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Kosmos
Astronomia Astrofizyka
Inne

Kultura
Sztuka dawna i współczesna, muzea i kolekcje

Metoda
Metodologia nauk, Matematyka, Filozofia, Miary i wagi, Pomiary

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Fizyka, chemia i inżynieria materiałowa

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Antropologia kulturowa Socjologia Psychologia Zdrowie i medycyna

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Przewidywania Kosmologia Religie Ideologia Polityka

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Geologia, geofizyka, geochemia, środowisko przyrodnicze

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Biologia, biologia molekularna i genetyka

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Technologia cyberprzestrzeni, cyberkultura, media i komunikacja

Działalność
Wiadomości | Gospodarka, biznes, zarządzanie, ekonomia

Technologie
Budownictwo, energetyka, transport, wytwarzanie, technologie informacyjne

Satellite retrieval of aerosol microphysical and optical parameters using neural networks: a new methodology applied to the Sahara desert dust peak

Satellite retrieval of aerosol microphysical and optical parameters using neural networks: a new methodology applied to the Sahara desert dust peakAtmospheric Measurement Techniques, 7, 3151-3175, 2014Author(s): M. Taylor, S. Kazadzis, A. Tsekeri, A. Gkikas, and V. AmiridisIn order to exploit the full-earth viewing potential of satellite
instruments to globally characterise aerosols, new algorithms are required
to deduce key microphysical parameters like the particle size distribution
and optical parameters associated with scattering and absorption from space
remote sensing data. Here, a methodology based on neural networks is
developed to retrieve such parameters from satellite inputs and to validate
them with ground-based remote sensing data. For key combinations of input
variables available from the MODerate resolution Imaging Spectro-radiometer (MODIS) and the Ozone Measuring Instrument (OMI) Level 3 data sets, a grid of 100
feed-forward neural network architectures is produced, each having a
different number of neurons and training proportion. The networks are
trained with principal components accounting for 98% of the variance of
the inputs together with principal components formed from 38 AErosol RObotic NETwork (AERONET) Level
2.0 (Version 2) retrieved parameters as outputs. Daily averaged, co-located
and synchronous data drawn from a cluster of AERONET sites centred on the
peak of dust extinction in Northern Africa is used for network training and
validation, and the optimal network architecture for each input parameter
combination is identified with reference to the lowest mean squared error.
The trained networks are then fed with unseen data at the coastal dust site
Dakar to test their simulation performance. A neural network (NN), trained with co-located
and synchronous satellite inputs comprising three aerosol optical depth
measurements at 470, 550 and 660 nm, plus the columnar water vapour (from
MODIS) and the modelled absorption aerosol optical depth at 500 nm (from
OMI), was able to simultaneously retrieve the daily averaged size
distribution, the coarse mode volume, the imaginary part of the complex
refractive index, and the spectral single scattering albedo – with moderate
precision: correlation coefficients in the range 0.368 ≤ R ≤ 0.514. The network failed to recover the spectral behaviour of the real part
of the complex refractive index. This new methodological approach appears to
offer some potential for moderately accurate daily retrieval of the total
volume concentration of the coarse mode of aerosol at the Saharan dust peak
in the area of Northern Africa.

Atmospheric Measurement Techniques 2014/09/26 - 15:30 Czytaj