Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 21
Articles | Volume 17, issue 21
https://doi.org/10.5194/amt-17-6485-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-6485-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 21
Research article
 | 
13 Nov 2024
Research article |  | 13 Nov 2024

NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations

Leon Kuhn, Steffen Beirle, Sergey Osipov, Andrea Pozzer, and Thomas Wagner

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Short summary
This paper presents a new machine learning model that allows us to compute NO2 concentration profiles from satellite observations. A neural network was trained on synthetic data from the regional chemistry and transport model WRF-Chem. This is the first model of its kind. We present a thorough model validation study, covering various seasons and regions of the world.
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