Transformation of reanalysis data for improved long-term estimation of wind speed and direction at a target site

This paper proposes the use of measure-correlate-predict (MCP) methods based on supervised machine learning (ML) techniques to transform reanalysis data from ERA5 and MERRA2, aiming to improve the long-term estimation of wind speed and direction at locations with limited on-site measurements. The study analyzes models that directly estimate the target variables—wind speed and direction—as well as two-stage models that first estimate the Cartesian components of wind velocity and subsequently transform them into polar coordinates. As a case study, hourly mean wind data recorded between 2001 and 2023 at 10 meters above ground level are used. The data were collected from an anemometric station located on the island of Gran Canaria (Canary Archipelago, Spain). Key findings include the following: (a) Reanalysis data underestimate actual wind speeds and fail to adequately represent the mean wind direction; (b) although reanalysis data poorly represent the daily wind speed profile, the MCP model significantly corrects this, achieving a Pearson correlation of 0.994; (c) the MCP method minimizes the differences between observed and estimated values (7.2 m/s vs. 7.13 m/s, and 4.49° vs. 4.50°, respectively); (d) the combination of ERA5 and MERRA2 consistently yields the lowest estimation errors, regardless of model type; (e) artificial neural networks outperform other ML techniques in all scenarios; and (f) the proposed method reduces the mean relative error in wind power density estimation to 13.89%, compared to 43% and 63.1% using MERRA2 and ERA5 alone, respectively.