Dirty or high CCN can either suppress or enhance precipitation processes, depending on environmental

1
Y2007 Progress from Goddard Mesoscale Dynamics
and Modeling Group
Aerosols and Deep Convective Precipitation
Cloud-Resolving Model Goddard Cloud Ensemble
Model with Spectral-Bin Microphysics Precipitatio
ns Systems Deep Convection, Mesoscale Convective
System (50 of global rainfall).
Surface rain time series Blue Clean Red Dirty
Observed Radar reflectivity
Rain Supression
Rain Enhacement
Rain Supression

• Dirty (or high) CCN can either suppress or
  enhance precipitation processes, depending on
  environmental conditions and cloud
  dynamics/microphysics interactions
• Clean (Low) CCN produces earlier rain onset and
  enhances surface rain only at initial stages
• Tao, W.-K., X. Li, A. Khain, T. Matsui, S. Lang,
  and J. Simpson, (2007), Role of atmospheric
  aerosol concentration on deep convective
  precipitation Cloud-resolving model simulations,
  Journal of Geophysical Research, 112, D24S18,
  doi10,1029/2007JD008728.

2
Y2008 Work Plan from Goddard Mesoscale Dynamics
and Modeling Group
Aerosols and Deep Convective Precipitation
Cloud-Resolving Model Goddard Cloud Ensemble
Model with Spectral-Bin Microphysics Cloud-Precip
itation Systems Deep Convection, MCS and Cirrus
Clouds (GSFC), Stratocumulus (DOE Brookhaven Lab.
and U. of Michigan)
GCE with SBM simulations
?
GCE-SDSU-Simulated Radar Statistics
TRMM-observed Radar Statistics

• Work Tasks
• Use Goddard Satellite Data Simulation Unit (SDSU)
  to compute satellite-consistent radar
  reflectivity and microwave brightness temperature
  from sensitivity simulations of GCE with SBM.
• Create statistics composites to examine the
  sensitivity of GCE-simulated satellite
  observation signals to aerosols concentrations.
• Find hotspot of aerosol-cloud-precipitation
  interactions on the global scale using TRMM
  satellites and A-Train constellation of
  satellites.