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Robert Conrick and Clifford F. Mass

Locatelli 1978 ), the Sierra Cooperative Pilot Project ( Reynolds and Dennis 1986 ), CASPII ( Cober et al. 1995 ), WISP ( Rasmussen et al. 1992 ), MAP ( Binder et al. 1996 ), and IMPROVE ( Stoelinga et al. 2003 )], but limitations in observing capabilities reduced their ability to evaluate parameterizations in mesoscale models. To address these limitations and to serve as ground validation for the NASA Precipitation Measurement Mission satellite for the Global Precipitation Mission ( Hou et al. 2014

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Robert Conrick and Clifford F. Mass

simulate this spatial variation in hydrometeor profiles. For example, in contrast to WRF simulations, DPR profiles had greater reflectivity values over land than water. Model performance was also evaluated by dividing the analysis period into prefrontal, warm, or postfrontal storm sectors based on integrated vapor transport (IVT), as in McMurdie et al. (2018) . Considering frequency distributions and vertical hydrometeor profiles, postfrontal sectors exhibited the greatest degree of agreement between

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

. In this study, we simulated AR events between 12–13 and 16–17 November 2015 that were both characterized by a warm prefrontal period with stable, blocked flow followed by less stable, unblocked flow. Recent studies have documented important features within the ARs during OLYMPEX ( Zagrodnik et al. 2018 ; McMurdie et al. 2018 ; Conrick et al. 2018 ; Conrick and Mass 2019 ), but more detailed modeling analyses are needed to fully evaluate and improve the performance of BMPs for these heavy

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Robert Conrick, Clifford F. Mass, and Qi Zhong

simulated waves did influence model microphysics and precipitation. Future work will focus on describing, quantifying, and evaluating the simulated microphysical processes that operate in simulated waves. Acknowledgments This research was supported by the National Science Foundation through Grant AGS-1349847. The authors would also like to thank three anonymous reviewers for their comments and suggestions, as well as Hannah Barnes, Robert Houze, Lynn McMurdie, and Joe Zagrodnik for the productive

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

; Zhang et al. 2011 , 2016 ). The outputs of a high-fidelity mesoscale simulation model are also used for further evaluation of the results over high altitudes, during the Olympic Mountains Experiment (OLYMPEX) in 2015 ( Houze et al. 2017 ). The paper is structured as follows. Section 2 briefly describes the database and the phase detection method used on the operational GPM radar and radiometer products. Section 3 elaborates on the effects of snow cover on passive microwave signal of snowfall at

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

have been the focus location of previous studies, which evaluated dynamical ( Anders et al. 2007 ; Minder et al. 2008 ) and statistical ( Daly et al. 2008 ) precipitation models, its historical lack of mountain observations allowed us to estimate how both approaches work at higher mountain elevations, where data were not previously available for model training and development. For the OLYMPEX campaign, we collected a unique set of independent snow depth and SWE observations (using cameras, poles

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

the temporal similarity among rain gauge, disdrometer, and the DOW estimates. To evaluate the performance of the SCOP-ME attenuation correction algorithm, first, the rain-path attenuation and VPR corrected horizontal polarization Z H (hereinafter referred to as corrected Z H ) reflectivity by the radar to the reflectivity calculated based on the T-matrix simulations of the observed raindrop spectra by the disdrometers are compared. Comparison is performed through scatterplots and by calculating

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

been run at resolutions of 4 km [recently increased to 1.33 km using the Weather Research and Forecasting (WRF) Model] over the Pacific Northwest since 1997 ( Mass et al. 2003 ). Anders et al. (2007) and Minder et al. (2008) evaluated the performance of these products over the Olympics. They found that the model simulated the windward ridge–valley pattern of orographic precipitation well at seasonal time scales, but there were major errors for individual events. They attributed this to

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Mircea Grecu, Lin Tian, Gerald M. Heymsfield, Ali Tokay, William S. Olson, Andrew J. Heymsfield, and Aaron Bansemer

performance of the nonparametric strategy in section 3 . Similar to more traditional approaches that are based on optimal-estimation theory (e.g., Delanoë and Hogan 2008 ; Grecu et al. 2011 ; Battaglia et al. 2016 ), the nonparametric method is robust in the presence of noise in the observations and uncertainties in the underlying forward models. However, unlike those approaches, this method does not require explicit assumptions on the PSD used in the forward models and does not require an explicit

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

unavailable. Such radar-based retrievals could help evaluate microphysical biases in regional numerical weather prediction models, such as the documented underprediction of liquid water content and precipitation over windward slopes of the Pacific Northwest ( Conrick and Mass 2019a , b ). In this paper, section 2 describes the data and method used to retrieve microphysical information from radar data using second-generation Particle Size and Velocity (PARSIVEL 2 ) disdrometer observations, and section

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