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.
References
- Vogelmann et al., Bull. Amer. Meteor. Soc., 2012
- Kollias, Remillard, Luke, and Szyrmer, J. Geophys.
Res., 2011
- Kollias, Szyrmer, Remillard, and Luke, J. Geophys.
Res., 2011
- Giangrande, Luke, and Kollias, J. Atmos. Oceanic
Technol., 2010
- Giangrande, Luke, and Kollias, J. Appl. Meteor. Clim.,
2012
- Huang, Johnson, Liu, and Wiscombe, Geophys. Res.
Lett., 2009
- Huang, Liu, Wiscombe, J. Geophys. Res., 2008
- Huang, Liu, and Wiscombe, J. Geophys. Res., 2008a
- Huang, Liu, and Wiscombe, J. Geophys. Res., 2008b
- Huang, D., Liu, Y., and Wiscombe, Remote. Sensing
Letters, 2010a.
- Huang, Gasiewski, and Wiscombe, Atmos. Chem. Phys.,
2010b.
- Huang, Gasiewski, and Wiscombe, Atmos. Chem. Phys.,
2010c.