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| Titel |
Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations |
| VerfasserIn |
A. Noia, O. P. Hasekamp, G. Harten, J. H. H. Rietjens, J. M. Smit, F. Snik, J. S. Henzing, J. Boer, C. U. Keller, H. Volten |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 8, no. 1 ; Nr. 8, no. 1 (2015-01-14), S.281-299 |
| Datensatznummer |
250116052
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| Publikation (Nr.) |
 copernicus.org/amt-8-281-2015.pdf |
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| Zusammenfassung |
| In this paper, the use of a neural network algorithm for the retrieval of the
aerosol properties from ground-based spectropolarimetric measurements is
discussed. The neural network is able to retrieve the aerosol properties with
an accuracy that is almost comparable to that of an iterative retrieval. By
using the outcome of the neural network as first guess in the iterative
retrieval scheme, the accuracy of the retrieved fine- and coarse-mode optical
thickness is further improved, while for the other parameters the improvement
is small or absent. The resulting scheme (neural network + iterative
retrieval) is compared to the original one (look-up table + iterative
retrieval) on a set of simulated ground-based measurements, and on a small
set of real observations carried out by an accurate ground-based
spectropolarimeter. The results show that the use of a neural-network-based
first guess leads to an increase in the number of converging retrievals, and
possibly to more accurate estimates of the aerosol effective radius and
complex refractive index. |
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