Peer Reviewed
Leufen, L. H., Kleinert, F., Schultz, M. G.:
Exploring decomposition of temporal patterns to facilitate learning of neural networks for ground-level daily maximum 8-hour average ozone prediction
Environmental Data Science Volume 1, 2022, e10
Betancourt, C., Stomberg, T. T., Edrich, A.-K., Patnala, A., Schultz, M. G., Roscher, R., Kowalski, J., Stadtler, S.:
Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
Geoscientific model development 15(11), 4331 – 4354 (2022)
Schröder, S., Epp, E., Mozaffari, A., Romberg, M., Selke, N., Schultz, M. G.:
Enabling Canonical Analysis Workflows Documented Data Harmonization on Global Air Quality Data
Data Intelligence (2022) 4 (2): 259–270
Stadtler, S., Betancourt, C., Roscher, R.:
Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset
Machine Learning and Knowledge Extraction. 2022; 4(1):150-171.
Schultz, M.G., Kleinert, F., Leufen, L.H., Betancourt, C., Schröder, S., Gong, B., Stadtler, S., Langguth, M., Mozaffari, A.:
Artificial intelligence for air quality
The project repository journal 12(1), 70 – 73 (2022)
Betancourt, C., Hagemeier, B., Schröder, S., Schultz, M.G.
Context aware benchmarking and tuning of a TByte-scale air quality database and web service
Earth Science Informatics, 2021
Betancourt, C., Stomberg, T., Stadtler, S., Roscher, R., and Schultz, M. G.:
AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics,
Earth Syst. Sci. Data, 13, 3013–3033, 2021
Leufen, L.; Kleinert, F.; Schultz, M.
MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series
Geoscientific model development 14(3), 1553 – 1574, 2021
Schultz, M.G.; Betancourt, C.; Gong, B.; Kleinert, F.; Langguth, M.; Leufen, L.H.; Mozaffari, A.; Stadtler, S.,
Can deep learning beat numerical weather prediction?
Philosophical Transactions of the Royal Society, Series A, 20200097, 2021
Kleinert, F. ; Leufen, L. ; Schultz, M.
IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany
Geoscientific Model Development 14, 1–25, 2021
Kaffashzadeh, N. ; Kleinert, F. ; Schultz, M.
A New Tool for Automated Quality Control of Environmental Data in Open Web Services
Preprint, 2019
Schultz M.G. and 96 co-authors
Tropospheric Ozone Assessment Report: Database and Metrics Data of Global Surface Ozone Observations.
Elementa: Science of the Anthropocene., 5: 58, 2017
Conference Contributions
Kesselheim, S., Herten, A., Krajsek, K., Ebert, J., Jitsev, J., Cherti, M., Langguth, M., Gong, B., Stadtler, S., Mozaffari, A., Cavallaro, G., Sedona, R., Schug, A., Strube, A., Kamath, R., Schultz, M.G., Riedel, M., Lippert, T.
JUWELS Booster: A Supercomputer for Large-Scale AI Research
ISC High-Performance Conference Digital, Workshop “Deep Learning on Supercomputers”, 02 July, 2021 (accepted paper).
Schultz, M.G.
Is bigger always better? Deep learning applications in air quality research
Invited talk, ISC High Performance 2021 Digital, Session “ML in Climate and Weather”, 29 June, 2021
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
vPICO presentation; European Geophysical Union General Assembly 2021, digital event, 19-30 April 2021
Kleinert, F., Leufen, L. H., Lupascu, A., Butler, T. and Schultz, M.G. (2021):
Representing chemical history for ozone time-series predictions – a method development study for deep learning models
vPICO presentation; European Geophysical Union General Assembly 2021, digital event, 19-30 April 2021
Betancourt, C., Schröder, S., Hagemeier, B., Schultz, M.G. (2020):
Performance analysis and optimization of a TByte-scale atmospheric observation database
European Geophysical Union Assembly 2020, online event, 04-08 May 2020
Gong, B., Hußmann, S., Mozaffari, A., Vogelsang, J., and Schultz, M.
Deep learning for short-term temperature forecasts with video prediction methods
European Geophysical Union Assembly 2020, online event, 04-08 May 2020
Kaffashzadeh, N., Chang, K.L., Schröder, S., and Schultz, M.G.
A Statistical Model for Automated Quality Assessment of the TOAR-II
European Geophysical Union Assembly 2020, online event, 04-08 May 2020
Mozaffari, A., Schröder, S., Apweiler, S., Saini, R., Hagemeier, B., Schultz, M.
FAIRness in the multi-services data infrastructure of the Tropospheric Ozone Assessment Report (TOAR) and Artificial Intelligence for Air Quality (IntelliAQ) project
Poster; RDA 15th Plenary Meeting, Melbourne, Australia and virtual, 18-20 March 2020
Schröder, S., Mozaffari, A., Apweiler, S., Saini, R., Hagemeier, B., Schultz, M.G.
FAIRness in the multi-service data infrastructure of the Tropospheric Ozone Assessment Report (TOAR) and Artificial Intelligence for Air Quality (IntelliAQ) project
Poster; RDA Germany Conference, Potsdam, Germany, 25-27 February 2020
Schröder, S., Apweiler, S., Saini, R., Hagemeier, B. Schultz, M.G. (2019):
Enhancing FAIRness of global air quality data: The Tropospheric Ozone Assessment Report database
Oral presentation; Book of Abstracts, page 360, GeoMünster conference, Münster, Germany, 22-25 Sept 2019
Schultz, M.G.
IntelliAQ and DeepRain: Using Deep Learning Approaches in Weather and Air Quality Forecasts
Oral presentation; Workshop on Machine Learning in Weather and Climate Research, Oxford, UK, 02-05 Sept 2019
Gong, B., Schultz, M.G., and Kleinert, F.
Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques
Oral presentation; European Geophysical Union Assembly 2019, Vienna, Austria, 07-12 April 2019
Kleinert, F. ; Gong, B. ; Götz, M. ; Schultz, M.
Near Surface Ozone Predictions Based on Multiple Artificial Neural Network Architectures
EGU General Assembly 2019, EGU2019, Wien, Austria, 7 Apr 2019 – 12 Apr 2019
Kaffashzadeh, N., F. Kleinert and M.G. Schultz
A New Tool for Automated Quality Control of Environmental Time Series (AutoQC4Env) in Open Web Services
In: Abramowicz W., Corchuelo R. (eds) Business Information Systems Workshops. BIS 2019. Lecture Notes in Business Information Processing, vol 373. Springer, Cham., December 2019
Master and Doctoral Theses
Falco Weichselbaum
Deep Neural Network techniques for Weather Forecasting
Master thesis, Universtiy Bonn, 2022
Vincent Gramlich
Deep learning methods for forecasting of extreme ambient ozone values
Master thesis, University Cologne, 2021
Hußmann, S.
Deep Learning for Future Frame Prediction of Weather Maps
Master thesis, Humboldt University Berlin, 2019
Other
Schultz, M.G.,
How can we use AI to fight air pollution?
Open Access Government, July 2022, pp. 396-397
Schultz, M.G., Kleinert, F., Leufen, L., Betancourt, C., Schröder, S., Gong, B., Stadtler, S., Langguth, M., Mozaffari, A.:
Artificial intelligence for air quality
The Project Repository Journal (PRj), 12(1), 70 – 73 (2022).