We are very proud to present our abstracts:
- END-TO-END PROCESS ORCHESTRATION OF EARTH OBSERVATION DATA WORKFLOWS WITH APACHE AIRFLOW ON HIGH PERFORMANCE COMPUTING
- MULTIMODAL SELF-SUPERVISED LEARNING FOR BOOSTING CROP CLASSIFICATION USING SENTINEL2 AND PLANETSCOPE
at IGARSS 2023 in Pasadena, California.
More Information will follow at https://2023.ieeeigarss.org/index.php
IntelliAQ is sponsoring two workshops in Cologne in early March 2023. The “IntelliAQ workshop on machine learning for air quality” (https://indico3-jsc.fz-juelich.de/event/68/) aims to bring together researchers from the air quality and machine learning communities for discussion of recent research progress and future priorities. Machine learning (ML) is rapidly gaining momentum as a new toolbox for analysing atmospheric data. While there are now several workshops, fora and conferences to discuss ML applications in the weather and climate domain, discussions on ML applications for air quality remain fragmented. The ERC project IntelliAQ has explored several modern ML concepts for air quality research and we would like to engage in a discussion with the international community about the potential and limitations of ML in this field. The second workshop, immediately following the first one, is a “Tropospheric Ozone Assessment Report (TOAR-II) science workshop” (https://indico3-jsc.fz-juelich.de/event/69/). IntelliAQ supports the development of the TOAR data infrastructure and draws on data and scientific insights from this global initiative.
JSC, the Otto von Guericke University of Magdeburg, and the Technical University of Munich jointly organised a workshop on “Transformers for Environmental Science” on September 22 and 23, 2022. The workshop was co-sponsored by the ERC grant IntelliAQ and brought together about 40 participants in Magdeburg and up to 20 additional online participants, who discussed the potential of this new AI technology for environmental applications. The program included lectures on recent advances in transformer architectures and transfer learning as well as on prototype developments focusing largely on atmospheric research and remote sensing. Keynote presentations were given by Peter Düben (ECMWF), Pedram Hassanzadeh (Rice University, Houston), Duncan Watson-Parris (Oxford University), Lucas Beyer (Google Brain) and Jonathan Godwin (Google Deepmind). A poster session and panel discussion provided opportunities for an exchange of ideas. Large transformer models require huge amounts of data and constitute an attractive application for accelerated supercomputers such as JUWELS Booster. Within the atmorep compute time project, first steps towards training such a model for atmospheric research are being taken.
Lukas H. Leufen, Felix Kleinert (both FZ Jülich and University of Bonn) and Martin G. Schultz (FZ Jülich) have published their latest research results of the study “Exploring decomposition of temporal patterns to facilitate learning of neural networks for ground-level daily maximum 8-hour average ozone prediction” in the Journal Environmental Data Science. The study shows how the accuracy of deep neural networks for forecasting ground-level ozone can be improved by splitting long-term and short-term weather patterns. The article is available at https://www.doi.org/10.1017/eds.2022.9 .
Felix Kleinert et al. submitted their article “Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework” to the Journal Geoscientific Model Development. It is now available as preprint for public discussion and review at https://gmd.copernicus.org/preprints/gmd-2022-122/ until July 6th 2022.
Scarlet Stadtler, Clara Betancourt (FZ Jülich) and Ribana Roscher (University of Bonn) published their study on “Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset” in the Machine Learning and Knowledge Extraction Journal. In their study, they gained insights into the AQ-Bench dataset on air quality using explainable machine learning. The article is available at http://dx.doi.org/10.3390/make4010008.
The results achieved so far within the IntelliAQ project have been summarised in the article “Artifical intelligence for air quality“. It has been published in The Project Repository journal, ISSN 2632-4067, volume 12, January 2022, pp. 70-74.
The preprint of the article “Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties” by Clara Betancourt et al. is now available at https://gmd.copernicus.org/preprints/gmd-2022-2/. It has been accepted for public discussion by the Journal Geoscientific Model Development.
The paper presents a data-driven ozone mapping workflow generating a transparent and reliable product. The global distribution of tropospheric ozone from sparse, irregularly placed measurement stations is mapped to a high-resolution regular grid using machine learning methods.
The Stifterverband awarded the project “TOAR App, Tropospheric Ozone Assessment Report database” with the second price of the Open Data Impact Award 2021 (https://www.stifterverband.org/pressemitteilungen/2021_12_14_open_data_impact_award): “The award highlights the potential of Open Research Data for innovation and society.
The TOAR database is one of the largest collections of global near-surface ozone measurements. To further promote the replication of this data, Clara Betancourt, Jianing Sun and Sabine Schröder from Forschungszentrum Jülich are developing a smartphone app that will allow farmers to quantify ozone-related damage to their crops.”
Scarlet Stadtler will present developments in the IntelliAQ project at Jülich’s End-of-Year Lecture Evening on 18 Nov. 2021 (https://juelich-eveninglecture.de). The focus of the presentation will be on her interdisciplinary approach to develop AI techniques to find patterns within climate and air quality data.