Articles | Volume 17, issue 21
https://doi.org/10.5194/amt-17-6485-2024
https://doi.org/10.5194/amt-17-6485-2024
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

Related authors

Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements
Kezia Lange, Andreas Richter, Tim Bösch, Bianca Zilker, Miriam Latsch, Lisa K. Behrens, Chisom M. Okafor, Hartmut Bösch, John P. Burrows, Alexis Merlaud, Gaia Pinardi, Caroline Fayt, Martina M. Friedrich, Ermioni Dimitropoulou, Michel Van Roozendael, Steffen Ziegler, Simona Ripperger-Lukosiunaite, Leon Kuhn, Bianca Lauster, Thomas Wagner, Hyunkee Hong, Donghee Kim, Lim-Seok Chang, Kangho Bae, Chang-Keun Song, Jong-Uk Park, and Hanlim Lee
Atmos. Meas. Tech., 17, 6315–6344, https://doi.org/10.5194/amt-17-6315-2024,https://doi.org/10.5194/amt-17-6315-2024, 2024
Short summary
On the influence of vertical mixing, boundary layer schemes, and temporal emission profiles on tropospheric NO2 in WRF-Chem – comparisons to in situ, satellite, and MAX-DOAS observations
Leon Kuhn, Steffen Beirle, Vinod Kumar, Sergey Osipov, Andrea Pozzer, Tim Bösch, Rajesh Kumar, and Thomas Wagner
Atmos. Chem. Phys., 24, 185–217, https://doi.org/10.5194/acp-24-185-2024,https://doi.org/10.5194/acp-24-185-2024, 2024
Short summary
The NO2 camera based on gas correlation spectroscopy
Leon Kuhn, Jonas Kuhn, Thomas Wagner, and Ulrich Platt
Atmos. Meas. Tech., 15, 1395–1414, https://doi.org/10.5194/amt-15-1395-2022,https://doi.org/10.5194/amt-15-1395-2022, 2022
Short summary

Related subject area

Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Remote sensing of lower-middle-thermosphere temperatures using the N2 Lyman–Birge–Hopfield (LBH) bands
Richard Eastes, J. Scott Evans, Quan Gan, William McClintock, and Jerry Lumpe
Atmos. Meas. Tech., 18, 921–928, https://doi.org/10.5194/amt-18-921-2025,https://doi.org/10.5194/amt-18-921-2025, 2025
Short summary
Retrievals of water vapour and temperature exploiting the far-infrared: application to aircraft observations in preparation for the FORUM mission
Sanjeevani Panditharatne, Helen Brindley, Caroline Cox, Richard Siddans, Jonathan Murray, Laura Warwick, and Stuart Fox
Atmos. Meas. Tech., 18, 717–735, https://doi.org/10.5194/amt-18-717-2025,https://doi.org/10.5194/amt-18-717-2025, 2025
Short summary
Global decadal measurements of methanol, ethene, ethyne, and HCN from the Cross-track Infrared Sounder
Kelley C. Wells, Dylan B. Millet, Jared F. Brewer, Vivienne H. Payne, Karen E. Cady-Pereira, Rick Pernak, Susan Kulawik, Corinne Vigouroux, Nicholas Jones, Emmanuel Mahieu, Maria Makarova, Tomoo Nagahama, Ivan Ortega, Mathias Palm, Kimberly Strong, Matthias Schneider, Dan Smale, Ralf Sussmann, and Minqiang Zhou
Atmos. Meas. Tech., 18, 695–716, https://doi.org/10.5194/amt-18-695-2025,https://doi.org/10.5194/amt-18-695-2025, 2025
Short summary
Forward model emulator for atmospheric radiative transfer using Gaussian processes and cross validation
Otto Lamminpää, Jouni Susiluoto, Jonathan Hobbs, James McDuffie, Amy Braverman, and Houman Owhadi
Atmos. Meas. Tech., 18, 673–694, https://doi.org/10.5194/amt-18-673-2025,https://doi.org/10.5194/amt-18-673-2025, 2025
Short summary
Developments on a 22 GHz microwave radiometer and reprocessing of 13-year time series for water vapour studies
Alistair Bell, Eric Sauvageat, Gunter Stober, Klemens Hocke, and Axel Murk
Atmos. Meas. Tech., 18, 555–567, https://doi.org/10.5194/amt-18-555-2025,https://doi.org/10.5194/amt-18-555-2025, 2025
Short summary

Cited articles

Anderson, G.: Error propagation by the Monte Carlo method in geochemical calculations, Geochim. Cosmochim. Acta, 40, 1533–1538, https://doi.org/10.1016/0016-7037(76)90092-2, 1976. a
Ardizzone, L., Kruse, J., Wirkert, S., Rahner, D., Pellegrini, E. W., Klessen, R. S., Maier-Hein, L., Rother, C., and Köthe, U.: Analyzing Inverse Problems with Invertible Neural Networks, https://doi.org/10.48550/ARXIV.1808.04730, 2018. a
Beckwith, M., Bates, E., Gillah, A., and Carslaw, N.: NO2 hotspots: Are we measuring in the right places?, Atmos. Environ. X, 2, 100025, https://doi.org/10.1016/j.aeaoa.2019.100025, 2019. a
Beirle, S., Dörner, S., Donner, S., Remmers, J., Wang, Y., and Wagner, T.: The Mainz profile algorithm (MAPA), Atmos. Meas. Tech., 12, 1785–1806, https://doi.org/10.5194/amt-12-1785-2019, 2019. a, b, c
Bergstra, J. and Bengio, Y.: Random Search for Hyper-Parameter Optimization, J. Mach. Learn. Res., 13, 281–305, 2012. a, b
Download
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.
Share