Temperature Inversion Prediction

  • Faculty: Weng-Keen Wong (OSU), Rebecca Hutchinson (OSU)
  • Senior Collaborators: Paola Gabriela Pesantez Cabrera (WSU, Data Scientist), Bryan Curtis (WSU AWN Data Scientist), Matthew Cann (WSU Postdoc)
  • Graduate students: Andrew Droubay (OSU, Ph.D. in CS)

  • Assemble and curate datasets that can be used to train and evaluate machine learning models for temperature inversion prediction. This objective involves integrating multiple data sources related to environmental features such as land cover, topographic information, tree canopy, etc. 
  • Develop a suite of machine learning models that can predict temperature inversions
  • Evaluate these machine learning models on the curated datasets.

  • Machine learning models that incorporate spatial and temporal information can provide more accurate predictions of temperature inversion strength. 

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