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185 results for “wind speed;”
Surface surface wind speed and its different grades over China during 1961-2020 based on a high-resolution observation dataset CN05.1
<p>The daily 10-m wind speed observation dataset CN05.1, which covers the period from 1961 to 2020, with horizontal resolution of 0.25° × 0.25° (latitude × longitude). This dataset was developed by Jia Wu from National Climate Center, China Meteorological Administration.</p> <p>References: Jia Wu, Xue-Jie Gao. A grided daily observation dataset over China region and comparison with the other datasets. <em>Chinese J. Geophys. </em>(in Chinese), 2013, 56(4): 1102-1111, doi: 10.6038/cjg20130406.</p>
Wind Data for Station-wise assessment of wind speed and direction under future climates across the United States
<p>This study employs statistical techniques to evaluate climate model performance in wind speed and direction and their projected future changes under the representative concentration pathway (RCP) 8.5 scenario over inland and offshore across the Continental United States (CONUS). It extends the scope of existing studies by characterizing the changes of the full range of the joint wind speed and direction distribution via a conditional approach. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the statistical significance of the future projections. The proposed conditional approach provides a better way to characterize the directional wind speed distributions that offers additional insights for the joint assessment of speed and direction. </p> <p>WRF data: We focus on seasonal (December-January-February (winter hereafter) and June-July-August (summer hereafter) statistics computed from the 3-hourly RCM outputs on both wind speed and direction over ten locations with different local topological features. We use three WRF simulations driven by Community Climate System Model 4 (CCSM4), the Geophysical Fluid Dynamics Laboratory Earth System Model 2 (GFDL-ESM2G), and the Hadley Centre Global Environment Model version 2 (HadGEM2-ES). These three GCMs represent a range of climate sensitivities that encompasses most of the coupled model intercomparison project phase 5 (CMIP5) GCMs when projecting future temperature changes. In this work, we focus on RCP 8.5 scenario for future projections. A 16-member ensemble of one-year of RCM simulation using bias corrected CCSM-driven WRF is also generated for analyzing the uncertainty due to the RCM's internal variability (IV). </p> <p>Benchmark data: Reanalysis data are used as a verification dataset in order to evaluate the RCMs' wind conditions under study for the historical time period. For the seven inland locations, we use the second phase of the multi-institution North American Land Data Assimilation System project, phase 2, at a spatial resolution of 12 km and hourly resolution. NLDAS-2 is an offline data assimilation system featuring uncoupled land surface models driven by observation-based atmospheric forcing. The non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are at a 32-km spatial resolution and 3-hourly temporal frequency.</p> <p>In-situ measurement: Since reanalysis data can present errors and uncertainties, ground measurements and offshore buoy measurements are used to consolidate the evaluation of RCMs' wind conditions for inland and offshore locations in historical climates. Observational data are extracted from the Automated Surface Observing System (ASOS) network that consists stations covers the U.S. territory, available at ftp://ftp.ncdc.noaa.gov/pub/data/asos-onemin. The offshore downscaled wind speeds from the historical decade are compared with National Data Buoy Center (NDBC) buoy observations of near-surface wind velocities available at https://www.ndbc.noaa.gov. The observed winds at the NBDC anemometers are adjusted to 10-m above ground height and at 3-hourly rate. </p> <p> </p> <p> </p>
water surface at about 32 m/s wind speed
<p>Filmed at University of Miami's SUSTAIN wind wave tank (http://sustain.rsmas.miami.edu/) with the imaging slope gauge (ISG) developed by the Air-Sea Interaction group of Heidelberg University (http://www.iup.uni-heidelberg.de/institut/forschung/groups/gw). More information about the ISG can be found at https://doi.org/10.2971/jeos.2014.14015.</p> <p>filmed at: 10000fps</p> <p>playback at: 60fps</p> <p>image size: approx, 28cmx24cm</p> <p>top down view</p>
water surface at about 39 m/s wind speed
<p>Filmed at University of Miami's SUSTAIN wind wave tank (http://sustain.rsmas.miami.edu/) with the imaging slope gauge (ISG) developed by the Air-Sea Interaction group of Heidelberg University (http://www.iup.uni-heidelberg.de/institut/forschung/groups/gw). More information about the ISG can be found at https://doi.org/10.2971/jeos.2014.14015.</p> <p>filmed at: 10000fps<br> playback at: 60fps<br> image size: approx, 28cmx24cm</p>
water surface at about 80 m/s wind speed
<p>Filmed at University of Miami's SUSTAIN wind wave tank (http://sustain.rsmas.miami.edu/) with the imaging slope gauge (ISG) developed by the Air-Sea Interaction group of Heidelberg University (http://www.iup.uni-heidelberg.de/institut/forschung/groups/gw). More information about the ISG can be found at https://doi.org/10.2971/jeos.2014.14015.<br> filmed at: 10000fps<br> playback at: 60fps<br> image size: approx, 28cmx24cm</p>
High-resolution (250x250m) gridded daily mean wind speed dataset for Austria spanning from 1961 to 2023
<p>Overview:</p> <p><strong>Resolution</strong>: 250x250 m<br><strong>Projection</strong>: EPSG 31287, Austria Lambert<br><strong>Extent</strong>: Austria <br><strong>Period </strong>: 1961-2023<br><strong>Format:</strong> NetCDF</p> <p>Application:</p> <p>High-resolution gridded climate data derived from in-situ observations play a crucial role in global and regional climatology. The data are a valuable input for further climate impact studies, particularly in ecological and energy modelling, and can be subsequently used for wind power potential analysis. Moreover, policymakers can make informed decisions based on accurate climate information derived from this dataset, enhancing the effectiveness of climate-related policies and interventions. Additionally, the data can be used for model evaluation and bias adjustment.</p> <p>Methods:</p> <p>1. Data homogenization</p> <p>Breaks in the station data time series were detected and corrected using the Standard Normal Homogeneity Test (SNHT), which identifies where the mean changes the most. If this change exceeds a certain threshold, the time series is adjusted by aligning the statistical distribution of values before the change to those after the change, assuming the most recent time series is correct. This adjustment is achieved using the Quantile Mapping (QM) approach.</p> <p>2. Spatial interpolation</p> <p>All methods were tested in a nested 10-fold cross-validation (CV) scheme. This means that 10 % of the stations are left out (outer loop), and the other 90 % are used for training the model (inner loop). The outer loop is solely used for validating the model, whereas the inner loop serves for model optimization.</p> <p>A two-stage approach was applied. Initially, a background field - the climatology for each month - was calculated using Random Forest Regression (RFR) with a defined set of predictors. Subsequently, model residuals were spatially interpolated using the 3D Inverse Distance Weighting (3D IDW) method. Differences between daily values and corresponding monthly climatologies were also interpolated using 3D IDW. The final daily mean wind speed field is calculated by adding the monthly climatology fields to the interpolated daily residuals.</p>
Lightning activity and wind speed variations in Tropical Cyclones of the Southwest Pacific Region
<p>Lightning data used in 'Lightning activity and wind speed variations in Tropical Cyclones of the Southwest Pacific Region'.</p>
Data for: Wind speed that can effect increasing COVID-19
<p>Several nations are currently experiencing a significant increase in coronavirus (COVID-19), including Indonesia. A total of 34,874,744 confirmed cases with 1,097,497 deaths (case fatality rate (CFR) 3.1%) were reported in 216 countries based on data from World Health Organization. COVID-19 remains public health problem around the world. It is possible the climate could affect the transmission of COVID-19. The wind is one of the climate factors besides temperature, humidity, and rainfall. Wind speed data can be used to study the spread of COVID-19 cases.</p>
Supplementary Data for "Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms"
<p>These are supplementary data for the paper "Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms". They are:</p> <p>- Python code to construct a neural network model</p> <p>- Saved optimal models (for 8-fold validation)</p> <p>- Selected y-label data (solar wind speed) and corresponding dates, which we eliminate the data identified as ICME</p>
Wind speed short-range forecasting verification dataset
<p>Verification files for the Verif program. See https://github.com/WFRT/verif for details.</p>
zEPHYR - Wind Speed - Nottingham
<p>Wind speed for 4 years at 4 different locations in Nottingham.</p>
Data for: Wind speed that can effect increasing COVID-19
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Influence of rotation speed and frequency on the decision of Columba livia domestica to cross the rotor-swept area of paper blades mimicking a wind turbine
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Forest bat activity declines with increasing wind speed in the proximity of operating wind turbines
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California Current Ecosystem site, station Lindbergh Field Airport, San Diego, CA, study of wind speed (mean) in units of metersPerSecond on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from California Current Ecosystem (CCE) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a monthly timescale.
California Current Ecosystem site, station Lindbergh Field Airport, San Diego, CA, study of wind speed (mean) in units of metersPerSecond on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from California Current Ecosystem (CCE) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a yearly timescale.
Konza Prairie site, station Headquarters, Meteorological Station 1, study of wind speed (mean) in units of metersPerSecond on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Konza Prairie (KNZ) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a monthly timescale.
Konza Prairie site, station Headquarters, Meteorological Station 1, study of wind speed (mean) in units of metersPerSecond on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Konza Prairie (KNZ) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a yearly timescale.
Luquillo Experimental Forest site, station Bisley Tower, study of wind speed (mean) in units of metersPerSecond on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Luquillo Experimental Forest (LUQ) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a monthly timescale.
Luquillo Experimental Forest site, station Bisley Tower, study of wind speed (mean) in units of metersPerSecond on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Luquillo Experimental Forest (LUQ) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a yearly timescale.
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