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Wind speed and direction data from benchmark stations at the HJ Andrews Experimental Forest, 1973 to present
A three-level hydro-climatological network for data monitoring was established in 1994. The networks at each level are nested to form a coordinated program of data acquisition and measurement. A future vision of linking the benchmark meteorological stations with regional weather stations to expand the future scope of studies was also considered in designing this network. The first-level in this top-down approach consists of Benchmark Meteorological Stations (BMS) and Benchmark Stream Stations. The BMS are designed to represent the environment across the Andrews. These stations are intended to provide complete, long-term, high temporal resolution, meso-scale hydroclimatological data. The location of the BMS network is based on factors such as elevation, aspect, vegetation gradients, and accessibility. Collected meteorological parameters are generally standardized across the BMS as well as methods and instrumentation. Secondary Meteorological Stations also follow standardized methods and serve similar purposes but are somewhat limited in meteorological parameters collected. The Primary Meteorological Station (PRIMET), Central Meteorological Station (CENMET), Upper Lookout Meteorological Station (UPLMET), and Vanilla Leaf Meteorological Station (VANMET) are the four Benchmark Stations, Climatic Station at Watershed 2 (CS2MET) and the Hi-15 Meteorological Station (H15MET) are Secondary Stations. These wind parameters were previously part of database code MS001, but were separated out into their own database in 2024.
Legacies of Historical Clearcutting, Wind and Salvage Logging in Pisgah State Forest 2016
Disturbance events affect forest composition and structure across a range of spatial and temporal scales, and subsequent forest development may differ after natural, anthropogenic, or compound disturbances. Following large, natural disturbances, salvage logging is a common and often controversial management practice in many regions of the globe. Yet, while the short-term impacts of salvage logging have been studied in many systems, the long-term effects remain unclear. We capitalized on over eighty years of data following an old-growth Tsuga canadensis-Pinus strobus forest in southwestern New Hampshire, USA after the 1938 hurricane, which severely damaged forests across much of New England. To our knowledge, this study provides the longest evaluation of salvage logging impacts, and it highlights developmental trajectories for Tsuga canadensis-Pinus strobus forests under a variety of disturbance histories. Specifically, we examined development from an old-growth condition in 1930 through 2016 across three different disturbance histories: (1) clearcut logging prior to the 1938 hurricane with some subsequent damage by the hurricane (“logged”), (2) severe damage from the 1938 hurricane (“hurricane”), and (3) severe damage from the hurricane followed by salvage logging (“salvaged”). At each site, 2-4 0.05 ha plots were established, and DBH, species, and distance and direction from plot center were recorded for each tree over 10 cm DBH. Stumps and snags were also measured. From the site center, coarse woody material was measured along three 34-m transects using the line-intersect method. Finally, a 1-ha plot was established around the site center, within which all pit-and-mound structures and stumps were recorded as well as boles associated with these structures.
Tropical Pacific SST and wind anomalies generated by a Nonlinear Inverse Model
<p>Tropical Pacific (40S-40N; 120E-50W) sea surface temperature (SST), zonal wind (U) and meridional wind (V) anomalies generated by the Nonlinear Inverse Model described in Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5). The data consists in 99 realizations (<a href="../api/records/10411023/draft/files/NLIM_output_085.nc/content" target="_blank" rel="noopener noreferrer">NLIM_output_XXX.nc</a>) of 1,000yrs each emulating SST, U, and V monthly anomalies conditions during 1980-2020 (<a href="../api/records/10411023/draft/files/Monthly_obs_1980_2020.nc/content" target="_blank" rel="noopener noreferrer">Monthly_obs_1980_2020.nc</a>) given in a 2.5deg-2.5deg grid. For observations, we used the NOAA Extended Reconstruction SST v5 reanalysis (SST; Huang et al., 2017) and NCEP-NCAR reanalysis (winds; Kalnay et al., 1996) The observed anomalies are calculated as described in Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5).</p> <p>Given that the stochastic forcing considered is white in time and space (https://doi.org/10.1038/s41612-024-00675-5; Methods, section "Offline simulation of SSH_{12}, PC2, and spatial patterns fron nonlinear inverse model output"), the spatial patterns and lead-lag relationships are better identified using composites. A modification of the methodology that allows for spatially coherent stochastic forcing will be implemented in a future article.</p> <p>When using the data please cite https://doi.org/10.5281/zenodo.10411023 (the data) and Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5; for the methodology). </p> <p>Any question, please contact Cristian Martinez-Villalobos at his email cristian.martinez.v@uai.cl</p> <p>References</p> <p>Martinez-Villalobos, C., Dewitte, B., Garreaud, R.D. <em>et al.</em> Extreme coastal El Niño events are tightly linked to the development of the Pacific Meridional Modes. <em>npj Clim Atmos Sci</em> <strong>7</strong>, 123 (2024). https://doi.org/10.1038/s41612-024-00675-5</p> <p>Huang, B. et al. Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate 30, 8179–8205 (2017).</p> <p>Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society 77, 437–471 (1996).</p> <p> </p>
High-frequency dissolved oxygen, water temperature, wind speed, and radiation data; stream and in-lake nutrient concentration data; and daily metabolism and nutrient loading estimates for 16 lakes in North America and Northern Europe.
In lakes, ecosystem structure and processes are influenced by gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP). The rates of these metabolic processes are often controlled by resource availability, which often reflects catchment loads. Although the relationship between catchment loads and in-lake nutrient concentrations may be well defined in specific lakes, we explored how watershed vs. in-lake predictors of metabolism compare across lake types. To do this, we combined stream loads of carbon (C), nitrogen (N), and phosphorus (P) with high frequency in situ monitoring of lake metabolism and in-lake C, N, and P concentrations from 16 lakes spanning a range of latitudes (39 to 64 degrees N), inflowing stream (0 - 6 streams), and trophic status (oligotrophic to eutrophic). The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP, R, and NEP derived from those data. In addition, the data package includes in-lake and stream concentrations of dissolved organic carbon, total nitrogen, and total phosphorus and stream discharge data. The package also includes estimates of daily carbon, nitrogen and phosphorus loading to each lake derived from the stream concentrations and discharge.
Hubbard Brook Experimental Forest: Wind Speed and Wind Direction Measurements, 1965 - present
Wind data have been measured by an anemometer mounted 3 m above the ground at Hubbard Brook Experimental Forest Headquarters since 1965. Prior to 1981, every mile of wind movement caused a tick mark on a strip-chart recorder, and wind direction as N, S, E, W, or a combination, was recorded continuously. From 1981-2003, wind speed and direction were measured with a MetOne wind speed sensor. In June 2003 the MetOne was replaced with a R.M. Young company wind speed direction sensor (model 05103). Since that time, wind direction (azimuth) is based on a 0 to 360 degree scale. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
EXPOS Model for Estimating Topographic Exposure to Wind
EXPOS is a simple model of topographic exposure to wind that utilizes digital elevation data to predict which points on a landscape are exposed to or protected from a given wind direction. The model was developed to approximate the effects of topography on hurricane wind flow at a landscape scale (~ 10 km). The model requires an accurate digital elevation map and a specified wind direction. Each point on the elevation map is then classified as protected or exposed, depending on whether or not it falls within the wind shadow cast by points upwind. The wind shadow is estimated by assuming that the wind bends downward no more than a fixed inflection angle from the horizontal as it passes over a height of land. The effects of topographic features beyond the margins of the input elevation map are of course not predicted by the model. For a given landscape and wind direction, predicted protected areas decrease in size as the inflection angle increases. Application of the model in studies of two recent hurricanes suggests that the probability of wind damage in protected areas also decreases as the inflection angle increases. An inflection angle of about 5 to 10 degrees was found to give good results. For more information on the EXPOS model, please see the published paper (Boose, E. R., D. R. Foster, and M. Fluet. 1994. Hurricane impacts to tropical and temperate forest landscapes. Ecological Monographs 64: 369-400). Additional information is contained in the documentation that accompanies the program. For an updated version of the EXPOS model in R and Python, please see HF447.
HURRECON Model for Estimating Hurricane Wind Speed, Direction and Damage
HURRECON is a simple meteorological model that estimates hurricane surface wind speed and direction based on the track, size, and intensity of a hurricane and the surface type (land or water). The model also estimates Fujita-scale wind damage as a function of peak 1/4 mile wind speed and wind gust factor. Estimates can be generated for a single site or a rectangular region. The model is based on published empirical studies of many hurricanes. HURRECON can be used to study the impacts of individual hurricanes or to reconstruct the hurricane disturbance regime for a particular region. For more information on the most recent version of the model please see the published paper (Boose, E. R., K. E. Chamberlin and D. R. Foster. 2001. Landscape and regional impacts of hurricanes in New England. Ecological Monographs 71: 27-48). Additional information is contained in the documentation that accompanies the program. For an updated version of the HURRECON model in R and Python, please see HF446.
North Temperate Lakes LTER: Spatial Variability of Wind Field at Lake Wingra (2004)
Wind speed and direction were measured at multiple locations around Lake Wingra. Two data tables are provided. The first has wind speed and direction measured at 7 sites around Lake Wingra, Dane County, WI, USA during the month of March, 2004 at a frequency of 10 minutes. The latter data table contains wind speed and direction measured at Vilas beach, Lake Wingra for the months July through September 2004 at a frequency of 2 minutes. Sampling Frequency: 2 minutes and 10 minutes Number of sites: 7 locations around Lake Wingra Instrument: http://www.campbellsci.com/03001-wind-sentry -- 03001-L R.M. Young Wind Sentry set
Laboratory measurements of wind, waves, and turbulence in hurricane conditions in the ASIST wind-wave facility
<p>Laboratory measurements of wind, waves, and turbulence in hurricane conditions, collected in September and October of 2018 and January of 2019 in the ASIST wind-wave facility, in the SUSTAIN laboratory at the University of Miami.</p> <p>This dataset includes two experiments, one with fresh water ("fresh") and another with seawater ("salt"), each in 10-m winds from 0 to approximately 42 m/s. Data include:</p> <ul> <li>3-dimensional wind velocity at 20 Hz sampling frequency from Campbell Scientific IRGASON sonic anemometer (collected in 2018)</li> <li>2-dimensional (along-tank and vertical) wind velocity at 1000 Hz sampling frequency from TSI IFA-300 hot film anemometer (collected in 2018)</li> <li>1-dimensional (along-tank) wind velocity at 10 Hz sampling frequency from a pitot anemometer (collected in 2018 and 2019)</li> <li>3-dimensional water velocity in the bottom 5 cm of the tank at 100 Hz sampling velocity from Nortek Vectrino velocimeter. (collected in 2018)</li> <li>Water elevation at 20 Hz sampling frequency at 6 locations in the tank from Senix Toughsonic 30 ultrasonic distance meters (collected in 2019)</li> <li>Along-tank static air pressure difference at 10 Hz sampling frequency from Baratron MKS 226 differential pressure transducer (collected in 2019)</li> </ul> <p>All data is in NetCDF4 format.</p> <p>Experiment set up and positions of instruments are documented in more detail in Curcic and Haus (2020), Revised estimates of ocean surface drag in strong winds, <em>Geophysical Research Letters</em>, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL087647">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL087647</a>.</p> <p>Produced as part of the National Science Foundation Award #1745384, titled "Air-Sea Momentum Transfer in Extreme Wind Conditions"<strong>.</strong></p> <p>Contact: Milan Curcic <mcurcic@miami.edu></p>
Storm Database Files for CLIMK–WINDS: A New Database of Extreme European Winter Windstorms
<p>This database is comprised of the four netCDF files containing the 50 most extreme European winter windstorms identified within the four input sources, with one netCDF file per source: ERA5 reanalysis, CCLM_ERA5_EUR-11 regional climate model simulation, COSMO-REA6 reanalysis, and CCLM_ERA5_CEU-3 regional climate model. This database was created by Clare Marie Flynn and its creation is described in the following paper: Flynn, C. M., Moemken, J., Pinto, J., Schutte, M., and Messori, G.: CLIMK–WINDS: A New Database of Extreme European Winter Windstorms, under review for final submission, Earth System Science Data, 2025.</p>
WILLOW - Norther: data set for the full-scale validation of model-based virtual sensing methods for an operational offshore wind turbine
<h1><em><strong>1. General description </strong></em></h1> <p>This data set contains as-build design information, as well as full-scale vibration response measurements from an operational offshore wind-turbine. The turbine is part of the Norther wind farm which is located in the Belgian North Sea<em> </em>and includes a total of 44 Vestas V164 (8.4MW) wind turbines on monopile foundations, see <a href="../api/records/11093262/draft/files/Fig1_Norther_locaction.png/content" target="_blank" rel="noopener noreferrer">Fig1_Norther_locaction.png</a>. This data set is intended to verify and validate model-based virtual sensing algorithms, using data as well as modeling information from a real turbine. </p> <h2><em><strong>1.1 Summary of the shared structural information</strong></em></h2> <p>The included information entails a detailed description of the geometric properties of the monopile and transition piece, distributed and lumped structural masses . All information shared in this record is conform the as-designed documentation. An example of the lumped masses considered in the model input files is presented in "<a href="../api/records/11093262/draft/files/Fig2_Sensor_Network.png/content" target="_blank" rel="noopener">Fig2_Sensor_Network.png"</a></p> <h2><em><strong>1.2 Summary of the shared geotechnical information</strong></em></h2> <p>Monopiles are distinguished by the significant role of soil-structure interaction. Ground reaction is most typically included in the structural model as non-linear p-y curves. Different p-y curves are available for a certain number of soils in the standards applicable to offshore structures (API RP 2GEO, 2011, and ISO 19901-4:2016(E), 2016).</p> <p>The required soil properties to define p-y curves according to the API framework are given in the soil profile provided in a separate Excel. Rather than symbols, the name of the soil properties is generally used as column header (e.g., <em>Undrained shear strength</em>). Therefore, it is straightforward to identify each soil parameter. The only soil parameter that might lead to confusion is:</p> <ul> <li><em>"epsilon50 [-]" </em>represents the vertical strain at half the maximum principal stress difference in a static undrained triaxial compression test on an undisturbed soil sample.</li> </ul> <p>It's worthy to note that estimates for the small shear strain stiffness, referred to as Gmax, are also included. Despite not being required as an input to define the API p-y curves, this parameter remains a key input for other soil reaction frameworks than the API (e.g., PISA). </p> <h2><em><strong>1.3 Summary of the shared measurement data</strong></em></h2> <p>Two sets of measurement data have been curated for validation purposes; the first interval has been collected during parked conditions, whereas the second interval has been collected during rated operational conditions. Both records have a length of 2 hours, and are subdivided into 10-minute data sets. Furthermore 1Hz SCADA data has been made available for the selected intervals. All different data sources are time synchronized and have been subjected to several internal quality checks. </p> <p>The sensor network on NRT-WTG is illustrated in in <strong>Fig. 2, </strong>whereas a description of the sensor types is presented in <strong>Tab.1.</strong> The acceleration sensors are installed in the horizontal plane, and measure tangential (Y) and orthogonal (X) to the wall, where the positive Y direction is pointing clockwise and the positive X direction is pointing inwards. All strain sensors are installed vertically and are located on the inside of the wall.</p> <table> <tbody> <tr> <td><strong>Data type </strong></td> <td><strong>Sensor type</strong></td> <td><strong>Fs (Hz)</strong></td> <td> <p><strong>Level mLAT (m)</strong></p> </td> <td><strong>Description </strong></td> </tr> <tr> <td>Acceleration (g) </td> <td>Piezo-electric acc. sensor (<strong>ACC</strong>)</td> <td>30</td> <td>15, 69, 97 </td> <td>3 Bi-directional accelerometers at different levels. LAT 15 installed at 240 degree heading; LAT 69 and 97 at 60 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Resistive strain gauge (<strong>SG</strong>)</td> <td>30</td> <td>14</td> <td>6 SGs: equally spaced around the inner circumference of the can. Headings: 50, 110, 170, 230, 290, 350 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Fiber-Bragg Grating strain gauge (<strong>FBG</strong>)</td> <td>100</td> <td>-17, -19</td> <td>2 FBGs per level at 165 and 255 degree respectively.</td> </tr> </tbody> </table> <p><strong>Table 1. Description of sensor types.</strong></p> <p>The FBG strain time series have been synchronized with the SG time series using using a cross-correlation based approach. Therefore the SG data has been used to genereate refrence strain time series at the headings of the FBG sensors; the FBG data is subsequently synchronized with regard to this reference time series. No synchronization of the acceleration data was needed, since these are collected using the same data aquisition system as the SG data. </p> <p>The SG strain time series have been calibrated and temperature compensated, whereas this is not the case for the FBG strain time series. The latter have a yet to be determined calibration offset. </p> <p>In conjunction to the sensor channels presented in <strong>Tab. 1</strong>, 1 Hz SCADA data is provided. A summary of the provided SCADA parameters, all sampled at 1Hz, is presented in <strong>Tab 2.</strong></p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Wind speed</td> <td>m/s</td> <td>Wind speed as recorded in the turbine SCADA</td> </tr> <tr> <td>Wind direction</td> <td>°</td> <td>Wind direction relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Yaw angle</td> <td>°</td> <td>Yaw orientation of the nacelle relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Pitch angle</td> <td>°</td> <td>Rotor blade pitch as recorded in the turbine SCADA</td> </tr> <tr> <td>Rotor speed</td> <td>rpm</td> <td>Rotor speed in rotations per minute as recorded in the turbine SCADA</td> </tr> <tr> <td>Power</td> <td>kW</td> <td>Active power of the turbine as recorded in the turbine SCADA</td> </tr> </tbody> </table> <p><strong>Table 2. </strong>List of provided SCADA parameters</p> <p> </p> <p>A summary of the selected intervals and relevant corresponding scada parameters is given in <strong>Tab 3</strong>.</p> <table> <tbody> <tr> <td><strong>Scenario </strong></td> <td><strong>T1 (UTC)</strong></td> <td><strong>T2 (UTC) </strong></td> <td><strong>Windspeed</strong></td> <td><strong>RPM </strong></td> <td><strong>Pitch </strong></td> </tr> <tr> <td>Parked</td> <td> <p>03/07 01:30</p> </td> <td> <p>03/07 03:30</p> </td> <td>< 4.5 m/s</td> <td>~1</td> <td>~18 °</td> </tr> <tr> <td>Rated</td> <td> <p>05/07 22:30</p> </td> <td> <p>06/07 00:30 </p> </td> <td>~15 m/s</td> <td>10.5</td> <td>8.1°</td> </tr> </tbody> </table> <p><strong>Table 3. </strong>Selected data intervals and relevant scada parameters</p> <p> </p> <h1><em><strong>2. Included in this version </strong></em></h1> <h2><em><strong>2.1 Version - 0.1.0</strong></em></h2> <ul> <li>Relevant Design information can be found in: <ul> <li>Geometry data for NRT-WTG: "WILLOW-Geometry_v4.xlsx"</li> <li>Best estimate soil profile: "WILLOW-BE_soil_profile.xlsx"</li> </ul> </li> <li>Acceleration, strain and scada data can be found in the following parquet files: <ul> <li>Measurement data for the parked case: "NRT-WTG_Parked.parquet.gz"</li> <li>Measurement data for the rated case: "NRT-WTG_Rated.parquet.gz"</li> </ul> </li> </ul> <p> </p> <h1><em><strong>3. Importing parquet files </strong></em></h1> <p>To import the measurement data into Python it is recommended to use pandas:</p> <pre>import pandas as pd<br># Read Parquet file with Pandas: relative_file_path = '<a href="../api/records/11093262/draft/files/NRT-WTG_Parked.parquet.gz/content" target="_blank" rel="noopener noreferrer">NRT-WTG_Parked.parquet.gz</a>' data = pd.read_parquet(relative_file_path ) <br><br>Once the dataframe has been imported, the users can process/re-arrange the raw data according the their needs; it should be noted that the imported dataframe contains NAN values - these are caused by the different sampling rates of the provided signals. </pre>
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
<p>This spatio-temporal dataset contains capacity factors timeseries for locations on a grid with 50km edge length in Europe. The data is resolved in one hour timesteps and comprises the years 2000--2016. It has been generated using <a href="https://www.renewables.ninja">Renewables.ninja</a> and is based on MERRA-2 reanalysis data. For each of the ~2700 onshore location, it contains one time series for onshore wind turbines and five time series for PV installations with different orientations and tilts. PV time series exist for (1) installations on open fields, (2) installations on all possible rooftops, (3) south-facing and flat rooftops, (4) east- and west-facing rooftops, (5) north-facing rooftops. For each of the ~2800 offshore location there is one timeseries for offshore wind turbines.</p> <p>Two GeoTIFF files contain spatial information of onshore and offshore locations. For each of the three technologies -- onshore wind, offshore wind, and PV -- there is one NetCDF file determining the temporal dimension and containing the data. The GeoTIFF and NetCDF files are linked through unique IDs for all locations.</p> <p>This data serves as input data to euro-calliope, a model of the European electricity system.</p> <p>The following parameters have been used to generate the timeseries:</p> <pre><code>resolution-grid: 50 # [km^2] corresponding to MERRA resolution pv-performance-ratio: 0.9 hub-height: onshore: 105 # m, median hub height of V90/2000 in Europe between 2010 and 2018 offshore: 87 # m, median hub height of SWT-3.6-107 in Europe between 2010 and 2018 turbine: onshore: "vestas v90 2000" # most built between 2010 and 2018 in Europe offshore: "siemens swt 3.6 107" # most built between 2010 and 2018 in Europe</code></pre> <p>CHANGELOG:</p> <p>Version 3 (2022-05-18)</p> <p>* Update spatial scope to include Iceland and its offshore EEZ.<br> * Update temporal scope to include 2017 and 2018.</p> <p>Effect of increasing spatial scope is a slight change in the spatial position of the data points.</p> <p>Version 2 (2020-06-18)</p> <p>* Add time series for rooftop PV with different orientations.</p>
Frøya wind data
<p>Herewith we present the dataset of wind measurements from a Skipheia meteorological station on the island of Frøya on the western coast of Norway, Trondelag.</p> <p>The site represents an exposed coastal wind climate with open sea, land and mixed fetch from various directions. UTM-coordinates of the Met-mast: 8.34251 E and 63.66638 N.</p> <p>Presented data were gathered between years 2009-2015;</p> <p>Hardware summary: 6 pairs of 2D sonic anemometers at 10, 16, 25, 40, 70, 100 m above the ground, independent temperature measurements at the same heights and near the ground; pressure and relative humidity from local meteostation (Sula, 20 km away).</p> <p>Database summary: approx. 180 000 of 10 min data samples of full data recovery. Wind speed and direction, temperature, pressure & relative humidity (from a nearby meteostation).</p> <p>Data description: Two data files of different formats are available: a ‘*.txt’ comma-separated values file and a native MATLAB ‘*.mat’ file. Both contain the same data, starting with the first column: timestamp, wind speed (m/s, columns WS1-WS12) for 6 anemometers pairs, wind direction (360 deg, columns WD1-WD12) for 6 anemometers pairs, temperature at 0.2 m (AT0), temperatures at levels of wind measurement (deg C, AT1-AT6), data from nearby meteostation Sula, pressure (hPa, PressureSula), relative humidity (%, RelHumSula), temperature (deg C, TempSula), wind direction (360 deg, WDSula) and wind speed (m/s, WSSula). Columns have headers describing the data (first row).</p> <p>Detailed site description with wind climate description can be found in attached analysis: Site analysys.pdf.</p> <p>Additional information and analysis can be found in listed below works, using data from Frøya site, or nearby sites:</p> <p><strong> </strong>Møller, M., Domagalski, P., and Sætran, L. R.: Comparing Abnormalities in Onshore and Offshore Vertical Wind Profiles, Wind Energ. Sci. https://wes.copernicus.org/articles/5/391/2020/ </p> <p>IEA Wind TCP Task 27 Compendium of IEA Wind TCP Task 27 Case Studies, Technical Report, Prepared by Ignacio Cruz Cruz, CIEMAT, Spain Trudy Forsyth, WAT, United States, October 2018; Chapter 1.8. <a href="https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0">https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0</a></p> <p>Domagalski, P., Bardal, L. M., & Satran, L. Vertical Wind Profiles in Non-neutral Conditions-Comparison of Models and Measurements from Froya. <em>Journal of Offshore Mechanics and Arctic Engineering,</em> doi: 10.1115/1.4041816, <a href="http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3">http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3</a></p> <p>Mathias Møller , Piotr Domagalski and Lars Roar Sætran, Characteristics of abnormal vertical wind profiles at a coastal site, <em>Journal of Physics: Conference Series</em>, IOPscience, under review (Feb 2019), DeepWind2019 conference poster available at: <a href="https://www.sintef.no/globalassets/project/eera-deepwind-2019/posters/c_moller_a4.pdf">https://www.sintef.no/globalassets/project/eera-deepwind-2019/posters/c_moller_a4.pdf</a></p> <p>Bardal, L. M., Onstad, A. E., Sætran, L. R., & Lund, J. A. (2018). Evaluation of methods for estimating atmospheric stability at two coastal sites. <em>Wind Engineering</em>, 0309524X18780378, <a href="https://doi.org/10.1177%2F0309524X18780378">https://doi.org/10.1177/0309524X18780378</a></p> <p>Bardal, L. M., & Sætran, L. R. (2016, September). Spatial correlation of atmospheric wind at scales relevant for large scale wind turbines. In <em>Journal of Physics: Conference Series</em> (Vol. 753, No. 3, p. 032033). IOP Publishing, doi:10.1088/1742-6596/753/3/032033, <a href="https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf">https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf</a></p> <p>Bardal, L. M., & Sætran, L. R. (2016). Wind gust factors in a coastal wind climate. <em>Energy Procedia,</em> 94, 417-424, <a href="https://doi.org/10.1016/j.egypro.2016.09.207">https://doi.org/10.1016/j.egypro.2016.09.207</a></p>
Vibration-based Monitoring of a Small-scale Wind Turbine Blade Under Varying Climate Conditions. Part I: An Experimental Benchmark
<p>This repository contains all publicly available data related to the experimental part of <a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2660">Sonkyo-Benchmark</a>. The data of each experimental case (R, A, B, C, D, E, F, G, H, I, J, K, L) and temperature point (-15, -10, -5, 0, 5, 10, 15, 20, 25, 30, 35, 40) are stored in a zip file named "Case_<em>X</em>_(<em>T</em>)", where <em>X</em> denotes the case label and <em>T</em> refers to the temperature value. Each file "Case_<em>X</em>_(<em>T</em>).zip" contains two folders "Case_<em>X</em>_(<em>T</em>)_1" and "Case_<em>X</em>_(<em>T</em>)_2", wherein the test results from the two sensor layouts are stored. </p>
A blind test on wind turbine wake modelling based on wind tunnel experiments: Phase I – The benchmark case
<p>This data set ("Data files.zip") contains the wind tunnel measurement data from Phase I of the Blind test on wind turbine wake modelling based on wind tunnel experiments organised during the TWEET-IE project (www.tweet-ie.eu).</p> <p>This updated version <strong>replaces</strong> the older versions 1.0.0 (https://doi.org/10.5281/zenodo.10566401), 1.1.0 (https://doi.org/10.5281/zenodo.11370112), 2.0 (https://doi.org/10.5281/zenodo.12188194) and 2.1 (https://doi.org/ 10.5281/zenodo.13918935). In comparison to the previous version 2.1 the data documentation has been updated to follow the template of the TWEET-IE project documents, indicating the Grant Agreement Number with the European Union and the Call Topic of the project.</p> <p>All tests were conducted in the closed-loop, low-speed boundary layer wind tunnel of the Chair of Aerodynamics and Fluid Mechanics at Technische Universität München (TUM). The experiments concerned two wind turbines, aligned with the flow, one downstream of the other, at a distance of 5 diameters. For Phase I, no control was applied to the wind turbine models, which were operating at constant RPM. The turbine models, designed and manufactured by TUM, were instrumented with multiple sensors and actuators and had a diameter of 1.1M. Measurements include velocity, power and loads on the turbines. A detailed description of the experimental set up can be found in the accompanying document ("Data documentation.pdf"). </p> <p>File "Submission procedure.zip" includes the format description and the templates of the output data that should be submitted by the participants in the blind test comparison.</p>
Wind Stress, Wind Stress Curl, and Upwelling Velocities in the Northwest Atlantic (80-45W, 30-45N) during 1980-2019
<p>This dataset contains three netcdf files that pertain to monthly, seasonal, and annual fields of surface wind stress, wind stress curl, and curl-derived upwelling velocities over the Northwest Atlantic (80-45W, 30-45N) covering a forty year period from 1980 to 2019. Six-hourly surface (10 m) wind speed components from the Japanese 55-year reanalysis (JRA-55; Kobayashi et al., 2015) were processed from 1980 to 2019 over a larger North Atlantic domain of 100W to 10E and 10N to 80N. Wind stress was computed using a modified step-wise formulation, originally based on (Gill, 1982) and a non-linear drag coefficient (Large and Pond, 1981), and later modified for low speeds (Trenberth et al., 1989). See Gifford (2023) for more details. </p> <p>After the six-hourly zonal and meridional wind stresses were calculated, the zonal change in meridional stress (curlx) and the negative meridional change in zonal stress (curly) were found using NumPy’s gradient function in Python (Harris et al., 2020) over the larger North Atlantic domain (100W-10E, 10-80N). The curl (curlx + curly) over the study domain (80-45W, 10-80N) is then extracted, which maintain a constant order of computational accuracy in the interior and along the boundaries for the smaller domain in a centered-difference gradient calculation. </p> <p>The monthly averages of the 6-hour daily stresses and curls were then computed using the command line suite climate data operators (CDO, Schulzweida, 2022) monmean function. The seasonal (3-month average) and annual averages (12-month average) were calculated in Python using the monthly fields with NumPy (NumPy, Harris et al., 2020). </p> <p>Corresponding upwelling velocities at different time-scales were obtained from the respective curl fields and zonal wind stress by using the Ekman pumping equation of the study by Risien and Chelton (2008; page 2393). Please see Gifford (2023) for more details. </p> <p>The files each contain nine variables that include longitude, latitude, time, zonal wind stress, meridional wind stress, zonal change in meridional wind stress (curlx), the negative meridional change in zonal wind stress (curly), total curl, and upwelling. Units of time begin in 1980 and are months, seasons (JFM etc.), and years to 2019. The longitude variable extends from 80W to 45W and latitude is 30N to 45N with uniform 1.25 degree resolution. </p> <p>Units of stress are in Pascals, units of curl are in Pascals per meter, and upwelling velocity is described by centimeters per day. The spatial grid is a 29 x 13 longitude x latitude array. </p> <p>Filenames: </p> <p><strong>monthly_windstress_wsc_upwelling.nc</strong>: 480 time steps from 80W to 45W and 30N to 45N.</p> <p><strong>seasonal_windstress_wsc_upwelling.nc</strong>: 160 time steps from 80W to 45W and 30N to 45N.</p> <p><strong>annual_windstress_wsc_upwelling.nc</strong>: 40 time steps from 80W to 45W and 30N to 45N.</p>
Monthly and Annual contour lines of the zero and the positive maximum of the Wind Stress Curl over Western North Atlantic during 1980-2019 and the Gulf Stream path during 1993-2019.
<p>This dataset includes multiple fields: (i) files for monthly and annual fields for the max curl line and the zero curl line at 0.1 degree longitudinal resolutions; (ii) files for monthly and annual GS path obtained from Altimetry and originally processed by Andres (2016) at 0.1 degree longitudinal resolution. The maximum curl line (MCL) and the zero curl line (ZCL) calculations are briefly described here and are based on the original wind data (at 1.25 x 1.25 degree) provided by the Japanese reanalysis (JRA-55; Kobayashi et al., 2015) and available at https://zenodo.org/record/8200832 (Gifford et al. 2023). For details see Gifford, 2023. </p> <p>The wind stress curl (WSC) fields used for the MCL and ZCL calculations extend from 80W to 45W and 30N to 45N at the 1.25 by 1.25-degree resolution. The MCL is defined as the maximum WSC values greater than zero within the domain per 1.25 degree longitude. As such, it is a function of longitude and is not a constant WSC value unlike the zero contour. High wind stress curl values that occurred near the coast were not included within this calculation. After MCL at the 1.25 resolution was obtained the line was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution. The smoothed MCL lines at 0.1 degree resolution are provided in separate files for monthly and annual averages (2 files). Similarly, 2 other files (monthly and annual) are provided for the ZCL. </p> <p>Like the MCL, the ZCL is a line derived from 1.25 degree longitude throughout the domain under the condition that it's the line of zero WSC. The ZCL is constant at 0 and does not vary spatially like the MCL. If there are more than one location of zero curl for a given longitude the first location south of the MCL is selected. Similar to the MCL, the ZCL was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution. </p> <p>The above files span the years from 1980 through 2019. So, the monthly files have 480 months starting January 1980, and the annual files have 40 years of data. The files are organized with each row being a new time step and each column being a different longitude. Therefore, the monthly MCL and ZCL files are each 480 x 351 for the 0.1 resolution data. Similarly, the annual files are 40 x 351 for the 0.1 degree resolution data. </p> <p><strong>Note that the monthly MCLs and ZCLs are obtained from the monthly wind-stress curl fields. The annual MCLs and ZCLs are obtained from the annual wind-stress curl fields.</strong></p> <p>Since the monthly curl fields preserves more atmospheric mesoscales than the annual curl fields, the 12-month average of the monthly MCLs and ZCLs will not match with the annual MCLs and ZCLs derived from the annual curl field. The annual MCLs and ZCLs provided here are obtained from the annual curl fields and representative metrics of the wind forcing on an annual time-scale. </p> <p>Furthermore, the monthly Gulf Stream axis path (25 cm isoheight from Altimeter, reprocessed by Andres (2016) technique) from 1993 through 2019 have been made available here. A total of 324 monthly paths of the Gulf Stream are tabulated. In addition, the annual GS paths for these 27 years (1993-2019) of altimetry era have been put together for ease of use. The monthly Gulf Stream paths have been resampled and reprocessed for uniqueness at every 0.1 degree longitude from 75W to 50W and smoothed with a 100 km (10 point) running average via matlab. The uniqueness has been achieved by using Consolidator algorithm (D’Errico, 2023). </p> <p>Each monthly or annual GS path has 251 points between 75W to 50W at 0.1 degree resolution. </p>
Metabolism dataset: one year of high-frequency temperature, dissolved oxygen, wind, photosynthetically active radiation observations and low-frequency nutrient data for 58 lakes in the Global Lake Ecological Observatory Network
Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We compiled data from 58 diverse and globally distributed and mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP and ER derived from those data. In addition, the data package includes median in-lake and stream concentrations of dissolved organic carbon and total phosphorus for a subset of 18 of those lakes.
Bonanza Creek LTER: Hourly Wind Speed and Direction at Various Heights from 1988 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
Hourly wind data from sefveral sites with the the Caribou Poker Creeks Research Watershed. Wind data should be used cautiously during winter months due to ice and snow build up on sensors.
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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.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.