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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-2019-143
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-143
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 08 May 2019

Submitted as: research article | 08 May 2019

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

A neural network radiative transfer model approach applied to TROPOMI’s aerosol height algorithm

Swadhin Nanda1,2, Martin de Graaf1, J. Pepijn Veefkind1,2, Mark ter Linden3, Maarten Sneep1, Johan de Haan1, and Pieternel F. Levelt1,2 Swadhin Nanda et al.
  • 1Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA De Bilt, The Netherlands
  • 2Delft university of Technology (TU Delft), Mekelweg 2, 2628 CD Delft, The Netherlands
  • 3S&T Corp, Delft, The Netherlands

Abstract. To retrieve aerosol properties from satellite measurements of the oxygen A-band in the near infrared, a line-by-line radiative transfer model implementation requires a large number of calculations. These calculations severely restrict a retrieval algorithm's operational capability as it can take several minutes to retrieve aerosol layer height for a single ground pixel. This paper proposes a forward modeling approach using artificial neural networks to speed up the retrieval algorithm. The forward model outputs are trained into a set of neural network models to completely replace line-by-line calculations in the operational processor. Results of comparing the forward model to the neural network alternative show encouraging results with good agreements between the two when applied to retrieval scenarios using both synthetic and real measured spectra from TROPOMI (TROPOspheric Monitoring Instrument) on board the ESA Sentinel-5 Precursor mission. With an enhancement of the computational speed by three orders of magnitude, TROPOMI's operational aerosol layer height processor is now able to retrieve aerosol layer heights well within operational capacity.

Swadhin Nanda et al.
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Swadhin Nanda et al.
Swadhin Nanda et al.
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Short summary
This paper discusses a neural network forward model used by the operational aerosol layer height (ALH) retrieval algorithm for the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Sentinel 5 Precursor satellite mission. This model replaces online radiative transfer calculations within the oxygen A-band, improving the speed of the algorithm by three orders of magnitude. With this advancement in the algorithm's speed, TROPOMI is set to deliver the ALH product operationally.
This paper discusses a neural network forward model used by the operational aerosol layer height...
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