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model (GCM) grid square; continue developing techniques to retrieve the properties of all clouds, with a special focus on ice clouds and mixed-phase clouds; develop a focused research effort on the indirect aerosol problem that spans observations, physical models, and climate model parameterizations; implement and evaluate an operational methodology to calculate broadband heating rates in the atmospheric columns at the ARM sites; develop and implement methodologies to use ARM data more effectively

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Maike Ahlgrimm, Richard M. Forbes, Jean-Jacques Morcrette, and Roel A. J. Neggers

. Operational forecast centers such as the ECMWF can provide a rigorous testing ground for new parameterizations as the model is confronted continuously with observations during data analysis, thus maintaining a model state close to reality. This allows a direct, day-to-day comparison between modeled clouds and observations such as those from the ARM sites. Forecast scores also routinely provide an objective measure of overall model performance to complement more process-oriented model evaluation. c. An

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large-eddy simulation (LES) and cloud ensemble models (CEMs) can be used to investigate basic physical questions, develop cloud amount parameterizations, and evaluate the sensitivity of model results to parameter changes. In support of ARM, SCMs and CEMs will be particularly valuable for testing parameterizations of cloud formation, maintenance, and dissipation. The data required to drive the SCMs, LES models, and CEMs, and to evaluate their performance, are not easy to obtain. ARM has the potential

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J.-L. F. Li, D. E. Waliser, G. Stephens, and Seungwon Lee

available for the ISCCP ( Rossow and Schiffer 1999 ) to serve for evaluation and intercomparison of climate model clouds (e.g., Norris and Weaver 2001 ; Lin and Zhang 2004 ; Zhang et al. 2005 ; Schmidt et al. 2006 ; Cole et al. 2011 ; Kay et al. 2012 ). More recently, the Cloud Feedback Model Intercomparison Project (CFMIP) (e.g., Bony et al. 2011 ) has been coordinating development of the CFMIP Observation Simulator Package (COSP) and includes a number of new satellite observations from the

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Steven K. Krueger, Hugh Morrison, and Ann M. Fridlind

2000 Cloud IOP. Xie et al. evaluated the overall performance of nine SCMs and four 2D CRMs in simulating a strong midlatitude frontal cloud system during the IOP, while Xu et al. focused on a 27-h period when only shallow frontal clouds were observed. Xie et al. (2005) found that all models captured the bulk characteristics of the frontal system and the frontal precipitation. However, there were significant differences in the detailed structures of the frontal clouds. All models overestimated high

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Minghua Zhang, Richard C. J. Somerville, and Shaocheng Xie

measurements are both used to provide an initial evaluation of the performance of the different parameterizations. The results of this evaluation are then used to develop improved cloud-precipitation schemes, and finally these schemes are tested in GCM experiments (e.g., Lee et al. 1997 ). An early example of using a single-column model in this way was described by Iacobellis and Somerville (1991a , b) . A major result of ARM is that SCMs have proven themselves capable of validating parameterization

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Robert G. Ellingson, Robert D. Cess, and Gerald L. Potter

data). The ICRCCM participants concluded that existing field observations, while they shed light on various issues facing ICRCCM, could not decisively resolve the large intermodel disagreements. As such they recommended a more sophisticated observational strategy, as follows ( Luther 1984 , p. 31): A dedicated field measurement program is recommended for the purpose of obtaining accurate spectral radiances rather than integrated fluxes as a basis for evaluating model performance. Following the 1988

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M. Haeffelin, S. Crewell, A. J. Illingworth, G. Pappalardo, H. Russchenberg, M. Chiriaco, K. Ebell, R. J. Hogan, and F. Madonna

/regional-scale processes typical to the urban–rural transition such as the formation mechanisms of gaseous and particulate pollution ( Freutel et al. 2013 ) or the effects of aerosols on fog and shallow cumulus ( Haeffelin et al. 2010 ) under high-pressure situations and larger-scale cloud–aerosol processes associated with baroclinic fronts. The SIRTA database is also geared toward global circulation model and numerical weather prediction model evaluations (e.g., Cheruy et al. 2013 ). Atmospheric process studies

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Larry K. Berg and Peter J. Lamb

. Their parameterization was consistent with the results of Zhu and Albrecht (2002) , who reported that the formation of the shallow clouds was a complicated function of processes within the boundary layer, including the fluxes at the top of the convective boundary layer. Other ARM-supported studies included efforts to evaluate the performance of large-eddy simulation (LES) models and their simulation of boundary layer clouds. In intermodel comparisons of an idealized case of shallow boundary layer

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Eli J. Mlawer, Michael J. Iacono, Robert Pincus, Howard W. Barker, Lazaros Oreopoulos, and David L. Mitchell

understanding of these radiative processes. This, in turn, necessitates a rigorous evaluation of our ability to compute spectrally resolved radiative fluxes for this range of conditions. Accomplishments in the ARM Program led to substantial advances with respect to both objectives. Mlawer and Turner (2016 , chapter 14) address the program’s accomplishments with regard to the observation and modeling of spectrally resolved radiation, while this chapter details achievements related to the modeling of

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