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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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Robert Conrick, Clifford F. Mass, and Qi Zhong

idealized or dry models. Large-eddy simulation (LES) experiments have investigated KH instability in a variety of real-world cases, including in a mesoscale convective system (MCS) over southern England ( Browning et al. 2012 ), within a hurricane boundary layer ( Nakanishi and Niino 2012 ; Na et al. 2014 ), during frontogenesis ( Samelson and Skyllingstad 2016 ), and for stratified flow over terrain ( Sauer et al. 2016 ). Recent studies have used full-physics NWP models to simulate KH waves. Mahalov

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Robert Conrick and Clifford F. Mass

precipitation. During the period of November 2015–February 2016, spatial errors of forecast precipitation from the University of Washington WRF mesoscale modeling system were examined. The results generally agreed with past studies in the Pacific Northwest, finding overprediction of precipitation on the windward slopes of the Oregon Cascades and considerable underprediction along the Pacific coastal zone. Errors in rain drop size distributions during this period showed significant overprediction of rain

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Robert Conrick and Clifford F. Mass

1. Introduction The evaluation of mesoscale weather prediction models over coastal mountains is of substantial importance because orographically forced coastal precipitation is critical for the water resources that serve many highly populated areas ( Barros 2013 ). The properties of coastal precipitation systems depend on the upstream environment over the ocean, where collecting observations is difficult ( Stoelinga et al. 2013 ). As a result, satellite-derived measurements are often the sole

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Aaron R. Naeger, Brian A. Colle, Na Zhou, and Andrew Molthan

-resolution Rapid Refresh model’s ability to predict mesoscale convective systems using object-based evaluation . Wea. Forecasting , 30 , 892 – 913 , https://doi.org/10.1175/WAF-D-14-00118.1 . 10.1175/WAF-D-14-00118.1 Poellot , M. R. , A. J. Heymsfield and A. Bansemer , 2017 : GPM ground validation UND citation cloud microphysics OLYMPEX. NASA Global Hydrology Resource Center DAAC, accessed 2 November 2016 , http://doi.org/10.5067/GPMGV/OLYMPEX/MULTIPLE/DATA201 . 10.5067/GPMGV

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David J. Purnell and Daniel J. Kirshbaum

frontal precipitation. Annual precipitation estimates from the Parameter-Elevation Relationships on Independent Slopes Model (PRISM) of Oregon State University ( Daly et al. 2008 ) shows a maximum of over 6600 mm over windward (west-southwest)-facing peaks and a leeside minimum of around 400 mm ( Fig. 1b ). Although such estimates are highly uncertain, they suggest remarkable mesoscale gradients in the regional climate. Fig . 1. (a) Terrain map of the Olympics and surrounding regions, along with

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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

determine the snow depth. The February flights occurred at a midseason time before maximum snow cover. The late March flights took place when winter snow cover was near maximum. SUCCESSFUL PROJECT COORDINATION. OLYMPEX operations required careful coordination of forecasting, decision-making, and scheduling of aircraft, radars, and soundings. The success of OLYMPEX operations is perhaps best illustrated in Fig. 5 showing how all three aircraft were positioned in the center of a GPM overpass at 1522 UTC

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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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Annareli Morales, Hugh Morrison, and Derek J. Posselt

are implemented in weather forecast models to represent the development of clouds and precipitation. Simulated orographic precipitation for both AR and non-AR events has been found to be sensitive to the choice of microphysics scheme ( Jankov et al. 2007 , 2009 ; Liu et al. 2011 ). Sensitivity studies exploring the effects of microphysical parameters on orographic precipitation show changes in these parameters can impact cloud and precipitation development. Colle and Mass (2000) found lower

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

warm advection and rising melting levels. The frontal sector is a broad, elongated cloud shield within which a cold or occluded front’s circulation generates banded precipitation. Sometimes the cold front is sharp, but more often over the ocean the temperature change in the cold-frontal zone is weak. Within these storm sectors are embedded rainbands and other mesoscale and convective elements of enhanced precipitation. The broad, moist warm sector located ahead of the cold-frontal zone and to the

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