Articles | Volume 12, issue 1
https://doi.org/10.5194/essd-12-429-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-429-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
18 Feb 2020
Data description paper |
| 18 Feb 2020
| 18 Feb 2020
The Tall Tower Dataset: a unique initiative to boost wind energy research
Barcelona Supercomputing Center (BSC), c/ Jordi Girona, 29, Barcelona 08034, Spain
Llorenç Lledó
Barcelona Supercomputing Center (BSC), c/ Jordi Girona, 29, Barcelona 08034, Spain
Núria Pérez-Zanón
Barcelona Supercomputing Center (BSC), c/ Jordi Girona, 29, Barcelona 08034, Spain
Albert Soret
Barcelona Supercomputing Center (BSC), c/ Jordi Girona, 29, Barcelona 08034, Spain
Francisco J. Doblas-Reyes
Barcelona Supercomputing Center (BSC), c/ Jordi Girona, 29, Barcelona 08034, Spain
ICREA, Pg. Lluís Companys 23, Barcelona 08010, Spain
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Cited
22 citations as recorded by crossref.
- Evaluation of two mesoscale wind farm parametrisations with offshore tall masts O. Garcia-Santiago et al. 10.1088/1742-6596/2265/2/022038
- Integration of Land Use Potential in Energy System Optimization Models at Regional Scale: The Pantelleria Island Case Study D. Mosso et al. 10.3390/su16041644
- Evaluating the mesoscale spatio-temporal variability in simulated wind speed time series over northern Europe G. Luzia et al. 10.5194/wes-7-2255-2022
- Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations A. Shestakova et al. 10.24057/2071-9388-2023-2782
- Attribution and projection of regional winter wind energy drought in Europe S. Yu & R. Vautard 10.1088/1748-9326/adb2a9
- Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review G. Gualtieri 10.1016/j.rser.2022.112741
- Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset M. Molina et al. 10.1002/joc.7103
- Validation of European-scale simulated wind speed and wind generation time series J. Murcia et al. 10.1016/j.apenergy.2021.117794
- Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations S. Kotthaus et al. 10.5194/amt-16-433-2023
- Evaluation of wind speed estimates in reanalyses for wind energy applications S. Brune et al. 10.5194/asr-18-115-2021
- Mind the (spectral) gap: how the temporal resolution of wind data affects multi-decadal wind power forecasts N. Effenberger et al. 10.1088/1748-9326/ad0bd6
- Evaluation of a Database of the Spanish Wind Energy Resources Derived from a Regional Reanalysis A. Jiménez-Garrote et al. 10.3390/en17071523
- A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models D. Millstein et al. 10.1038/s41597-023-02804-w
- Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions Z. Allal et al. 10.1016/j.jenvman.2024.120392
- High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs R. McKenna et al. 10.1016/j.renene.2021.10.027
- Exploring the Complementarity of Offshore Wind Sites to Reduce the Seasonal Variability of Generation I. Fernandes et al. 10.3390/en15197182
- Analysis of turbulence intensity in the megacity of Beijing by High-frequency observations on a 325-m Tower L. Liu et al. 10.1016/j.renene.2023.119184
- Prediction and forecast of surface wind using ML tree-based algorithms M. ElTaweel et al. 10.1007/s00703-023-00999-6
- A collection and categorization of open‐source wind and wind power datasets N. Effenberger & N. Ludwig 10.1002/we.2766
- Wind power estimation on local scale—A case study of representativeness of reanalysis data and data-driven analysis I. Schicker et al. 10.3389/fclim.2023.1017774
- A perfect prognosis downscaling methodology for seasonal prediction of local-scale wind speeds J. Ramon et al. 10.1088/1748-9326/abe491
- Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers G. Gualtieri 10.3390/en14144169
22 citations as recorded by crossref.
- Evaluation of two mesoscale wind farm parametrisations with offshore tall masts O. Garcia-Santiago et al. 10.1088/1742-6596/2265/2/022038
- Integration of Land Use Potential in Energy System Optimization Models at Regional Scale: The Pantelleria Island Case Study D. Mosso et al. 10.3390/su16041644
- Evaluating the mesoscale spatio-temporal variability in simulated wind speed time series over northern Europe G. Luzia et al. 10.5194/wes-7-2255-2022
- Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations A. Shestakova et al. 10.24057/2071-9388-2023-2782
- Attribution and projection of regional winter wind energy drought in Europe S. Yu & R. Vautard 10.1088/1748-9326/adb2a9
- Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review G. Gualtieri 10.1016/j.rser.2022.112741
- Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset M. Molina et al. 10.1002/joc.7103
- Validation of European-scale simulated wind speed and wind generation time series J. Murcia et al. 10.1016/j.apenergy.2021.117794
- Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations S. Kotthaus et al. 10.5194/amt-16-433-2023
- Evaluation of wind speed estimates in reanalyses for wind energy applications S. Brune et al. 10.5194/asr-18-115-2021
- Mind the (spectral) gap: how the temporal resolution of wind data affects multi-decadal wind power forecasts N. Effenberger et al. 10.1088/1748-9326/ad0bd6
- Evaluation of a Database of the Spanish Wind Energy Resources Derived from a Regional Reanalysis A. Jiménez-Garrote et al. 10.3390/en17071523
- A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models D. Millstein et al. 10.1038/s41597-023-02804-w
- Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions Z. Allal et al. 10.1016/j.jenvman.2024.120392
- High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs R. McKenna et al. 10.1016/j.renene.2021.10.027
- Exploring the Complementarity of Offshore Wind Sites to Reduce the Seasonal Variability of Generation I. Fernandes et al. 10.3390/en15197182
- Analysis of turbulence intensity in the megacity of Beijing by High-frequency observations on a 325-m Tower L. Liu et al. 10.1016/j.renene.2023.119184
- Prediction and forecast of surface wind using ML tree-based algorithms M. ElTaweel et al. 10.1007/s00703-023-00999-6
- A collection and categorization of open‐source wind and wind power datasets N. Effenberger & N. Ludwig 10.1002/we.2766
- Wind power estimation on local scale—A case study of representativeness of reanalysis data and data-driven analysis I. Schicker et al. 10.3389/fclim.2023.1017774
- A perfect prognosis downscaling methodology for seasonal prediction of local-scale wind speeds J. Ramon et al. 10.1088/1748-9326/abe491
- Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers G. Gualtieri 10.3390/en14144169
Latest update: 23 Apr 2025
Short summary
A dataset containing quality-controlled wind observations from 222 tall towers has been created. Wind speed and wind direction records have been collected from existing tall towers in an effort to boost the utilization of these non-standard atmospheric datasets. Observations are compiled in a unique collection with a common format, access, documentation and quality control (QC). For the latter, a total of 18 QC checks have been considered to ensure the high quality of the wind data.
A dataset containing quality-controlled wind observations from 222 tall towers has been created....
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