Artificial Intelligence for Air Quality

Informazioni relative al progetto

IntelliAQ

ID dell’accordo di sovvenzione: 787576

DOI

10.3030/787576

Progetto chiuso

Data della firma CE 13 Giugno 2018

Data di avvio 1 Ottobre 2018

Data di completamento 30 Settembre 2023

Finanziato da

• EXCELLENT SCIENCE - European Research Council (ERC)

Costo totale

€ 2 498 761,00

Contributo UE

€ 2 498 761,00

Coordinato da

FORSCHUNGSZENTRUM JULICH GMBH
Germany

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Open Research Data Pilot (1)

Data Management Plan (DMP)

A document describing the data management strategy with respect to Open data provision

Pubblicazioni

Conference proceedings (22)

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

Other (7)

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

Peer reviewed articles (8)

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

Non-peer reviewed articles (11)

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, U
Pubblicato in: Elementa Science of the Anthropocene, Numero (2017) 5: 58, 2017, ISSN 2325-1026
Editore: University of California Press
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

Book chapters (1)

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

Diritti di proprietà intellettuale

Other (3)

IntelliO3-ts (v1.0): Source code and data

Numero candidatura/pubblicazione: http://doi.org/10.23728/b2share.5042cda41a4c49769c B2SHARE
Data: 2020-11-13
Candidato/i: FORSCHUNGSZENTRUM JULICH GMBH

MLAir (v1.0.0) - a tool to enable fast and flexible machine learning on air data time series - Source Code

Numero candidatura/pubblicazione: https://doi.org/10.34730/5a6c3533512541a79c5c41061 B2SHARE
Data: 2021-02-11
Candidato/i: FORSCHUNGSZENTRUM JULICH GMBH

AQ-Bench

Numero candidatura/pubblicazione: http://doi.org/10.23728/b2share.30d42b5a87344e8285 B2SHARE
Data: 2020-11-05
Candidato/i: FORSCHUNGSZENTRUM JULICH GMBH