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| Titel |
A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO2 using MODIS data |
| VerfasserIn |
A. Piscini, M. Picchiani, M. Chini, S. Corradini, L. Merucci, F. Del Frate, S. Stramondo |
| 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 ; 7, no. 12 ; Nr. 7, no. 12 (2014-12-01), S.4023-4047 |
| Datensatznummer |
250115969
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| Publikation (Nr.) |
 copernicus.org/amt-7-4023-2014.pdf |
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| Zusammenfassung |
In this work neural networks (NNs) have been used for the retrieval of
volcanic ash and sulfur dioxide (SO2) parameters based on Moderate
Resolution Imaging Spectroradiometer (MODIS) multispectral measurements.
Different neural networks were built in order for each parameter to be
retrieved, for experimenting with different topologies and evaluating their
performances. The neural networks' capabilities to process a large amount of
new data in a very fast way have been exploited to propose a novel
applicative scheme aimed at providing a complete characterization of
eruptive products.
As a test case, the May 2010 Eyjafjallajókull eruption has been considered.
A set of seven MODIS images have been used for the training and validation
phases.
In order to estimate the parameters associated to the volcanic eruption,
such as ash mass, effective radius, aerosol optical depth and SO2
columnar abundance, the neural networks have been trained using the
retrievals from well-known algorithms. These are based on simulated
radiances at the top of the atmosphere and are estimated by radiative
transfer models.
Three neural network topologies with a different number of inputs have been
compared: (a) three thermal infrared MODIS channels, (b) all multispectral
MODIS channels and (c) the channels selected by a pruning procedure applied
to all MODIS channels.
Results show that the neural network approach is able to estimate the
volcanic eruption parameters very well, showing a root mean square error
(RMSE) below the target data standard deviation (SD). The network built
considering all the MODIS channels gives a better performance in terms of
specialization, mainly on images close in time to the training ones, while
the networks with less inputs reveal a better generalization performance
when applied to independent data sets. In order to increase the network's
generalization capability and to select the most significant MODIS channels,
a pruning algorithm has been implemented. The pruning outcomes revealed that
channel sensitive to ash parameters correspond to the thermal infrared,
visible and mid-infrared spectral ranges.
The neural network approach has been proven to be effective when addressing the
inversion problem for the estimation of volcanic ash and SO2 cloud
parameters, providing fast and reliable retrievals, important requirements
during volcanic crises. |
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