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Ousmane O. Sy, Simone Tanelli, Stephen L. Durden, Andrew Heymsfield, Aaron Bansemer, Kwo-Sen Kuo, Noppasin Niamsuwan, Robert M. Beauchamp, V. Chandrasekar, Manuel Vega, and Michael P. Johnson

number of case studies using data from specific events in GCPEx and OLYMPEx have been completed ( Heymsfield et al. 2018 ; Huang et al. 2019 ; Leinonen et al. 2018 ; Chase et al. 2018 ; Durden et al. 2020 ). In this paper, we use all the in situ, APR and D3R data from GCPEx and OLYMPEx. Fontaine et al. (2014) , Wood et al. (2015) , Falconi et al. (2018) , and Finlon et al. (2019) have conducted analyses similar to ours using data from Cirrus Regional Study of Tropical Anvils and Cirrus

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

distributions (PSD) and observed or assumed ice particle shapes (e.g., Sekhon and Srivastava 1970 ). Shape information is necessary not only because the backscatter cross section is approximately proportional to the square of the ice particle mass but also because, as the radar wavelength decreases, non-Rayleigh scattering effects become increasingly more important. Non-Rayleigh scattering and particle shape strongly affect the shape-dependent backscatter cross sections ( σ ) from the ice hydrometeors

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

blockage, range effects, and other causes ( Hunter 1996 ). However, the main drawback in mountainous regions is a lack of low-level coverage due to terrain blockage ( Maddox et al. 2002 ). As noted above, gauge-based precipitation products may not be reliable in areas with sparse gauge distributions ( Henn et al. 2015 , 2018 ). Given that gauges provide accurate point rainfall measurements while radar provides rainfall estimates at a larger spatial coverage, a great deal of effort has been devoted to

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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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Hannah C. Barnes, Joseph P. Zagrodnik, Lynn A. McMurdie, Angela K. Rowe, and Robert A. Houze Jr.

with ρ HV values between 0.95 and 0.85. Note that these ρ HV thresholds slightly vary with wavelength because of resonance effects. For a comprehensive description of dual-polarization radar variables, see Bringi and Chandrasekar (2001) . NASA’s dual-polarization S-band radar (NPOL) was deployed during IFloodS and OLYMPEX ( Petersen and Krajewski 2018 ; Petersen et al. 2017 ). During IFloodS, NPOL was located near Waterloo, Iowa ( Fig. 2a ; 42.5°N, 92.3°W), and was operated primarily by

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Robert Conrick, Clifford F. Mass, and Qi Zhong

University boundary layer scheme ( Hong et al. 2006 ). Following Barnes et al. (2018) , the NCEP North American Regional Reanalysis (NARR; Mesinger et al. 2006 ) was used to evaluate the simulated synoptic conditions for the two case studies. Figure 2 presents a map of the OLYMPEX observing locations used in this study and the regional terrain. Barnes et al. (2018) and Houze et al. (2017) described the OLYMPEX instrumentation used to observe KH waves, including the NASA polarimetric (NPOL) radar

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Robert Conrick and Clifford F. Mass

mesoscale conditions varying substantially between storm sectors (prefrontal, warm sector, and postfrontal). Because IVT was realistically simulated by the University of Washington (UW) WRF during OLYMPEX (Conrick and Mass 2018, manuscript submitted to J. Hydrometeor. ), we define storm sectors in terms of IVT as in McMurdie et al. (2018) , using values from the North American Regional Reanalysis (NARR; Mesinger et al. 2006 ) grid point nearest to the OLYMPEX NASA S-Band Dual Polarimetric (NPOL

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Robert Conrick, Joseph P. Zagrodnik, and Clifford F. Mass

by latitude. The results of the above studies and others suggest that DSD radar retrieval algorithms developed for convective storms may not be applicable to locations where stratiform precipitation is dominant or where topography is a key controller of regional precipitation distributions, such as the Pacific Northwest ( Chow et al. 2013 ). Zagrodnik et al. (2018) , using DSD observations from the Olympic Mountains Experiment (OLYMPEX; Houze et al. 2017 ), showed that DSDs varied considerably

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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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