Lightning prediction models for the province of Alberta, Canada

  • Author / Creator
    Blouin, Karen D
  • Lightning is widely acknowledged as a major cause of wildland fires in Canada. On average, 250,000 cloud-to-ground lightning strikes occur in Alberta every year. Lightning-caused wildland fires in remote areas have considerably larger suppression costs and a much greater chance of escaping initial attack. Geographic and temporal covariates were paired with Reanalysis and Radiosonde observations to generate a series of 6-hour and 24-hour lightning prediction models valid from April to October. These models, based on cloud-to-ground lightning from the CLDN, were developed and validated for the province of Alberta, Canada. The ensemble forecasts produced from these models were most accurate in the Rocky Mountain and Foothills Natural Regions achieving hits rates of ~85%. The Showalter index, convective available potential energy, Julian day, and geographic covariates were highly important predictors. Random forest classification is introduced as a viable modelling method to generate lightning forecasts. Limitations and recommendations are also discussed.

  • Subjects / Keywords
  • Graduation date
    Spring 2014
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
  • Specialization
    • Forest Biology and Management
  • Supervisor / co-supervisor and their department(s)
  • Examining committee members and their departments
    • Nielsen, Scott (Renewable Resources)
    • Reuter, Gerhard (Earth and Atmospheric Sciences)
    • Kochtubajda, Bob (Environment Canada)
    • Wang, Xianli (Renewable Resources)