Physics
Scientific paper
Apr 2005
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005georl..3207814p&link_type=abstract
Geophysical Research Letters, Volume 32, Issue 7, CiteID L07814
Physics
18
Atmospheric Composition And Structure: Cloud Physics And Chemistry, Atmospheric Processes: Global Climate Models (1626, 4928)
Scientific paper
Forecasts from seven air quality models and ozone data collected over the eastern USA and southern Canada during July and August 2004 are used in creating a simple method to improve ensemble-based forecasts of maximum daily 1-hr and 8-hr averaged ozone concentrations. The method minimizes least-square error of ensemble forecasts by assigning weights for its members. The real-time ozone (O3) forecasts from this ensemble of models are statistically evaluated against the ozone observations collected for the AIRNow database comprising more than 350 stations. Application of this method is shown to significantly improve overall statistics (e.g., bias, root mean square error, and index of agreement) of the weighted ensemble compared to the averaged ensemble or any individual ensemble member. If a sufficient number of observations is available, we recommend that weights be calculated daily; if not, a longer training phase will still provide a positive benefit.
Bouchet V.
Dévényi Dezső
Gong Wenlin
Grell Georg A.
McHenry J.
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