Quantitative Winter Ozone Forecasts

Project Updates

• Major Findings:
  
  • The Clyfar ozone forecasting system can provide early indications of potential ozone events up to two weeks in advance, offering valuable lead time for understanding and preparing for possible air quality impacts. 
    
    
• Current and Upcoming Work:
  • Additional inputs and features are being incorporated into the model to further improve its accuracy and overall forecasting performance. 
    
    
  • Enhancing the online interface to make the model's forecasts and outputs more accessible and easier to use for stakeholders and members of the public. 
    
    
  • Evaluating the model's performance and preparing a preprint manuscript describing the methods and results for publication.
    
    
• Problems:
  
  • No problems to report at this time.

Example Clyfar model output showing Optimistic, Neutral, and Pessimistic represent the low, medium, and high range of forecast ozone values, respectively.

Improve Quantitative Winter Ozone Forecasts

Winter inversions are difficult to predict due to their inherent uncertainty, such as their sensitivity to snow depth, wind speeds, and temperature profiles. Small errors in the initial state of the atmosphere will become large errors in the coming forecast. Also, issuing solely “yes” or “no” forecasts for ozone events masks the true uncertainty of the prediction and can be misleadingly confident. In our team’s past attempts to forecast winter ozone episodes, our prediction methods compared well against historical datasets but performed poorly when we used them for real-time prediction (Lyman et al., 2020; Mansfield, 2018). Because of these failures, we have thus far relied on qualitative forecasts rather than numerical prediction methods for our Ozone Alert program.

To give the forecasting team more options to guide issued Alerts, we have constructed a fuzzy logic-based ozone prediction system named Clyfar that is thus performing better than past efforts (Lawson and Lyman, 2024), in part due to the addition of risk communication.  This system can predict high ozone events, including providing likelihoods of different ozone concentration thresholds, so users have more detailed information with which to make decisions.  We have also created a website to display products from Clyfar: https://basinwx.com.

Over the coming year, we will continue improving forecasts for winter ozone and promote the basinwx.com website to the Uinta Basin community, current subscribers to our Ozone Alert program, and others.  We will also use machine learning (a sort of artificial intelligence) to tune Clyfar for improved accuracy. Results will be published in leading, peer-reviewed scientific journals; simpler explanations will be written in blogs on the team’s side website (www.jrl.ac) and in social media short videos, with assistance from on-campus branding and outreach staff. We will investigate meteorological tipping points and other factors that hinder accurate winter ozone forecasting,.  We will seek feedback from from potential users, and from those using products in real-time, to ensure that the public-facing web interface is useful to them.