Developing a new wind dataset by blending satellite data and WRF model wind predictions

Nadia Salvação, Abderrahim Bentamy and C. Guedes Soares

Renewable Energy, 2022, vol. 198, issue C, 283-295

Abstract: This paper presents an approach to improve wind datasets developed using the regional atmospheric model Weather Research Forecasting by combining its predictions with remotely sensed wind observations in enhanced wind speed analyses that leads to blended winds. In this study, satellite data derived from scatterometers, radiometers, and synthetic aperture radar are used. The spatial and temporal features of each wind product are thoroughly analysed. For the probabilistic evaluation of their skill, comprehensive comparisons with available buoy data are carried out. The statistical analysis shows that the combined use of satellite and numerical weather prediction model data improves the agreement with buoy measurements, demonstrating the added value of using the blended product. As an application of the method, new improved satellite wind speeds are presented in the form of a wind energy assessment along the Iberian coastal area. From inspection of the provided wind power maps, northern and central regions emerge as the most promising areas for wind harnessing offshore despite some seasonal variations. Finally, potential wind farm sites are provided, along with insights into multi-year wind speed distribution. The results show how the new dataset can be used for the selection of promising areas for wind exploitation.

Keywords: WRF; Satellite wind; Blended data; Wind energy (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148122010461
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:198:y:2022:i:c:p:283-295

DOI: 10.1016/j.renene.2022.07.049

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().