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108 results for “wind modelling”
SCATSAT-1 Inter-Calibrated ESDR Level 2 Observed and Modeled Spatial Derivatives of Surface Wind and Wind Stress Version 1.0
This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations aboard SCATSAT-1, representing the first science quality release of these data funded under the MEaSUREs program. This product from SCATSAT-1 has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit. There are typically 14 orbits per day, and the thumbnail preview shows coverage for the first ten orbits in an example day. Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables.<br><br>The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.
Metop-A ASCAT Inter-Calibrated ESDR Level 2 Observed and Modeled Spatial Derivatives of Surface Wind and Wind Stress Version 1.0
This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations (the MetOp-A ASCAT scatterometer), representing the first science quality release of these data funded under the MEaSUREs program. This product from MetOp-A ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-B, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit - the thumbnail preview shows data for all orbits over a day (typically 14 orbits). Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables.<br><br>The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.
Climate model data for "Atmosphere-ocean feedback from wind-driven sea spray aerosol production"
<p>Data from atmosphere-only and coupled climate model simulations performed for "Atmosphere-ocean feedback from wind-driven sea spray aerosol production". Files are in netCDF format.</p>
Effects of solar wind density and velocity variations on the Martian ionosphere and plasma transport—a MHD model study
<p>MHD simulation data for paper: Effects of solar wind density and velocity variations on the Martian ionosphere and plasma transport—a MHD model study</p>
Skillful bias correction of offshore near-surface wind speed and wind direction forecasting based on a multi-task machine learning model
<h3>Dataset</h3> <p>1. observation data over 14 weather stations</p> <p>Variables: hourly near-surface 2-min average wind speed, wind direction </p> <p>2. ECMWF-IFS forecast data over 14 weather stations</p> <p>Variables: hourly predictors at surface level and upper level in next 48 hours (shown in Table 1. and Table 2.)</p> <p>Table 1. ECMWF-IFS forecast data at surface level</p> <div> <table> <tbody> <tr> <td> <p>Predictors</p> </td> <td> <p>Abbreviation</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>Temperature at 2 m</p> </td> <td> <p>2t</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Sea surface temperature</p> </td> <td> <p>sst</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Dewpoint temperature at 2 m</p> </td> <td> <p>2d</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Convective precipitation in the past hour</p> </td> <td> <p>cp</p> </td> <td> <p>mm</p> </td> </tr> <tr> <td> <p>Mean sea level pressure</p> </td> <td> <p>msl</p> </td> <td> <p>hPa</p> </td> </tr> <tr> <td> <p>Zonal component of wind speed at 10 m</p> </td> <td> <p>10u</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind speed at 10 m</p> </td> <td> <p>10v</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at 10 m</p> </td> <td> <p>10ws</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at 10 m</p> </td> <td> <p>10wd</p> </td> <td> <p>°</p> </td> </tr> <tr> <td> <p>Zonal component of wind speed at 100 m</p> </td> <td> <p>100u</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind speed at 100 m</p> </td> <td> <p>100v</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at 100 m</p> </td> <td> <p>100ws</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at 100 m</p> </td> <td> <p>100wd</p> </td> <td> <p>°</p> </td> </tr> </tbody> </table> </div> <div> </div> <p>Table 2. ECMWF-IFS forecast data at upper level</p> <table> <tbody> <tr> <td> <p>Predictors</p> </td> <td> <p>Abbreviation</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>Relative humidity at xxx hPa</p> </td> <td> <p>r_Lxxx</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Temperature at xxx hPa</p> </td> <td> <p>t_Lxxx</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Vertical velocity of wind at xxx hPa</p> </td> <td> <p>w_Lxxx</p> </td> <td> <p>Pa s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Zonal component of wind at xxx hPa</p> </td> <td> <p>u_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind at xxx hPa</p> </td> <td> <p>v_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at xxx hPa</p> </td> <td> <p>ws_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at xxx hPa</p> </td> <td> <p>wd_Lxxx</p> </td> <td> <p>°</p> </td> </tr> </tbody> </table> <div> </div> <p>3. key variables constructed by feature engineering</p> <p>(1) sort-term statistics, including <em>maximum, minimum, mean </em>and <em>variance</em> of key variables (<em>2t</em>,<em> 10u</em>, <em>10v </em>and <em>10ws</em>) from ECMWF-IFS model during the next 48 hours,</p> <p> (2) long-term statistics, including <em>mean </em>and <em>deviation</em> of key variables (<em>2t</em>,<em> 10u</em>, <em>10v </em>and <em>10ws</em>) from ECMWF-IFS model during history 3-yr period (January 2020–December 2022),</p> <p> (3) thermodynamic factors, including the low-level wind shear between <em>10ws</em> and <em>100ws</em>, vertical wind shear between 200 hPa and 850 hPa<em>, </em>the differences between <em>sst</em><em> </em>and <em>2t</em><em>.</em></p> <h3>Scripts</h3> <p>1. Random Forest model training code</p> <p>2. LightGBM model training code</p> <p>3. XGBoost model training code</p> <p>4. TabNet-MTL model training code</p> <p> </p>
A spatially-explicit dataset of global wind erosion based on distributed RWEQ model
<p>This is a global wind erosion estimation dataset obtained based on the distributed RWEQ model with a resolution of 0.05°. It's the raw data of the article “Global Wind Erosion Reduction Driven by Changing Climate and Land Use” (https://doi.org/10.1029/2024EF004930). The dataset is NetCDF (Network Common Data Format) and contains 38 years between 1982 and 2019. Dimension is the year information for wind erosion, beginning in 1982 and ending in 2019.</p> <p>Coordinate system: WGS_84 (EPSG:4326)</p> <p>NoData Value=-10000</p> <p>NETCDF_VARNAME=SoilLoss</p> <p> </p> <h3><strong>Please cite this dataset via the Earth's Future journal article</strong><strong>“Global Wind Erosion Reduction Driven by Changing Climate and Land Use” </strong></h3> <h3><strong>https://doi.org/10.1029/2024EF004930</strong></h3> <p>Citation:<br>Sun, R., He, H., Jing, Y., Leng, S., Yang,G., Lü, Y., et al. (2024). Global winderosion reduction driven by changingclimate and land use. Earth's Future, 12,e2024EF004930. https://doi.org/10.1029/2024EF004930</p> <p> </p> <p>For access to the data, please contact the corresponding author.</p>
A Generative Super-resolution Model for Enhancing Tropical Cyclone Wind Field Intensity and Resolution
<p>Data for paper in review: A Generative Super-resolution Model for Enhancing Tropical Cyclone Wind Field Intensity and Resolution</p>
Near-surface model wind
<p>Near surface u and v wind, from Unified Model nesting suite u-ct753. Data is for 20200701-20200704 at hourly intervals. Data is on a dx=dy=1.5 km grid. Using a rotated coordinate system. The central latitude and longitude of each limited area model (LAM) is given in the .dat file. Map of locations is in .png file. Land-sea mas for each domain is in .maskfiles. This data is intended for looking at near-surface wind over the sea, so only LAMs minaly over the sea have their u and v wind uploaded.</p>
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International Brain Laboratory public data
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OpenNeuro
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