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Fig. 2 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea

Fig. 2: (A) Central points chosen as reference for the meteorological conditions of the Aegean Sea (black dot in the middle of the basin). Wind time series during the 2008 (B) and 2009 (C) experiments.

Fig. 5 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea

Fig. 5: Same as Figure 4, but of the southern triplet drifters during the 2008 experiment: A) drifter a1; B) drifter a2 and C) drifter a3.

Fig. 1 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea

Fig. 1: Geographical references and deployment positions during 2008 (yellow dots) and 2009 (magenta dots). The bathymetry is saturated at - 600 m. SB: Singitikos Bay; SM: Samothraki Island; L: Lemnos Island; DS: Dardanelles Strait; AE: Agios Efstratios Island; S: Skyros Island; A: Andros Island; T: Tinos Island; MY: Mykonos Island; M: Milos Island.

Fig. 4 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea

Fig. 4: Six-hourly interpolated trajectories of the northern triplet drifters during the 2008 experiment superimposed on the bathymetry: A) drifter b1 (red curve) and drifter b2 (yellow curve) and B) drifter b3. The dots show the drifter position every 6 hours and the black arrows, indicating the direction of the drifters, are depicted every 5 days. See Table 1 for drifter attributes. Depth colourbar is the same as in Figure 1.

Fig. 8A in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea

Fig. 8A: Three-day drifter trajectories superimposed on the SST composite of the period 28-30 August 2008. The dot symbols correspond to the end of the 3-day trajectories.

Fig. 3 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea

Fig. 3: Drifter trajectories during the 2008 (A) and 2009 (B) experiments. The drifters of the northern triplets are indicated in magenta colour, while the drifters of the southern triplets are depicted in yellow colour. Depth colourbar is the same as in Figure 1.

opencc-by-4.0Jun 2024View details →

Wind and Wave Measurements in the Oslo Fjord

Measurements of surface waves were conducted with the aim of measuring waves in the capillary-gravity regime as part of a master's thesis. An in-house built sensor equipped with an IMU, which measures acceleration and angular velocity in the unit's frame of reference, was used for a period of 26 hours. The sensor has a length of 2.5 cm and a width of 2 cm. The setup included the preprogrammed logger, SparkFun OpenLog Artemis, to record the sensor data.  Wind measurements were also made during the same period using a commercial 3-cup anemometer, although wind direction was not recorded. The equipment was mounted on a jetty at Lindøya, in the inner Oslo Fjord.

opencc-zeroJun 2024View details →

Dataset: Global X Wind Energy ETF (WNDY) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-by-4.0Jun 2024View details →

Wind turbine condition monitoring dataset of Fraunhofer LBF

<h3>Fraunhofer wind turbine dataset  contains monitoring data from a 750 W wind turbine (WT), including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures of the structure, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings’ outer race, inner race and roller element have been implemented. The availability of time series data makes the dataset well suited for both machine learning and signal processing-based condition monitoring (CM) applications. The availability of heterogeneous sensors has created a dataset particularly suited for information fusion, data fusion, multi-sensor approaches, and holistic monitoring. Experiments were conducted in real-world conditions outside of a controlled laboratory environment, thereby introducing challenges such as variable rotor speed, noise, overloads, and other environmental factors. Consequently, the dataset is qualified for tasks involving uncertainty quantification and signal pre-processing. This document will detail the test equipment, experimental procedures, simulated damage cases, measurement parameters, data specifics, and preliminary analysis aimed at validating data quality.</h3> <h3>See the full data descriptor at: https://doi.org/10.1038/s41597-024-03934-5</h3>

Wind Value: Second Conference 2024 Situation in Italy Carla De Laurentis Video

Video 30 minutes 41 seconds, of the presentation by Carla De Laurentis on the topic " Factors shaping end-of-life decisions of ageing wind infrastructure in Italy". The presentation considers the issues behind repowering, life extension and decommissioning decisions in Italy. This took place at the second Wind Value conference on 29th May 2024 in the Ellen Hutchins Building, of the Environmental Research Institute of University College Cork, Ireland.

Wind Value: Second Conference 2024 Real Option Analysis Peter Deeney Video

Video 13mins 31 seconds, of the presentation by Peter Deeney on the topic " Planning is Optional". The presentation considers the preparatory work before a wind farm, to be a European call option. It also looks at the decisions at end-of-life to decommission, extend life or repower the wind farm. This took place at the second Wind Value conference on 29th May 2024 in the Ellen Hutchins Building, of the Environmental Research Institute of University College Cork, Ireland.

Plasma Parameters From Wind Mission Radio Observations Using Quasi-Thermal Noise Spectroscopy

Database of the high-resolution Velocity Diftribution Function (VDF) moments from Wind Thermal Noise Receiver (TNR) using  Quasi-Thermal Noise (QTN) Spectroscopy

opencc-by-4.0Feb 2024View details →

The effects of increased thermal insulation in timber-framed external walls with thin gypsum board as a wind barrier - Dataset

This dataset includes measured data from the field measurement of four timber-framed exterior wall constructions. All the walls were equipped with gypsum board wind barrier having a minimal thermal resistance. Insulation thicknesses of 150 mm and 300 mm, demonstrating a moderate and a very effective levels of thermal insulation, were compared. Wooden cladding and brick veneer were compared as façade materials. Measurements were done in a test building of Tampere University, Tampere, Finland. The coordinates of the campus are 61°27' N, 23°52’ E. Ground height at test building site is approximately 135 m above sea level. The site is rather protected area, with the modest wind and driving rain load.  The test was performed between 13 September 2020 and 30 November 2021.

opencc-by-4.0Mar 2024View details →

Dataset of historical hourly information of four european wind farms for wind energy forecasting and maintenance

If you use this dataset please cite this paper: Sánchez-Soriano, J.; Paniagua-Falo, P.J.; Gómez Muñoz, C.Q. Historical Hourly Information of Four European Wind Farms for Wind Energy Forecasting and Maintenance. Data 2025, 10, 38. <a href="https://doi.org/10.3390/data10030038" target="_blank" rel="noopener">https://doi.org/10.3390/data10030038</a> For an electric company, having an accurate forecast of the expected electrical production and maintenance from its wind farms is crucial. This information is essential for operating in various existing markets such as Iberian Energy Market Operator - Spanish Hub (OMIE in its Spanish acronym), Portuguese Hub (OMIP in its Spanish acronym), and Iberian electricity market between the Kingdom of Spain and the Portuguese Republic (MIBEL in its Spanish acronym), among others. The accuracy of these forecasts is vital for estimating the costs and benefits of the handling of electricity. This article explains the process of creating the complete dataset, which includes the acquisition of the hourly information of four European wind farms as well as a description of the structure and content of the dataset which amounts to 2 years of hourly information. The wind farms are in three countries, two from Auvergne-Rhône-Alpes (France), Aragon (Spain) and the Piemonte region (Italy). The presented dataset is available and accessible to improve the forecasting and management of wind farms, especially for the detection of faults and the elaboration of a preventive maintenance plan. The full description of the characteristics of the dataset, as well as its components, format and methodology, can be found here: "Historical Hourly Information of Four European Wind Farms for Wind Energy Forecasting and Maintenance". Data 2025, 10, 38. <a href="https://doi.org/10.3390/data10030038" target="_blank" rel="noopener">https://doi.org/10.3390/data10030038</a>

opencc-by-4.0Nov 2019View details →

Figure 3 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast

Figure 3. The dependence of the speed wind at a height Figure 4. In-situ MS data breakdown scheme for a

opencc-by-4.0Nov 2019View details →

Figure 5 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast

Figure 5. The dependence of the correlation coefficient between in-situ wind speed at the WS and remote sensing data on the orientation angle of the main quadrants (a) and their position relative to the White Sea coastline (b).

CESM2 data for "Internal Wind Driven Ocean Circulation Variability Delays the Time of Emergence of Externally Forced Sea Surface Temperature Trends" - submitted to GRL

CESM2 Experiment names: <ul> <li>MDM = mechanically decoupled model (referred to as MDM in paper)</li> <li>FCM = fully coupled model (referred to as FCM in paper)</li> </ul> Details for files cesm2.[experiment name].SST.noise.nc <ul> <li>These files include the unfiltered time-varying SST noise </li> <li>"noise" refers to ensemble standard deviation (no 10-yr running mean has been applied) </li> <li>"SST" is the annual mean SST</li> <li>Time period is 1900-2014</li> </ul> For the ensemble mean SST, see previously created Zenodo repository by Fu et al: https://zenodo.org/records/10484207 For other ensemble mean variables, see previously created Zenodo repository by McMonigal et al: https://zenodo.org/records/7154374

opencc-by-4.0Apr 2024View details →

Fig. 3 in Gone with the wind? Condica capensis (Guenée 1852), a migrant species new for Italy (Lepidoptera: Noctuidae)

Fig. 3 - Distribution of Condica capensis in Europe with year of record. Green circle: probably resident population; yellow circles: migrant individuals; red circle: first migrant individual in Italy (from GoogleEarth). / Distribuzione di Condica capensis in Europa con anno di segnalazione. Cerchio verde: popolazione probabilmente residente; cerchi gialli: individui migranti; cerchio rosso: primo individuo migrante in Italia (da GoogleEarth).

opencc-by-4.0Apr 2024View details →

Fig. 4 in Gone with the wind? Condica capensis (Guenée 1852), a migrant species new for Italy (Lepidoptera: Noctuidae)

Fig. 4 - Wind maps of the Mediterranean area from 27th October to 1st November 2023 at 20:00 h. Maps refer to wind at 1000 hPa/100 m of elevation (left) and at 850 hPa/1500 m of elevation (right) (Beccario, 2020). / Mappe dei venti dell'area mediterranea dal 27 ottobre al 1 novembre 2023 alle ore 20:00. Le mappe si riferiscono al vento a 1000 hPa/100 m di altitudine (a sinistra) e a 850 hPa/1500 m di altitudine (a destra) (Beccario, 2020).