Aerosol remote sensing to derive global CCN maps

The student will analyze and examine links between aerosol remote sensing data from ground and space.  The overall goal is to establish global maps for cloud condensation nuclei (CCN) or ice nuclei (IN) based on satellite retrieved aerosol properties. This will be achieved by locally calibrating usually less accurate data from space with much more accurate column (and profiling) data of ground-based sun-/sky-photometry networks, such as AERONET (/MPLnet) or SKYnet.

Background and tasks:
Aerosol particles are an essential parameter in the formation of cloud droplets. Thus, available aerosol particles will modulate onset and life-cycles of clouds, including precipitation processes.  However, not all atmospheric aerosol particles automatically are potential seeds for water cloud droplets (usually referred to as cloud condensation nuclei or CCN) or ice crystals (ice nuclei or IN). While it is generally true, that larger aerosol concentrations usually yield larger CCN/IN concentrations, there are also strong CCN/IN concentration dependences associated with aerosol particle size, shape and composition. In addition, also environmental conditions have an considerable impact such as the available atmospheric water vapor and (especially in conjunction with) atmospheric temperature.  For the atmospheric environment, however, climatological statistics will be employed to shift the focus to aerosol properties.

Highly accurate local data on atmospheric column properties for aerosol amount, size and composition (and even vertical distribution) are examined: Statistics on aerosol column data are locally available worldwide at about 400 land-sites via (day-time) sun-/sky-photometer networks (e.g. AERONET, SKYnet) and limited profile information at few sites is provided by lidar instruments. These data allow estimates for CCN and IN, but only locally.

Satellite data, in contrast, provide global (and annual) coverage. However, the retrieved aerosol products are highly uncertain, involving many assumptions, mainly to the aerosol composition. In addition, the accuracy of passive remote suffers from assumptions to surface properties (which are needed at high accuracy) and from limitations in identification process of cloud-free scenes. In order to reduce at least compositional and environmental limitations comparisons to reasonable assumptions suggested by global modeling will be applied. 

The goal is to extend the ground-based knowledge of aerosol, that can serve as CCN or IN to data from satellite remote sensing. The expected results are statistically sound relationships for CCN as a function of satellite retrieved aerosol properties for amount and size (which are usually provided via the aerosol optical depth and its spectral dependence). Such relationships are highly desirable for applications in parameterizations of processes involving clouds, precipitation and the hydrological cycle.


Supervisor: Stefan Kinne