Articles | Volume 15, issue 11
Geosci. Model Dev., 15, 4331–4354, 2022
https://doi.org/10.5194/gmd-15-4331-2022

Special issue: Benchmark datasets and machine learning algorithms for Earth...

Geosci. Model Dev., 15, 4331–4354, 2022
https://doi.org/10.5194/gmd-15-4331-2022
Model description paper
03 Jun 2022
Model description paper | 03 Jun 2022

Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties

Clara Betancourt et al.

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Cited articles

Amante, C. and Eakins, B. W.: ETOPO1 arc-minute global relief model: procedures, data sources and analysis, Tech. rep., NOAA National Geophysical Data Center, Boulder, Colorado, https://doi.org/10.7289/V5C8276M, 2009. a, b
Bastin, J.-F., Finegold, Y., Garcia, C., Mollicone, D., Rezende, M., Routh, D., Zohner, C. M., and Crowther, T. W.: The global tree restoration potential, Science, 365, 76–79, https://doi.org/10.1126/science.aax0848, 2019. a
Betancourt, C., Stomberg, T., Stadtler, S., Roscher, R., and Schultz, M. G.: AQ-Bench, B2SHARE [data set], https://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f, 2020. a
Betancourt, C., Stadtler, S., Stomberg, T., Edrich, A.-K., Patnala, A., Roscher, R., Kowalski, J., and Schultz, M. G.: Global fine resolution mapping of ozone metrics through explainable machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7596, https://doi.org/10.5194/egusphere-egu21-7596, 2021. a
Betancourt, C., Stomberg, T., Roscher, R., Schultz, M. G., and Stadtler, S.: AQ-Bench: a benchmark dataset for machine learning on global air quality metrics, Earth Syst. Sci. Data, 13, 3013–3033, https://doi.org/10.5194/essd-13-3013-2021, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n
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Short summary
Ozone is a toxic greenhouse gas with high spatial variability. We present a machine-learning-based ozone-mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.