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C. Hogrefe, W. Hao, K. Civerolo, J.-Y. Ku, G. Sistla, R. S. Gaza, L. Sedefian, K. Schere, A. Gilliland, and R. Mathur

, building upon the operational NWS/NOAA/EPA ozone forecasts while also including the simulation of PM 2.5 . Of special interest is the potential utility of PM 2.5 simulations from CMAQ in supporting the routine air quality forecasting program already established by NYSDEC that is based on statistical techniques and expert judgment ( Gaza 1998 ; NYSDEC 2005 ). Section 2 provides a brief overview of the modeling system as well as the observational databases used in model evaluation. Section 3

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John S. Irwin, William B. Petersen, and Steven C. Howard

). Because ( α ) is an average over all β , it is only a function of α . The modeled concentrations C m can be envisioned as where f  ( α ) is the deterministic error in the estimate of ( α ) and d (Δ α ) represents the effects of uncertainty in specifying the model inputs. The next two terms in (2) are not present in most current operational atmospheric transport and diffusion models, because they represent an attempt to estimate the unresolved variability, c ″( α , β ), and any

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George Kallos, Marina Astitha, Petros Katsafados, and Chris Spyrou

observations, ground-based measurements, and operational modeling forecasts, there is a large seasonal variability of the dust mobilization that depends on the source characteristics as well as the global atmospheric circulation ( di Sarra et al. 2001 ; Ozsoy et al. 2001 ). During winter and spring, the GMR is affected by two upper-air jet streams: the polar-front jet stream, originally located over Europe, and the subtropical jet stream, which is typically located over northern Africa. The combined

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