How well do atmospheric reanalyses reproduce observed winds in coastal regions of Mexico?

Thomas, S., Nicolau, S., Martinez-Alvarado, O. ORCID: https://orcid.org/0000-0002-5285-0379, Drew, D. and Bloomfield, H. ORCID: https://orcid.org/0000-0002-5616-1503 (2021) How well do atmospheric reanalyses reproduce observed winds in coastal regions of Mexico? Meteorological Applications, 28 (5). e2023. ISSN 1469-8080 doi: 10.1002/met.2023

Abstract/Summary

Atmospheric reanalyses are widely used for understanding the past and present climate. They have become increasingly used within the renewable energy sector for assessing wind and solar resources for different regions of the globe in conjunction with observations. Mexico is a country with considerable potential for wind energy production, especially around coastal sites and therefore the characterisation of wind resource in these areas of the country is imperative for the most beneficial use of these resources. In this work, we assess how well three global reanalyses, namely ERA-Interim, ERA5 and MERRA-2, can reproduce wind observations at a number of key sites across the country. We find that the reanalyses’ ability to reproduce these observations is highly variable between different regions in Mexico. Correlation coefficients are around 0.9 in the South of the country where the winds are strongest, but much lower (around 0.5) in Baja Cal24 ifornia Sur due to the complex coastal topography of the region. ERA5 outperforms ERA-Interim and MERRA-2 consistently across the vast majority of sites and so this reanalysis is recommended for local wind power studies. The consistently improved performance compared to ERA-Interim shows the value of the increased spatial resolution of ERA5. However, in the South and East of Mexico, despite having the highest correlations, ERA5 also has the largest bias, meaning that it underestimates winds consistently across most of the country. Poor correlations between ERA5 and the observations in Veracruz are considered as a case study to understand potential drivers of low wind biases.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/99586
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Royal Meteorological Society
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