Cloud Aerosol Interaction and Precipitation Enhancement (CAIPEEX)


A. Conduct collocated airborne and integrated ground based observations at different places and seasons to understand

a) The cloud and precipitation microphysical changes in the clouds in response to changes in aerosol particles

b) Cloud microphysical and dynamical controls on the rain formation and their linkages and controlling factors for cloud growth to formulate guidelines for cloud seeding

c) How physical and chemical properties of aerosols may impact radiative forcing and cloud formation

d) To establish the link between the boundary layer CCN and the cloud base CCNwith the help of aerosol-CCN-droplet closure studies

e) For the exchange of mass momentum and heat within the boundary layer and the cloud layer

f) How direct and indirect effect of aerosols may be quantified, and make observations of black carbon aerosols and investigate their role in cloud burning/cloud forming and eventually on the radiation balance

g) Understand formation and dissipation of fog and its life cycle to improve fog parameterization in models

h) Utilization of observations in formulating physical parameterization for monsoon clouds.

B.Conduct radar monitoring of cloud microphysical characteristics (Ka band) and precipitation (C-band) over the seeded areas

a) to document the cloud and precipitation microphysical and dynamical evolution of seeded cloudsand mesoscale convective clusters

b) toevaluate amenability and to derive statistical confidence in the seeding procedure.

C.Conduct laboratory and numerical simulations to understand the fundamental cloud dynamical and microphysical processes

a) To establish a fluid dynamics laboratory to study laboratory analogues of boundary layer and cloud flows

b) To establish a cloud seeding flare material testing aerosol and cloud physics laboratory

D.Undertake initiatives to investigate the cloud physics and dynamics in the subgrid scales through turbulence simulations to scale up and scale down the information by using combined observations and numerical simulations in the DNS-LES-RANS framework.  

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