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Yi-Hung Kuo, J. David Neelin, Chih-Chieh Chen, Wei-Ting Chen, Leo J. Donner, Andrew Gettelman, Xianan Jiang, Kuan-Ting Kuo, Eric Maloney, Carlos R. Mechoso, Yi Ming, Kathleen A. Schiro, Charles J. Seman, Chien-Ming Wu, and Ming Zhao

-organized and smaller-scale convection ( Schiro et al. 2018 ; Schiro and Neelin 2019 ). The relationship to convective instability has been examined in a single GCM ( Sahany et al. 2012 , 2014 ; Kuo et al. 2017 ). Here we systematically evaluate the performance of multiple GCMs in simulating key features of tropical precipitation and deep convection with such diagnostics. KSN18 has detailed observational aspects of the convective transition statistics over tropical oceans using satellite retrievals and

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Stephen E. Lang, Wei-Kuo Tao, Jiun-Dar Chern, Di Wu, and Xiaowen Li

Mission Large-Scale Biosphere–Atmosphere Experiment in Amazonia (TRMM LBA). The model results are evaluated using radar reflectivity contoured frequency with altitude diagrams (CFADs; Yuter and Houze 1995 ). Validation via comparison with in situ aircraft data can provide a very detailed look at the performance of microphysical schemes (e.g., Molthan and Colle 2012 ); however, such data are limited and difficult to compare against (if even available) when it comes to convective cores and are

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Woosub Roh, Masaki Satoh, and Tomoe Nasuno

1. Introduction The evaluation of clouds and precipitation is important in high-resolution models such as cloud-system-resolving models (CSRMs). CSRMs are generally defined as nonhydrostatic models with horizontal grid spacing that is sufficiently fine to explicitly simulate individual cloud systems. CSRMs more realistically represent microphysical processes of precipitation and calculate the time evolution, structure, and life cycle of cloud systems than general circulation models (GCMs

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Timothy Glotfelty, Kiran Alapaty, Jian He, Patrick Hawbecker, Xiaoliang Song, and Guang Zhang

. Model evaluation Since Glotfelty et al. (2019) conducted a detailed evaluation of precipitation, radiation, and cloud properties within WRF-ACI at 12 km grid spacing for the full summer 2006 season, the current study exclusively focuses on evaluating the scale dependency of precipitation performance across grid spacings ranging from 36 to 1 km. Precipitation evaluation Domain-wide mean bias (MB) and root-mean-square error (RMSE) statistics derived from stage-IV precipitation estimates that have

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Woosub Roh, Masaki Satoh, Tempei Hashino, Hajime Okamoto, and Tatsuya Seiki

models. Forbes and Ahlgrimm (2014) showed that a reduction in the ice deposition rate at the cloud top significantly improves the occurrence of supercooled water clouds and their radiative impacts. Furtado and Field (2017) found that a modification of the riming parameterization improves the mean-state biases of clouds over the Southern Ocean. The satellite data are useful to evaluate the thermodynamic phases of clouds (e.g., Deschamps et al. 1994 ; Platnick et al. 2003 ). Active sensors are

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Takanobu Yamaguchi, W. Alan Brewer, and Graham Feingold

increasingly been applied in LES or near-LES mode (e.g., Moeng et al. 2007 ; Wang and Feingold 2009a , b ; Feingold et al. 2010 ; Kazil et al. 2011 ; Solomon et al. 2011 ; Blossey et al. 2013 ) and therefore it behooves the community to evaluate its performance, not only against other LES models, but equally importantly, against observations. ARW has been tested on several LES intercomparison cases of the Global Atmospheric System Studies (GASS, formerly GCSS) ( Stevens et al. 2005 , hereafter S05

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Anne K. Smith, Nicholas M. Pedatella, Daniel R. Marsh, and Tomoko Matsuo

constraining the dynamical fields with observations or observation-based reanalyses over part or all of the model domain. The constraints are performed by nudging (relaxing with a specified time constant) to global reanalysis fields or by assimilating observational data. There are several reasons that simulations with constraint are used. One is that a model simulation whose dynamical fields correspond to observations during a specific period gives an important tool for evaluating chemical composition

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Luke Oman, Darryn W. Waugh, Steven Pawson, Richard S. Stolarski, and J. Eric Nielsen

models to evaluate its robustness. This could be a very helpful method for understanding future stratospheric water vapor trends. Acknowledgments We thank Paul Newman and Anne Douglass for their helpful comments and suggestions and Stacey Frith for helping with the data processing. Thanks to Stefan Fueglistaler and two anonymous reviewers for their helpful comments on improving this manuscript. We also appreciate Don Anderson of NASA’s MAP Program for funding, those involved in model development at

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Y. P. Zhou, W.-K. Tao, A. Y. Hou, W. S. Olson, C.-L. Shie, K.-M. Lau, M.-D. Chou, X. Lin, and M. Grecu

footprints with cloud optical thickness greater than 10, cloud height greater than 10 km, and 100% cloudy conditions within each footprint. Their simulations are only run for 24 h for each cloud object using a 2D model. While this method provides a large number of cloud objects for different times and locations, it is hard to evaluate how the model simulates entire synoptic systems. In this study, we will examine the cloud and precipitation properties observed from the CERES and TRMM instruments against

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Woosub Roh and Masaki Satoh

. P. Lane , P. T. May , C. Jakob , S. T. Siems , M. J. Manton , and J. Pinto , 2013 : Statistical assessment of tropical convection-permitting model simulations using a cell-tracking algorithm . Mon. Wea. Rev. , 141 , 557 – 581 , doi: 10.1175/MWR-D-11-00274.1 . Delanoe , J. , R. J. Hogan , R. M. Forbes , A. Bodas-Salcedo , and T. H. M. Stein , 2011 : Evaluation of ice cloud representation in the ECMWF and UK Met Office models using CloudSat and CALIPSO data

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