HR: 1340h
AN: A33C-0929    [Abstracts]
TI: Aerosol Characterisation Using the SeaWiFS Sensor and Surface Data
AU: Robinson, E
EM: emr1@cec.wustl.edu
AF: Washington University, 1 Brookings Drive, St. Louis, MO 63130 United States
AU: * Husar, R B
EM: rhusar@me.wustl.edu
AF: Washington University, 1 Brookings Drive, St. Louis, MO 63130 United States
AB: The main scientific challenge in the study of particulate matter (natural or man made) is to understand the immense structural and dynamic complexity of the aerosol system. Dynamic complexity arises from chemical and physical interactions of particles with the non-particle world. The structural complexity arises from the large dimensionality of the aerosol system which include four dimensions that characterize gaseous concentrations g (X, Y, Z, T), four extra dimensions that characterize the aerosol system. a (X, Y, Z, T, D, C, F, M): particle size D, composition C, shape factor, F, and external/internal mixture ratio, M. These latter four dimensions are indispensable for establishing the sources and effects of natural aerosols. Each sensor/network covers only a limited fraction of the 8-D data space. Some measuring devices (e.g. single particle electron microscopy) measure only a small subset of the PM pollution data. The interpretation of this type of data yields considerable detail of a small `data-cube' but extrapolating this small data cube to a larger data space (e.g. larger space-time domains) is problematic. On the other extreme, some instruments provide broad integral measures of the aerosol system. Satellites, for example, integrate over atmospheric height, particle size, composition, shape, and mixture dimensions. The interpretation of these integral data requires considerable de-convolution of the integral measures. While aerosols are dynamically and structurally complex, it is precisely this complexity that provides unique benefits in understanding aerosols. Given its many dimensional properties, the aerosol system is to a large extent self-describing. In other words, once the aerosol is characterized through painstaking measurement efforts, (size-composition, shape) and the spatio-temporal patterns are established, the aerosol system describes much of its history through the properties and pattern, e.g. the source type (dust, smoke, haze), and some of the atmospheric interactions can be inferred from the data. The analyst's challenge is first to integrate the multidimensional data to obtain the most detailed aerosol characterization possible, and then to use the obtained aerosol pattern to derive the responsible causal factors such as aerosol sources, transformations and transport processes. The analysis tools for deciphering the "handwriting" hidden in the aerosol pattern include data de-convolution and fusion, multidimensional data extrapolation, meteorological transport analysis, and chemical fingerprinting/source apportionment. The paper will discuss recent results of aerosol characterization using five years of SeaWiFS-derived data over the US, along with companion surface observations.
DE: 0305 Aerosols and particles (0345, 4801, 4906)
DE: 0345 Pollution: urban and regional (0305, 0478, 4251)
SC: Atmospheric Sciences [A]
MN: Fall Meeting 2005