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Joseph P. Zagrodnik, Lynn McMurdie, and Robert Conrick

small-scale ridges ( Minder et al. 2008 ), and the semi-idealized nature of the simulations in Purnell and Kirshbaum (2018) makes it difficult to directly compare with Zagrodnik et al. (2018 , 2019) . By examining microphysical output from realistic Weather Research and Forecasting (WRF) simulations, this study evaluates the relative importance of warm and cold precipitation processes on the full barrier scale as well as on localized sub-barrier ridges and valleys. The model setup, evaluation

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Minda Le and V. Chandrasekar

rate. Brief algorithm descriptions are available in section 2 . The algorithm provides a surface snowfall flag (1 or 0 product) at each valid DPR Ku- and Ka-band matched footprint. Le et al. (2017) showed initial qualitative evaluations of the algorithm with promising results when compared to some of the Next Generation Weather Radars (NEXRAD; or WSR-88D). In this paper, we focus on performing more extensive ground validations in both qualitative and quantitative manner with NEXRAD, NASA

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Robert A. Houze Jr., Lynn A. McMurdie, Walter A. Petersen, Mathew R. Schwaller, William Baccus, Jessica D. Lundquist, Clifford F. Mass, Bart Nijssen, Steven A. Rutledge, David R. Hudak, Simone Tanelli, Gerald G. Mace, Michael R. Poellot, Dennis P. Lettenmaier, Joseph P. Zagrodnik, Angela K. Rowe, Jennifer C. DeHart, Luke E. Madaus, Hannah C. Barnes, and V. Chandrasekar

understand orographic modification of frontal precipitation processes but also to satisfy the need for further development and refinement of algorithms used to convert GPM’s satellite measurements to precipitation amounts in midlatitudes. The algorithms applied to TRMM satellite data over a nearly 17-yr period have been very successful for rain measurement and characterizing tropical convection ( Simpson 1988 ; Simpson et al. 1996 ; Kummerow et al. 1998 ; Zipser et al. 2006 ; Huffman et al. 2007

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Joseph P. Zagrodnik, Lynn A. McMurdie, Robert A. Houze Jr., and Simone Tanelli

modification of precipitation processes over West Coastal mountains (e.g., Anders et al. 2007 ; Hobbs et al. 1971 ; Houze et al. 1976 ; Hobbs 1978 ; Matejka et al. 1980 ; Bond et al. 1997 ; Ralph et al. 1999 ; Stoelinga et al. 2003 ; Houze and Medina 2005 ; Medina et al. 2007 ; Minder et al. 2008 ; Barrett et al. 2009 ; Viale et al. 2013 ; Massmann et al. 2017 ; Zagrodnik et al. 2018 ). Ground-based scanning radars have been especially valuable in determining the specific processes that lead

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Yagmur Derin, Emmanouil Anagnostou, Marios Anagnostou, and John Kalogiros

and C band radars. Although the range of low-power X-band radar systems is relatively short, their small size, mobility, higher spatial and temporal resolution, and stronger differential phase signals make them a convenient tool for hydrometeorological studies over complex terrain or regions that lack adequate coverage by the operational weather radar networks ( Brotzge et al. 2006 ; Matrosov et al. 2005 ). Moreover, short-wavelength radars can monitor rainfall variability at smaller scales and

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David J. Purnell and Daniel J. Kirshbaum

, an operational radar was also added to the National Weather Service (NWS) Doppler network at Langley Hill, Washington. Located southwest of the Olympics, this radar complements the Camano Island radar on the opposite side ( Fig. 1a ). Building on this newfound infrastructure, the Olympics Mountains Experiment (OLYMPEX) in winter 2015/16 ( Houze et al. 2017 ) intensively observed numerous Olympics precipitation events. OLYMPEX sought to gain process understanding and to verify satellite

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Hannah C. Barnes, Joseph P. Zagrodnik, Lynn A. McMurdie, Angela K. Rowe, and Robert A. Houze Jr.

study. The Center for Severe Weather Research (CSWR) deployed one of its X-band National Science Foundation (NSF)-sponsored Doppler on Wheels (DOW) radars near Amanda Park, Washington (47.5°N, 123.9°W), in the foothills of the Olympic Mountains. The DOW was at an elevation of 72 m and approximately 45 km northeast of NPOL and D3R. This dual-polarization radar conducted RHI scans up the Quinault Valley and provided data at low levels in the valley below NPOL’s beam. The RHI scans conducted by NPOL, D

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William Ryan Currier, Theodore Thorson, and Jessica D. Lundquist

1. Introduction Quantifying the amount of precipitation that falls as snow in complex terrain, where we have limited observations, remains a challenge. Methods that produce estimates of spatially distributed precipitation range from physically based numerical weather models, such as the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ), to statistical models that spatially interpolate surface precipitation observations. A widely used statistical model is the Parameter

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Stephanie M. Wingo, Walter A. Petersen, Patrick N. Gatlin, Charanjit S. Pabla, David A. Marks, and David B. Wolff

1. Introduction NASA’s Global Precipitation Measurement (GPM) mission aims to advance understanding of Earth’s water and energy cycles and has a broader goal of improving prediction capability for high-impact weather and climate events in order to benefit society ( Hou et al. 2014 ; Skofronick-Jackson et al. 2017 ). Recent decades have seen tremendous precipitation science advancement, and there is now an unprecedented suite of space- and ground-based precipitation sensors in use around the

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