Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-1-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
04 Jan 2021
Model description paper |
| 04 Jan 2021
| 04 Jan 2021
IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
Institute of Geosciences, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
Lukas H. Leufen
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
Institute of Geosciences, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
Martin G. Schultz
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
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Cited
21 citations as recorded by crossref.
- Can deep learning beat numerical weather prediction? M. Schultz et al. 10.1098/rsta.2020.0097
- Short-term forecasting of ozone air pollution across Europe with transformers S. Hickman et al. 10.1017/eds.2023.37
- Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties C. Betancourt et al. 10.5194/gmd-15-4331-2022
- Correcting ozone biases in a global chemistry–climate model: implications for future ozone Z. Liu et al. 10.5194/acp-22-12543-2022
- Unprecedented decline in summertime surface ozone over eastern China in 2020 comparably attributable to anthropogenic emission reductions and meteorology H. Yin et al. 10.1088/1748-9326/ac3e22
- Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017 M. DeLang et al. 10.1021/acs.est.0c07742
- Global impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone C. Keller et al. 10.5194/acp-21-3555-2021
- A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China W. Han et al. 10.5194/gmd-15-4225-2022
- Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data C. Betancourt et al. 10.1021/acs.est.3c05104
- Development of a recurrent spatiotemporal deep-learning method coupled with data fusion for correction of hourly ozone forecasts J. Li et al. 10.1016/j.envpol.2023.122291
- Implementation of HONO into the chemistry–climate model CHASER (V4.0): roles in tropospheric chemistry P. Ha et al. 10.5194/gmd-16-927-2023
- Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework F. Kleinert et al. 10.5194/gmd-15-8913-2022
- AQ-Bench: a benchmark dataset for machine learning on global air quality metrics C. Betancourt et al. 10.5194/essd-13-3013-2021
- Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration J. Becker et al. 10.1525/elementa.2022.00025
- MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series L. Leufen et al. 10.5194/gmd-14-1553-2021
- A daily highest air temperature estimation method and spatial–temporal changes analysis of high temperature in China from 1979 to 2018 P. Wang et al. 10.5194/gmd-15-6059-2022
- Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0 J. Gil et al. 10.5194/gmd-16-5251-2023
- Trend detection of atmospheric time series K. Chang et al. 10.1525/elementa.2021.00035
- Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information K. Ko et al. 10.3390/s22207864
- Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset S. Stadtler et al. 10.3390/make4010008
- Exploring decomposition of temporal patterns to facilitate learning of neural networks for ground-level daily maximum 8-hour average ozone prediction L. Leufen et al. 10.1017/eds.2022.9
21 citations as recorded by crossref.
- Can deep learning beat numerical weather prediction? M. Schultz et al. 10.1098/rsta.2020.0097
- Short-term forecasting of ozone air pollution across Europe with transformers S. Hickman et al. 10.1017/eds.2023.37
- Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties C. Betancourt et al. 10.5194/gmd-15-4331-2022
- Correcting ozone biases in a global chemistry–climate model: implications for future ozone Z. Liu et al. 10.5194/acp-22-12543-2022
- Unprecedented decline in summertime surface ozone over eastern China in 2020 comparably attributable to anthropogenic emission reductions and meteorology H. Yin et al. 10.1088/1748-9326/ac3e22
- Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017 M. DeLang et al. 10.1021/acs.est.0c07742
- Global impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone C. Keller et al. 10.5194/acp-21-3555-2021
- A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China W. Han et al. 10.5194/gmd-15-4225-2022
- Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data C. Betancourt et al. 10.1021/acs.est.3c05104
- Development of a recurrent spatiotemporal deep-learning method coupled with data fusion for correction of hourly ozone forecasts J. Li et al. 10.1016/j.envpol.2023.122291
- Implementation of HONO into the chemistry–climate model CHASER (V4.0): roles in tropospheric chemistry P. Ha et al. 10.5194/gmd-16-927-2023
- Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework F. Kleinert et al. 10.5194/gmd-15-8913-2022
- AQ-Bench: a benchmark dataset for machine learning on global air quality metrics C. Betancourt et al. 10.5194/essd-13-3013-2021
- Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration J. Becker et al. 10.1525/elementa.2022.00025
- MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series L. Leufen et al. 10.5194/gmd-14-1553-2021
- A daily highest air temperature estimation method and spatial–temporal changes analysis of high temperature in China from 1979 to 2018 P. Wang et al. 10.5194/gmd-15-6059-2022
- Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0 J. Gil et al. 10.5194/gmd-16-5251-2023
- Trend detection of atmospheric time series K. Chang et al. 10.1525/elementa.2021.00035
- Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information K. Ko et al. 10.3390/s22207864
- Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset S. Stadtler et al. 10.3390/make4010008
- Exploring decomposition of temporal patterns to facilitate learning of neural networks for ground-level daily maximum 8-hour average ozone prediction L. Leufen et al. 10.1017/eds.2022.9
Latest update: 24 Apr 2024
Short summary
With IntelliO3-ts v1.0, we present an artificial neural network as a new forecasting model for daily aggregated near-surface ozone concentrations with a lead time of up to 4 d. We used measurement and reanalysis data from more than 300 German monitoring stations to train, fine tune, and test the model. We show that the model outperforms standard reference models like persistence models and demonstrate that IntelliO3-ts outperforms climatological reference models for the first 2 d.
With IntelliO3-ts v1.0, we present an artificial neural network as a new forecasting model for...
