TITLE:
Comparative Analysis of NASA POWER and ERA5 Reanalysis Data for Assessing Wind Energy Potential in Kassa (Guinea)
AUTHORS:
Mohamed Ansoumane Camara, Ouaïdou Emmanuel, Souleymane Soumah, Kadiatou Aïssatou Barry, Oumar Kourouma, Sidiki Fatta Condé, Amadou Oury Diallo
KEYWORDS:
Wind Energy Potential, ERA5 Reanalysis, NASA Power, MERRA-2, Wind Speed Assessment, MBE, RMSE, Spearman Correlation (ρ)
JOURNAL NAME:
Journal of Sustainable Bioenergy Systems,
Vol.16 No.3,
August
21,
2026
ABSTRACT: This study aims to evaluate the consistency and reliability of mean wind speed data derived from two widely used atmospheric reanalysis datasets, namely NASA POWER (based on MERRA-2) and ERA5, in the context of wind resource assessment. In the absence of in situ measurements, an inter-source comparative approach was adopted to examine the differences and similarities between these two datasets for the Kassa site (Guinea). The methodology is based on the use of three complementary statistical indicators: Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Spearman’s rank correlation coefficient (ρ), which were used to assess, respectively, the systematic error, the overall deviation, and the monotonic relationship between the two datasets. For each month, these indicators were first calculated from the six annual pairs of wind speed data obtained from the NASA POWER and ERA5 databases over the 2020-2025 period. The values reported in this abstract correspond to the arithmetic means of the twelve monthly statistics, thus providing a synthetic assessment of the agreement between the two datasets. The results indicate an average Mean Bias Error (MBE) of 0.336 m/s, reflecting a slight overall overestimation of wind speeds by ERA5 relative to NASA POWER. The average Root Mean Square Error (RMSE) is 0.783 m/s, indicating a moderate overall level of discrepancy between the two datasets. The average Spearman’s rank correlation coefficient is ρ = 0.324, indicating an overall weak positive monotonic relationship between the two datasets. However, the monthly analysis reveals strong seasonal variability, with some periods showing excellent agreement, while others exhibit weak or even negative correlations. Overall, the results demonstrate satisfactory agreement between the two data sources, although noticeable differences remain, mainly due to the specific characteristics of the reanalysis models, particularly in terms of spatial resolution and data assimilation methods. These differences may influence wind resource assessment and should therefore be taken into account in energy-related studies. This study therefore confirms the value of reanalysis datasets as an alternative to direct measurements for the preliminary assessment of wind energy potential in regions with limited observational data, while emphasizing the need for local validation using in situ measurements to improve the accuracy and robustness of the estimates.