into the following zip files.</p> <p><em><strong>Zip files</strong></em></p> <p>SL88.zip contains netCDF files of SL88 data structured into subdirectories (one subdirectory for each month, yyyymm, and one for each day, yyyymmdd).</p> <p>SLXR142.zip contains netCDF files of SLXR142 data structured into subdirectories (one subdirectory for each month, yyyymm).</p> <p><em><strong>NetCDF files for uncorrected SL88 data</strong></em></p> <p>Stare_88_yyyymmdd_HH_l0.nc contains vertical stare measurements aggregated together in one netCDF file for each hour (uncorrected level0 data).</p> <p><em><strong>NetCDF files for SLXR142 data products</strong></em></p> <p>yyyymmdd.nc contains vertical profiles of the horizontal wind vector derived from PPI scans by applying the VAD technique. Each vertical profile is based on several PPI scans conducted at an elevation angle of 70&deg; within 10 minutes. Hence, each profile represents a 10-min average. Profiles are aggregated together for each day in a separate netCDF file.</p> <p><strong>6. Contact</strong></p> <p>Contact alexander.gohm(at)uibk.ac.at for any questions regarding the data set.</p> <p><strong>7. References</strong></p> <p>Karl, T., A. Gohm, M.W. Rotach, H.C. Ward, M. Graus, A. Cede, G. Wohlfahrt, A. Hammerle, M. Haid, M. Tiefengraber, C. Lamprecht, J. Vergeiner, A. Kreuter, J. Wagner, M. Staudinger, 2020: Studying urban climate and air quality in the Alps: The Innsbruck Atmospheric Observatory. <em>Bulletin of the American Meteorological Society,</em> <strong>101,</strong> E488&ndash;E507, <a href="https://doi.org/10.1175/bams-d-19-0270.1">https://doi.org/10.1175/bams-d-19-0270.1</a></p> <p>Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubi&scaron;ić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, 2020: <em>Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment.</em> Innsbruck University Press. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a></p> <p>Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubi&scaron;ic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J.&nbsp; Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, 2022: A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. <em>Bulletin of the American Meteorological Society,</em> <strong>103,</strong> E1282&ndash;E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a></p>

opencc-by-4.0May 2023View details →
zenodo40/100

LiDAR Cluster Statistic of Wind Turbine Wakes

<p>Mean and standard deviation of the wake velocity field generated by utility-scale wind turbines for different turbulence intensity of the incoming wind and rotor thrust coefficient. Statistics are retrieved from wind LiDAR measurements. More details in this paper&nbsp;https://onlinelibrary.wiley.com/doi/full/10.1002/we.2430&nbsp;</p>

opencc-by-4.0Jan 2020View details →
zenodo40/100

A two-year intercomparison of CW focusing wind lidar and tall mast wind measurements at Cabauw

<p>Dataset (.csv files) and software (python scripts) for generating figures, including data analysis, in our manuscript &quot;A two-year intercomparison of CW focusing wind lidar and tall mast wind measurements at Cabauw&quot;, submitted to Atmos. Meas. Tech.</p>

opencc-by-4.0Jul 2020View details →
zenodo40/100

SKiYMET Meteor Radar Horizontal Wind at Andes Lidar Observatory 2009-2014

<p>This is horizontal wind measured by a&nbsp;SKiYMET Meteor Radar near Andes Lidar Observatory in Cerro Pach&oacute;n, Chile (30.05 S, 70.82 W) from Sep 2009 to Aug 2014.&nbsp; The radar was previously installed at Maui, Hawaii and is described in the paper</p> <p>Franke, S. J., X. Chu, A. Z. Liu, W. K. Hocking (2005), Comparison of meteor radar and Na Doppler lidar measurements of winds in the mesopause region above Maui, Hawaii, <em>J. Geophys. Res.</em>, <em>110</em>, D09S02, doi:10.1029/2003JD004486.</p> <p>The data is in NetCDF&nbsp;format, at 1 hr and&nbsp;2 km resolution from 80 to 100 km altitude.&nbsp; Time is in UT.&nbsp; Both time and altitude refer&nbsp;to the center of the 1 hr bin.&nbsp; Wind rms errors and numbers of meteor detections used for wind retrieval are also inicluded.</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2020View details →
zenodo40/100

Vertical Wind and Temperature Gravity Wave Perturbations Derived from Na Lidar Observations

<p>The gravity wave perturbations associated with vertical wind and temperature in the mesopause region for heat flux calculations.&nbsp;</p>

opencc-by-4.0Jul 2020View details →
zenodo40/100

Doppler lidar wind profiles from Granada

<p>This is data set includes Doppler wind lidar quantities which were calculated from&nbsp;measurements performed between 2016&nbsp;and 2020&nbsp;at&nbsp;<em>Andalusian Global Observatory of the Atmosphere</em>, AGORA, in particular, at the UGR station, Andalusian Institute for Earth System Research (IISTA-CEAMA) in Granada, Spain&nbsp;(37.16&ordm;N, 3.61&ordm;W, 680 m a.s.l.).</p> <p>The system is a Doppler lidar Stream Line (Halo Photonics), which&nbsp;is part of ACTRIS-Cloudnet (Illingworth et al., 2007). The system laser emits at 1.5 &mu;m and the detector is&nbsp;heterodyne using fiber-optic technology. The measurements for this data set consisted of&nbsp;conical scans with constant elevation of 75&deg; and 12 equidistant azimuth points performed every 10 min. A more detailed description of the instrument can be found in (Ortiz-Amezcua et al., 2022)</p>

opencc-by-4.0Jun 2022View details →
zenodo40/100

UW-Madison SSEC Lidar Wind Profiler for WiscoDISCO 21

<p>Data from a doppler lidar wind profiler at the Chiwaukee Prairie, WI air monitoring site.&nbsp;There are two datastreams: chiwaukee_wind_profiles_YYYYMMDD.cdf and chiwaukee_stare_YYYYMMDD.cdf due to the way the data are collected. The lidar was programmed to carry out a vertical wind profile every 5 mins. In between, the lidar is staring vertically in zenith-pointing mode. &nbsp;Therefore, there are two separate temporal resolutions.&nbsp;&nbsp;The wind_profiles files contain wind speed, wind direction, vertical velocity, and signal-to-noise ratio. The data were thresholded at SNR == 0.008 during the processing, so that worse SNRs than that are not included. The stare files contain data on backscatter, vertical velocity, and intensity, with better representation of the vertical coordinate.&nbsp;</p> <p>Vertical velocity is given in each of the two datastreams. There are two ways to get at the vertical velocity: &nbsp;one, as a direct measurement of the along-beam doppler velocity when it is in zenith mode, and two, as the residual in the calculation of the horizontal wind vector from the non-zenith stares at various azimuths. &nbsp;The latter is convenient in that it represents the w component of the wind on the same time/height grid as the u and v components, but it is&nbsp;more temporally coarse than the vertical stare measurement. &nbsp;</p> <p>The wind profiles are processed using code developed by Rob Newsom (Dept of Energy Pacific Northwest National Laboratory) and Dave Turner (NOAA Earth Systems Research Lab) and used operationally at the DOE Atmospheric Radiation Measurement (ARM) sites throughout the world.&nbsp;</p>

opencc-by-4.0Aug 2021View details →
zenodo40/100

Dataset used for the study of "Validation of Aeolus wind profiles using ground-based lidar and radiosonde observations at La Réunion Island and the Observatoire de Haute Provence"

<p>Datasets used to create the figures and statistical study in &quot;Validation of Aeolus wind profiles using ground-based lidar and radiosonde observations at La R&eacute;union Island and the Observatoire de Haute Provence&quot;&nbsp;&nbsp;</p>

opencc-by-4.0Dec 2022View details →
zenodo40/100

TEAMx-PC22 (TEAMx pre-campaign 2022) – DWD Doppler wind lidar data set (SLXR172)

<p>This dataset contains data measured by DWD with a Doppler Wind Lidar SLXR172 during the TEAMx pre-campaign 2022. More details about TEAMx can be found at <a href="http://www.teamx-programme.org/">http://www.teamx-programme.org</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Measurement location and time period </strong></p> <p>Measurements with the SLXR172 were collected at the site of Brannenburg (47.741547 N / 12.122187 E / 456 m MSL) between 15.June&nbsp; &ndash; 19 October 2022.</p> <p><strong>2. Measurement setup</strong></p> <p>During the measurement period, two different scanning modes were applied:</p> <p>15. June - 26. July 2022 and&nbsp; 13. August &ndash; 19. October 2022 (VAD_CSM).</p> <ul> <li><strong>VAD (velocity-azimuth display) scans in </strong><strong>continuous scanning mode</strong><strong>&nbsp; : </strong>These scans were conducted at an elevation angle of 35&deg;. Azimuth angle interval of the CSM data sampling was about 1.1&deg;.&nbsp;</li> </ul> <p>27.July &ndash; 12. August 2022 (VAD_RHI)</p> <ul> <li><strong>VAD scans in step-stare mode: &nbsp;</strong>Step-stare scans were conducted at an elevation angle of 35&deg; and with azimuth steps of 15&deg;.</li> <li><strong>RHI (</strong><strong>range-height indicator) scans into the Inn Valley</strong>; The RHI scans were performed for 10 azimuth angles from 151&deg; to 160&deg; and covered elevation angles from 3&deg; to 51&deg;.</li> </ul> <p>&nbsp;</p> <p><strong><em>3. Data processing, corrections and filter</em></strong></p> <p>For <strong><em>VAD scans in continuous scanning mode</em></strong> the processed wind fields are provided. The data have not been corrected. The data can be filtered using the parameters R<sup>2 </sup>(coefficient of determination), CN (condition number) and NVRAD (number of radial velocities) as described in P&auml;schke (2015):</p> <p>R<sup>2</sup>&gt; 0.95 and CN&lt;10 and NVRAD&gt;12 &nbsp;</p> <p>Please note that in the postprocessing of the VAD CSM scans, the R<sup>2</sup> filter criterion was set to R<sup>2</sup>&gt;0 in order to include all data and therefore might also include scans where the assumptions of homogeneity are not fulfilled. The parameter qwind is therefore not meaningful due to this configuration and should not be used to filter the data. We recommend the use of the above criterion from P&auml;schke.</p> <p>For scans from the <strong><em>VAD scans in step stare mode</em></strong> as well as the <strong><em>RHI scans</em></strong> the raw data files are provided. They have not been corrected nor filtered.</p> <p><strong>4. Data file structure</strong></p> <p>The data are provided in NetCDF format. File names contain date and time information in UTC. The following wildcard characters are used in the file examples below: yyyy - year; mm - month, dd - day; HH - hour, MM - minute, `SS` - second. Files are sorted in monthly folders.</p> <p>The data are provided in two zip-files.</p> <ul> <li>VAD_CSM contains the processed wind fields from 15. June - 26. July 2022 and from 13. August &ndash; 19. October 2022)</li> <li>VAD+RHI the raw data files for 27.July &ndash; 12. August 2022.</li> </ul> <p>Raw data files of the VAD CSM scans can be provided upon request.</p> <p><strong>5. Contact</strong></p> <p>Contact Katrin.sedlmeier(at)dwd.de.at for any questions regarding the data set.</p> <p><strong>6. References</strong></p> <p>P&auml;schke, E., Leinweber, R., and Lehmann, V.: An assessment of the performance of a 1.5 &mu;m Doppler lidar for operational vertical wind profiling based on a 1-year trial, Atmos. Meas. Tech., 8, 2251&ndash;2266, https://doi.org/10.5194/amt-8-2251-2015, 2015.</p>

opencc-by-4.0Jul 2023View details →
zenodo40/100

Doppler lidar vertical wind profiles from Rzecin during POLIMOS 2018

<p>This is data set includes Doppler wind lidar quantities which were calculated from&nbsp;measurements performed between May and September 2018&nbsp;at&nbsp;<em>PolWET&nbsp;</em>site in Rzecin, Poland (52.75&deg;N, 16.30&deg;E, 59&nbsp;m&nbsp;a.s.l.) of the Poznan University of Life Sciences</p> <p>The system is a Doppler lidar Stream Line (Halo Photonics), which&nbsp;is part of ACTRIS-Cloudnet (Illingworth et al., 2007). The system laser emits at 1.5 &mu;m and the detector is&nbsp;heterodyne using fiber-optic technology. The measurements for this data set consisted of&nbsp;continuous vertically pointing measurements with a temporal resolution of 2&nbsp;s. A more detailed description of the instrument can be found in (Ortiz-Amezcua et al., 2022)</p>

opencc-by-4.0Jul 2023View details →
zenodo40/100

TEAMx-PC22 (TEAMx pre-campaing 2022) – GeoSphere Austria Doppler wind lidar data

<p><strong>ABSTRACT</strong></p> <p><a href="https://www.geosphere.at/">GeoSpere Austria</a> operated a Doppler lidar (<a href="https://metek.de/product/wind-ranger-100-200/">METEK Wind Ranger 200</a>) during the TEAMx pre-campaign 2022 (TEAMx-PC22) from August 17, 2022 to October 3, 2022 next to the <a href="https://oscar.wmo.int/surface/index.html#/search/station/stationReportDetails/0-20000-0-11130">meteorological station at Kufstein</a>, Austria. The wind lidar data from this campaign is provided here. Standard meteorological data is available on the <a href="https://data.hub.geosphere.at/">GeoSphere Austria data hub</a>.</p> <p>The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in <a href="https://www.uibk.ac.at/iup/buch_pdfs/10.1520399106-003-1.pdf">Serafin et al. (2020) </a>and in <a href="https://journals.ametsoc.org/view/journals/bams/103/5/BAMS-D-21-0232.1.xml">Rotach et al. (2022)</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p>The windlidar is operated at Kufstein next to the meteorological station (12.1628&deg;E 47.5753&deg;N 490m asl). 1 VAD scan is measured per second, 100 radial measurements per VAD scan.</p> <p>Provided are daily NetCDF data sets, so-called &ldquo;averaged files&rdquo;, i.e. 10min averaged profiles calculated from the instantaneous profiles provided by the operational software of the instrument.</p> <p><strong>Description of variables:</strong></p> <table> <tbody> <tr> <td> <p>lat</p> </td> <td> <p>&nbsp;Latitude</p> </td> </tr> <tr> <td> <p>lon</p> </td> <td> <p>&nbsp;Longitude</p> </td> </tr> <tr> <td> <p>alt</p> </td> <td> <p>&nbsp;Altitude</p> </td> </tr> <tr> <td> <p>height</p> </td> <td> <p>&nbsp;Measuring height</p> </td> </tr> <tr> <td> <p>pitch</p> </td> <td> <p>&nbsp;Tilt towards north arrow</p> </td> </tr> <tr> <td> <p>roll</p> </td> <td> <p>&nbsp;Tilt clockwise looking along north arrow</p> </td> </tr> <tr> <td> <p>heading</p> </td> <td> <p>&nbsp;Azimuth alignment (should be zero)</p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>&nbsp;Time stamp (seconds since 01.01.1970 00:00 UTC).</p> </td> </tr> <tr> <td> <p>VEL</p> </td> <td> <p>&nbsp;Wind Velocity (vectorial average)</p> </td> </tr> <tr> <td> <p>VEL_SC</p> </td> <td> <p>Wind Velocity (scalar average)</p> </td> </tr> <tr> <td> <p>DIR</p> </td> <td> <p>&nbsp;Direction (vectorial average)</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p>&nbsp;West-East wind component</p> </td> </tr> <tr> <td> <p>V</p> </td> <td> <p>&nbsp;South-North wind component</p> </td> </tr> <tr> <td> <p>W</p> </td> <td> <p>&nbsp;Upward wind component</p> </td> </tr> <tr> <td> <p>SU</p> </td> <td> <p>Standard deviation of U</p> </td> </tr> <tr> <td> <p>SV</p> </td> <td> <p>Standard deviation of V</p> </td> </tr> <tr> <td> <p>SW</p> </td> <td> <p>Standard deviation of W</p> </td> </tr> <tr> <td> <p>SVEL</p> </td> <td> <p>Mean square deviation of radial wind components from fitted values</p> </td> </tr> <tr> <td> <p>DQ</p> </td> <td> <p>Fraction of valid radial components per VAD</p> </td> </tr> <tr> <td> <p>MDT</p> </td> <td> <p>Mean distance to target (Measured distance of focus)</p> </td> </tr> <tr> <td> <p>SNR</p> </td> <td> <p>Signal to noise ratio in dB</p> </td> </tr> <tr> <td> <p>SPW</p> </td> <td> <p>Spectral width (for internal use only)</p> </td> </tr> </tbody> </table> <p>&nbsp;</p> <p>Contact: kathrin.baumann-stanzer@geosphere.at</p>

opencc-by-4.0Aug 2023View details →
zenodo36/100

Atmospheric visibility inferred from continuous-wave Doppler wind lidar, data set

<p>Visibility data from Pershore, UK, between 2018 and 2020</p>

opencc-by-4.0Mar 2022View details →
zenodo36/100

Doppler lidar wind profiles from Kumpula

<p>This data set contains Doppler lidar wind profiles calculated from VAD (Velocity-Azimuth Display) scans by a Halo Photonics Streamline Doppler lidar between 10 April 2018 and 30 September 2020 at Kumpula, Finland (60.333 N, 25.6 E, 45 m.a.s.l.). A more detailed description of the instrument specification and operating parameters is given in Hirsikko et al. (2014).</p>

opencc-by-4.0May 2022View details →

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Allen Brain Atlas

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Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

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DANDI Archive for NWB datasets

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electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

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Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record