Artificial Intelligence for Air Quality

Near-surface temperature forecasting by deep learning

Autori: Gong, Bing; Langguth, Michael; Mozaffari, Amirpasha; Ji, Yianing ; Stadtler, Scarlet; Mache, Karim; Schultz, Martin
Pubblicato in: ML for Earth System Modelling and Analytics workshop 2021, online, Germany,, 2021
Editore: Forschungszentrum Jülich

Performance analysis and optimization of a TByte-scale atmospheric observation database

Autori: Clara Betancourt; Sabine Schröder; Björn Hagemeier; Martin Schultz
Pubblicato in: EGU2020: Sharing Geoscience Online, EGU2020, online, online conference, 2020-05-04 - 2020-05-08, 2020
Editore: Copernicus
DOI: 10.5194/egusphere-egu2020-13637

A Statistical Model for Automated Quality Assessment of the TOAR-II

Autori: Najmeh Kaffashzadeh; Kai-Lan Chang; Sabine Schröder; Martin G. Schultz
Pubblicato in: "EGU2020: Sharing Geoscience Online, #shareEGU20, Vienna, Austria, 2020-05-04 - 2020-05-08", 2020
Editore: Copernicus
DOI: 10.5194/egusphere-egu2020-13357

TOAR-II data portal for global measurements of ozone and its precursors

Autori: Schröder, Sabine; Mozaffari, Amirpasha; Romberg, Mathilde; Selke, Niklas; Leufen, Lukas Hubert; Ahring, Jessica; Schultz, Martin
Pubblicato in: CEOS Atmospheric Composition Virtual Constellation AC-VC-18, virtual, Belgium, 2022
Editore: Forschungszentrum Jülich

Global fine resolution mapping of ozone metrics through explainable machine learning

Autori: Clara Betancourt; Scarlet Stadtler; Timo Stomberg; Ann-Kathrin Edrich; Ankit Patnala; Ribana Roscher; Julia Kowalski; Martin G. Schultz
Pubblicato in: EGU General Assembly 2021, vEGU21, Online, Online, 2021-04-19 - 2021-04-30, 2021
Editore: Copernicus
DOI: 10.13140/rg.2.2.17134.13123

FAIRness in the multi-service data infrastructure of the Tropospheric Ozone Assessment Report (TOAR) and Artificial Intelligence for Air Quality (IntelliAQ) project

Autori: Amirpasha Mozaffari; Schröder, Sabine; Apweiler, Sander; Rajveer Saini; Hagemeier, Björn; Schultz, Martin G.
Pubblicato in: RDA Deutschland Tagung 2020, Berlin, Germany, 2020-02-25 - 2020-02-27, Numero 1, 2020
Editore: Research Data Alliance
DOI: 10.13140/rg.2.2.24046.13123

Enhancing FAIRness of global air quality data: The Tropospheric Ozone Assessment Report database

Autori: Schröder, Sabine. Apweiler, Sander. Saini, Rajveer. Hagemeier, Björn. Schultz, Martin. G.
Pubblicato in: Enhancing FAIRness of global air quality data: The Tropospheric Ozone Assessment Report database, 2019
Editore: Zenodo
DOI: 10.5281/zenodo.3549626

Automatic quality control and quality control schema in the Observation to Archive

Autori: Silva, Brenner; Kaffashzadeh, Najmeh; Nixdorf, Erik; Immoor, Sebastian; Fischer, Philipp; Anselm, Norbert; Gerchow, Peter; Schäfer, Angela; Koppe, Roland
Pubblicato in: "EGU2020: Sharing Geoscience Online, #shareEGU20, Vienna, Austria, 2020-05-04 - 2020-05-08", Numero 1, 2021
Editore: Copernicus
DOI: 10.5194/egusphere-egu2020-15961

FAIRness in Air Quality and Weather Forecast

Autori: Mozaffari, Amirpasha; Schröder, Sabine; Apweiler, Sander; Saini, Rajveer; Hagemeier, Björn; Schultz, Martin
Pubblicato in: Research Data Alliance 15th Plenary Meeting, RDA 15, Melbourne, Australia, 2020-03-18 - 2020-03-20, 2020
Editore: Research Data Alliance

The TOAR database: metadata harmonization and data quality assurance on global air quality data

Autori: Selke, N. Schröder, S. Romberg, M. Ahring, J. Leufen, L. H. Apweiler, S. Schultz, M.
Pubblicato in: 2021
Editore: FZJ

TOAR-II Data Workshop

Autori: Schultz, Martin. Schröder, Sabine. Selke, Niklas. Epp, Eleonora. Romberg, Mathilde. Sun, Janing. Ahring, Jessica. Mozaffari, Amirphasha. Lensing, Max. Betancourt, Clara. Leufen, Lukas. Hubert. Hagemeier, Björn. Saini, Rajvee.
Pubblicato in: 2021
Editore: Forschungszentrum Jülich

Deep learning for short-term temperature forecasts with video prediction methods

Autori: Gong, Bing; Stadtler, Scarlet; Langguth, Michael; Mozaffari, Amirpasha; Vogelsang, Jan; Schultz, Martin
Pubblicato in: European Geosciences Union 2020, EGU2020, Virtual, Austria, 2020-05-04 - 2020-05-08, 2020
Editore: Copernicus

Geodata enrichment for air quality

Autori: Selke, Niklas; Leufen, Lukas Hubert; Mozaffari, Amirpasha; Schröder, Sabine; Schultz, Martin
Pubblicato in: Living Planet Symposium 2022, LPS2022, Bonn, Germany, 2022
Editore: Forschungszentrum Jülich

Prediction of Daily Maximum Ozone Threshold Exceedances by Artificial Intelligence Techniques in Germany

Autori: Gong, Bing; Kleinert, Felix; Schultz, Martin
Pubblicato in: EGU General Assembly 2019, EGU2019, Vienna, Austria, 2019-04-07 - 2019-04-12, 2019
Editore: Copernicus

Near Surface Ozone Predictions Based on Multiple ANN Architectures

Autori: Kleinert, Felix; Gong, Bing; Götz, Markus; Schultz, Martin
Pubblicato in: EGU General Assembly 2019, EGU2019, Wien, Austria, 2019-04-07 - 2019-04-12, 2019
Editore: Copernicus

A Web Service Architecture for Objective Station Classification Purposes

Autori: Martin G. Schultz, Sander Apweiler, Jan Vogelsang, Bjorn Hagemeier, Felix Kleinert, Daniel Mallmann
Pubblicato in: 2018 IEEE 14th International Conference on e-Science (e-Science), 2018, Pagina/e 283-284, ISBN 978-1-5386-9156-4
Editore: IEEE
DOI: 10.1109/escience.2018.00051

On the use of containers for machine learning and visualization workflows on JUWELS

Autori: Gong, Bing; Vogelsang, Jan; Amirpasha Mozaffari; Schultz, Martin
Pubblicato in: NIC Symposium 2020, Jülich, Germany, 2020-02-27 - 2020-02-28, 2020
Editore: Forschungszentrum Jülich
DOI: 10.13140/rg.2.2.27401.57442

A Novel Concept for Automated Quality Control of Atmospheric Time Series

Autori: Kaffashzadeh, Najmeh; Schröder, Sabine; Schultz, Martin
Pubblicato in: European Geoscience Union (EGU), Vienna, Austria, 2019-04-07 - 2019-04-12, 2019
Editore: Copernicus

TOAR-II Overview and Database

Autori: Cooper, Owen; Schröder, Sabine; Romberg, Mathilde; Selke, Niklas; Leufen, Lukas Hubert; Ahring, Jessics; Mozaffari, Amirpasha; Schultz, Martin
Pubblicato in: 2022
Editore: Forschungszentrum Jülich

Tropospheric Ozone Assessment Report (TOAR) Data Infrastructure

Autori: Schröder, Sabine; Epp, Eleonora; Leufen, Lukas Hubert; Mozaffari, Amirpasha; Romberg, Mathilde; Schultz, Martin; Sun, Jianing
Pubblicato in: WMO Data Conference, Virtual, Virtual, 2020-11-16 - 2020-11-19, 2020
Editore: World Meteorological Organisation

Deep Learning for Weather Forecasting and Climate Prediction

Autori: Martin Schultz
Pubblicato in: 2022
Editore: aiforgood.itu.int

Advancing FAIRness for global air quality data analyses

Autori: Mozaffari, Amirpasha; Schröder, Sabine ; Romberg, Mathilde ; Epp, Eleonora ; Ahring, Jessica Betancourt, Clara ; Leufen, Lukas Hubert; Kleinert, Felix ; Schultz, Martin
Pubblicato in: International Data Week 2022, IDW2022, Seoul, South Korea, 2022
Editore: Forschungszentrum Jülich

JUWELS Booster – A Supercomputer for Large-Scale AI Research

Autori: Kesselheim, Stefan; Herten, Andreas; Cavallaro, Gabriele; Sedona, Rocco; Schug, Alexander; Strube, Alexandre; Kamath, Roshni; Schultz, Martin G.; Riedel, Morris; Lippert, Thomas; Krajsek, Kai; Ebert, Jan; Jitsev, Jenia; Cherti, Mehdi; Langguth, Michael; Gong, Bing; Stadtler, Scarlet; Mozaffari, Amirpasha
Pubblicato in: Lecture Notes in Computer Science ISBN: 9783030905385, Numero 1, 2022
Editore: Arxiv-Preprints
DOI: 10.1007/978-3-030-90539-2_31

O3ResNet: A Deep Learning–Based Forecast System to Predict Local Ground-Level Daily Maximum 8-Hour Average Ozone in Rural and Suburban Environments

Autori: Lukas Hubert Leufen; Felix Kleinert; Martin G. Schultz
Pubblicato in: Artificial Intelligence for the Earth Systems, Numero Volume 2, Numero 3, 2023
Editore: American Meteorological Society
DOI: 10.1175/aies-d-22-0085.1

AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning

Autori: Christian Lessig, Ilaria Luise, Bing Gong, Michael Langguth, Scarlet Stadtler, Martin Schultz
Pubblicato in: Arxiv, 2023
Editore: Cornell University
DOI: 10.48550/arxiv.2308.13280

Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017

Autori: Marissa N. DeLang, Jacob S. Becker, Kai-Lan Chang, Marc L. Serre, Owen R. Cooper, Martin G. Schultz, Sabine Schröder, Xiao Lu, Lin Zhang, Makoto Deushi, Beatrice Josse, Christoph A. Keller, Jean-François Lamarque, Meiyun Lin, Junhua Liu, Virginie Marécal, Sarah A. Strode, Kengo Sudo, Simone Tilmes, Li Zhang, Stephanie E. Cleland, Elyssa L. Collins, Michael Brauer, and J. Jason West*
Pubblicato in: Environmental Science & Technology, Numero 2021,55,8, 2021, Pagina/e 4389-4398
Editore: ACS Publications
DOI: 10.1021/acs.est.0c07742

Multi-decadal surface ozone trends at globally distributed remote locations

Autori: Owen R. Cooper, Martin G. Schultz, Sabine Schröder, Kai-Lan Chang, Audrey Gaudel, Gerardo Carbajal Benítez, Emilio Cuevas, Marina Fröhlich, Ian E. Galbally, Suzie Molloy, Dagmar Kubistin, Xiao Lu, Audra McClure-Begley, Philippe Nédélec, Jason O’Brien, Samuel J. Oltmans, Irina Petropavlovskikh, Ludwig Ries, Irina Senik, Karin Sjöberg, Sverre Solberg, Gerard T. Spain, Wolfgang Spangl, Martin
Pubblicato in: Knowledge Domain: Atmospheric Science, Numero Elementa: Science of the Anthropocene (2020) 8: 23., 2020
Editore: ucpress.edu
DOI: 10.1525/elementa.420

Trend detection of atmospheric time series: Incorporating appropriate uncertainty estimates and handling extreme events

Autori: Kai-Lan Chang, Martin G. Schultz, Xin Lan, Audra McClure-Begley, Irina Petropavlovskikh, Xiaobin Xu, Jerald R. Ziemke
Pubblicato in: Elementa Science of the Anthropocene, Numero (2021) 9 (1): 00035, 2021
Editore: ucpress.edu
DOI: 10.1525/elementa.2021.00035

Advancing caching and automation with FDO

Autori: Amirpasha Mozaffari, Niklas Selke, Martin Schultz
Pubblicato in: Research Ideas and Outcomes, Numero 8, e94856, 2022
Editore: Pensoft
DOI: 10.3897/rio.8.e94856

AQ-Bench: A Benchmark Dataset for Machine Learning on GlobalAir Quality Metrics

Autori: Clara Betancourt; Timo Stomberg; Scarlet Stadtler; Ribana Roscher; Martin G. Schultz
Pubblicato in: Earth System Science Data, 2021, ISSN 1866-3516
Editore: Copernicus
DOI: 10.5194/essd-2020-380

MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series

Autori: Lukas Hubert Leufen, Felix Kleinert, Martin G. Schultz
Pubblicato in: Geoscientific Model Development, Numero 14/3, 2021, Pagina/e 1553-1574, ISSN 1991-9603
Editore: Copernicus
DOI: 10.5194/gmd-14-1553-2021

Can deep learning beat numerical weather prediction?

Autori: M. G. Schultz, C. Betancourt, B. Gong, F. Kleinert, M. Langguth, L. H. Leufen, A. Mozaffari, S. Stadtler
Pubblicato in: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Numero 379/2194, 2021, Pagina/e 20200097, ISSN 1364-503X
Editore: Royal Society of London
DOI: 10.1098/rsta.2020.0097

Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset

Autori: Scarlet Stadtler; Ribana Roscher; Clara Betancourt
Pubblicato in: Machine Learning and Knowledge Extraction; Volume 4; Numero 1; Pages: 150-171, Numero 1, 2022, ISSN 2504-4990
Editore: MDPI
DOI: 10.3390/make4010008

Artificial Intelligence for Air Quality

Autori: Martin Schultz
Pubblicato in: The Project Repository Journal (PRj), Numero Volume 6, June 2020, 2020, Pagina/e 30-32, ISSN 2632-4067
Editore: http://www.europeandissemination.eu/

IntelliO3-ts v1.0: A neural network approach to predict near-surface ozone concentrations in Germany

Autori: Lukas Hubert Leufen; Martin Schultz; Felix Kleinert
Pubblicato in: Geoscientific model development 14(1), 1 - 25 (2021). doi:10.5194/gmd-14-1-2021, 2021, ISSN 1991-9603
Editore: Copernicus
DOI: 10.5194/gmd-2020-169

IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany

Autori: Felix Kleinert, Lukas H. Leufen, Martin G. Schultz
Pubblicato in: Geoscientific Model Development, Numero 14/1, 2021, Pagina/e 1-25, ISSN 1991-9603
Editore: Copernicus GmbH
DOI: 10.5194/gmd-14-1-2021

Context aware benchmarking and tuning of a TByte-scale air quality database and web service

Autori: Clara Betancourt, Björn Hagemeier, Sabine Schröder, Martin G. Schultz
Pubblicato in: Earth Science Informatics, Numero 14/3, 2021, Pagina/e 1597-1607, ISSN 1865-0473
Editore: Springer Verlag
DOI: 10.1007/s12145-021-00631-4

Enabling Canonical Analysis Workflows Documented Data Harmonization on Global Air Quality Data

Autori: Sabine Schröder, Eleonora Epp, Amirpasha Mozaffari, Mathilde Romberg, Niklas Selke, Martin G. Schultz
Pubblicato in: Data Intelligence, Numero Volume 4, Numero 2, 2022, Pagina/e 259 - 270, ISSN 2641-435X
Editore: MIT Press Cambridge
DOI: 10.1162/dint_a_00130

Canonical Workflows to Make Data FAIR

Autori: Peter Wittenburg, Alex Hardisty, Yann Le Franc, Amirpasha Mozaffari, Limor Peer, Nikolay A. Skvortsov, Zhiming Zhao, Alessandro Spinuso
Pubblicato in: Data Intelligence, Numero Volume 4, Numero 2, 2022, Pagina/e 286–305, ISSN 2641-435X
Editore: MIT Press Cambridge
DOI: 10.1162/dint_a_00132

Exploring decomposition of temporal patterns to facilitate learning of neural networks for ground-level daily maximum 8-hour average ozone prediction

Autori: Lukas Hubert Leufen, Felix Kleinert, Martin G. Schultz
Pubblicato in: Environmental Data Science, 2022, ISSN 2634-4602
Editore: Cambridge University Press
DOI: 10.1017/eds.2022.9

Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration

Autori: Jacob S. Becker, Marissa N. DeLang, Kai-Lan Chang, Marc L. Serre, Owen R. Cooper, Hantao Wang, Martin G. Schultz, Sabine Schröder, Xiao Lu, Lin Zhang, Makoto Deushi, Beatrice Josse, Christoph A. Keller, Jean-François Lamarque, Meiyun Lin, Junhua Liu, Virginie Marécal, Sarah A. Strode, Kengo Sudo, Simone Tilmes, Li Zhang, Michael Brauer, J. Jason West
Pubblicato in: Elementa: Science of the Anthropocene, Numero (2023) 11 (1): 00025, 2023, ISSN 2325-1026
Editore: University of California Press
DOI: 10.1525/elementa.2022.00025

Editors’ Note: Special Issue on Canonical Workflow Frameworks for Research

Autori: Peter Wittenburg, Alex Hardisty, Amirpasha Mozzafari, Limor Peer, Nikolay Skvortsov, Alessandro Spinuso, Zhiming Zhao
Pubblicato in: Data Intelligence, Numero Volume 4, Numero 2, 2022, Pagina/e 149–154, ISSN 2641-435X
Editore: MIT Press Cambridge
DOI: 10.1162/dint_e_00122

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

Autori: Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler
Pubblicato in: Geoscientific Model Development, Numero Volume 15, Numero 11, 2022, Pagina/e 4331-4354, ISSN 1991-9603
Editore: Copernicus Publications
DOI: 10.5194/gmd-15-4331-2022

Tropospheric Ozone Assessment Report: Database and metrics data of global surface ozone observations

Autori: Martin G. Schultz, Sabine Schröder, Olga Lyapina, Owen R. Cooper, Ian Galbally, Irina Petropavlovskikh, Erika von Schneidemesser, Hiroshi Tanimoto, Yasin Elshorbany, Manish Naja, Rodrigo J. Seguel, Ute Dauert, Paul Eckhardt, Stefan Feigenspan, Markus Fiebig, Anne-Gunn Hjellbrekke, You-Deog Hong, Peter Christian Kjeld, Hiroshi Koide, Gary Lear, David Tarasick, Mikio Ueno, Markus Wallasch, Darrel B
Pubblicato in: Elementa Science of the Anthropocene, Numero 5, 2017, Pagina/e 58, ISSN 2325-1026
Editore: BioOne
DOI: 10.1525/elementa.244

Generating Views Using Atmospheric Correction for Contrastive Self-Supervised Learning of Multispectral Images

Autori: Ankit Patnala; Scarlet Stadtler; Martin G. Schultz; Juergen Gall
Pubblicato in: IEEE Geoscience and Remote Sensing Letters, Numero Volume 20, 2023, ISSN 1558-0571
Editore: IEEE journal
DOI: 10.1109/lgrs.2023.3274493

Temperature forecasting by deep learning methods

Autori: Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz
Pubblicato in: Geoscientific Model Development, Numero Volume 15, Numero 23, 2022, Pagina/e 8931–8956, ISSN 1991-9603
Editore: Copernicus
DOI: 10.5194/gmd-15-8931-2022

HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction

Autori: Amirpasha Mozaffari, Michael Langguth, Bing Gong, Jessica Ahring, Adrian Rojas Campos, Pascal Nieters, Otoniel José Campos Escobar, Martin Wittenbrink, Peter Baumann, Martin G. Schultz
Pubblicato in: Data Intelligence, Numero Volume 4, Numero 2, 2022, Pagina/e 271-285, ISSN 2641-435X
Editore: MIT Press Cambridge
DOI: 10.1162/dint_a_00131

Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework

Autori: Felix Kleinert, Lukas H. Leufen, Aurelia Lupascu, Tim Butler, and Martin G. Schultz
Pubblicato in: Geoscientific Model Development, Numero Volume 15, Numero 23, 2022, Pagina/e 8913–8930, ISSN 1991-9603
Editore: Copernicus Publications
DOI: 10.5194/gmd-15-8913-2022

A New Tool for Automated Quality Control of Environmental Time Series (AutoQC4Env) in Open Web Services

Autori: Najmeh Kaffashzadeh, Felix Kleinert, Martin G. Schultz
Pubblicato in: Business Information Systems Workshops - BIS 2019 International Workshops, Seville, Spain, June 26–28, 2019, Revised Papers, Numero 373, 2019, Pagina/e 513-518, ISBN 978-3-030-36690-2
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-36691-9_43

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