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Daniel T. Eipper, George S. Young, Steven J. Greybush, Seth Saslo, Todd D. Sikora, and Richard D. Clark

additional mechanisms supporting InPen. At the same time, our results indicate the importance of a favorable thermodynamic environment—especially a deep CBL—to InPen 20 . 2) Inland plume-focusing hypothesis A second hypothesis for InPen is that InPen is enhanced when the plume of buoyancy and moisture associated with the LLAP band is focused into a narrow ribbon downwind of the parent lake. This inland plume-focusing hypothesis follows the experience of operational forecasters as reported in Niziol et

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Leah S. Campbell, W. James Steenburgh, Peter G. Veals, Theodore W. Letcher, and Justin R. Minder

. D. Hall , 1994 : Numerical simulations of convective snow clouds over the Sea of Japan: 2-dimensional simulations of mixed-layer development and convective snow cloud formation . J. Meteor. Soc. Japan , 72 , 43 – 62 . Nakai , S. , and T. Endoh , 1995 : Observation of snowfall and airflow over a low mountain barrier . J. Meteor. Soc. Japan , 73 , 183 – 199 . Niziol , T. A. , 1987 : Operational forecasting of lake effect snowfall in western and central New York . Wea

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Peter G. Veals, W. James Steenburgh, and Leah S. Campbell

and Kristovich 2002 ). The strength of the fluxes and height of the cap in turn affect the behavior and intensity of the lake-effect convection. Larger fluxes and a higher cap enable deeper, stronger convection and greater LPE downwind of the lake (e.g., Braham 1983 ; Niziol 1987 ; Hjelmfelt 1990 ; Byrd et al. 1991 ; Smith and Boris 2017 ). For operational forecasting, the potential for boundary layer growth and lake-effect convection is often assessed using estimates of the lake

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Seth Saslo and Steven J. Greybush

. Kadowaki , 2010 : Ensemble Kalman filter and 4D-Var intercomparison with the Japanese operational global analysis and prediction system . Mon. Wea. Rev. , 138 , 2846 – 2866 , doi: 10.1175/2010MWR3209.1 . 10.1175/2010MWR3209.1 Niziol , T. A. , 1987 : Operation forecasting of lake effect snowfall in western and central New York . Wea. Forecasting , 2 , 310 – 321 , doi: 10.1175/1520-0434(1987)002<0310:OFOLES>2.0.CO;2 . 10.1175/1520-0434(1987)002<0310:OFOLES>2.0.CO;2 Niziol , T. A. , W. R

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David A. R. Kristovich, Richard D. Clark, Jeffrey Frame, Bart Geerts, Kevin R. Knupp, Karen A. Kosiba, Neil F. Laird, Nicholas D. Metz, Justin R. Minder, Todd D. Sikora, W. James Steenburgh, Scott M. Steiger, Joshua Wurman, and George S. Young

they form. DOW and Geostationary Operational Environmental Satellite (GOES) imagery are being used to examine the convective field. Weather Research and Forecasting (WRF) Model reanalysis using OWLeS observations is providing the 4D thermal advection and diabatic heating fields necessary to fully explore the boundary layer destabilization mechanisms. MUPS observations are also being employed to describe the surface forcing. Influence of upwind lakes. Most large field projects on LeSs were

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W. James Steenburgh and Leah S. Campbell

played an unexpectedly prominent role in simulations exploring orographic effects over Tug Hill ( Campbell and Steenburgh 2017 ). In the next section, we describe the datasets and modeling system used for our analysis. Sections 3 – 6 then use operational analyses, Weather Research and Forecasting (WRF) Model simulations, trajectories, and frontogenesis diagnostics to show how the large-scale flow, shape of the Lake Ontario shoreline, and differential surface heating and roughness contribute to the

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Peter G. Veals and W. James Steenburgh

to provide new insights into lake-effect precipitation east of Lake Ontario and over Tug Hill, with relevance for operational forecasting, regional climate applications, and improved knowledge of lake-effect and orographic precipitation processes. The methods used for the radar-based climatology are described in section 2 , with detailed analysis of the regional lake-effect characteristics and influence of Tug Hill presented in section 3 . Conclusions and future work are summarized in section

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David A. R. Kristovich, Luke Bard, Leslie Stoecker, and Bart Geerts

1. Introduction Forecasts of lake-effect snowstorms have become increasingly accurate as numerical atmospheric simulations have improved. However, critical details of the mesoscale structure, spatial and temporal distribution of snowfall, snowband movement, and precipitation intensity continue to be difficult to predict (e.g., Niziol et al. 1995 ). Much of the forecast difficulty is due to smaller-scale processes within the lake-effect boundary layer ( Saslo and Greybush 2017 ), many of which

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Daniel T. Eipper, Steven J. Greybush, George S. Young, Seth Saslo, Todd D. Sikora, and Richard D. Clark

cover all periods with dominant bands in OWLeS with the exception of the 15–16 December 2013 storm. Following the identification of these time periods, reanalysis simulations were generated for each period with the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ). These reanalyses were generated by assimilating hourly in situ observations (e.g., operational surface, aircraft, and radiosonde data were assimilated; radar reflectivities and OWLeS field observations were not

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Leah S. Campbell and W. James Steenburgh

1. Introduction Lake-effect snowstorms generated over the Great Lakes of North America and other bodies of water can produce intense, extremely localized snowfall (e.g., Andersson and Nilsson 1990 ; Steenburgh et al. 2000 ; Eito et al. 2005 ; Laird et al. 2009 ; Kindap 2010 ). Forecasters still struggle, however, to accurately predict the timing and location of the heaviest snowfall during lake-effect events, which disrupt local and regional transportation, education, utilities, and

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