 |
| Titel |
Multi-modal analysis of aerosol robotic network size distributions for remote sensing applications: dominant aerosol type cases |
| VerfasserIn |
M. Taylor, S. Kazadzis, E. Gerasopoulos |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1867-1381
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 7, no. 3 ; Nr. 7, no. 3 (2014-03-31), S.839-858 |
| Datensatznummer |
250115651
|
| Publikation (Nr.) |
 copernicus.org/amt-7-839-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
| To date, size distributions obtained from the aerosol robotic network (AERONET) have
been fit with bi-lognormals defined by six secondary microphysical
parameters: the volume concentration, effective radius, and the variance of
fine and coarse particle modes. However, since the total integrated volume
concentration is easily calculated and can be used as an accurate
constraint, the problem of fitting the size distribution can be reduced to
that of deducing a single free parameter – the mode separation point. We
present a method for determining the mode separation point for
equivalent-volume bi-lognormal distributions based on optimization of the
root mean squared error and the coefficient of determination. The extracted
secondary parameters are compared with those provided by AERONET's Level 2.0
Version 2 inversion algorithm for a set of benchmark dominant aerosol types,
including desert dust, biomass burning aerosol, urban sulphate and sea
salt. The total volume concentration constraint is then also lifted by
performing multi-modal fits to the size distribution using nested Gaussian
mixture models, and a method is presented for automating the selection of the
optimal number of modes using a stopping condition based on Fisher
statistics and via the application of statistical hypothesis testing. It is
found that the method for optimizing the location of the mode separation
point is independent of the shape of the aerosol volume size distribution (AVSD), does not require the
existence of a local minimum in the size interval 0.439 μm ≤ r ≤ 0.992 μm, and shows some potential for optimizing the bi-lognormal
fitting procedure used by AERONET particularly in the case of desert dust
aerosol. The AVSD of impure marine aerosol is found to require three modes. In
this particular case, bi-lognormals fail to recover key features of the
AVSD. Fitting the AVSD more generally with multi-modal models allows
automatic detection of a statistically significant number of aerosol modes,
is applicable to a very diverse range of aerosol types, and gives access to
the secondary microphysical parameters of additional modes currently not
available from bi-lognormal fitting methods. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|