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1. Introduction Lake-effect long-lake-axis-parallel (LLAP) snowbands 1 are known to produce heavy snowfall downwind of the Great Lakes ( Jiusto and Kaplan 1972 ; Niziol et al. 1995 ; Veals and Steenburgh 2015 ; Campbell et al. 2016 ). Consequently, accurately predicting the inland extent of LLAP-band snowfall is important for public safety (e.g., Villani et al. 2017 ). Although LLAP bands have been studied for several decades (e.g., Peace and Sykes 1966 ; Holroyd 1971 ; Kelly 1986
1. Introduction Lake-effect long-lake-axis-parallel (LLAP) snowbands 1 are known to produce heavy snowfall downwind of the Great Lakes ( Jiusto and Kaplan 1972 ; Niziol et al. 1995 ; Veals and Steenburgh 2015 ; Campbell et al. 2016 ). Consequently, accurately predicting the inland extent of LLAP-band snowfall is important for public safety (e.g., Villani et al. 2017 ). Although LLAP bands have been studied for several decades (e.g., Peace and Sykes 1966 ; Holroyd 1971 ; Kelly 1986
references regarding physics schemes]. The initial and boundary conditions are taken from the same GFS deterministic forecast as the IC ensemble but are not perturbed. As a final component to this experiment, each unique physics combination is given to one member of the IC/BC ensemble to create a new “IC/BC PHYS” ensemble to examine any nonlinear contributions to ensemble spread. c. Data assimilation Data assimilation (DA) has been used for decades as a tool to improve numerical model forecasts by
references regarding physics schemes]. The initial and boundary conditions are taken from the same GFS deterministic forecast as the IC ensemble but are not perturbed. As a final component to this experiment, each unique physics combination is given to one member of the IC/BC ensemble to create a new “IC/BC PHYS” ensemble to examine any nonlinear contributions to ensemble spread. c. Data assimilation Data assimilation (DA) has been used for decades as a tool to improve numerical model forecasts by
