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Title: Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts

Abstract

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.

Authors:
; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
899093
Report Number(s):
UCRL-JRNL-218640
TRN: US200706%%488
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Journal Article
Journal Name:
Atmospheric Environment, vol. 40, no. 18, June 1, 2006, pp. 3240-3250
Additional Journal Information:
Journal Name: Atmospheric Environment, vol. 40, no. 18, June 1, 2006, pp. 3240-3250
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; AIR QUALITY; FORECASTING; MONITORS; OZONE; PERFORMANCE; NORTH AMERICA

Citation Formats

Pagowski, M O, Grell, G A, Devenyi, D, Peckham, S E, McKeen, S A, Gong, W, Monache, L D, McHenry, J N, McQueen, J, and Lee, P. Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts. United States: N. p., 2006. Web. doi:10.1016/j.atmosenv.2006.02.006.
Pagowski, M O, Grell, G A, Devenyi, D, Peckham, S E, McKeen, S A, Gong, W, Monache, L D, McHenry, J N, McQueen, J, & Lee, P. Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts. United States. https://doi.org/10.1016/j.atmosenv.2006.02.006
Pagowski, M O, Grell, G A, Devenyi, D, Peckham, S E, McKeen, S A, Gong, W, Monache, L D, McHenry, J N, McQueen, J, and Lee, P. 2006. "Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts". United States. https://doi.org/10.1016/j.atmosenv.2006.02.006. https://www.osti.gov/servlets/purl/899093.
@article{osti_899093,
title = {Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts},
author = {Pagowski, M O and Grell, G A and Devenyi, D and Peckham, S E and McKeen, S A and Gong, W and Monache, L D and McHenry, J N and McQueen, J and Lee, P},
abstractNote = {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.},
doi = {10.1016/j.atmosenv.2006.02.006},
url = {https://www.osti.gov/biblio/899093}, journal = {Atmospheric Environment, vol. 40, no. 18, June 1, 2006, pp. 3240-3250},
number = ,
volume = ,
place = {United States},
year = {Thu Feb 02 00:00:00 EST 2006},
month = {Thu Feb 02 00:00:00 EST 2006}
}