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| Titel |
A neural network algorithm for cloud fraction estimation using NASA-Aura OMI VIS radiance measurements |
| VerfasserIn |
G. Saponaro, P. Kolmonen, J. Karhunen, J. Tamminen, G. Leeuw |
| 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 ; 6, no. 9 ; Nr. 6, no. 9 (2013-09-09), S.2301-2309 |
| Datensatznummer |
250085057
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| Publikation (Nr.) |
 copernicus.org/amt-6-2301-2013.pdf |
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| Zusammenfassung |
| The discrimination of cloudy from cloud-free pixels is required in almost any
estimate of a parameter retrieved from satellite data in the ultraviolet
(UV), visible (VIS) or infrared (IR) parts of the electromagnetic spectrum.
In this paper we report on the development of a neural network (NN) algorithm
to estimate cloud fractions using radiances measured at the top of the
atmosphere with the NASA-Aura Ozone Monitoring Instrument (OMI). We present
and discuss the results obtained from the application of two different types
of neural networks, i.e., extreme learning machine (ELM) and back propagation
(BP). The NNs were trained with an OMI data sets existing of six orbits, tested
with three other orbits and validated with another two orbits. The results were
evaluated by comparison with cloud fractions available from the MODerate
Resolution Imaging Spectrometer (MODIS) flying on Aqua in the same
constellation as OMI, i.e., with minimal time difference between the OMI and
MODIS observations. The results from the ELM and BP NNs are compared. They
both deliver cloud fraction estimates in a fast and automated way, and they
both performs generally well in the validation. However, over highly
reflective surfaces, such as desert, or in the presence of dust layers in the
atmosphere, the cloud fractions are not well predicted by the neural network.
Over ocean the two NNs work equally well, but over land ELM performs better. |
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