Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

Pablo Durán, Sukanta Basu, Cathérine Meißner, Muyiwa S. Adaramola

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low-level jets, near-uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.

Original languageEnglish
Pages (from-to)898-914
Number of pages17
JournalWind Energy
Volume23
Issue number4
DOIs
Publication statusPublished - 2020

Keywords

  • low-level jets
  • mesoscale modeling
  • neural networks
  • planetary boundary layer
  • self-organizing maps
  • vertical wind profile

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