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185 results for “Wind speed”

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zenodo32/100

Everest Model Output Statistics: improved wind speed forecasts

<p>Data and pre-trained random forest models necessary to correct GFS forecast data and produce improved Everest Forecasts.</p> <p>Please find the associated code at:&nbsp;github.com/MaxVWDV/Everest_wind_forecast</p>

opencc-by-4.0May 2023View details →
zenodo32/100

Hourly wind speed, solar radiation and load demand data

<p>Hourly data for wind velocity, solar radiation and load demand as time series of 10 years length,&nbsp;used within the simulation of a hybrid renewable energy system in the island of Sifnos, Greece.&nbsp;</p>

opencc-by-4.0Jun 2023View details →
zenodo32/100

Data for the publication: Ice supersaturation variability in cirrus clouds: Role of vertical wind speeds and deposition coefficients

<p>The files contain the datasets shown in the publication &quot;Ice supersaturation variability in cirrus clouds: Role of vertical wind speeds and deposition coefficients&quot; to appear in J. Geophys. Res. Atmos. (revised manuscript submitted). The files are xmgrace plot files containing the research data (ASCII) shown in all figures in the main text and Appendix A.</p>

opencc-by-4.0Aug 2023View details →
dryad32/100

Data from: Flight paths of seabirds soaring over the ocean surface enable measurement of fine-scale wind speed and direction

Open the record for dataset details and reuse information.

publicJul 2017View details →
dryad32/100

Data from: The influence of sea ice, wind speed and marine mammals on Southern Ocean ambient sound

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publicDec 2016View details →
dryad32/100

Data from: A global review of Procellariiform flight height, flight speed and nocturnal activity: Implications for offshore wind farm collision risk

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publicMay 2025View details →
nasa32/100

Aquarius CAP Level 2 Sea Surface Salinity, Wind Speed & Direction Data V5.0

The version 5.0 Aquarius CAP Level 2 product contains the fourth release of the AQUARIUS/SAC-D orbital/swath data based on the Combined Active Passive (CAP) algorithm. CAP is a P.I. produced dataset developed and provided by JPL. This Level 2 dataset contains sea surface salinity (SSS), wind speed and wind direction data derived from 3 different radiometers and the onboard scatterometer. The CAP algorithm simultaneously retrieves the salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. Each L2 data file covers one 98 minute orbit. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

restrictednotspecifiedApr 2025View details →
nasa32/100

Aquarius CAP Level 3 Wind Speed Standard Mapped Image Monthly Data V5.0

Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the monthly wind speed V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

restrictednotspecifiedApr 2025View details →
nasa32/100

NOAA CYGNSS Level 2 Science Wind Speed 25-km Product Version 1.2

This dataset contains the Version 1.2 NOAA CYGNSS Level 2 Science Wind Speed Product Version 1.2 which provides the time-tagged and geolocated average wind speed (m/s) in 25x25 kilometer grid cells along the measurement tracks from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. This version corresponds to the second science-quality released through the PO.DAAC, as produced by NOAA/NESDIS using a specific geophysical model function (GMF version 1.0) and a track-wise debiasing algorithm as part of the wind speed retrieval process. The reported retrieval locations are determined by averaging the specular point locations falling within each 25 km grid cell. Version 1.2 includes four major updates compared to Version 1.1 ( https://doi.org/10.5067/CYGNN-22511 ), namely: 1) the inclusion of data associated to a spacecraft roll angle exceeding +/- 5 degrees; 2) an improved wind speed performance in the higher wind speed regime; 3) a full revision of the quality flags; 4) the inclusion of a wind speed retrieval error variable. Only one netCDF-4 data file is produced for each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Formatting of the data variables and metadata designed to be consistent with the netCDF-4 formatting provided by the legacy CYGNSS mission Level 2 wind speed science data record (SDR).

restrictednotspecifiedApr 2025View details →
nasa32/100

Aquarius CAP Level 3 Wind Speed Standard Mapped Image 7-Day Data V5.0

Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the 7-Day running mean wind speed V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

restrictednotspecifiedApr 2025View details →
zenodo28/100

Variable Structure Control of a Small Ducted Wind Turbine in the Whole Wind Speed Range Using a Luenberger Observer

<p>1) Files.csv :&nbsp;Dataset acquired on the experimental laboratory setup used for the emulation of a Ducted Horizontal Axis Wind Turbine.</p> <p>2) DHAWT.xlsx&nbsp;: aerodynamic characteristics of the Ducted Horizontal Axis Wind Turbine chosen as case of study.</p>

opencc-by-4.0Jun 2020View details →
zenodo28/100

Data Used for Article: Speeding up large wind farms layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout

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opencc-by-4.0Dec 2023View details →
zenodo28/100

GGWS-PCNN: A global gridded wind speed dataset (1973/01-2021/12; Ongoing Update)

<p><strong>Profile of the dataset</strong></p> <ul> <li>The GGWS-PCNN&nbsp;is a global gridded monthly dataset of 10-m wind speed based on an artificial intelligence algorithm (the partial convolutional neural network), observations from weather stations (the HadISD dataset), and 34 climate models from CMIP6.</li> <li>It has&nbsp;a resolution of 1.25&deg; &times; 2.5&deg; (latitude &times; longitude).&nbsp; We will update this dataset as soon as the new HadISD version is accessible.</li> <li>For more details about the dataset and its reconstructed processes, please see our paper &quot;<strong>An artificial intelligence reconstruction of global gridded surface winds</strong>&quot; published in the <em>Science Bulletin</em>.</li> </ul> <p><strong>Notice</strong></p> <ul> <li>The HadISD discovered an issue in the wind data after 2013. So in their version&nbsp;3.3.0.202201p and later, they fixed this issue. Find the website<strong>&nbsp;</strong><a href="https://www.metoffice.gov.uk/hadobs/hadisd/">Met Office Hadley Centre observations datasets</a>&nbsp;for more details.</li> <li>Due to the limitations of existing AI algorithms in reconstructing data with many missing values, our product has a small number of outliers (e.g. wind speeds less than zero or very high), most of which are located in the Antarctic region. We recommend you remove these outliers&nbsp;before using this dataset.</li> </ul> <p><strong>Reference</strong></p> <p>Lihong Zhou, Haofeng Liu, Xin Jiang, et al. (2022). <a href="https://www.researchgate.net/publication/363806982_An_artificial_intelligence_reconstruction_of_global_gridded_surface_winds">An artificial intelligence reconstruction of global gridded surface winds</a>. Science Bulletin.</p>

opencc-by-4.0Sep 2022View details →
zenodo28/100

An Investigation of Climate Change Effects on Design Wind Speeds along the US East and Gulf Coasts

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opencc-by-4.0May 2024View details →
zenodo28/100

Figure 1 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast

Figure 1. Maps of the White Sea. Dashed lines show boundaries of the sea and their internal parts (Lebedev et al., 2011).

opencc-by-4.0Nov 2019View details →
dryad28/100

The data of COVID-19 and their correlation with wind speed

<p>In 2020 the world was presently burdened with the COVID-19 pandemic. World Health Organization confirms 34,874,744 cases with 1,097,497 deaths (case fatality rate (CFR) 3.1%) were reported in 216 countries. In Indonesia, the number of people who have been infected and the number who have died are approximately 287,008 and 10,740 (CFR 3.7%), respectively, with the most predominant regions being Jakarta (73,700), East Java (43,536) and Central Java (22,440). Many factors can increase the transmission of COVID-19. One of them is wind speed. This data set contains covid-19 data in DKI Jakarta from June 2020 until August 2022 and wind speed in daily power point form. This data can be analyzed to see the correlation between wind speed and the COVID-19 cases.</p>

opencc-zeroDec 2022View details →
zenodo28/100

Datasets of GEV distribution values of observed surface pressure, rainfall and westward wind speed

<p>These are&nbsp;the generalized extreme value (GEV) distribution results&nbsp;obtained from Figure 3.5B, Figure 3.6B, Figure 3.7B and Figure C.4d in Peirson et al. (2011) and Figure 4, Figure 5, Figure 6 in Peirson et al. (2014). These datasets had&nbsp;been used to evaluate the climate model&nbsp;characterization of extreme storms.&nbsp; The evaluation results have been summarized in a paper which submitted to Journal of Climate: Wenjun Zhu et al., 2023: &quot;An Assessment of Model Projections of Climate-change Induced Extreme Storms on the South-eastern Coast of Australia&quot; (submitted).</p>

opencc-by-4.0Jan 2023View details →
dryad28/100

The data of COVID-19 and their correlation with wind speed

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publicDec 2022View details →
nasa28/100

Aquarius Official Release Level 3 Wind Speed Standard Mapped Image Ascending 7-Day Data V5.0

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal timescales. This particular data set is the 7-Day, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

restrictednotspecifiedApr 2025View details →
nasa28/100

Aquarius Official Release Level 3 Wind Speed Standard Mapped Image Descending 7-Day Data V5.0

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day,Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

restrictednotspecifiedApr 2025View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

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abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
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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.

dandi-nwb
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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
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