Environmental Data Science (EDS) - an open-access journal at Cambridge University Press - is pleased to be partnering with a workshop organised by the National Spatial Remote Sensing Program (PNTS) on the theme of Artificial Intelligence and Physical Modeling in Earth Observation: Synergy or Competition?
Authors participating in the workshop are encouraged to submit their work to EDS to be considered for a special collection (a virtual special issue) dedicated to this theme. We also encourage authors doing relevant research who are not presenting at the workshop to take part.
Scope
Artificial intelligence (AI) has experienced advances in various fields in recent years, particularly in the observation and modeling of the Earth system. Recently, some tasks typically performed by physical models, such as weather forecasting, have faced competition from AI-based systems (e.g., GraphCast). One of the strengths of AI algorithms is their significant capacity to learn tasks from large amounts of data. Once an AI model is "trained," its application requires relatively few computational resources, making it very competitive with physically based simulations, which are traditionally very costly. Many AI applications have a direct link to physical modeling. For example, AI systems for weather prediction are generally trained on ERA5 reanalyses, based on a physical model. These links between AI and physical modeling are not one-way. AI systems can be influenced by physics, whether through learning, as in GraphCast, through constraints on architecture (Physics-Informed Neural Networks - PINN), or through hybrid systems combining AI components with physical models. Conversely, AI systems can be analyzed to improve our understanding of physical processes (Explainable AI - XAI).
This special issue welcomes contributions for exploring the links between AI and physical modeling in their competitive aspects, but also in the various possible complementarities through applications related to Earth observation by satellite remote sensing in the fields of continental surfaces, ocean (physics and biogeochemistry), atmosphere, solid Earth, cryosphere, and human sciences. Algorithmic approaches can be related to (but are not restricted to) Physics-Informed Neural Networks, model parametrization, explainable AI, emulator of physical models, and data-driven models with physical constraints.
Timetable
- Workshop takes place 14 May 2024 in Grenoble, France.
- Deadline to submit to EDS: as soon as ready, with final deadline of 18 November 2024
We encourage submissions both from authors participating in the workshop and those not taking part. Articles will be peer-reviewed according to the standard EDS process (see below) and will be published as soon as possible after acceptance, in the interest of allowing authors to disseminate their work without unnecessary delay, and added to a special collection page.
Peer review
Articles will undergo the standard EDS peer review process, which consists of obtaining two reviewers per research paper (with a mix of data science and domain knowledge). EDS runs a transparent peer review model in which review reports are published alongside the accepted article. Reviewers are given the option of being anonymous or named in the published review report. Read more about the EDS peer review process here.
How to submit
Please note the following key details, with more information available in the EDS Instructions for Authors:
Article Types: Authors should make sure that they select the most appropriate article type when they submit their work to EDS. The standard EDS article types are:
- Application papers: Research progress, or tackling a real-world problem, in an environmental field, enabled by data science. For example, AI or data science could be used for understanding of environmental processes, or improving forecasting tools.
- Methods papers: Novel data science methodology inspired by an environmental problem or application. Typically the methodology should be demonstrated in one or more environmental applications.
- Data papers that describe in a structured way, with a short narrative and accompanying metadata, important and re-usable environmental data sets that reside in publicly accessible repositories. These papers promote data transparency and data re-use.
- Survey papers: providing a systematic overview of a method, tool or approach, or a field or subfield that is relevant to environmental data science.
Templates: Authors have the option of using the following EDS templates to help structure their submission:
- LaTeX template files
- Word template
- Overleaf (a collaborative authoring tool based on LaTeX; authors are able to submit directly to through Overleaf; read more about benefits here)
Authors not using our templates are reminded to include:
- An impact Statement: 120 words beneath the abstract describing the significance of the findings in language that can be understood by a wide audience
And at the back of the article:
- Author contributions (using the CRedIT taxonomy as a guide)
- Competing interest statement
- Data availability statement
- Funding statement
See the EDS Instructions for Authors for more details about these statements.
Submission portal: Authors should submit via the EDS ScholarOne site and select the 'AI and Physical Modelling in Earth Observation: Synergy or Competition?' tag from the dropdown menu when prompted to identify whether the article is for a special issue. Authors who presented at PNTS 2024 should select this when asked in the submission system whether the article derives from a conference or event
Open access
Any author can publish on an open access basis in EDS if accepted, irrespective of their funding situation or institutional affiliation. There are no financial barriers to publication. Many articles have publishing costs covered through the Transformative Agreements that Cambridge has set up with universities worldwide. If the corresponding author on an article is affiliated with a Transformative Agreement this effectively covers publishing costs. Authors not affiliated with these agreements who have received a grant that budgets for open access publication are encouraged to pay an article processing charge (APC). However, if an author has no funding and no institutional agreement, the charge will be waived. Please do not let concerns about your financial situation or affiliation put off your submission.
Open materials
Authors are encouraged to make code and data that supports the findings openly available in a recognised repository and to link to them in the Data Availability Statement in the article. We recognise this may not be possible in all circumstances. See the EDS Research Transparency policy. Open Data and Open Materials badges will be displayed on published articles that link to replication materials, as a recognition of open practices.
Editors
Editors of EDS related to the special issue:
- Claire Monteleoni (University of Colorado Boulder & INRIA Paris)
- Julien Brajard (Nansen Center NERSC, Norway & Sorbonne University, France)
- Anastase Charantonis (ENSIIE, Évry, France)
Guest Editors:
- Fatima Karbou (Météo-France & CNRS, France)
- Raul Lopez-Lozano (INRAE, France)
- Karine Adeline (ONERA, France)