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

1. Introduction Quantifying the amount of precipitation that falls as snow in complex terrain, where we have limited observations, remains a challenge. Methods that produce estimates of spatially distributed precipitation range from physically based numerical weather models, such as the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ), to statistical models that spatially interpolate surface precipitation observations. A widely used statistical model is the Parameter

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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

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Robert Conrick and Clifford F. Mass

issues are explored in this paper. The aim of this study is to evaluate precipitation biases and low-level microphysics in the Weather Research and Forecasting (WRF) Model, comparing observed and simulated rain drop size distributions and precipitation during the OLYMPEX winter experiment and two heavy precipitation events. Our goal is to explore the following questions: What biases exist in simulated precipitation over the Pacific Northwest in current microphysical parameterization schemes? How do

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Robert Conrick and Clifford F. Mass

properties and rainfall include those from the Tropical Rainfall Measurement Mission (TRMM; Kummerow et al. 1998 ), the Moderate Resolution Imaging Spectroradiometer (MODIS), and CloudSat ( Stephens et al. 2002 ). Using MODIS observations of a midlatitude cyclone, Otkin and Greenwald (2008) demonstrated an overprediction of cloud depth over the North Atlantic in the Weather Research and Forecasting (WRF) Model. Bodas-Salcedo et al. (2012) used a variety of satellite datasets, including MODIS, to

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

( Doswell et al. 1990 ) for the presented results in Figs. 7 – 9 . We also compare the algorithm outputs with the precipitation phase products of the MRMS on a seasonal basis ( Figs. 10 , 11 ). Finally, some results are presented at a storm scale to demonstrate the detection capabilities of the algorithm for a few precipitation events that are coincidentally captured by the DPR and high-resolution ground-based radars ( Figs. 12 , 13 ) and simulated by the Weather Research and Forecasting (WRF) Model

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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

determine the snow depth. The February flights occurred at a midseason time before maximum snow cover. The late March flights took place when winter snow cover was near maximum. SUCCESSFUL PROJECT COORDINATION. OLYMPEX operations required careful coordination of forecasting, decision-making, and scheduling of aircraft, radars, and soundings. The success of OLYMPEX operations is perhaps best illustrated in Fig. 5 showing how all three aircraft were positioned in the center of a GPM overpass at 1522 UTC

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David J. Purnell and Daniel J. Kirshbaum

calculated over all hourly samples. Fig . 7. Composites of mean retrieved precipitation rate during (a) WF, (b) WS, and (c) PF events. As a second verification, we compare the retrievals to the National Centers for Environmental Prediction (NCEP) Stage-IV product, a mosaic of regional multisensor analyses generated by the NWS River Forecast Centers (RFCs) ( Lin and Mitchell 2005 ). Each RFC uses multiple quality-control measures, including correcting for terrain beam blocking, quality-controlling low

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

during different storm sectors of midlatitude cyclones impacting the Olympic Peninsula of Washington State. In addition, they found that periods warm-rain enhancement were associated with DSDs having a large number of small drops. The development of machine learning techniques and their implementations (e.g., the “scikit-learn” Python package; Pedregosa et al. 2011 ) provide new opportunities for the atmospheric sciences, particularly with regard to statistical weather forecasting ( Herman and

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