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Application of Dynamic Linear Regression To Improve The Skill of Ensemble-based Deterministic Ozone Forecasts


Forecasts from seven air quality models and surface ozone data collected over the eastern USA and southern Canada during July and August 2004 provide a unique opportunity to assess benefits of ensemble-based ozone forecasting and devise methods to improve ozone forecasts. In this investigation, past forecasts from the ensemble of models and hourly surface ozone measurements at over 350 sites are used to issue deterministic 24-h forecasts using a method based on dynamic linear regression. Forecasts of hourly ozone concentrations as well as maximum daily 8-h and 1-h averaged concentrations are considered. It is shown that the forecasts issued with the application of this method have reduced bias and root mean square error and better overall performance scores than any of the ensemble members and the ensemble average. Performance of the method is similar to another method based on linear regression described previously by Pagowski et al., but unlike the latter, the current method does not require measurements from multiple monitors since it operates on individual time series. Improvement in the forecasts can be easily implemented and requires minimal computational cost.

Article / Publication Data
Available Metadata
Accepted On
February 15, 2006
Fiscal Year
Publication Name
Atmospheric Environment
Published On
June 01, 2006
Publisher Name
Pergamon Elsevier Science Ltd
Print Volume
Print Number
Submitted On
November 16, 2005


Not available


Authors who have authored or contributed to this publication.

  • Stuart A. McKeen - Not Positioned Csl
    Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder
    NOAA/Global Systems Laboratory