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Joseph P. Zagrodnik, Lynn McMurdie, and Robert Conrick

small-scale ridges ( Minder et al. 2008 ), and the semi-idealized nature of the simulations in Purnell and Kirshbaum (2018) makes it difficult to directly compare with Zagrodnik et al. (2018 , 2019) . By examining microphysical output from realistic Weather Research and Forecasting (WRF) simulations, this study evaluates the relative importance of warm and cold precipitation processes on the full barrier scale as well as on localized sub-barrier ridges and valleys. The model setup, evaluation

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Robert Conrick and Clifford F. Mass

events are documented in section 4 . Finally, section 5 offers concluding remarks. 2. Model configurations and precipitation data a. Model configuration During OLYMPEX, real-time operational forecasts were produced by the University of Washington (UW) using version 3.7.1 of the Advanced Research version of the WRF Model (WRF-ARW; Skamarock et al. 2008 ). A 36–12–4–1.33-km domain configuration was utilized with 38 vertical levels ( Fig. 1a ), with the innermost domain covering Washington State

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Robert Conrick and Clifford F. Mass

configuration and GPM data a. Model configuration During OLYMPEX, operational forecasts were produced at the University of Washington using the WRF ( Skamarock et al. 2008 ) Model, version 3.7.1. As in Conrick and Mass (2018, manuscript submitted to J. Hydrometeor. ), we use archived WRF forecasts, valid every 6 h from 0600 to 2400 UTC and initialized daily at 0000 UTC, for the period from 1 November 2015 to 1 February 2016. A 36–12–4–1.33-km domain configuration was utilized with 38 vertical levels, with

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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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Robert A. Houze Jr., Lynn A. McMurdie, Walter A. Petersen, Mathew R. Schwaller, William Baccus, Jessica D. Lundquist, Clifford F. Mass, Bart Nijssen, Steven A. Rutledge, David R. Hudak, Simone Tanelli, Gerald G. Mace, Michael R. Poellot, Dennis P. Lettenmaier, Joseph P. Zagrodnik, Angela K. Rowe, Jennifer C. DeHart, Luke E. Madaus, Hannah C. Barnes, and V. Chandrasekar

storms vary strongly in the vertical. In contrast, scanning of operational radars provides broad horizontal coverage from sequences of plan position indicator (PPI) azimuthal scans at a discrete set of elevation angles. OLYMPEX used the operational scans of the Langley Hill and Camano Island, Washington, National Weather Service (NWS) Weather Surveillance Radar-1988 Dopplers (WSR-88Ds) ( Figs. 1 and 2 ) to provide the background horizontal mapping, while the OLYMPEX scanning radars operated in

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

estimate rain rate R at the surface using a power-law relation, known as a Z – R relationship. A number of such Z – R retrievals have since been developed for a variety of meteorological situations and locations, and they continue to be used by the U.S. National Weather Service to produce operational quantitative precipitation estimates ( Kuligowski 1997 ; Fulton 2002 ; Apffel et al. 2015 ). With the introduction of dual-polarization radars for research and operational applications ( Kumjian

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David J. Purnell and Daniel J. Kirshbaum

, an operational radar was also added to the National Weather Service (NWS) Doppler network at Langley Hill, Washington. Located southwest of the Olympics, this radar complements the Camano Island radar on the opposite side ( Fig. 1a ). Building on this newfound infrastructure, the Olympics Mountains Experiment (OLYMPEX) in winter 2015/16 ( Houze et al. 2017 ) intensively observed numerous Olympics precipitation events. OLYMPEX sought to gain process understanding and to verify satellite

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Ali Tokay, Leo Pio D’Adderio, David A. Marks, Jason L. Pippitt, David B. Wolff, and Walter A. Petersen

Andsager et al. (1999) for drops up to 6 mm and equilibrium drop shapes ( Beard and Chuang 1987 ) for drops larger than 6 mm; Z H and Z DR were derived for S-band wavelength (NPOL’s operational frequency) following Rayleigh–Gans theory ( Tokay et al. 2002 ). The Z DR is the ratio of reflectivity at horizontal polarization Z H to reflectivity at vertical polarization Z V . The reflectivity at horizontal and vertical polarization is expressed as a function of wavelength λ , dielectric constant

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

observations from the CloudSat Profiling Radar (CPR), the AMSU-B, and NOAA’s Microwave Humidity Sounder (MHS). More recently, Sims and Liu (2015) used the CloudSat radar and multiple ground-based reanalysis data, including near-surface air temperature, atmospheric moisture, low-level vertical temperature lapse rate, surface skin temperature, surface pressure, and land cover types to diagnose precipitation phase partitioning. This algorithm is deployed in the GPM operational precipitation retrievals

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Yagmur Derin, Emmanouil Anagnostou, Marios Anagnostou, and John Kalogiros

. However, radar quantitative precipitation estimation (QPE) accuracy over complex terrain is affected by beam blockage when the radar is close to a mountain range or by the height of the beam and the width of the sampling volume when the radar is far away. To overcome these limitations, locally deployed low-power polarimetric X-band radars are used to fill gaps in radar networks ( Maki et al. 2010 ). The use of locally deployed X-band polarimetric radars has many advantages compared to operational S

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