The Influence of Large-Scale Weather Patterns on Potential Wind Energy Fields over Mexico
Abstract
The assessment of wind energy resources in large regions presents a substantial challenge for energy system operators due to the intricacies involved in accurately describing all atmospheric circulation patterns and, even more so, modeling their inherent dynamics. This research primarily aimed to identify the prevailing synoptic weather patterns over continental Mexico and its surrounding regions. The study utilized ERA5 Reanalysis daily mean sea level pressure anomaly data and two widely recognized clustering techniques, k-means and self-organizing maps, to achieve this objective. Research findings indicate that four weather patterns, estimated using k-means clustering, are sufficient to explain daily atmospheric variability over continental Mexico. However, the fifteen weather patterns identified through the self-organizing-maps technique suggest that the k-means-determined patterns can be complemented by interconnected transition patterns, which represent a more intricate dynamics within the tropical and subtropical atmosphere. The second primary objective of this research is to assess and analyze the impact of weather patterns on the wind velocity field and energy availability across continental Mexico. This assessment employed a probabilistic methodology grounded in the L-moments theory and the inference of a regional wind-speed model, which was supported by the Weibull and Kappa distributions. The findings indicate that fluctuations in large-scale weather patterns across the continental Mexican influence the potential wind energy production significantly. For instance, in the Oaxaca region, the potential wind energy fluctuates between 189 and 734 W/m^2. These results emphasize the crucial influence of these weather patterns on energy production and highlight the need for a comprehensive study within a forecasting framework to enhance the current operation of the Mexican electrical system.
Related
- Influence of natural and anthropogenic forcings on western US winter fire weather and the 2025 Los Angeles wildfires
- Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry
- Butterfly Effect and the Kinetic Energy Cascade in Probabilistic Machine Learning Weather Prediction Models
- An Electron Relay Driven by Built‐in Electric Fields for Self‐Sustaining and External‐Energy‐Free Fenton‐Like Catalysis
- Geographic Range and Body Size Influence Abundance Patterns in Reef Fishes
BibTeX
@misc{penarandavelez2026influence,
title = {{The Influence of Large-Scale Weather Patterns on Potential Wind Energy Fields over Mexico}},
author = {Victor Manuel Peñaranda-Vélez and Carlos Abraham Ochoa-Moya and Arturo I. Quitanar Isaias},
howpublished = {EarthArXiv},
year = {2026},
month = sep,
doi = {10.31223/x5mb8g},
url = {https://eartharxiv.org/repository/view/15200/},
note = {CC BY-ND 4.0},
}
RIS
TY - UNPB
TI - The Influence of Large-Scale Weather Patterns on Potential Wind Energy Fields over Mexico
AU - Victor Manuel Peñaranda-Vélez
AU - Carlos Abraham Ochoa-Moya
AU - Arturo I. Quitanar Isaias
PY - 2026
DA - 2026/09/26
PB - EarthArXiv
DO - 10.31223/x5mb8g
UR - https://eartharxiv.org/repository/view/15200/
AB - The assessment of wind energy resources in large regions presents a substantial challenge for energy system operators due to the intricacies involved in accurately describing all atmospheric circulation patterns and, even more so, modeling their inherent dynamics. This research primarily aimed to identify the prevailing synoptic weather patterns over continental Mexico and its surrounding regions. The study utilized ERA5 Reanalysis daily mean sea level pressure anomaly data and two widely recognized clustering techniques, k-means and self-organizing maps, to achieve this objective. Research findings indicate that four weather patterns, estimated using k-means clustering, are sufficient to explain daily atmospheric variability over continental Mexico. However, the fifteen weather patterns identified through the self-organizing-maps technique suggest that the k-means-determined patterns can be complemented by interconnected transition patterns, which represent a more intricate dynamics within the tropical and subtropical atmosphere. The second primary objective of this research is to assess and analyze the impact of weather patterns on the wind velocity field and energy availability across continental Mexico. This assessment employed a probabilistic methodology grounded in the L-moments theory and the inference of a regional wind-speed model, which was supported by the Weibull and Kappa distributions. The findings indicate that fluctuations in large-scale weather patterns across the continental Mexican influence the potential wind energy production significantly. For instance, in the Oaxaca region, the potential wind energy fluctuates between 189 and 734 W/m^2. These results emphasize the crucial influence of these weather patterns on energy production and highlight the need for a comprehensive study within a forecasting framework to enhance the current operation of the Mexican electrical system.
ER -