Peer Reviewed
Becker J. S. ; DeLang M. N. ; Chang K.-L. ; Serre M. L. ; Cooper O. R. ; Wang H. ; Schultz M. G. ; Schröder S. ; Lu X. ; Zhang L. ; Deushi M. ; Josse B. ; Keller C.A. ; Lamarque J.F. ; Lin M. ; Liu J. ; Marécal V. ; Strode S.A. ; Sudo K. ; Tilmes S. ; Zhang L. ; Brauer M. ; West J.J.:
Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration
Elementa: Science of the Anthropocene (2023) 11 (1): 00025, October 2023
Leufen L.H. ; Kleinert F. ; Schultz M.G.:
O3ResNet: A Deep Learning–Based Forecast System to Predict Local Ground-Level Daily Maximum 8-Hour Average Ozone in Rural and Suburban Environments
Artificial Intelligence for the Earth Systems, Volume 2, Issue 3, 12. July 2023
Patnala, A. ; Stadtler, S. ; Schultz, M. G. ; Gall, J.:
Generating Views Using Atmospheric Correction for Contrastive Self-Supervised Learning of Multispectral Images
IEEE geoscience and remote sensing letters 20(2502305), 1 – 5 (2023), May 2023
2023
Gong, B. ; Langguth, M. ; Ji, Y. ; Mozaffari, A. ; Stadtler, S. ; Mache, K. ; Schultz, M. G.:
Temperature forecasting by deep learning methods
Geoscientific model development 15(23), 8931 – 8956 (2022), Dec 2022
Kleinert, F. ; Leufen, L. H. ; Lupascu, A. ; Butler, T. ; Schultz, M. G.:
Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework
Geoscientific model development 15(23), 8913 – 8930 (2022), special issue: “Benchmark datasets and machine learning algorithms for Earth system science data”, Oct 2022
Mozaffari, A. ; Selke, N. ; Schultz, M.:
Advancing caching and automation with FDO
Research ideas and outcomes 8, e94856 (2022), Oct 2022
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, Jul 2022
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), Jun 2022
Wittenburg, P. ; Hardisty, A. ; Mozaffari, A. ; Peer, L. ; Skvortsov, N. A. ; Spinuso, A. ; Zhao, Z.:
Editors’ Note: Special Issue on Canonical Workflow Frameworks for Research
Data Intelligence (2022) 4 (2): 149-154, Apr 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, Apr 2022
Mozaffari, A. ; Langguth, M. ; Gong, B. ; Ahring, J. ; Campos, A. R. ; Nieters, P. ; Escobar, O. J. C. ; Wittenbrink, M. ; Baumann, P. ; Schultz, M. G.:
HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction
Data Intelligence (2022) 4 (2): 271-285, Apr 2022
Wittenburg, P. ; Hardisty, A. ; Franc, Y. L. ; Mozaffari, A. ; Peer, L. ; Skvortsov, N. A. ; Zhao, Z. ; Spinuso, A.:
Canonical Workflows to Make Data FAIR
Data Intelligence (2022) 4 (2): 286-305, Apr 2022
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, Feb 2022
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), Jan 2022
2022
Chang, K.-L.; Schultz, M. ; Lan, X. ; McClure-Begley, A. ; Petropavlovskikh, I. ; Xu, X. ; Ziemke, J. R.:
Trend detection of atmospheric time series
Earth Science Informatics, Jun 2021
Betancourt, C., Hagemeier, B., Schröder, S., Schultz, M.G.
Context aware benchmarking and tuning of a TByte-scale air quality database and web service
Elementa: Science of the Anthropocene (2021) 9 (1): 00035., Dec 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, Jun 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, Apr 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, Mar 2021
DeLang, M. N. ; Becker, J. S. ; Chang, K.-L. ; Serre, M. L. ; Cooper, O. R. ; Schultz, M. ; Schröder, S. ; Lu, X. ; Zhang, L. ; Deushi, M. ; Josse, B. ; Keller, C. A. ; Lamarque, J.-F. ; Lin, M. ; Liu, J. ; Marécal, V. ; Strode, S. A. ; Sudo, K. ; Tilmes, S. ; Zhang, L. ; Cleland, S. E. ; Collins, E. L. ; Brauer, M. ; West, J. J.:
Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017
Environmental science & technology 55(8), 4389 – 4398 (2021), Mar 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, Jan 2021
2021
Cooper, O. ; Schultz, M. G.; Schröder, S. ; Chang, K.-L. ; Gaudel, A. ; Benítez, G. C. ; Cuevas, E. ; Fröhlich, M. ; Galbally, I. E. ; Molloy, S. ; Kubistin, D. ; Lu, X. ; McClure-Begley, A. ; Nédélec, P. ; O’Brien, J. ; Oltmans, S. J. ; Petropavlovskikh, I. ; Ries, L. ; Senik, I. ; Sjöberg, K. ; Solberg, S. ; Spain, G. T. ; Spangl, W. ; Steinbacher, M. ; Tarasick, D. ; Thouret, V. ; Xu, X..:
Multi-decadal surface ozone trends at globally distributed remote locations
Elementa: Science of the Anthropocene (2020) 8: 23. , Jun 2020
2020
Kaffashzadeh, N. ; Kleinert, F. ; Schultz, M.
A New Tool for Automated Quality Control of Environmental Data in Open Web Services
Preprint, Jul 2019
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, Oct 2017
Conference Contributions
Mozaffari, A. ; Schröder, S. ; Romberg, M. ; Epp, E. ; Ahring, J. ; Betancourt, C. ; Leufen, L. H. ; Kleinert, F. ; Schultz, M.:
Advancing FAIRness for global air quality data analyses Poster, International Data Week 2022, IDW2022, Seoul, South Korea, 20 Jun 2022 – 23 Jun 2022
Selke, N. ; Leufen, L. H. ; Mozaffari, A. ; Schröder, S. ; Schultz, M.:
Geodata enrichment for air quality Poster, Living Planet Symposium 2022, LPS2022, Bonn, Germany, 23 May 2022 – 27 May 2022
Cooper, O. ; Schröder, S. ; Romberg, M. ; Selke, N.; Leufen, L. H. ; Ahring, J. ; Mozaffari, A. ; Schultz, M.G::
TOAR-II Overview and Database Presentation, Spring 2022 Meetings of the Task Force on Hemispheric Transport of Air Pollution, virtual, virtual, 17 May 2022 – 25 May 2022
Schröder, S. ; Mozaffari, A. ; Romberg, M. ; Selke, N. ; Leufen, L. H. ; Ahring, J. ; Schultz, M.:
TOAR-II data portal for global measurements of ozone and its precursors Presentation, CEOS Atmospheric Composition Virtual Constellation AC-VC-18, virtual, Belgium, 14 Mar 2022 – 18 Mar 2022
Schultz, M.:
Deep Learning for Weather Forecasting and Climate Prediction AI4Good, AI4Good, virtual, Switzerland, 12 Jan 2022 – 12 Jan 2022
2022
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 Jul, 2021 (accepted paper).
Selke, N. Schröder, S. Romberg, M. Ahring, J. Leufen, L. H. Apweiler, S. Schultz, M..:
The TOAR database: metadata harmonization and data quality assurance on global air quality data EOSC Symposium 2021, virtual, virtual, 15 Jun 2021 – 18 Jun 2021
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 Jun, 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 Apr 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 Apr 2021
2021
Schröder, S. ; Epp, E. ; Leufen, L. H. ; Mozaffari, A. ; Romberg, M. ; Schultz, M.G: ; Sun, J.:
Tropospheric Ozone Assessment Report (TOAR) Data Infrastructure WMO Data Conference, Virtual, 16 Nov 2020 – 19 Nov 2020
Betancourt, C., Schröder, S., Hagemeier, B., Schultz, M.G.:
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.G.:
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
Silva, B.; Kaffashzadeh, N.* ; Nixdorf, E. ; Immoor, S. ; Fischer, P. ; Anselm, N. ; Gerchow, P. ; Schäfer, A. ; Koppe, R. .:
Automatic quality control and quality control schema in the Observation to Archive
European Geophysical Union Assembly 2020, online event, 04-08 May 2020
Mozaffari, A., Schröder, S., Apweiler, S., Saini, R., Hagemeier, B., Schultz, M.G.:
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 Mar 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 Feb 2020
2020
Schröder, S., Apweiler, S., Saini, R., Hagemeier, B. Schultz, M.G.
Enhancing FAIRness of global air quality data: The Tropospheric Ozone Assessment Report database
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
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
Presentation, European Geophysical Union 2019, Wien, Austria, 7 Apr 2019 – 12 Apr 2019
Kleinert, F. ; Gong, B. ; Götz, M. ; Schultz, M.
Near Surface Ozone Predictions Based on Multiple Artificial Neural Network Architectures
Presentation, European Geophysical Union 2019, Wien, Austria, 7 Apr 2019 – 12 Apr 2019
Kaffashzadeh, N. ; Schröder, S. ; Schultz, M.:
A Novel Concept for Automated Quality Control of Atmospheric Time Series
Presentation, European Geophysical Union 2019, Wien, Austria, 7 Apr 2019 – 12 Apr 2019
Gong, B.; Vogelsang, J.; Mozaffari A.; Schultz, M.G.:
On the use of containers for machine learning and visualization workflows on JUWELS
Presentation, NIC Symposium 2020, Jülich, Germany, 27 Feb 2022 – 28 Feb 2020
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., Dec 2019
2019
Schultz, M. ; Apweiler, S. ; Vogelsang, J. ; Hagemeier, B. ; Kleinert, F. ; Mallmann, D.
A Web Service Architecture for Objective Station Classification Purposes
14th International Conference on e-Science and Grid Computing, IEEE, Amsterdam, Netherlands, 29. October 2018 – 01. November 2018
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).
Schultz, M.G.; Schröder, S.; Selke, N.; Epp, E.; Romberg, M.; Sun, J.; Ahring, J.; Mozaffari, A.; Lensing, M.; Betancourt, C.; Leufen, L.H.; Hagemeier, B.; Saini, R.:
TOAR-II Data Workshop
TOAR-II Manuscript Scoping Workshop, TOAR-II_2.02, virtual, Germany, 14 Nov 2021 – 16 Nov 2021
Gong, B. ; Langguth, M. ; Mozaffari, A. ; Ji, Y. ; Stadtler, S. ; Mache, K. ; Schultz, M.
Near-surface temperature forecasting by deep learning
ML for Earth System Modelling and Analytics workshop 2021, online, Germany, 3 May 2021 – 4 May 2021