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185 results for “wind speed;”
Harvard Forest site, station Fisher Meteorological Station at Harvard Forest, 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 Harvard Forest (HFR) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a yearly timescale.
Jornada Basin LTER/Jornada Experimental Range site, station LTER Weather Station at Jornada Basin LTER, 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 Jornada Basin LTER/Jornada Experimental Range (JRN) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a monthly timescale.
Jornada Basin LTER/Jornada Experimental Range site, station LTER Weather Station at Jornada Basin LTER, 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 Jornada Basin LTER/Jornada Experimental Range (JRN) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a yearly timescale.
Central Arizona - Phoenix Urban LTER site, stations Litchfield (11)Buckeye meteorological station, 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 Central Arizona - Phoenix Urban LTER (CAP) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a monthly timescale.
Central Arizona - Phoenix Urban LTER site, stations Litchfield (11)Buckeye meteorological station, 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 Central Arizona - Phoenix Urban LTER (CAP) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a yearly timescale.
Kellogg Biological Station site, station KBS LTER weather station, 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 Kellogg Biological Station (KBS) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a monthly timescale.
Kellogg Biological Station site, station KBS LTER weather station, 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 Kellogg Biological Station (KBS) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a yearly timescale.
Wind speed and direction on Godwin Island, 2013-2014
A RM Young wind sensor was installed on top of a oyster hut located on Godwin Island and mean wind speed and direction measurements were made every 10 minutes. Elevation was approximately 5 meters above mean sea level.
Data of 'Estimating Wind Speed and Direction Using Wave Spectra'
<p>This set contains data of a Spotter wave buoy deployment and of the RMSE of wind speed and direction estimates as described in 'Estimating Wind Speed and Direction Using Wave Spectra', submitted for review to Journal of Geophysical Research.</p>
Data of tangential wind speed of Typhoon Trami (2018) derived by Tsujino et al. (2021)
<p>Time series data of tangential wind speed in the eye and eyewall of Typhoon Trami (2018) from 0000 UTC 25 to 0600 UTC 27 September 2018, derived by Tsujino et al. (2021) using the data observed by Himawari-8 satellite. More information is also available at <a href="http://wwwoa.ees.hokudai.ac.jp/people/horinouchi-lab/TC/data_en.html">http://wwwoa.ees.hokudai.ac.jp/people/horinouchi-lab/TC/data_en.html</a>.</p>
Evaluation and Projection of Surface Wind Speed over China Based on CMIP6 GCMs
<p>This file is for the upload of CN05.1 data for 2020JD033611RR.</p>
Data from: The influence of sea ice, wind speed and marine mammals on Southern Ocean ambient sound
This paper describes the natural variability of ambient sound in the Southern Ocean, an acoustically pristine marine mammal habitat. Over a 3-year period, two autonomous recorders were moored along the Greenwich meridian to collect underwater passive acoustic data. Ambient sound levels were strongly affected by the annual variation of the sea-ice cover, which decouples local wind speed and sound levels during austral winter. With increasing sea-ice concentration, area and thickness, sound levels decreased while the contribution of distant sources increased. Marine mammal sounds formed a substantial part of the overall acoustic environment, comprising calls produced by Antarctic blue whales (Balaenoptera musculus intermedia), fin whales (Balaenoptera physalus), Antarctic minke whales (Balaenoptera bonaerensis) and leopard seals (Hydrurga leptonyx). The combined sound energy of a group or population vocalizing during extended periods contributed species-specific peaks to the ambient sound spectra. The temporal and spatial variation in the contribution of marine mammals to ambient sound suggests annual patterns in migration and behaviour. The Antarctic blue and fin whale contributions were loudest in austral autumn, whereas the Antarctic minke whale contribution was loudest during austral winter and repeatedly showed a diel pattern that coincided with the diel vertical migration of zooplankton.
Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (vertical slices of wind speed)
<p>Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (vertical slices of wind speed) using the WRF-LES-GAD approach. Supplementary material part of the paper "Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign", submitted to the Wind Energy Science journal.</p>
Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (wind speed at 90 m AGL)
<p>Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (wind speed at 90 m AGL) using the WRF-LES-GAD approach. Supplementary material part of the paper "Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign", submitted to the Wind Energy Science journal.</p>
Data for Nicolas & Boos, "Sensitivity of tropical orographic precipitation to wind speed with implications for future projections"
<p><span>This directory contains all data used in producing the plots in Nicolas & Boos (2024), "Sensitivity of tropical orographic precipitation to wind speed with implications for future projections". It is divided in four subdirectories:</span></p> <p><span> - wrfData contains processed simulation output needed to reproduced figures 1, 2, and SI figure 1.</span></p> <p><span> - regionsData and globalData contain processed observational (APHRODITE and IMERG) and ERA5 data necessary to reproduce figure 3 and SI figures 2 and 3.</span></p> <p><span> - cmipData contains processed CMIP surface wind data necessary to reproduce figure 4.</span></p> <p><span>Code used in producing these figures will be made available and linked to this dataset once any needed revisions are complete.</span></p>
GNSS and Wind Speed Dataset from North Sea Wave Glider 2016 Deployment
<p>5 Hz GNSS RINEX and 10 min wind speed data collected on-board an SV2 Wave Glider in the North Sea from 28 July to 10 August 2016 used in Penna et al, “Sea Surface Height Measurement Using a GNSS Wave Glider”, submitted to Geophysical Research Letters.</p> <p> </p>
water surface at about 68 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>
Monthly RACMO2.4p1 data for Greenland (11 km) and Antarctica (27 km) for SMB, SEB, near-surface temperature and wind speed (2006-2015)
<p>Version 2: Updated missing months in the Antarctic data set.</p> <p>Monthly-accumulated (named monthlyS) and monthly-averaged (named monthlyA) data for RACMO2.4p1 for Greenland (GRN) and Antarctica (ANT) on a 11 km and 27 km horizontal resolution grid, respectively, are presented in this data set and are available for 2006 until 2015. The data include the surface mass balance (SMB), snow melt (mltgl), refreezing (rfrzgl), precipitation (pr), runoff (totrunoff), drifting snow erosion (sndiv), sublimation (sublgl) and sublimation due to blowing snow (sublsd), all in kg m-2 mo-1. For the surface energy balance (SEB): the downward shortwave radiation (rsds), shortwave upward radiation (rsus), downward longwave radiation (rlds), upward longwave radiation (rlus), sensible heat flux (hfss) and latent heat flux (hfls) are available. The SEB components are in J m-2. To convert to W m-2, divide by the amount of seconds in a month. In addition, the near-surface temperature (tas), in K, and near-surface wind speed (sfcwind), in m s-1, are included.</p> <p><br>This data set does not represent new surface mass balance and climate products for Greenland and Antarctica. This will follow in later publications, where RACMO2.4 simulations are presented covering the full historical time period of ERA5 with higher horizontal resolution. </p>
Dataset for "Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet"
<p>The dataset supports the article "Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet", which is accepted to be published in the Environmental Data Science journal. <br><br>The description of the files is as follows:</p> <ol> <li>ERA5.nc: <br> <ul> <li>Dimensions: (location: 11, time: 166560)<br>Coordinates:<br> longitude (location) float32 ...<br> latitude (location) float32 ...<br> * time (time) datetime64[ns] 2000-01-01 ... 2018-12-31T23:00:00<br> year (time) int64 ...</li> <li>Variables: 10ws, 100ws, 100alpha, 975ws, 950ws, 975wsgrad, 950wsgrad, zust, i10fg, t2m, skt, stl1, d2m, msl, blh, cbh, ishf, ie, tcc, lcc, cape, cin, bld, t_975, t_950, 2mtempgrad, sktempgrad, dewtempsprd, 975tempgrad, 950tempgrad, sinHR, cosHR, sinJDAY, cosJDAY, 10ws_delta1, 10ws_delta2, 10ws_delta3, 10ws_delta4, 10ws_delta5, 10ws_delta6, 100ws_delta1, 100ws_delta2, 100ws_delta3, 100ws_delta4, 100ws_delta5, 100ws_delta6, 975ws_delta1, 975ws_delta2, 975ws_delta3, 975ws_delta4, 975ws_delta5, 975ws_delta6, 950ws_delta1, 950ws_delta2, 950ws_delta3, 950ws_delta4, 950ws_delta5, 950ws_delta6</li> </ul> </li> <li>2000.nc: <ul> <li>Dimensions: (obs: 11, time: 8784, heightAboveGround: 12)<br>Coordinates:<br> lat (obs) float64 ...<br> lon (obs) float64 ...<br> * time (time) datetime64[ns] 2000-01-01 ... 2000-12-31T23:00:00<br> * heightAboveGround (heightAboveGround) float64 10.0 15.0 ... 400.0 500.0<br>Dimensions without coordinates: obs<br>Data variables:<br> data (obs, time, heightAboveGround) float64 ...</li> </ul> </li> <li> 2001.nc: <ul> <li>Dimensions: (obs: 11, time: 8760, heightAboveGround: 12)<br>Coordinates:<br> lat (obs) float64 ...<br> lon (obs) float64 ...<br> * time (time) datetime64[ns] 2001-01-01 ... 2001-12-31T23:00:00<br> * heightAboveGround (heightAboveGround) float64 10.0 15.0 ... 400.0 500.0<br>Dimensions without coordinates: obs<br>Data variables:<br> data (obs, time, heightAboveGround) float64 ...</li> <li>Data is the wind speed at multiple height levels</li> </ul> </li> </ol>
Dataset and R code for "Relationship between wind speed and plant hydraulics at the global scale"
<p><strong><span>Data collection</span></strong><strong><span> </span></strong></p> <p><span> Plant hydraulic traits and height data were obtained from three sources: (1) field measurements of plant hydraulics for 210 forest species in China; (2) the TRY Plant Traits Database (https://www.try-db.org/TryWeb/Home.php; Kattge et al., 2020); and (3) published literature. For the latter we conducted searches on Web of Science, Google Scholar, and China National Knowledge Infrastructure (http://www.cnki.net) using keywords such as “hydraulic traits,” “xylem hydraulic conductivity,” “xylem vulnerability,” “water potential at 50% loss of hydraulic conductivity,” “xylem embolism resistance,” and “plant water conductivity.” A substantial portion of data in our study were obtained from published literature (Choat et al., 2012; Gleason et al., 2016) and the Xylem Functional Traits Database (XFT; </span><span><a href="https://xylemfunctionaltraits.org/"><span>https://xylemfunctionaltraits.org</span></a></span><span>).</span></p> <p><span>To minimize ontogenetic and methodological variation, we only included data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources; and (e) data values > 3 SD (standard deviation) were removed to reduce the effect of outliers (Carmona et al., 2021); (f) <span>height data were reported at the same site where plant hydraulic traits were measured.</span> </span></p> <p><span>Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2; Fick & Hijmans, 2017; Table 1) if the original data were not available. The following variables measured at ~1 km<sup>2</sup> scale were extracted from WorldClim: mean annual wind speed (<span>μ</span>), mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, wind seasonality (<span>μS; </span>coefficient of variation across monthly measurements × 100), precipitation of driest month, and minimum temperature of coldest month. The VPD data were extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html; Abatzoglou et al., 2018). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data; Zomer et al., 2008). Moisture index (MI), which is the ratio of precipitation to PET. </span></p> <p><strong><span>Data analysis</span></strong></p> <p><span>Trait and environment data were log<sub>10</sub>-transformed to achieve approximate normality, except for <em>P</em><sub>50</sub> and temperature data. We first calculated correlations among all climatic variables and for subsequent analyses retained only those variables with correlation coefficients lower than |0.7| (Dormann et al., 2013). We then ran independent multiple linear models for each trait of interest using the retained climatic variables. Model selection based on a corrected Akaike information criterion and using the R package glmulti (Calcagno & de Mazancourt, 2010), identified the best linear model for each trait. The R package ‘visreg’ (Breheny & Burchett, 2017) was used to visualize the partial relationships between wind speed and hydraulic traits. Two-dimensional contour plots were then used to explore and visualise how plant hydraulic traits varied simultaneously with wind speed and moisture index.</span></p> <p><span>To quantify the strength of wind effects on plant hydraulics, models with wind parameters μ and μS included were compared to those without these wind parameters. </span></p> <p><span>To test for differences in the relationship between hydraulic traits and wind speed among species grouped into different climatic regions (i.e., dry <em>vs</em>. wet sites, and tropical <em>vs</em>. temperate regions), we used standardized major axis (SMA) analyses using the R package ‘smatr’ (Warton et al., 2012).</span><span> </span><span>A grouping factor was added in each SMA to test whether species groups share a common slope, with <em>p</em> > 0.05 indicating species groups share a common slope. </span></p> <p><span>Variance partitioning analysis was performed using the ‘rdacca. hp’ R package to quantify the degree to which the effect of wind speed was independent from other climatic variables (Lai et al., 2022). The individual contribution of each predictor was estimated in this analysis. This analysis also helped to illustrate the significant values of climatic variables on plant hydraulics. </span></p> <p><span>A Random Forest </span><span>machine-learning algorithm (implemented using the R package ‘randomForest’) was utilized to further assess the relative importance of environmental variables for each plant hydraulic trait (Breiman, 2001). To avoid multicollinearity, this analysis only included variables with correlation coefficients lower than |0.7|. A higher value of the mean decrease in accuracy (%IncMSE) indicates the increased importance of a variable (e.g., a %IncMSE value of 50 indicates that the overall mean square error would increase by 50% if that variable were to be excluded from the analysis). This provides a measure of a variable's importance in estimating the value of the target variable across the trees in the forest. </span></p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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