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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-228
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-228
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 11 Jun 2019

Research article | 11 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Neural Network for Aerosol Retrieval from Hyperspectral Imagery

Steffen Mauceri1,2, Bruce Kindel1, Steven Massie1, and Peter Pilewskie1,2 Steffen Mauceri et al.
  • 1Department of Atmospheric and Oceanic Sciences, CU Boulder, Boulder, Colorado, USA
  • 2Laboratory for Atmospheric and Space Physics, Boulder, Colorado, USA

Abstract. We retrieve aerosol optical thickness (AOT) independently for brown carbon-, dust and sulfate from hyperspectral image data. The model, a neural network, is trained on atmospheric radiative transfer calculations from MODTRAN 6.0 with varying aerosol- concentration and type, surface albedo, water vapor and viewing geometries. From a set of test radiative transfer calculations, we are able to retrieve AOT with a standard error of better than ±0.05. No a priori information of the surface albedo or atmospheric state is necessary for our model. We apply the model to AVIRIS-NG imagery from a recent campaign over India and demonstrate its performance under high and low aerosol loadings and different aerosol types.

Steffen Mauceri et al.
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Status: open (until 06 Aug 2019)
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Steffen Mauceri et al.
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Latest update: 23 Jun 2019
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Short summary
Aerosols are fine particles that are suspended in Earth’s atmosphere. Better understanding of aerosols is important to lower uncertainties in climate predictions. We propose to measure aerosols from satellites and airplanes equipped with hyperspectral cameras using an artificial neural network, a form of machine learning. We applied our neural network to hyperspectral observations from a recent airplane flight over India and find general agreement with independent aerosol measurements.
Aerosols are fine particles that are suspended in Earth’s atmosphere. Better understanding of...
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