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J. A. Milbrandt and R. McTaggart-Cowan

with a given selection of prognostic and diagnostic variables in order to guide cloud modelers as to the selection of prognostic moments in BMSs. The paper is organized as follows. Section 2 describes the experimental design and the approach used to evaluate the different bulk model simulations. Section 3 provides a summary of the bulk results using different combinations of prognostic moments and presents an analysis of the errors. Section 4 discusses two alternatives to controlling

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Kevin Wolf, André Ehrlich, Mario Mech, Robin J. Hogan, and Manfred Wendisch

subpixel scales. To bridge the gap between satellite and ground-based observations, airborne measurements of spectral irradiance are a useful tool for direct model evaluation. In this regard, some airborne campaigns were conducted, operating instruments to measure the spectral solar radiation reflected by clouds, e.g., Wendisch and Keil (1999) , Wendisch et al. (2005 , 2007) , Jacob et al. (2010) , or Smith et al. (2017) . In spite of that, the natural variability of clouds might not be covered

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Andrew J. Heymsfield, Carl Schmitt, Chih-Chieh-Jack Chen, Aaron Bansemer, Andrew Gettelman, Paul R. Field, and Chuntao Liu

focused areas of model performance to investigate. Acknowledgments NASA supported NCAR investigators for their work on this effort through Grant NNX16AP28G. We wish to thank Norm Wood for helping us with the CloudSat data and Meg Miller for her editorial support. GPCP precipitation data acquired through NOAA/OAR/ESRL PSD, Boulder, Colorado, from their website at https://www.esrl.noaa.gov/psd/ . APPENDIX A Evaluation of Collocated CPR–GPM Dataset The coincident satellite-based dataset 2B

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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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Kentaroh Suzuki, Graeme Stephens, Alejandro Bodas-Salcedo, Minghuai Wang, Jean-Christophe Golaz, Tokuta Yokohata, and Tsuyoshi Koshiro

-observed microphysical statistics. This inconsistency between two different levels of model performance implies the presence of compensating errors at a fundamental level in the model. The purpose of the present study is to extend these model diagnostic approaches to multiple state-of-the-art global atmosphere models to evaluate different models in the context of their representations of warm rain formation processes over the global ocean against satellite observations. As will be discussed below, such model

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Hanii Takahashi, Alejandro Bodas-Salcedo, and Graeme Stephens

from cloud to drizzle and drizzle to rain. Many previous studies have used this conventional CFODD approach to evaluate model performance (e.g., Michibata et al. 2014 ; Suzuki et al. 2015 ; Jing et al. 2019 ). Fig . 1. Conventional CFODDs based on the observations and different HadGEM3 runs over global ocean, classified according to R e into four groups: R e = 5–10, 10–15, 15–20, and 20–25 μ m. Compared to the observations, the four HadGEM3 experiments each exhibit coalescence associated

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Judith L. Lean

. 2007 , 2013 ; McPeters and Labow 2012 ) and known drivers of ozone variability, the evolution of total atmospheric ozone is projected into the twenty-first century by inputting assumed changes in each of the drivers. Using historical information about the drivers, total atmospheric ozone is also reconstructed since 1900 to provide a past perspective for the present and future. The evolution of total atmospheric ozone in the twenty-first century evaluated using the statistical models with a range

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Hyunho Lee, Ann M. Fridlind, and Andrew S. Ackerman

1. Introduction Modeling of clouds and precipitation has made remarkable advances over the last several decades. Today, many microphysical processes occurring in clouds can be evaluated over a wide range of spatial and temporal scales. However, in spite of these advances, the representation of cloud processes in models still suffers from large uncertainties, which limits accurate weather forecasting and climate prediction. For instance, interactions of aerosol and clouds are relatively poorly

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Roel A. J. Neggers, Martin Köhler, and Anton C. M. Beljaars

operational GCMs because of its low computational cost (e.g., Tiedtke 1989 ). In the other extreme, (1) is applied to many individual updrafts in the so-called “multiple updraft” models (e.g., Neggers et al. 2002 ; Cheinet 2003 ). These more elaborate models better resolve cumulus ensemble statistics, allowing scientific study of associated impacts on convective transport and clouds. Large-eddy simulation (LES) of boundary layer convection has enabled the development and evaluation of

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Alexandra L. Jones and Larry Di Girolamo

model applicable to a wide range of the electromagnetic spectrum; capable of simulating scattering, absorption, and thermal emission; computing spectral radiance, irradiance, and heating rates from solar and internal sources; and optimized for parallel computing, with an object-oriented, open-source design that the community can build upon. The theoretical basis for the internal emission component of this model and the thorough benchmarking of its accuracy and performance is the subject of this

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