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

mechanisms for InPen. In particular, we investigate the role of vertically differential temperature advection on InPen, which is not addressed by Villani et al. (2017) . Additional insight into the large-scale predictors of InPen will further equip forecasters to accurately leverage observations and numerical weather prediction (NWP) model guidance. In the remainder of the paper, we first describe the datasets and data processing techniques used in this study. We then investigate physical mechanisms and

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Seth Saslo and Steven J. Greybush

reducing initial condition error and involves statistically combining model forecasts and observations ( Kalnay 2003 ). In particular, the ensemble Kalman filter (EnKF; Evensen 1994 ) is a DA technique that uses a flow-dependent background error covariance from a forecast model ensemble, which often improves the final analysis compared to other methods, such as three-dimensional variational data assimilation (3DVAR; e.g., M. Zhang et al. 2011 ; Miyoshi et al. 2010 ; Buehner et al. 2010 ), and has

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

:// . 10.1175/MWR-D-15-0412.1 Eipper , D. T. , G. S. Young , S. J. Greybush , S. Saslo , T. D. Sikora , and R. D. Clark , 2018 : Predicting the inland penetration of long-lake-axis-parallel snowbands . Wea. Forecasting , 33 , 1435 – 1451 , . 10.1175/WAF-D-18-0033.1 Grell , G. A. , and D. Dévényi , 2002 : A generalized approach to parameterizing convection combining ensemble and data assimilation techniques . Geophys. Res

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