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Joseph P. Zagrodnik, Lynn McMurdie, and Robert Conrick

coastal California mountains and coastal Chilean mountains ( White et al. 2003 ; Kingsmill et al. 2006 ; Martner et al. 2008 ; Massmann et al. 2017 ) used the presence of a bright band in vertically profiling radar to separate periods of exclusively warm (nonbrightband) rain from periods when ice-generated particles might be collecting cloud or rainwater. Surprisingly, the warm rain periods, characterized by large concentrations of small raindrops, were found to produce rain rates up to 20 mm h −1

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

-dimensional optical array cloud probe (2D-C; Heymsfield et al. 2017 ) and high-volume particle spectrometer (HVPS) for permitting retrievals of ice particle number concentrations for 41 size bins ranging from 0.04 to 30 mm ( Poellot et al. 2017 ). We derived D m from the ice number concentrations using the Heymsfield et al. (2004) methodology, which is valid for snow aggregates, and particles smaller than 100 μ m were disregarded due to large uncertainties in the sample area of the probe ( Strapp et al

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

sectors (moist-neutral upstream stability combined with high horizontal water vapor flux), the precipitating cloud over terrain was enhanced in the ice layer throughout the depth of the precipitating system. Zagrodnik et al. (2018) showed that the periods of heaviest rainfall on the windward slopes often contained large concentrations of small drops, suggesting that warm precipitation processes are dominant during the strongest terrain-induced enhancement. Based on quasi-idealized model simulations

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Robert Conrick and Clifford F. Mass

; Skofronick-Jackson et al. 2017 ), the OLYMPEX field program was conducted during the winter of 2015/16. A variety of midlatitude frontal systems were sampled during OLYMPEX by an extensive collection of satellite, aircraft, surface, and radar observations that provided a comprehensive microphysical description of these systems. Additional details of the OLYMPEX field campaign are found in Houze et al. (2017) . Ice microphysics impact the fidelity of simulated orographic precipitation (e.g., Hobbs et al

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

. (2006) used profiling radars to show that collision–coalescence can produce precipitation on the low-elevation windward slopes of the California coastal range without radar indication of ice-phase hydrometeors. Martner et al. (2008) further illustrated that this “nonbrightband rain” was associated with larger concentrations of small drops, while the presence of melting ice hydrometeors from deeper clouds, when present, shifted the drop size distribution (DSD) toward larger drop sizes and smaller

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Qian Cao, Thomas H. Painter, William Ryan Currier, Jessica D. Lundquist, and Dennis P. Lettenmaier

Juan de Fuca to the north, and Puget Sound to the east (see Fig. 1 ). Elevations range from sea level to 2427 m at the top of Mt. Olympus in the interior of the Peninsula. Precipitation in this area is winter dominant, with over 80% of the annual total (on average over our domain) falling between October and April. The southwestern and western slopes of the Olympic Mountains are covered by dense temperate rain forest and receive plentiful winter precipitation due to orographic enhancement of

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Bin Pei and Firat Y. Testik

DSD parameters retrieved through the Bayesian approach. In another study, Posselt et al. (2015) analyzed the information content of the selected dual-polarization radar observables in the mixed- and ice-phase regions of a convective storm. Both observational and modeling uncertainties of the radar observables were quantified using the Bayesian approach similar to that implemented in Cao et al. (2010) . The likelihood equation, which may be considered equivalent to a cost function, was utilized

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Minda Le and V. Chandrasekar

observations from NPOL and APR3. The surface snowfall identification is based on studying the vertical profiles of Ku and Ka band from storm top toward the surface. To avoid the instability of the algorithm due to the clutter contamination, we calculate the snow index based on range bin from storm top to three bins above the clutter free. In the case above, melting happens at around 1 km, roughly four bins above sea level. The surface snowfall identification algorithm has difficulty catching the

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David J. Purnell and Daniel J. Kirshbaum

–density (or )-weighted mean wind vector over the sounding depth. Then, I is the vertically integrated component of the horizontal moisture flux parallel to . To calculate M , we vertically average N (computed separately for each level) and the wind component aligned with from the surface to m, the Olympics crest height. The relative humidity (RH) is computed relative to liquid for and ice for , where T is temperature and C. If RH at a given level is greater than 98%, we use the saturated

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