Probabilistic aspects of meteorological and ozone regional ensemble forecasts Page: 4 of 51
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Abstract. This study investigates whether probabilistic ozone forecasts from an
ensemble can be made with skill; i.e., high verification resolution and reliability.
Twenty-eight ozone forecasts were generated over the Lower Fraser Valley, British
Columbia, Canada, for the 5-day period 11-15 August 2004, and compared with 1-hour
averaged measurements of ozone concentrations at five stations. The forecasts were
obtained by driving the CMAQ model with four meteorological forecasts and seven
emission scenarios: a control run, 50% NOx, 50% VOC, and 50% NOx combined
with VOC. Probabilistic forecast quality is verified using relative operating characteristic
curves, Talagrand diagrams, and a new reliability index.
Results show that both meteorology and emission perturbations are needed to have a
skillful probabilistic forecast system -- the meteorology perturbation is important to
capture the ozone temporal and spatial distribution, and the emission perturbation is
needed to span the range of ozone-concentration magnitudes. Emission perturbations are
more important than meteorology perturbations for capturing the likelihood of high ozone
concentrations. Perturbations involving NOx resulted in a more skillful probabilistic
forecast for the episode analyzed, and therefore the 50% perturbation values appears to
span much of the emission uncertainty for this case. All of the ensembles analyzed show
a high ozone concentration bias in the Talagrand diagrams, even when the biases from
the unperturbed emissions forecasts are removed from all ensemble members. This result
indicates nonlinearity in the ensemble, which arises from both ozone chemistry and its
interaction with input from particular meteorological models.
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Monache, L D; Hacker, J; Zhou, Y; Deng, X & Stull, R. Probabilistic aspects of meteorological and ozone regional ensemble forecasts, article, March 20, 2006; Livermore, California. (https://digital.library.unt.edu/ark:/67531/metadc897093/m1/4/: accessed April 25, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.