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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

/scattering signals of the surface and the atmosphere at 13 frequency channels ranging from 10 to 183 GHz. On the one hand, observations by the DPR and the GMI high-frequency channels (>80 GHz) provide information about the microwave signature of precipitation and more specifically about snowfall ice scattering. On the other hand, observations by the low-frequency channels (>80 GHz) add information about the land surface characteristics that leads to improved detection skill by the presented algorithm. This study

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Annareli Morales, Hugh Morrison, and Derek J. Posselt

the large WRA simulation. To briefly summarize, perturbations to WRA demonstrate the nonlinear interactions between cloud water and surface precipitation. Reducing the rain accretion process initially reduces PE and precipitation rate and increases cloud water in the atmosphere, but the increased cloud water is associated with a deeper cloud layer (lower cloud base), leading to an increase in 〈COND〉 that compensates for the decrease in PE. The opposite occurs for large WRA, thus resulting in

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Robert Conrick, Clifford F. Mass, and Qi Zhong

et al. (2011) , using the Weather Research and Forecasting (WRF) Model, demonstrated that terrain-induced KH waves in the upper troposphere were not resolved at 3-km horizontal grid spacing but were at 1 km. Efimov (2017) used the WRF Model to simulate KH waves over Crimea, and Trier et al. (2012) simulated turbulence arising from KH instability in a winter cyclone. Thompson (2007) simulated the formation of KH instability in a sea-breeze front using the U.S. Navy’s Coupled Ocean–Atmosphere

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

.1175/JHM-D-16-0209.1 Purnell , D. J. , and D. J. Kirshbaum , 2018 : Synoptic control over orographic precipitation distributions during the Olympics Mountains Experiment (OLYMPEX) . Mon. Wea. Rev. , 146 , 1023 – 1044 , . 10.1175/MWR-D-17-0267.1 Ralph , F. M. , and Coauthors , 1999 : The California Land-Falling Jets Experiment (CALJET): Objectives and design of a coastal atmosphere–ocean observing system deployed during a strong El Niño. Preprints

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

2016 and 29–30 March 2016, and the data were processed to a 3-m gridded resolution of snow depth. The accuracy of ASO in a nonforested, flat, 15 m × 15 m area has been shown to have a mean absolute error of less than 8 cm, with an overall bias of less than 1 cm ( Painter et al. 2016 ). In appendix A , we compare the ASO data with our snow depth pole measurements. In this paper, we focus on snowfall accumulation and not on forest–snow interactions. Therefore, we used the classification from the

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

1. Introduction Bulk microphysical parameterization (BMP) schemes are a critical component of operational weather forecasting models, as they must simulate the formation and development of hydrometeor species, including their interaction, growth, and precipitation processes. Thus, model performance and precipitation forecasts can be strongly dependent on the parameterizations and assumptions used to represent these complex processes within the BMPs ( Lin and Colle 2009 ; Morrison et al. 2009

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