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1,855 results for “winds”
Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bioindicators in San Jose, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Indicator for the current and future socio-ecological burden caused by the expansion of wind energy in German districts
<p>The data table lists the calculated present burden (IST-Belastungsgrad) based on the year 2014 and the maximum possible burden (MAX-Belastungsgrad) on the population caused by the further expansion of wind energy. The so-called burden level is calculated accounting for the area occupied by wind turbines, the total area of a district and the population density. Additionally the table holds data on possible future burden levels based on two scenarios for the year 2050. Each district can be identified by its key, "Regionalschlüssel", and corresponding geo data (EPSG: 25832) of the administrative area provided by the Federal Agency for Cartography and Geodesy: © GeoBasis-DE / BKG 2014 (data was changed).</p> <p>The dataset was created in the context of the interdisciplinary research project VerNetzen and is described in detail in the final project report: VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., pp. 98-118, 143-145.</p> <p><strong><em>Deutsch:</em></strong></p> <p>Die Tabelle enthält u.a. den derzeitigen Belastungsgrad, festgestellt für das Jahr 2014, und den maximal möglichen Belastungsgrad je Landkreis. Der Belastungsgrad ist ein Indikator für die durch den Zubau von Windenergie betroffene Bevölkerung und berechnet sich aus der Gesamtfläche eines Landkreises, der für die Windenergie genutzten Fläche und der Bevölkerungsdichte. In der Tabelle sind ebenfalls mögliche zukünftige Belastungsgrade auf Grundlage zweier Projektszenarien für das Jahr 2050 enthalten. Die jeweiligen Landkreise können mit dem Regionalschlüssel oder den geographischen Daten (EPSG: 25832) des Bundesamtes für Kartographie und Geodäsie zugeordnet werden: © GeoBasis-DE / BKG 2014 (Daten verändert).</p> <p>Der Datensatz ist im Kontext des interdisziplinären Forschungsprojekts VerNetzen entstanden und ist ausführlich im Projektabschlussbericht beschrieben: VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., S.98-118, S.143-145.</p> <p> </p> <p> </p>
Indicator for the current and future socio-ecological burden caused by the expansion of wind energy in German districts - auxiliary values
<p>The table contains the population and the size of the total area for each German district as of 2013. Furthermore it contains the size of those areas per district, that potentially could be used for wind energy.</p> <p>The data on the population is provided by the Federal Statistical Office and the statistical Offices of the Länder: © Federal Statistical Office and the statistical Offices of the Länder, Regionaldatenbank Deutschland, December 2014, Datenlizenz by-2-0 (https://www.govdata.de/dl-de/by-2-0) (data was changed). The total district area is derived from geo data provided by the Federal Agency for Cartography and Geodesy: © GeoBasis-DE / BKG 2014 (data was changed).</p> <p>For further information on potential areas see VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., pp. 105-109.</p> <p><em><strong>Deutsch:</strong></em></p> <p>Die Tabelle umfasst die Bevölkerungsanzahl und Flächengröße je deutschem Landkreis für das Jahr 2013. Außerdem ist die Größe jener Fläche angegeben, die potentiell für die Windenergie genutzt werden könnte.</p> <p>Die Bevölkerungszahlen werden von den Statistischen Ämtern des Bundes und der Länder zur Verfügung gestellt: © Statistische Ämter des Bundes und der Länder, Regionaldatenbank Deutschland, Dezember 2014, Datenlizenz by-2-0 (https://www.govdata.de/dl-de/by-2-0) (Daten geändert). Die Landkreisflächen werden auf Grundlage von Geodaten des Bundesamtes für Kartographie und Geodäsie berechnet: © GeoBasis-DE / BKG 2014 (Daten geändert).</p> <p>Für weitere Informationen bzgl. der Potentialflächen siehe VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., S. 105-109.</p>
Dataset for article: Integer programming for optimal yaw control of wind farms
<div> <p>This is the dataset for the article "Integer programming for optimal yaw control of wind farms". We provide the integer programs (lp-files) and corresponding solver log files for each case of our series of experiments. Submission of manuscript: September 2024 (v1). Major revision of manuscript: February 2025 (v2). Minor revision of manuscript: April 2025 (v3).</p> </div>
Behavior of Telecommunication Lattice Towers to Thunderstorm Winds
<h1>Dataset Description</h1> <p>This work aims at closing the knowledge gap between the wind field monitoring of real structures and wind tunnel testing by simulating real atmospheric boundary layer (ABL) and thunderstorm events in the Wind Energy, Environment, Engineering (WindEEE) research facility. The real events were acquired by a wind and structural monitoring system installed on a 50 m telecommunication lattice tower located in Sânnicolau Mare, Romania. The study reproduces complex downburst wind systems, in a controlled laboratory environment, like those observed in the field monitoring. The wind-induced response of two typical telecommunication lattice towers of different heights, i.e. 50 m and 90 m is investigated by means of both aerodynamic and aeroelastic tests. The acquired data will allow to compare and calibrate wind tunnel test results with field monitoring structural data measured during intense ABL and thunderstorm winds by the Sânnicolau Mare monitoring system. This extends the wind field and aerodynamic database which can be further utilized for codification purposes and for validating numerical and analytical models. The proposed work aims to advance code-based design of telecom lattice towers to thunderstorm winds.</p> <p>This work involved three areas of testing – wind field characterization to determine the best settings to match full scale / realistic wind loads, aeroelastic tests of both a 90m and 50m full towers (1:100 scale) using strain gauges as well as force balances, and a 1:10 sectional model of the top of the 50m tower to study the aerodynamics of the tower both with and without ancillaries added</p> <p> </p> <h2>S0. Documentation</h2> <p>Contains information documents regarding the instrumentation specifications, test plan, and other important diagrams.</p> <p> </p> <h2>S1. Wind Profile Stand</h2> <p>A vertical stand of 11 TFI cobra probes measured wind field data at heights of 50, 100, 150, 200, 300, 400, 500, 600, 700, 800, and 900 mm from the ground surface. For each experiment described below, the Cobra Probe stand was located in select locations to capture the near-surface flow.</p> <h3>E1. ABL Profile Development</h3> <p>The 60-fan wall located on one side of the hexagonal shaped WindEEE test chamber was used to generate the various ABL flows for this experiment. Each fan on this wall is individually controlled allowing a versatile range of ABL flow conditions. In addition, the ABL flow turbulence and boundary layer gradient were fine-tuned using roughness elements and spires.</p> <h3>E2. Downburst Profile Development</h3> <p>An impinging-jet style downburst is generated at the WindEEE dome through the release of pressure from a plenum above the testing chamber. The plenum is pressurized with six large fans for an adjustable amount of time or until a certain pressure is achieved. The built pressure then releases through a bell mouth with variable orifice sizes, D, to achieve a rapid downdraft of air. Given WindEEE’s unique 3-D test chamber, measurements were taken at various angles, theta, and radius, r, from the centre of the bell mouth. Commonly, these measurement locations are indicated by a non-dimensional parameter, r/D, and the angle, ϑ (theta).</p> <h3>E3. Combined Downburst and ABL Profile Development</h3> <p>With the unique capability of the WindEEE test chamber, profile measurements were taken while operating various combinations of the ABL and downburst-like flow configurations. The natural occurrence of a downburst in a storm acted as a driver for this segment of profile development.</p> <h3>E4. Downburst with Radial Trip Profile Development</h3> <p>For this test, wooden trips about 15cm tall were evenly placed all around the edge of the turntable. The downburst-like flow was generated similar to the downburst profile development section.</p> <p> </p> <h2>S2. 1-100 Scaled 50m Lattice Tower Model (T50)</h2> <p>This specimen included a triangular lattice tower of 50m built to a scale of 1:100. The model was made from a mixture of stainless-steel tubing for the spines and bracing elements while the joints were made from 3D printed PolyJet material. The models were fastened to a steel base measuring 14 by 14cm that is 1.27cm thick. A gradual ramp that sloped at a 1:12 angle was placed around this base extending 30.48cm. The model was placed on a mobile setup to be placed in experiment specific locations, as described below.</p> <h3>E1. ABL Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 1.</p> <h3>E2. Downburst Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 2.</p> <h3>E3. Combined Downburst and ABL Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 3.</p> <h3>E4. Downburst with Radial Trip Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 4.</p> <p> </p> <h2>S3. 1-100 Scaled 90m Lattice Tower Model (T90)</h2> <p>This specimen included a triangular lattice tower of 50m built to a scale of 1:100. The model was made from a mixture of stainless-steel tubing for the spines and bracing elements while the joints were made from 3D printed PolyJet material. The models were fastened to a steel base measuring 14 by 14cm that is 1.27cm thick. A gradual ramp that sloped at a 1:12 angle was placed around this base extending 30.48cm. The model was placed on a mobile setup to be placed in experiment specific locations, as described below.</p> <h3>E1. ABL Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 1.</p> <h3>E2. Downburst Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 2.</p> <h3>E3. Combined Downburst and ABL Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 3.</p> <p> </p> <h2>S4. Aerodynamic lattice tower sectional model</h2> <p>This specimen included a 1 m tall section of the top of the 50 m tower at a scale of 1:10. It is constructed of brass, steel, 3D printed nylon, PolyJet 3-D printed material, and steel screws. The antennas, railing and central ladder are all removable. The model was mounted on a rig made up of a 12.7 cm diameter steel pipe and a wooden base plate. The rig stands 60cm tall so that the model is above the sheared surface flow. The base and top plates of the rig were 90cm in diameter. The experiments were performed with three different configurations of the model.</p> <h3>E1. Aerodynamics of the Bare structure without Top Plate</h3> <p>During this test, the model was measured as a bare structure (no antennas, ladders, or other components). The model was tested under ABL flow to outline the aerodynamic effects of the baseline model.</p> <h3>E2. Aerodynamics of the Structure with Top Plate</h3> <p>During this test, a top plate hovered over the model for the entirety of the test program. This plate encourages 2-D flow properties in ABL flow to mimic aerodynamic properties seen in horizontal testing in traditional wind tunnels.</p> <h3>E3. Aerodynamics of the Structure with Ancillary Components</h3> <p>During this test, ancillary components including ladders, railing, and antenna were attached to the model. These items act to increase the frontal area of the model which are expected to change the aerodynamic properties of the model.</p> <p> </p> <p><strong>Note:</strong> Given the number of data files captured in this program, the files required to be uploaded in compressed '.zip' folders.</p>
Kelmarsh wind farm data
<p>This dataset contains:</p> <ul> <li>A kmz file for Kelmarsh wind farm in the UK (for opening in e.g. <a href="https://www.google.com/intl/en-GB/earth/versions/#earth-pro">Google Earth</a>)</li> <li>Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)</li> <li>10-minute SCADA and events data from the 6 Senvion MM92's at Kelmarsh wind farm, grouped by year from 2016 to end 2024, which was extracted from Cubico's secondary SCADA system (Greenbyte). Note not all signals are available for the entire period</li> <li>Data mappings from primary SCADA to csv signal names</li> <li>Site substation/PMU meter data where available for the same period</li> <li>Site fiscal/grid meter data where available for the same period</li> </ul> <p>The dataset has been released by <a href="https://www.cubicoinvest.com/">Cubico Sustainable Investments Ltd</a> under a <a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a> open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data.</p> <p>Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry.</p> <p>If you have any questions or want to discuss open data and this or other initiatives, please either:</p> <ol> <li>Contact me on <a href="https://www.linkedin.com/in/charlie-plumley-8b8b613b">LinkedIn</a>, and I will endeavour to help</li> <li>Or in the <a href="https://www.wedowind.ch/">WeDoWind</a> platform in the ODE space</li> </ol> <p>I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager and team for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way.</p> <p>You can also access data from Penmanshiel wind farm <a href="https://doi.org/10.5281/zenodo.5946807">here</a>.</p>
Penmanshiel wind farm data
<p>This dataset contains:</p> <ul> <li>A kmz file for Penmanshiel wind farm in the UK (for opening in e.g. <a href="https://www.google.com/intl/en-GB/earth/versions/#earth-pro">Google Earth</a>)</li> <li>Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)</li> <li>10-minute SCADA and events data from the 14 Senvion MM82's at Penmanshiel wind farm, grouped by year from 2016 to end of 2024, which was extracted from our secondary SCADA system (Greenbyte). Note not all signals are available for the entire period, and there is no turbine WT03</li> <li>Data mappings from primary SCADA to csv signal names</li> <li>Site substation/PMU meter data where available for the same period</li> <li>Site fiscal/grid meter data where available for the same period</li> </ul> <p>The dataset has been released by <a href="https://www.cubicoinvest.com/">Cubico Sustainable Investments Ltd</a> under a <a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a> open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data.</p> <p>Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry.</p> <p>If you have any questions or want to discuss open data and this or other initiatives, please either:</p> <ol> <li>Contact me on <a href="https://www.linkedin.com/in/charlie-plumley-8b8b613b">LinkedIn</a>, and I will endeavour to help</li> <li>Or in the <a href="https://www.wedowind.ch/">WeDoWind</a> platform in the ODE space</li> </ol> <p>I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager and team for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way.</p> <p>You can also access data from Kelmarsh wind farm <a href="https://doi.org/10.5281/zenodo.5841833">here</a>.</p>
Numerical simulation of the equatorial plasma bubble: the effect of seeding by the vertical winds and random background noise perturbations.
<p>A wide variety of small-amplitude waves widely exist in the ionosphere and have significant effects on the evolution of equatorial plasma bubbles. In this paper, we simulated equatorial plasma bubbles (EPB) seeded by vertical neutral wind perturbations with wavelengths of 125 km and 250 km, and compared the morphology characteristics of plasma bubble structures with those under random noise perturbations in the background density. The numerical results showed that both vertical winds and random background noise perturbations can contribute to the growth of plasma bubbles, and the perturbations under additional random background noise can promote the growth of the plasma bubble structures faster. Additionally, several processes of the nonlinear behavior of bifurcated EPB structures, including bifurcation, pinching, and small-scale turbulent structures, were successfully obtained. Our simulation captured supersonic flows within the low-density plasma structures characterized by vertical velocities of about 1.5 km/s, which is consistent with experimental studies found in the literature.</p>
A dataset of seabird collision and displacement vulnerability factors relatively to marine wind farms in Portugal
<p>The implementation of marine wind farms has grown considerably along northern European's northern Atlantic coasts (e.g. Baltic and North Sea) and a boom in these infrastructures is expected to take place along Europe's entire Atlantic and Mediterranean coasts. Accordingly, the Portuguese government has recently proposed priority sites for the construction of wind farms along the mainland coast. We used sensitivity mapping (Garthe & Hüppop, 2004) to assess which areas along the Portuguese coast are most sensitive for seabirds and to what extent the proposed sites for wind farm construction overlap with these areas.</p><p>This dataset contains the base data to estimate a seabird Species Sensitivity Index (SSI) (following Bradbury et al., 2014, Certain et al., 2015), including scores for 11 species-specific ecological and behavioural factors related with seabird species' (i) vulnerability to collision with wind farms (4 factors), (ii) vulnerability to displacement due to disturbance by wind farms and associated maintenance (3 factors), and (iii) conservation status (4 factors). </p><p>We reviewed the literature to mine and compile data on these factors for 34 seabird species that regularly occur along the Portuguese mainland coast. We updated factor scores, particularly for those factors that have been studied in greater detail in recent years using tracking technologies (Clairbaux & Jessopp, 2021). However, in many cases empirical data were unavailable and we used the scores presented in previous sensitivity mapping studies (Garthe & Hüppop, 2004; Bradbury et al., 2014; Certain et al., 2015; Wade et al., 2016; Serratosa & Allinson, 2022).</p>
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF
<p>This repository contains namelists needed to reproduce the WRF(V4.4) simulations analyzed in "Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF"</p>
Supplementary material (part 2) for "On the effect of tributary valleys on thermally driven winds in the main valley: a case study in the Inn Valley"
<p>Part 2 of the supplementary material for the master's thesis "On the effect of tributary valleys on thermally driven winds in the main valley: a case study in the Inn Valley." (Deidda 2023, available at <a href="https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-134436">https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-134436</a>). The supplementary material consists in two parts: part 1 includes scripts, datasets, model setup files and figures (available at <a href="https://zenodo.org/records/8089010">https://zenodo.org/records/8089010</a>), while part 2 includes the model output needed to create the figures (see description below).</p><p>This directory contains part of the model output used for the thesis. The model used is WRF-ARW version 4.4 (Skamarock et al. 2021). Information on the model setup is available in the thesis, Section 2.2. For storage limitations, only the files needed to create the graphs presented in the thesis are available.</p><p>The file format is <i>type_domain_date.nc </i>where <i>domain</i> is "d01" or "d02" (respectively for the outer or inner domain, see Section 2.2 in the thesis), while <i>type</i> is "wrfout", "mean_out", or "Averaged", where:</p><ul><li>"wrfout" is the instantaneous WRF output;</li><li>"mean_out" is the time-averaged WRF output, computed using the fork <a href="https://github.com/matzegoebel/WRFlux">WRFlux</a>.</li><li>"Averaged" is a combination of time-averaged and instantaneous outputs. These files were created using the the script <i>Create_intermediate_files.py, </i>available in the <a href="https://zenodo.org/records/8089010">software directory </a>(in Scripts.zip). These files were computed to have a lighter and WRF-like formatted data containing the averaged fields needed for the analysis.</li></ul>
Vertical profiles of air temperature, relative humidity, wind speed and direction observed using UAV over the Mukhrino peatland in June 2022
<p>Vertical profiles of air temperature and relative humidity were measured using the iMetXQ2 sensor onboard DJI Phantom 4 quad-copter; vertical profiles of wind speed and direction were obtained from the Phantom 4 flight logs as produced by the DJI proprietary algorithm. </p>
Wind Value Summer Seminar 2023 Kevin Campbell on Compliance Bonds
<p>A 20 minute video on MP4 format of a talk given by Kevin Campbell at the Wind Value, Industry Meets Academia Summer Seminar, held on 31st August 2023 in the Environmental Research Institute, University College Cork, Ireland.</p>
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></p>
zEPHYR - Audio files of recorded and auralized wind turbine noise
<p>Audio files associated with the publication "Wind farm noise prediction and auralization", Andrea P. C. Bresciani, Julien Maillard, Arthur Finez, submitted to Acta Acustica in Dec. 2023.</p> <p>Audio 1: Auralized noise for OC1 and SB1<br>Audio 2: Auralized noise for OC1 and SB2<br>Audio 3: Auralized noise for OC1 and SB3<br>Audio 4: Auralized noise for OC2 and SB1<br>Audio 5: Auralized noise for OC2 and SB2<br>Audio 6: Auralized noise for OC2 and SB3<br>Audio 7: Recorded noise for OC1 and SB1<br>Audio 8: Recorded noise for OC1 and SB2<br>Audio 9: Recorded noise for OC1 and SB3<br>Audio 10: Recorded noise for OC2 and SB1<br>Audio 11: Recorded noise for OC2 and SB2<br>Audio 12: Recorded noise for OC2 and SB3<br>Audio 13: Auralized noise for OC2 and SB2 without amplitude fluctuations</p>
Data for "Disrupted connectivity within a metapopulation of a wind-pollinated declining conifer Taxus baccata L."
<p>A spreadsheet contains microsatellite genotypes and population coordinates necessary for estimating seed and pollen migration rates. In addition, a spreadsheet contains detailed individual data necessary for parentage analysis.</p> <p>For more details, see:</p> <p>Chybicki IJ, Robledo-Arnuncio JJ, Bodziarczyk J, Widlak M, Meyza, K, Oleksa A, Ulaszewski B (2024) Disrupted connectivity within a metapopulation of a wind-pollinated declining conifer, Taxus baccata L. Forest Ecosystems 100240 (https://www.sciencedirect.com/science/article/pii/S2197562024000769)</p>
Wind and SOLAR RES predicted production data for Crete and Peloponnese - ONENET WP8
<p>WP8 aimed at the development and implementation of a web based app that enhances Active Power Management necessary for coordination of a TSOs and DSOs, using AI methods and cloud calculation engines that was tested in Peloponnese and Crete regions. Full description of the scope and results of WP8 Greek demo can be found in the relative deliverable <em>D8.2: Development and implementation of the “F-Channel” platform</em> (https://www.onenet-project.eu//wp-content/uploads/2023/10/OneNet_D8.2_V1.0.pdf). For purpose of this project similar, historical weather data in 1 hour resolution have been used in order to obtain behavior patterns of climatic parameters (daily, monthly, season) throughout region of interest. For this purpose various ERA5 climatic datasets has been used and AI algorithms applied in combination with terrain orography data. Modeled results was <strong>compared </strong>with <strong>operational data </strong>from TSO/DSO and appropriate model calibration has been provided based on deep learning AI algorithms.</p> <p>The data for Wind power plant modelled production is given in file <<a href="../api/records/10848817/draft/files/wind_res_onenet_wp8.csv/content" target="_blank" rel="noopener noreferrer">wind_res_onenet_wp8.csv</a>> in the following columns <time> ; <aa> ; <pw> ; <ws> ; <wp_name> . Columns are related to: Hourly time, wind park code, power [MWh], wind speed [m/s] and wind park name, respectively.</p> <p>The data for Solar power plant modelled production is given in file <<a href="../api/records/10848817/draft/files/solar_onenet_wp8.csv/content" target="_blank" rel="noopener noreferrer">solar_onenet_wp8.csv</a>> in the following columns <time> ; <name> ; <pw> ; <ta> ; <ghi> . Columns are related to: Hourly time, solar park name, power in MWh, ambient temperature and Global Horizontal irradiance [W/m2], respectively.</p>
Source data for "Halving the North Sea's offshore wind energy carbon footprint"
<p>This dataset provides source data for the paper "Halving the North Sea’s offshore wind energy carbon footprint". It contains basic geographical factors, including wind speed, water depth, and distance from shore, and environmental impact intensities, including steel, Cu, and Al use, climate change, marine ecotoxicity, and marine eutrophication impacts. For more details, please refer to https://pubs.acs.org/doi/full/10.1021/acs.est.2c02183 and https://www.sciencedirect.com/science/article/pii/S1364032122004993. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.