Publications

Peer Reviewed

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

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

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

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

Hußmann, S.
Deep Learning for Future Frame Prediction of Weather Maps
Master thesis, Humboldt University Berlin, 2019

Other

Schultz, M.
Artificial Intelligence for Air Quality
The Project Repository Journal (PRj), Vol. 6, p. 30-32, June 2020