Wind Data
IHData provide information from atmospheric hindcast developed by IHCantabria.. To obtain a detailed description of the wind fields at the regional scale, the hindcast are developed using the atmospheric model Weather and Research Forecast (WRF) model that is Limited Area Models (LAMs). The configurations are focused on providing wind information with the highest quality, but we have also available more variables associate with the atmospheric parameters: air temperature, pressure, humidity or visibility.
The inputs are the global reanalysis data that are downscaled to obtain the atmospheric fields to a better spatial resolution. The hindcast data can be considered proper representations of the atmospheric conditions and are considered as quasi-observational datasets in many fields.
IHData contains two historical wind reconstruction products:
- SeaWind: is focused on Offshore and Coastal Regional Winds
- SeaWind HR: Offshore and Coastal Winds at High Resolution.
The wind information is generated with the WRF-ARW model forced by different atmospheric reanalysis and is configured with the best configuration to increase the wind quality in different target areas, our atmospheric hindcast datasets are available in a wide range of zones along the world and covers different temporal period depending on the zone. If you contact us, we will provide you the best option for you project. All our wind data provided by our Atmospheric hindcast are validates and a example of the results are the next, the wind information is available at different heights.
EXAMPLE OF Validation results show the Pearson correlation, BIAS error and Scatter Index statistics of diagnosis obtained with the comparison of wind speed from an atmospheric SeaWind hindcast focused on the north coast of Spain against scatterometer measurements of wind speed at 10 m.
Wind speed roses at 10 m height in two buoy locations in the north coast of spain, the rose with the instrumental data on the left and the rose with the atmospheric hindcast data on the right.
Scientific References
- Acevedo, A., Menéndez, M., Tomas, A., Iturrioz, A., (2019). A hybrid downscaling approach for short-term forecasting offshore surface atmospheric variables in coastal areas. 21(ii), 17908. https://meetingorganizer.copernicus.org/EGU2019/EGU2019-17908.pdf
- Azorin-Molina, C., Menendez, M., McVicar, T. R., Acevedo, A., Vicente-Serrano, S. M., Cuevas, E., … & Chen, D. (2018). Wind speed variability over the Canary Islands, 1948–2014: focusing on trend differences at the land–ocean interface and below–above the trade-wind inversion layer. Climate Dynamics, 50, 4061-4081.
- Camus P., Mendez F.J. Medina R., Cofiño, A.S. (2011). Analysis of clustering and selection algorithms for the study of multivariate wave climate.Coastal Engineering, 58, pp 453-462.
- Fernández-Quiruelas, V., Blanco, C., Cofiño, A. S., & Fernández, J. (2015). Large-scale climate simulations harnessing clusters, grid and cloud infrastructures.Future Generation Computer Systems, 51, 36–44.
- Menendez M., García-Díez M., Fita L., Fernández J., Méndez FJ., Gutiérrez JM. (2014).High-resolution sea wind hindcasts over the Mediterranean area. Climate Dynamics, 42:1857–1872.
- Menéndez, M., Tomás, A., Camus, P., García-Díez, M., Fita, L., Fernández, J., Méndez, F. J., Losada, I. J. (2011).A methodology to evaluate regional-scale offshore wind energy resources. OCEANS’11 IEEE, Santander. 978-1-61284-4577-0088-0/11/$26.00 ©2011 IEEE.
- Susini, S., Menendez, M., Eguia, P., & Blanco, J. M. (2022). Climate Change Impact on the Offshore Wind Energy Over the North Sea and the Irish Sea. Frontiers in Energy Research, 10.