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Atmospheric System Research (ASR)

Cloud Life Cycle Research

Despite ever increasing computational power and climate model sophistication, the poor representation of cloud processes continues to be one of the major sources of uncertainty in numerical simulations of climate and weather.  Improvement of the representation of clouds in numerical models requires fundamental process studies on all scales important to cloud formation, evolution and dissipation. Since many of these processes operate at scales smaller than the grid scales used in climate and weather models, the sub-grid scale processes must be represented by parameterizations. New observational capabilities are crucial because many cloud processes, including precipitation formation and entrainment, remain insufficiently understood mechanistically. Thus, improving cloud processes in models requires:

  • developing methods that fill observational gaps and,
  • where observations are sufficient, using them to evaluate and improve parameterizations.  

To improve the representation of cloud properties and processes in climate models, BNL has been actively engaged in innovative remote sensing technique development (surface-based and satellite), cloud process analysis, theoretical development, and the infusion of these data and theory into models.  Core capabilities are: comprehensive radar expertise (particularly 3D and radar Doppler techniques, radar polarimetry), cloud tomography, and aerosol, cloud microphysics and precipitation theory.  Our efforts particularly focus on using radar and other instruments at Atmospheric Radiation Measurement (ARM) Climate Research Facility sites to improve our understanding of how dynamics and microphysics interact and evolve at small scales to ultimately affect mesoscale cloud features.  

Selected Research Accomplishments

  • BNL scientists (Jensen, Giangrande) led the Midlatitude Continental Convective Clouds Experiment (MC3E) in April-June 2011. MC3E represents the first deployment of a new network of ARM radar systems that were used to provide a holistic view of the lifecycle of convective cloud systems.
  • BNL led a first-of-a-kind, extended-term cloud aircraft campaign to obtain an in-situ statistical characterization of continental boundary-layer clouds needed to investigate cloud processes and refine retrieval algorithms1.
  • Developed a novel approach to spectra-based retrievals of drizzling maritime stratus cloud properties, which suggests that radar observables commonly attributed to drizzle onset are actually closely tied to accretion but not auto-conversion, thereby implying that prevailing methods of drizzle observation may require revision2-3.
  • Used cloud radar Doppler spectra to retrieve vertical air motion and precipitation drop size distribution slope and shape parameters in light-to-moderate precipitation4-5.
  • Developed a novel method that uses dual-frequency radar attenuation from collocated ARM radars (35 and 95 GHz) to retrieve vertical profiles of cloud liquid water content6.
  • Developed a new cloud tomographic method that uses scanning microwave sensors to retrieve the 3D spatial variation of cloud water at a resolution of a few tens of meters in the vertical and a few hundred meters in the horizontal7-12.


  1. Vogelmann et al., Bull. Amer. Meteor. Soc., 2012
  2. Kollias, Remillard, Luke, and Szyrmer, J. Geophys. Res., 2011
  3. Kollias, Szyrmer, Remillard, and Luke, J. Geophys. Res., 2011
  4. Giangrande, Luke, and Kollias, J. Atmos. Oceanic Technol., 2010
  5. Giangrande, Luke, and Kollias, J. Appl. Meteor. Clim., 2012
  6. Huang, Johnson, Liu, and Wiscombe, Geophys. Res. Lett., 2009
  7. Huang, Liu, Wiscombe, J. Geophys. Res., 2008
  8. Huang, Liu, and Wiscombe, J. Geophys. Res., 2008a
  9. Huang, Liu, and Wiscombe, J. Geophys. Res., 2008b
  10. Huang, D., Liu, Y., and Wiscombe, Remote. Sensing Letters, 2010a.
  11. Huang, Gasiewski, and Wiscombe, Atmos. Chem. Phys., 2010b.
  12. Huang, Gasiewski, and Wiscombe, Atmos. Chem. Phys., 2010c.