Scholarlink

Abstract <p>In 2023, more than a third of dangerous weather events in the Siberian Federal District were associated with strong wind, which emphasizes the importance of improving the accuracy and timing of its forecasting. Modern numerical simulation and machine learning methods make it possible to improve forecasts; however, the task of direct calculation of wind gusts remains topical due to the limited resolution of models. An original method is proposed for correcting the results of short-term forecast of wind gusts obtained on the basis of mesoscale models of numerical weather forecasting using advance measurements and artificial neural networks. The results show that the proposed correction method makes it possible to improve the forecast of wind gusts by various semiempirical methods. The results can be applied in meteorology, energy engineering, transportation, and other industries to minimize damage from dangerous weather events.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Correction of Numerical Forecasts of Wind Gusts Using Artificial Neural Networks and Observations

  • 期刊论文

  • 发表日期：

    2025年8月1日

查看全文
  • I. V. Del,
  • A. V. Starchenko

摘要

Abstract

In 2023, more than a third of dangerous weather events in the Siberian Federal District were associated with strong wind, which emphasizes the importance of improving the accuracy and timing of its forecasting. Modern numerical simulation and machine learning methods make it possible to improve forecasts; however, the task of direct calculation of wind gusts remains topical due to the limited resolution of models. An original method is proposed for correcting the results of short-term forecast of wind gusts obtained on the basis of mesoscale models of numerical weather forecasting using advance measurements and artificial neural networks. The results show that the proposed correction method makes it possible to improve the forecast of wind gusts by various semiempirical methods. The results can be applied in meteorology, energy engineering, transportation, and other industries to minimize damage from dangerous weather events.