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Joseph P. Zagrodnik, Lynn McMurdie, and Robert Conrick

windward (dark blue) domains used in the WRF CFADs. Lines denote the prefrontal (light green) and warm sector (dark green) DC-8 flight paths used in the APR-3 CFADs. The orange dot denotes the sounding launch location ( Table 3 ). The red lines denote the cross section in Figs. 8 , 9 and 10 . Darker gray shades over land denote higher terrain. The black outline over land denotes the Quinault River basin, which includes the Quinault valley. Model kinematic and thermodynamic output was validated by

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Robert Conrick and Clifford F. Mass

profiles of simulated reflectivity consistent with observed hydrometeor profiles? Do varying environmental conditions, such as those of different midlatitude storm sectors, influence model skill? Does the accuracy of simulated rain rate or reflectivity depend on surface type (ocean vs land)? This paper is organized as follows: section 2 reviews GPM data and model configuration, section 3 describes results of our model evaluation using GPM, and section 4 offers concluding remarks. 2. Model

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

blizzard storm with the mesoscale MM5 model and a delta-Eddington-type radiative transfer (RT) model to produce a storm-scale database for snowfall retrieval using AMSU-B observations. Noh et al. (2009) used a large number of snowfall profiles from airborne, surface, and satellite radars, as well as an atmospheric RT model ( Liu 1998 ) to generate a regional database for snowfall retrievals using the AMSU-B data. The study used the NESDIS Microwave Land Surface Emissivity Model ( Weng et al. 2001

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

jets. Over the last decade, several studies have been performed that use remotely sensed snow data to help infer the spatial distribution of precipitation. Durand et al. (2008) and Girotto et al. (2014a , b ) used satellite-derived snow covered area (SCA) data, after converting to SWE using a snow depletion curve, to update the precipitation disaggregation weights in a land surface model via smoothing methods. Livneh et al. (2014) used the seasonal peak value of SWE via reconstructions based

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

time-varying surface roughness length taken from WRF, and z d is the zero-plane displacement height. In this manuscript, we took z d to be the simulated snow depth from WRF’s embedded land surface model (Noah). All model forcing data for SUMMA is summarized in Table B1 . Table B1. SUMMA model forcing data used in the calibration and precipitation evaluation phase. The dash indicates that the source of the model forcing data is the same as that in the column to the left. APPENDIX C Adjusted

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David J. Purnell and Daniel J. Kirshbaum

. 8. Grid configuration, land coverage, and terrain height h (filled contours) for the numerical simulations. Water bodies are shown in blue. Physical parameterizations include Thompson microphysics ( Thompson et al. 2008 ) with a maritime cloud-droplet concentration of 100 cm −3 , the Yonsei University planetary boundary layer scheme coupled to a surface layer using Monin–Obukhov similarity theory ( Hong et al. 2006 ), horizontal mixing along model surfaces using Smagorinsky closure, and a

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Robert Conrick, Clifford F. Mass, and Qi Zhong

: A numerical study on sea/land breezes as a gravity current: Kelvin–Helmholtz billows and inland penetration of the sea-breeze front . J. Atmos. Sci. , 48 , 1649 – 1665 , https://doi.org/10.1175/1520-0469(1991)048<1649:ANSOSB>2.0.CO;2 . 10.1175/1520-0469(1991)048<1649:ANSOSB>2.0.CO;2 Skamarock , W. C. , 2004 : Evaluating mesoscale NWP models using kinetic energy spectra . Mon. Wea. Rev. , 132 , 3019 – 3032 , https://doi.org/10.1175/MWR2830.1 . 10.1175/MWR2830.1 Skamarock , W. C. , J

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Andrew Heymsfield, Aaron Bansemer, Norman B. Wood, Guosheng Liu, Simone Tanelli, Ousmane O. Sy, Michael Poellot, and Chuntao Liu

1. Introduction Accurate quantification of the amount, vertical distribution, and phase (liquid or ice) of precipitation is critical for hydrological applications and for understanding the current state of Earth’s climate and its future changes ( Stephens et al. 2002 ; Trenberth et al. 2007 ). Ground-based measurements of precipitation are commonly considered to be the most accurate, but they are located almost exclusively over populated land and thus not available in many regions of the world

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

lee side is very dry ( Fig. 1 ). In a recent study using OLYMPEX data, Purnell and Kirshbaum (2018 , hereafter PK18 ) used rain gauges and operational National Weather Service radars to show that orographic precipitation distributions are highly sensitive to the upstream static stability, horizontal moisture flux, and the presence of preexisting precipitation associated with the large-scale synoptic storm sectors. McMurdie et al. (2018) found that when the large-scale conditions resembled warm

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Robert Conrick and Clifford F. Mass

. Initialization and boundary conditions were driven by the 0.5° NOAA/National Weather Service (NWS) Global Forecast System (GFS) gridded forecasts, with some surface parameters initialized from other sources. 1 Boundaries were updated and the 36-km grid nudged every 3 h using the GFS forecasts. Parameterization options included the Noah-MP land surface model ( Niu et al. 2011 ), the RRTMG radiation scheme ( Iacono et al. 2008 ), the Yonsei University (YSU; Hong et al. 2006 ) boundary/surface layer (PBL

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