メキシコにおける潜在的風力エネルギー場に対する大規模気象パターンの影響
The Influence of Large-Scale Weather Patterns on Potential Wind Energy Fields over Mexico (原題)
Peñaranda-Vélez. Victor Manuel, Ochoa-Moya. Carlos Abraham, Quitanar Isaias. Arturo I.
🤖 gxceed AI 要約
日本語
メキシコ本土および周辺域の卓越する総観気象パターンをERA5再解析データとk-means・自己組織化マップで同定した研究。4つのk-meansパターンで日々の大気変動を説明でき、SOMの15パターンは遷移的動態を補完する。L-moments理論とWeibull・Kappa分布に基づく確率論的手法で風速場とエネルギー賦存量を評価し、オアハカ地域では189〜734 W/m²と大きく変動することを示した。気象パターンが風力発電量に強く影響するため、予測枠組みの必要性を強調している。
English
This study identifies prevailing synoptic weather patterns over continental Mexico using ERA5 reanalysis data and k-means/self-organizing maps clustering. Four k-means patterns explain daily atmospheric variability, while fifteen SOM patterns capture transition dynamics. Using L-moments theory and Weibull/Kappa distributions, it assesses wind speed and energy availability, finding that potential wind energy in Oaxaca fluctuates between 189 and 734 W/m². The results highlight the strong influence of weather patterns on wind power production and the need for forecasting frameworks.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では再エネ導入拡大と電力系統の安定化が課題であり、気象パターンと風力発電量の関係を定量化する本手法は、日本の風力資源評価や出力予測の高度化に示唆を与える。特に、地域別の変動特性を把握することは、系統運用や再エネ調達戦略に有用である。
In the global GX context
While focused on Mexico, this work contributes to global energy transition scholarship by demonstrating how synoptic weather patterns drive wind energy variability. It offers a transferable probabilistic framework for resource assessment that can inform grid integration and renewable procurement strategies in other regions, aligning with broader decarbonization and energy security goals.
👥 読者別の含意
🔬研究者:気象パターンと再エネ賦存量の関係を確率論的に評価する手法に関心のある研究者に有用。
🏢実務担当者:風力発電事業の開発・運用担当者が、地域別の資源変動リスクを理解するための参考になる。
🏛政策担当者:再エネ導入政策や系統計画において、気象変動を考慮した資源評価の重要性を示唆。
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- EarthArXiv https://eartharxiv.org/repository/object/15200/download/26366/first seen 2026-09-26 04:13:03 · last seen 2026-09-29 04:14:00
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