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Ryan R. Neely III, Louise Parry, David Dufton, Lindsay Bennett, and Chris Collier

the presence of ice in the sampling volume, which may bias QPEs ( Herzegh and Jameson 1992 ; Fabry and Zawadzki 1995 ; Straka et al. 2000 ; Liu and Chandrasekar 2000 ; Rico-Ramirez et al. 2005 ; Bechini et al. 2013 ; Picca et al. 2014 ; Hall et al. 2015 ). An essential issue in dual-polarization radar rainfall estimation is determining which set of polarimetric observables and which method to employ for given meteorological conditions. For example, under what circumstances does K dp

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Alyssa M. Stansfield, Kevin A. Reed, Colin M. Zarzycki, Paul A. Ullrich, and Daniel R. Chavas

discussed further in appendix B . b. Model description The model used for this study is the Community Earth System Model (CESM), a fully coupled global climate model with atmosphere, ocean, land, and sea ice components. CAM is the atmospheric component of CESM, and specifically CAM5 ( Neale et al. 2012 ) with the spectral element (SE) dynamical core ( Taylor et al. 1997 ; Taylor 2011 ; Dennis et al. 2012 ) is utilized. CAM5 is a comprehensive atmospheric GCM that includes parameterizations of shallow

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Hengchun Ye, Judah Cohen, and Michael Rawlins

because of significant drops in station operation from 1990 to 1993. For this study we chose the time period from 1966 to 2000 because of more consistent observational practices. Among these 757 stations, 547 stations are retained for this study, having record lengths ranging from 27 to 35 years. The synoptic observation records include sea level pressure, station pressure, air temperature, vapor pressure, relative humidity, wind speed, wind direction, cloud cover and type, and weather condition. The

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Carolina A. Bieri, Francina Dominguez, and David M. Lawrence

.9° × 1.25°). The FHIST component set was used in CESM. CESM is composed of several Earth system component models: ocean, land, atmosphere, sea ice, land ice, river, and ocean wave. There are many defined component sets available to CESM users; each determines the active component(s) for a given model run. Component sets beginning with “F” have active land and atmosphere components while all other components are inactive or prescribed. “HIST” component sets use transient forcing valid for

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Zhichang Guo and Paul A. Dirmeyer

root-zone soil moisture is much slower than that of other land variables, subsurface soil moisture is the major potential predictor for subseasonal predictability. Here only the subsurface soil moisture is prescribed in the S experiment, and the impact of subsurface soil moisture on precipitation is considered. In the standard GLACE experiments, the AGCM simulations are forced only by a particular year's sea surface temperatures (SSTs), and the subsurface soil moisture values for each ensemble

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Allison B. Marquardt Collow, Haiden Mersiovsky, and Michael G. Bosilovich

. Alexander , D. P. Rowell , E. C. Kent , and A. Kaplan , 2003 : Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century . J. Geophys. Res. , 108 , 4407 , . 10.1029/2002JD002670 Reichle , R. H. , Q. Liu , R. D. Koster , C. S. Draper , S. P. Mahanama , and G. S. Partyka , 2017 : Land surface precipitation in MERRA-2 . J. Climate , 30 , 1643 – 1664 ,

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Erin Dougherty, Erin Sherman, and Kristen L. Rasmussen

. The PGW simulations perturb the horizontal wind, geopotential, temperature, specific humidity, sea surface temperature, soil temperature, sea level pressure, and sea ice. Large-scale spectral nudging was applied to these simulations above the planetary boundary layer on horizontal scales greater than 2000 km in order to minimize long-term climate drift ( Feser et al. 2011 ). Nudging also allows for specific synoptic weather events to be reproduced, but subsynoptic scales can freely evolve, thus

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Hanh Nguyen, Jason A. Otkin, Matthew C. Wheeler, Pandora Hope, Blair Trewin, and Christa Pudmenzky

-0149.1 Rayner , N. A. , D. E. Parker , E. B. Horton , C. K. Folland , L. V. Alexander , D. P. Rowell , E. C. Kent , and A. Kaplan , 2003 : Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century . J. Geophys. Res. , 108 , 4407 , . 10.1029/2002JD002670 Reisinger , A. , and Coauthors , 2014 : Australasia . Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B

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Wonbae Bang, GyuWon Lee, Alexander Ryzhkov, Terry Schuur, and Kyo-Sun Sunny Lim

1. Introduction Different climatology and regional differences of the atmospheric environment can significantly affect the microphysical characteristics of precipitation ( Bringi et al. 2003 ). When compared with Oklahoma (OKL), United States, the atmospheric environment of the southern Korean Peninsula (KOR) is characterized by abundant lower-level moisture caused by an ample supply of water vapor from the sea during the summer, which leads to deficient ice crystals at upper levels ( Sohn et

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Graham A. Sexstone, Colin A. Penn, Glen E. Liston, Kelly E. Gleason, C. David Moeser, and David W. Clow

. Itkin , J. King , I. Merkouriadi , and J. Haapala , 2018 : A distributed snow-evolution model for sea-ice applications (SnowModel) . J. Geophys. Res. Oceans , 123 , 3786 – 3810 , . 10.1002/2017JC013706 López-Moreno , J. I. , S. R. Fassnacht , S. Beguería , and J. B. P. Latron , 2011 : Variability of snow depth at the plot scale: Implications for mean depth estimation and sampling strategies . Cryosphere , 5 , 617 – 629 , https

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