A neural network radiative transfer model approach applied to the Tropospheric Monitoring Instrument aerosol height algorithm

Nanda, Swadhin; de Graaf, Martin; Veefkind, J. Pepijn; ter Linden, Mark; Sneep, Maarten; de Haan, Johan; Levelt, Pieternel F.

doi:https://doi.org/10.5194/amt-12-6619-2019

Articles | Volume 12, issue 12

https://doi.org/10.5194/amt-12-6619-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

TROPOMI on Sentinel-5 Precursor: first year in operation (AMT/ACP...

https://doi.org/10.5194/amt-12-6619-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 12, issue 12

Research article

|

13 Dec 2019

Research article |

| 13 Dec 2019

A neural network radiative transfer model approach applied to the Tropospheric Monitoring Instrument aerosol height algorithm

Swadhin Nanda, Martin de Graaf, J. Pepijn Veefkind, Mark ter Linden, Maarten Sneep, Johan de Haan, and Pieternel F. Levelt

Data sets

MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1 km MODIS Science Data Support Team (SDST) https://doi.org/10.5067/MODIS/MOD021KM.006

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 3 orders of magnitude. With this advancement in the algorithm's speed, TROPOMI is set to deliver the ALH product operationally.