Articles | Volume 15, issue 23
https://doi.org/10.5194/gmd-15-8913-2022
https://doi.org/10.5194/gmd-15-8913-2022
Model experiment description paper
 | 
13 Dec 2022
Model experiment description paper |  | 13 Dec 2022

Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework

Felix Kleinert, Lukas H. Leufen, Aurelia Lupascu, Tim Butler, and Martin G. Schultz

Related authors

MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series
Lukas Hubert Leufen, Felix Kleinert, and Martin G. Schultz
Geosci. Model Dev., 14, 1553–1574, https://doi.org/10.5194/gmd-14-1553-2021,https://doi.org/10.5194/gmd-14-1553-2021, 2021
Short summary
IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany
Felix Kleinert, Lukas H. Leufen, and Martin G. Schultz
Geosci. Model Dev., 14, 1–25, https://doi.org/10.5194/gmd-14-1-2021,https://doi.org/10.5194/gmd-14-1-2021, 2021
Short summary

Related subject area

Atmospheric sciences
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025,https://doi.org/10.5194/gmd-18-3359-2025, 2025
Short summary
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025,https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025,https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025,https://doi.org/10.5194/gmd-18-3211-2025, 2025
Short summary
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025,https://doi.org/10.5194/gmd-18-3175-2025, 2025
Short summary

Cited articles

Abdi‐Oskouei, M., Carmichael, G., Christiansen, M., Ferrada, G., Roozitalab, B., Sobhani, N., Wade, K., Czarnetzki, A., Pierce, R., Wagner, T., and Stanier, C.: Sensitivity of Meteorological Skill to Selection of WRF‐Chem Physical Parameterizations and Impact on Ozone Prediction During the Lake Michigan Ozone Study (LMOS), J. Geophys. Res.-Atmos., 125, e2019JD031971, https://doi.org/10.1029/2019JD031971, 2020. a
Aliaga, D., Sinclair, V. A., Andrade, M., Artaxo, P., Carbone, S., Kadantsev, E., Laj, P., Wiedensohler, A., Krejci, R., and Bianchi, F.: Identifying source regions of air masses sampled at the tropical high-altitude site of Chacaltaya using WRF-FLEXPART and cluster analysis, Atmos. Chem. Phys., 21, 16453–16477, https://doi.org/10.5194/acp-21-16453-2021, 2021. a
Archibald, A. T., Neu, J. L., Elshorbany, Y. F., Cooper, O. R., Young, P. J., Akiyoshi, H., Cox, R. A., Coyle, M., Derwent, R. G., Deushi, M., Finco, A., Frost, G. J., Galbally, I. E., Gerosa, G., Granier, C., Griffiths, P. T., Hossaini, R., Hu, L., Jöckel, P., Josse, B., Lin, M. Y., Mertens, M., Morgenstern, O., Naja, M., Naik, V., Oltmans, S., Plummer, D. A., Revell, L. E., Saiz-Lopez, A., Saxena, P., Shin, Y. M., Shahid, I., Shallcross, D., Tilmes, S., Trickl, T., Wallington, T. J., Wang, T., Worden, H. M., and Zeng, G.: Tropospheric Ozone Assessment Report: A critical review of changes in the tropospheric ozone burden and budget from 1850 to 2100, Elementa, 8, 034, https://doi.org/10.1525/elementa.2020.034, 2020. a
Avnery, S., Mauzerall, D. L., Liu, J., and Horowitz, L. W.: Global crop yield reductions due to surface ozone exposure: 1. Year 2000 crop production losses and economic damage, Atmos. Environ., 45, 2284–2296, https://doi.org/10.1016/j.atmosenv.2010.11.045, 2011. a
Download
Short summary
We examine the effects of spatially aggregated upstream information as input for a deep learning model forecasting near-surface ozone levels. Using aggregated data from one upstream sector (45°) improves the forecast by ~ 10 % for 4 prediction days. Three upstream sectors improve the forecasts by ~ 14 % on the first 2 d only. Our results serve as an orientation for other researchers or environmental agencies focusing on pointwise time-series predictions, for example, due to regulatory purposes.
Share