News

Paper on “Can deep learning beat numerical weather prediction?” published

Today, the Open Access article by Martin Schultz et.al. has been published in the Philosophical Transactions of the Royal Society A, theme issue “Machine learning for weather and climate modelling”. The paper discusses the question, whether it is possible to completely replace  the  current  numerical  weather  models  and data  assimilation  systems  with  deep learning  approaches. It is available at https://royalsocietypublishing.org/doi/10.1098/rsta.2020.0097.

AQ-Bench preprint available for public discussion

The preprint of the article “AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics”  by Clara Betancourt et al. is now in the public discussion phase. The manuscript is available at https://essd.copernicus.org/preprints/essd-2020-380/. It has been accepted for public discussion by the Inter-Journal Special issue “Benchmark datasets and machine learning algorithms for Earth system science data” of the Journals Earth System Science Data and Geoscientific Model Development.
The AQ-Bench dataset contains air quality data and metadata at more than 5500 air quality observation stations all over the world. It offers a low-threshold entrance to machine learning on a real world environmental dataset. The dataset itself is available at https://b2share.eudat.eu/records/30d42b5a87344e82855a486bf2123e9f . To start machine learning on the AQ-Bench dataset directly in your browser, visit the code repository (https://gitlab.version.fz-juelich.de/toar/ozone-mapping) and launch the binder!

IntelliO3-ts v1.0 is open access publication of the month

The Central Library of the Research Centre Jülich has elected “IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany” by Felix Kleinert, Lukas H. Leufen, and Martin G. Schultz as the open access publication of the month.

IntelliO3-ts v1.0 paper published

The article “IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany” by Felix Kleinert, Lukas H. Leufen, and Martin G. Schultz has been published in Geoscientific Model Development (GMD). The article is available from https://doi.org/10.5194/gmd-14-1-2021 under the Creative Commons Attribution 4.0 License.

MLAir preprint available for public discussion

The preprint of a paper on the machine learning workflow tool MLAir by Lukas Leufen et.al. is now in the public discussion phase. MLAir enables to easily build your custom workflow to train a machine learning algorithm on time series data. Check out the manuscript “MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series” available at https://gmd.copernicus.org/preprints/gmd-2020-332/ or go straight to the code repository at https://gitlab.version.fz-juelich.de/toar/mlair and start your contribution.

Documentation of workshop on ozone pollution in Germany

The ozone situation in Germany -state of knowledge, research gaps and recommendations- was discussed at a workshop organized by the German Environment Agency (Umweltbundesamt) and the Institute for Advanced Sustainability Studies (IASS Potsdam) in November 2019. The documentation is now available at https://www.umweltbundesamt.de/publikationen/the-ozone-situation-in-germany

Preprint available for public discussion

A preprint of the manuscript “IntelliO3-ts v1.0: A neural network approach to predict
near-surface ozone concentrations in Germany” by Felix Kleinert at. al. has been made available by the Geoscientific Model Development (GMD) journal. It can be accessed at https://gmd.copernicus.org/preprints/gmd-2020-169/ and is open for interactive public discussion until 12 Oct 2020.
Referees, authors, and other members of the scientific community can post interactive comments alongside the preprint. These comments are fully citable and archived.

AutoQA4Env presentation at the EGU 2020 conference

AutoQA4Env has been presented by Najmeh Kaffashzadeh in the virtual EGU conference session on atmospheric composition variability and trends. Many scientific and statistical efforts are devoted to developing advanced analytic tools or methods, but a better quantification of trend and uncertainty cannot be achieved without proper data quality control (QC). Automated QC tools are needed to allow better use of existing data, for example in the machine learning applications of the IntelliAQ project. The presentation discusses the challenges involved and presents a methodology for automated QC and its integration into the workflow used for the TOAR-database

Presentation given at workshop “AI for weather and climate studies”

Bing Gong attended the Eumetnet workshop on “AI for weather and climate studies” held at Royal Meteorological Institute of Belgium. In the two-day workshop, 20 presentations were given on the existing or exploring potential weather and climate application of AI. Bing Gong gave a presentation on “Parallel deep learning workflow for short-term temperature forecasts with video frame prediction methods”.

Workshop: Machine Learning in weather and climate modelling

Martin Schultz and Lukas Leufen attended a workshop on “Machine Learning in weather and climate modelling” at Corpus Christi college in Oxford. This workshop assembled more than 100 top-notch climate scientists and experts in HPC computer science and machine learning to present ongoing work and discuss the way forward. It became clear from the start that machine learning can likely play an important role in almost all stages of a weather and climate modelling workflow. Much discussed topics were the perceived need to impose physical constraints on the machine learning algorithms and quantify uncertainties. Martin Schultz’s presentation on the IntelliAQ and DeepRain projects was well received and the positive response confirmed the research strategy followed by these projects.