Ensemble‐based air quality forecasts: A multimodel approach applied to ozone
By V. Mallet, Bruno Sportisse. 2006
59 citations recorded by the source corpus.
Subfields: Atmospheric Science, Environmental Engineering, Health, Toxicology and Mutagenesis
View Paper: https://www.semanticscholar.org/paper/e28e16702991d7234ec13155988ee1cca1f5c2f4
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In Atmospheric Science
In Environmental Engineering
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Citations in the corpus
- A Kalman-filter bias correction method applied to deterministic, ensemble averaged and probabilistic forecasts of surface ozone (2008, methodology): “…ozone forecasts can be improved using ensemble methods (e.g. Delle Monache and Stull, 2003; McKeen et al., 2005; Delle Monache et al., 2006a, Mallet and Sportisse, 2006; Wilczak et al., 2006; Thunis et al., 2007; van Loon et al., 2007), by combining weighted ensemble…”
- A review of parameterizations for modelling dry deposition and scavenging of radionuclides (2007, background): “Due to the large amount of uncertainties, one conclusion is that using ensemble forecast on the basis of a set of different parameterizations and of different meteorological data is required for modelling the dispersion of radionuclides (Galmarini et al., 2004; Mallet and Sportisse, 2006).”