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Joseph P. Zagrodnik, Lynn McMurdie, and Robert Conrick

are especially important on smaller-scale ridges where any condensate formed by ascent on the windward side has a short amount of time to reach precipitation size and fall out. Many studies have observed or modeled processes resembling the “seeder–feeder” effect ( Bergeron 1968 ), which refers to the enhancement of precipitation on a small ridge when condensate falling from a preexisting higher cloud collects cloud drops from a separate, lower-level cloud, resulting in a more efficient

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Robert Conrick and Clifford F. Mass

mean vertical profiles of cloud water, rainwater, and snow mixing ratios averaged by storm sector. Postfrontal sectors generally had profiles with the smallest mixing ratios of all sectors. Considering that warm-sector environments are often associated with strong synoptic forcing and large IVT (e.g., Zhu and Newell 1994 , Zagrodnik et al. 2018 ), it was not unexpected that those environments had the greatest mixing ratios among sectors. Compared to GMI, cloud water profiles during pre- and

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Annareli Morales, Hugh Morrison, and Derek J. Posselt

), cloud water evaporation rate (blue contours = −1 × 10 −3 g kg −1 s −1 , cyan contours = −5 × 10 −4 g kg −1 s −1 ), and freezing level (thick black line). (a) WRA = 0.5, (b) WRA = 2, (c) ECI = 0.3, (d) ECI = 1. This point can be illustrated clearly for moist pseudoadiabatic descent; while the model has diabatic forcing due to mixing and other microphysical processes, moist pseudoadiabatic descent can serve as a useful guide for explaining this behavior. For reversible moist pseudoadiabatic

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Robert Conrick and Clifford F. Mass

have focused on improving the representation of cloud condensation nuclei (CCN) and ice nuclei (IN) in bulk microphysics schemes. Simulations of aerosol impacts on precipitation have found that a reduction in CCN can invigorate warm rain processes ( Alizadeh-Choobari and Gharaylou 2017 ; Li et al. 2011 ; Khain 2009 ; Khain et al. 2012 ; Tao et al. 2012 ; Thompson and Eidhammer 2014 ; Nugent et al. 2016 ), which may be important along the relatively warm coastal areas of the Pacific Northwest

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

highly varying concentrations of large drops, suggesting both warm rain (e.g., collision–coalescence) and cold rain processes (e.g., melting). Purnell and Kirshbaum (2018) noted the presence of cold rain via an active seeder–feeder process during warm frontal and sector conditions throughout OLYMPEX from the synthesis of observations and model simulations in which “seeder” clouds initiate precipitation growth that falls into orographically enhanced (“feeder”) clouds at lower levels ( Cotton et al

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David J. Purnell and Daniel J. Kirshbaum

cloud depth (not shown). To systematically quantify the sensitivity of OPEs to , we conduct sets of WF and WS simulations where in (5) is progressively varied. To limit expense, these experiments use km, which does not affect the basic model sensitivities (not shown). Nine values of are considered: 0, 0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.3, and 0.5 m s −1 , encompassing the control values for both the WF and WS cases. Because synoptic forcing is limited to the midtroposphere ( km), the

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

force had an eastward component near the Washington coast ( Fig. 9b ), and the low-level winds were veering ( Fig. 10 ). The melting level was low compared to the region south of 40°N ( Fig. 9a ). At sea level, a weak pressure trough separated a colder air mass to the north, and an elongated band of IVT >400 kg m −1 s −1 stretched for more than 4000 km across the Pacific Ocean ( Fig. 9b ). A long band of clouds ( Fig. 11a ) coincided with the IVT band. Cold air remained north of the Olympic

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

significant uncertainty in spatially distributed precipitation estimates ( Gutmann et al. 2012 ; Livneh et al. 2014 ; Henn et al. 2016 ) due to a sparse network of gauges ( Lundquist et al. 2003 ) and observational uncertainty at the gauge itself ( Goodison et al. 1998 ; Rasmussen et al. 2012 ). WRF or PRISM are frequently used to force hydrologic models, which guide decisions regarding avalanche control, reservoir storage, and flood forecasting. Therefore, uncertainties in the estimation of

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

1. Introduction Precipitation over midlatitude West Coastal mountain ranges is usually associated with baroclinic frontal cyclones containing distinct cloud patterns, which are modified during passage over complex terrain ( Nagle and Serebreny 1962 ; Medina et al. 2007 ). Observations from numerous past field programs have characterized the complex ways in which warm processes (condensation/collision–coalescence) and cold processes (riming, accretion, and aggregation) contribute to the

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Robert Conrick, Clifford F. Mass, and Qi Zhong

variability from 2000 to 2300 UTC ( Fig. 8 ): Simulated vertical velocity oscillations exceeded 5 m s −1 within the 0.5–3-km layer, which impacted surface mass-weighted mean drop diameter and mixing ratios of cloud and rainwater. Precipitation in the 444-m domain was modulated by the waves, albeit with a smaller amplitude than observed. Fig . 8. Simulated vertical profiles at Bishop Field for 2000–2300 UTC of (a) cloud water mixing ratio (gray fill; gray dashed contours), rainwater mixing ratio (red

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