• Astin, I., L. Di Girolamo, and H. M. van de Poll (2001), Bayesian confidence intervals for true fractional coverage from finite transect measurements: Implications for cloud studies from space, J. Geophys. Res., 106, 17,30317,310.
  • Baldauf, M., A. Seifert, J. Forstner, D. Majewski, M. Raschendorfer, and T. Reinhardt (2011), Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities, Mon. Wea. Rev., 139, 38873905.
  • Bouniol, D., et al. (2010), Using continuous ground-based radar and lidar measurements for evaluating the representation of clouds in four operational models, J. Appl. Meteor., 49, 19711991.
  • Boutle, I. A., S. J. Abel, P. G. Hill, and C. J. Morcrette (2013), Spatial variability of liquid cloud and rain: Observations and microphysical effects, Q. J. Roy. Meteorol. Soc., doi:10.1002/qj.2140.
  • Duynkerke, P. G., P. J. Jonker, A. Chlond, M. C. Van Zanten, J. Cuxart, P. Clarke, E. Sanchez, G. Martin, G. Lenderink, and J. Teixeira (1999), Intercomparison of three- and one-dimensional model simulations and aircraft observations of stratocumulus, Bound.-Layer Meteor., 92, 453487.
  • Gal-Chen, T., and R. C. J. Sommerville (1975), On the use of a coordinate transformation for the solution of the Navier-Stokes equations, J. Comput. Phys., 17, 209228.
  • Golaz, J.-C., V. E. Larson, and W. R. Cotton (2002), A PDF-based model for boundary layer clouds. Part I: Method and model description, J. Atmos. Sci., 59, 35523571.
  • Hennemuth, B., A. Weiss, J. Bösenberg, D. Jacob, H. Linné, G. Peters, and S. Pfeifer (2005), Quality assessment of water cycle parameters in REMO by radar-lidar synergy, Atmos. Chem. Phys., 8, 287308.
  • Hinkelman, L. M., T. P. Ackerman, and R. T. Marchand (1999), An evaluation of NCEP Eta model predictions of surface energy budget and cloud properties by comparison with measured ARM data, J. Geophys. Res., 104, 19,53519,594.
  • Hogan, R. J., C. Jakob, and A. J. Illingworth (2001), Comparison of ECMWF winter season cloud fraction with radar-derived values, J. Appl. Meteorol., 40, 513525.
  • Illingworth, A. J., et al. (2007), CloudNet - continuous evaluation of cloud roles in seven operational models using ground-based observations, B. Am. Meteorol. Soc., 88, 883898.
  • King, M. D., W. P. Menzel, Y. J. Kaufman, D. Tanre, B. C. Gao, S. Platnic, S. A. Ackerman, L. A. Remer, R. Pincus, and P. A. Hubanks (2003), Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS, IEEE T. Geosci. Remote Sens., 41, 442458.
  • LeTreut, H., and Z.-X. Li (1991), Sensitivity of an atmospheric general circulation model to prescribed SST changes: Feedback effects associated with the simulation of cloud optical properties, Clim. Dyn., 5, 175187.
  • LeTreut, H., R. Somerville, U. Cubasch, Y. Ding, C. Mauritzen, A. Mokssit, T. Peterson, and M. Prather (2007), Historical overview of climate change, in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., Cambridge Univ. Press, Cambridge, United Kingdom and New York, NY, USA.
  • Löhnert, U., S. Crewell, and C. Simmer (2004), An integrated approach toward retrieving phyically consistent profiles of temperature, humidity, and cloud liquid water, J. Appl. Meteorol., 43, 12951307.
  • Mace, G. G., C. Jakob, and K. P. Moran (1998), Validation of hydrometeor occurrence predicted by the ECMWF model using millimeter wave radar data, Geophys. Res. Let., 25, 16451648.
  • Morcrette, C. J. (2012), Prognostic-cloud-scheme increment diagnostics: A novel addition to the case-study tool kit, Atmosph. Sci. Lett., 13, 200207, doi:10.1002/asl.380.
  • Morcrette, C. J., E. J. O'Connor, and J. C. Petch (2012), Evaluation of two cloud parameterization schemes using ARM and Cloud-Net observations, Q. J. R. Meteorol. Soc., 138, 964979.
  • Quaas, J. (2012), Evaluating the critical relative humidity as a measure of subgrid-scale variability of humidity in general circulation model cloud cover parameterizations using satellite data, J. Geophys. Res., 117, D09208, doi:10.1029/2012JD017495.
  • Stevens, B., et al. (2013), The atmospheric component of the MPI-M Earth System Model: ECHAM6, J. Adv. Model. Earth Syst., 5, 146172, doi:10.1002/jame.20015.
  • Tompkins, A. M. (2002), A prognostic parameterization for the sub-scale variability of water vapor and clouds in large scale models and its use to diagnose cloud cover, J. Atmos. Sci., 59, 19171942.
  • Tompkins, A. M. (2003), Impact of temperature and humidity variability on cloud cover assessed using aircraft data, Q. J. R. Meteorol. Soc., 129, 21512170.
  • Uttal, T., and R. A. Kropfli (2001), The effect of radar pulse length on cloud reflectivity statistics, J. Atmos. Oceanic Technol., 18, 947961.
  • van Meijgaard, E., and S. Crewell (2005), Comparison of model predicted liquid water path with ground-based measurements during CLIWA-NET, Atmos. Res., 75, 201226.
  • Weber, T., J. Quaas, and P. Räisänen (2011), Evaluation of the statistical cloud scheme in the ECHAM5 model using satellite data, Quart. J. Roy. Meteor. Soc., 137, 20792091.
  • Zhu, P., and P. Zuidema (2009), On the use of PDF schemes to parameterize sub-grid clouds, Geophys. Res. Lett., 36, L05807, doi:10.1029/2008GL036817.