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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> </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> </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 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 'CSV file detailed description' 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> </p> <p>Filename 03122013-07082014_10min_res.csv contains:</p> <p>10 minute averaged data from: 03/12/2013 to 07/08/2014.</p> <p>Measurement altitudes: 148 m, 90 m, 50 m, 35 m, 15 m above instrument level.</p> <p>Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).</p> <p> </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> </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) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] </p> <p>Horizontal min [m/s] </p> <p>Horizontal max [m/s] </p> <p>TI (turbulence intensity) []</p> <p> </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 [unitless] Higher values indicate more rain during the averaging interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer).</p> <p> </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] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p> </p> <p><strong>4.2 Data with high time resolution (~23 s):</strong></p> <p> </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> </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> </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> </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) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] not defined as measurement interval is too short.</p> <p>Horizontal min [m/s] not defined as measurement interval is too short. </p> <p>Horizontal max [m/s] not defined as measurement interval is too short. </p> <p>TI (turbulence intensity) [] not defined as measurement interval is too short.</p> <p> </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 [unitless] Higher values indicate more rain during the scanning interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.</p> <p> </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] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p><strong>4.3 Quality control indicators:</strong></p> <p> </p> <p>9998 atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog</p> <p>9999 high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path</p> <p>Status Flag 'Green' => good</p> <p>=======================================================</p> <p> </p>
Fiber-optic Distributed Temperature Sensing and Wind Profiler Data during the Shallow Cold Pool Experiment
<p>The <a href="https://www.eol.ucar.edu/field_projects/scp">Shallow Cold Pool (SCP) experiment</a> was an <a href="https://www.eol.ucar.edu/observing_facilities/isfs">Integrated Surface Flux System (ISFS)</a> deployment conducted by the <a href="https://ncar.ucar.edu/">National Center for Atmospheric Research (NCAR)</a>, the <a href="https://ceoas.oregonstate.edu/">College of Earth, Ocean and Atmospheres (CEOAS)</a>, the <a href="https://bee.oregonstate.edu/">Department of Biological & Ecological Engineering (BEE)</a>, and the <a href="https://ctemps.org/">Center for Transformative Environmental Monitoring Programs (CTEMPS)</a> of <a href="https://oregonstate.edu/">Oregon State University</a>, in a shallow gully within the Pawnee Grasslands, Coloradp, USA. The primary goal of SCP was to examine the formation and maintenance of common shallow cold pools. These cold pools had not been previously examined with turbulence measurements and very little was known about their dynamics and interaction with gravity waves and other submesoscale motions.</p> <p>SCP consisted of a dense network of ultrasonic anemometers with 19 units being installed at 1m above ground level (agl) and 8 being mounted at different heights on a 20m high tower. In addition, air temperature, humidity, and carbon dioxide concentrations measurements were taken. This data can be found on <a href="https://data.eol.ucar.edu/project/SCP">https://data.eol.ucar.edu/project/SCP</a>.</p> <p>The unique observational technique featured in SCP was a cross-valley transect of the innovative active and passive fiber-optic distributed sensing technique (FODS) using a Distributed Temperature Sensing (DTS) unit (Model Ultima SR, Silixa, London, UK) as well as a ground-based acoustic wind profiler (SODAR, PCS2000-24, Metek GmbH, Elmshorn, Germany) in addition to the classical sonic anemometer network. The data archived in this submission publishes the FODS data and contains data for nine (9) nights between 16th November until 27th November between the hours of 19:00 and 05:00 MST (Local time). Details of the FODS setup are contained in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a> and <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. (2015</a>).<br> The fiber-optic cross-valley transect was 240m long and stretched from the North to the South shoulder of the gully and contained FODS observations at three heights (0.5m, 1m, 2m agl). By combining passive and active FODS, air temperatures and wind speeds were measured spatially continuously with a temporal and spatial resolution of 5s and 25cm, respectively. Air temperatures were measured with an unheated white-PVC jacketed optical glass fiber cable with an outer diameter of 0.9mm, while for the wind speed measurements an additional actively heated stainless-steel uncoated fiber-optic cable (1.3mm outer diameter) was deployed. Wind speeds were derived from the difference between the heated and unheated fiber-optic pair similar to a hotwire anemometer (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. 2015</a>).<br> The acoustic wind profiler (Sound Detection and Ranging, SODAR) was installed at the gully bottom about 200m down the gully from the fiber-optic transect (between station A18 and A19) and measured with a 5-min resolution, a 10-m gate range, and 17000 Hz, see map in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a>. The observational range was between 10m to 320m agl. The data provided is the cluster data output of the wind profiler, which is quality-controlled by the internal data processing software. The published data include horizontal wind speed (speed), wind direction (direction), unrotated along-wind component (u_unrot), unrotated cross-wind component (v_unrot), and unrotated vertical-wind component (w_unrot).</p> <p>By combining the fiber-optic distributed sensing, the sensor network, and the wind profiler, we were able to investigate specific class of submeso-scale motions in detail. The submeso-scale motion occurred frequently during SCP, significantly impacted air temperature, wind speed and direction, as well as the near-surface turbulence within less than a few minutes. These motions are not described or categorized by existing boundary layer regimes or concepts. Consequently, further research on submeso-scale motions using continuous FODS measurements is necessary to better understand the stable boundary layer.</p> <p> </p> <p>Pfister, L., Sayde, C., Selker, J., Mahrt, L., & Thomas, C. K. (2019). Classifying the Nocturnal Atmospheric Boundary Layer into Temperature and Flow Regimes. <em>Quart. J. Roy. Meteorol. Soc.</em>, <em>145</em>(721), 1515–1534. <a href="https://doi.org/10.1002/qj.3508">https://doi.org/10.1002/qj.3508</a></p> <p> </p> <p>Sayde, C., Thomas, C. K., Wagner, J., & Selker, J. S. (2015). High-resolution wind speed measurements using actively heated fiber optics. <em>Geophys. Res. Lett.</em>, <em>42</em>(22), 10,064–10,073. <a href="https://doi.org/10.1002/2015GL066729">https://doi.org/10.1002/2015GL066729</a></p>
Pertubation Profiles Dataset used for "Convection-generated gravity waves in the tropical lower stratosphere from Aeolus wind profiling and ERA5 reanalysis"
<p>These are the perturbation profiles, from 5km to 29.5km, with a 500m grid. In the study, we picked up the data between tropopause-1km to 22km, which was then squared, smoothed, and averaged into one value. We used a 14 points moving average for the smoothing.</p> <p>The data is from 2018-09 to 2022-09, based on the Aeolus L2B Rayleigh clear wind, using only quality flag 1 data.</p> <p>Please email me at mathieu.ratynski@estaca.eu if you're interested in the 100m resolution version, used in the final version of the manuscript.</p>
Low-level updraft intensification in response to environmental wind profiles
<p>Supercell storms can develop a "dynamical response" whereby upward accelerations in the lower troposphere amplify as a result of rotationally induced pressure falls aloft. These upward accelerations likely modulate a supercell's ability to stretch near-surface vertical vorticity to achieve tornadogenesis. This study quantifies such a dynamical response as a function of environmental wind profiles commonly found near supercells. Self-organizing maps (SOMs) were used to identify recurring low-level wind profile patterns from 20,194 model-analyzed, near-supercell soundings. The SOM nodes with larger 0–500 m storm-relative helicity (SRH) and streamwise vorticity (ω<sub>s</sub>) corresponded to higher observed tornado probabilities. The distilled wind profiles from the SOMs were used to initialize idealized numerical simulations of updrafts. In environments with large 0–500 m SRH and large ω<sub>s</sub>, a rotationally induced pressure deficit, increased dynamic lifting, and a strengthened updraft resulted. The resulting upward-directed accelerations were an order of magnitude stronger than typical buoyant accelerations. At 500 m AGL, this dynamical response increased the vertical velocity by up to 25 m s<sup>–1</sup>, vertical vorticity by up to 0.2 s<sup>–1</sup>, and pressure deficit by up to 5 hPa. This response specifically augments the near-ground updraft (the midlevel updraft properties are almost identical across the simulations). However, dynamical responses only occurred in environments where 0–500 m SRH and ω<sub>s</sub> exceeded 110 m<sup>2</sup> s<sup>–2</sup> and 0.015 s<sup>–1</sup>, respectively. The presence vs. absence of this dynamical response may explain why environments with higher 0–500 m SRH and ω<sub>s</sub> correspond to greater tornado probabilities.</p>
Vertical profiles of air temperature, relative humidity, wind speed and direction observed using UAV over the Mukhrino peatland in June 2022
<p>Vertical profiles of air temperature and relative humidity were measured using the iMetXQ2 sensor onboard DJI Phantom 4 quad-copter; vertical profiles of wind speed and direction were obtained from the Phantom 4 flight logs as produced by the DJI proprietary algorithm. </p>
Doppler lidar wind profiles from Granada
<p>This is data set includes Doppler wind lidar quantities which were calculated from measurements performed between 2016 and 2020 at <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 (37.16ºN, 3.61ºW, 680 m a.s.l.).</p> <p>The system is a Doppler lidar Stream Line (Halo Photonics), which is part of ACTRIS-Cloudnet (Illingworth et al., 2007). The system laser emits at 1.5 μm and the detector is heterodyne using fiber-optic technology. The measurements for this data set consisted of conical scans with constant elevation of 75° 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>
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. 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. Therefore, there are two separate temporal resolutions. 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. </p> <p>Vertical velocity is given in each of the two datastreams. There are two ways to get at the vertical velocity: 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. 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 more temporally coarse than the vertical stare measurement. </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. </p>
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 "Validation of Aeolus wind profiles using ground-based lidar and radiosonde observations at La Réunion Island and the Observatoire de Haute Provence" </p>
Doppler lidar vertical wind profiles from Rzecin during POLIMOS 2018
<p>This is data set includes Doppler wind lidar quantities which were calculated from measurements performed between May and September 2018 at <em>PolWET </em>site in Rzecin, Poland (52.75°N, 16.30°E, 59 m a.s.l.) of the Poznan University of Life Sciences</p> <p>The system is a Doppler lidar Stream Line (Halo Photonics), which is part of ACTRIS-Cloudnet (Illingworth et al., 2007). The system laser emits at 1.5 μm and the detector is heterodyne using fiber-optic technology. The measurements for this data set consisted of continuous vertically pointing measurements with a temporal resolution of 2 s. A more detailed description of the instrument can be found in (Ortiz-Amezcua et al., 2022)</p>
Low-level updraft intensification in response to environmental wind profiles
Open the record for dataset details and reuse information.
A temporally continuous divergence and vorticity dataset in Beijing derived from the radar wind profiler mesonet during 2023
<p>A temporally continuous horizontal divergence and vertical vorticity dataset is produced by horizontal winds derived from the radar wind profilers mesonet in Beijing by applying the triangle method. This dataset covers the period of 2023 with a temporal resolution of 6-minute and a vertical resolution of 120 m. The dataset is of significance for a multitude of scientific research and applications, including air quality, convection initiation and so on. The latest version includes rain flags of triangles.</p>
Hourly wind and solar generation profiles for every EIA 2020 plant in the CONUS
<p>Historical hourly time series of wind and solar generation profiles for every plant within the United States (US) that is part of the Energy Information Administration (EIA) 2020 dataset for the years 1980 through 2022. The data uses regional atmospheric climate model simulations and 2020 wind and solar power plant configurations across the entire contiguous US. This data is designed to be be aggregated to the Balancing Authority (BA) scale, or to other scales such as to the nodes of a production cost model which would allow the data to be used to perform reliability assessments and evaluations of technology innovation. There are ongoing efforts to extend this dataset for future climate projections, which additionally require the projection of future infrastructure under a wide range of uncertainties. This historical dataset is a benchmark for those projections and can be used to understand sensitivity to historical inter-annual variability, seasonality, and recent extreme events.</p> <p>For more information please refer to the <a href="https://www.nature.com/articles/s41597-024-03894-w">Scientific Data paper</a> and the <a href="https://github.com/GODEEEP/tgw-gen">code repository</a>.</p> <p>The dataset consists of two components:</p> <ul> <li>Plant configuration files - The plant configuration files (<code>eia_solar_configs.csv</code> and <code>eia_wind_configs.csv</code>) contain all the plant data that is relevant to a generation model, derived from EIA 860 2020 data. Each row corresponds to a single logical plant. In some cases actual plants were split into two logical plants for modeling purposes.</li> <li>Generation data files - The generation data resides in the <code>solar/</code> and <code>wind/</code> directories and consists of one file per year. Each year contains an 8760 (hourly) profile of generation for every plant. The first column in each csv file is the datetime in Coordinated Universal Time (UTC), and the subsequent columns correspond to the <code>plant_code_unique</code> column in the configuration files. Data in these generation files is expressed as a capacity factor, which is generation divided by the plant capacity. To obtain the actual Megawatts (MW) generated, multiply the capacity factor for a plant by the value in the <code>system_capacity</code> column from the respective configuration file.</li> </ul> <p>This research was supported under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p>Version 1.1.0 adds bias corrected solar and extends the data through 2022</p> <p>Version 1.1.1 adds missing wind years 2021 and 2022</p> <p>Version 1.2.0 Extends the data through 2024</p> <p>Corresponding Author:</p> <ul> <li>Cameron Bracken, cameron.bracken@pnnl.gov</li> </ul>
Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution
<h2>Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution</h2><p><br>This dataset uses regional atmospheric climate model output to simulate wind and solar power generation across the entire contiguous US. This data is particularly useful for obtaining power production profiles at new locations or locations where only short records exist, or to study climate change impacts. We assume that a generic power plant exists at each grid cell and model the power output for solar at the surface and wind at 80, 100 and 125 meter hub heights. The data consists of a historical period 1980-2022 and several future scenarios that extend from 2020-2099.</p><p><br>- Code: <a href="https://github.com/GODEEEP/tgw-gen/">https://github.com/GODEEEP/tgw-gen/</a><br>- Underlying climate data and description of the future scenarios: <a href="https://tgw-data.msdlive.org/">https://tgw-data.msdlive.org/</a><br>- The NREL reV model was used to produce these profiles: <a href="https://github.com/NREL/reV">https://github.com/NREL/reV</a></p><h3>Data</h3><p>The dataset consists of a series of netcdf files, one per year, grouped together based on resource type, climate scenario, and year range. The data is available as capacity factors which can be scaled to any desired plant size.</p><p> </p><p>Please see the data directory below to find the appropriate record for your purposes.</p><ul><li>solar<ul><li>historical<ul><li><a href="https://doi.org/10.5281/zenodo.10138040">solar historical 1980-2022</a></li></ul></li><li>rcp45cooler<ul><li><a href="https://doi.org/10.5281/zenodo.10138850">solar rcp45cooler 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10139076">solar rcp45cooler 2060-2099</a></li></ul></li><li>rcp45hotter<ul><li><a href="https://doi.org/10.5281/zenodo.10139819">solar rcp45hotter 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10139994">solar rcp45hotter 2060-2099</a></li></ul></li><li>rcp85cooler<ul><li><a href="https://doi.org/10.5281/zenodo.10140410">solar rcp85cooler 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10140517">solar rcp85cooler 2060-2099</a></li></ul></li><li>rcp85hotter<ul><li><a href="https://doi.org/10.5281/zenodo.10140685">solar rcp85hotter 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10140758">solar rcp85hotter 2060-2099</a></li></ul></li></ul></li><li>wind<ul><li>historical<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10182689">wind 80m historical 1980-2000</a></li><li><a href="https://doi.org/10.5281/zenodo.10182848">wind 80m historical 2001-2022</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10143911">wind 100m historical 1980-2000</a></li><li><a href="https://doi.org/10.5281/zenodo.10144158">wind 100m historical 2001-2022</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10161120">wind 125m historical 1980-2000</a></li><li><a href="https://doi.org/10.5281/zenodo.10161276">wind 125m historical 2001-2022</a></li></ul></li></ul></li><li>rcp45cooler<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10183152">wind 80m rcp45cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10183308">wind 80m rcp45cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10183360">wind 80m rcp45cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10192154">wind 80m rcp45cooler 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10145097">wind 100m rcp45cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10145630">wind 100m rcp45cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10145720">wind 100m rcp45cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10145987">wind 100m rcp45cooler 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10161377">wind 125m rcp45cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10161494">wind 125m rcp45cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10161683">wind 125m rcp45cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10162136">wind 125m rcp45cooler 2080-2099</a></li></ul></li></ul></li><li>rcp45hotter<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10193740">wind 80m rcp45hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10198849">wind 80m rcp45hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10198912">wind 80m rcp45hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10198962">wind 80m rcp45hotter 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10146056">wind 100m rcp45hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10146108">wind 100m rcp45hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10146118">wind 100m rcp45hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10146159">wind 100m rcp45hotter 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10162368">wind 125m rcp45hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10162463">wind 125m rcp45hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10162561">wind 125m rcp45hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10162689">wind 125m rcp45hotter 2080-2099</a></li></ul></li></ul></li><li>rcp85cooler<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10199074">wind 80m rcp85cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10199103">wind 80m rcp85cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10199362">wind 80m rcp85cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10199411">wind 80m rcp85cooler 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10146346">wind 100m rcp85cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10150299">wind 100m rcp85cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10152763">wind 100m rcp85cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10152830">wind 100m rcp85cooler 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10162728">wind 125m rcp85cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10163251">wind 125m rcp85cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10171504">wind 125m rcp85cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10178654">wind 125m rcp85cooler 2080-2099</a></li></ul></li></ul></li><li>rcp85hotter<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10199444">wind 80m rcp85hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10199543">wind 80m rcp85hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10199587">wind 80m rcp85hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10199638">wind 80m rcp85hotter 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10152864">wind 100m rcp85hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10152945">wind 100m rcp85hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10160579">wind 100m rcp85hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10160776">wind 100m rcp85hotter 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10182205">wind 125m rcp85hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10182349">wind 125m rcp85hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10182514">wind 125m rcp85hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10182574">wind 125m rcp85hotter 2080-2099</a></li></ul></li></ul></li></ul></li></ul><p> </p><p>This research was supported by the <a href="https://godeeep.pnnl.gov">Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP)</a> Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p><p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Stratosphere-Troposphere wind profiler radar data
<p>Horizontal wind profiles from ST radar data at Cochin (10.04N, 76.33 E) during mosoon seasons (June to September) for three years (2019-2021)</p>
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>
Reproduction package for the paper "Constraining planetary mass-loss rates by simulating Parker wind profiles with Cloudy"
<p>This is a basic reproduction package for the paper "Constraining planetary mass-loss rates by simulating Parker wind profiles with Cloudy" by Linssen et al. (2022). It provides the data products necessary to reproduce the figures of the paper.</p>
Hourly wind profile measurements at Beijing weather station for the year 2020
<p>This dataset contains the wind profiler from 0.14 to 10.38 km above ground level at 1-h intervals for the Beijing Observatory station, which is obtained from the measurements of the Radar wind profiler for the year 2020. It is stored in Matlab format, and is organized by a matrix of 21*8784. the field "h" represents its corresponding height.</p>
Turbulent dissipation rate and velocity spectrum at two radar wind profiler sites in North China
<p>This dataset includes the monthly mean velocity spectrum width and turbulence dissipation rate from 0900 local standard time (LST) to 1700 LST in 2021, which are retrieved from the radar wind profiler measurements at Baoding (urban) and Zhangbei (plateau) stations. Each data file is stored in xlsx format and contains two sheets, and each sheet is a two-dimensional data, including velocity spectrum width and turbulence dissipation rate. These two variables are stored in a matrix of 30 rows and 9 columns. The row refers to the height at an interval of 120 meters, and the columns refers to time which corresponds to 0900 to 1700 LST. </p>
Doppler lidar wind profiles from Rzecin during POLIMOS 2018
<p>This is data set includes Doppler wind lidar quantities which were calculated from measurements performed between May and September 2018 at <em>PolWET </em>site in Rzecin, Poland (52.75°N, 16.30°E, 59 m a.s.l.) of the Poznan University of Life Sciences</p> <p>The system is a Doppler lidar Stream Line (Halo Photonics), which is part of ACTRIS-Cloudnet (Illingworth et al., 2007). The system laser emits at 1.5 μm and the detector is heterodyne using fiber-optic technology. The measurements for this data set consisted of conical scans (VAD) with constant elevation of 70° and 12 equidistant azimuth points performed every 30 min. A more detailed description of the instrument can be found in (Ortiz-Amezcua et al., 2022)</p>
Wind velocity vertical profile in a 50 m wind mast at Sisal, Yucatán, Mexico
<p>A meteorological mast, instrumented with sonic anemometers was implemented during the years 2010-2014, in order to study high frequency wind data at five different heights above the ground. The mast is 50 m height, located about 100 m from the shoreline at the Sisal campus of UNAM. An automated acquisition system recorded raw data in a database (server) directly through a RF link.</p> <p>For more information visit: http://ocse.mx/en/experimento/torre-sisal</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.
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.