4.7 Article

2022 ECMWF-ESA workshop report: current status, progress and opportunities in machine learning for Earth System observation and prediction

Journal

NPJ CLIMATE AND ATMOSPHERIC SCIENCE
Volume 6, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41612-023-00387-2

Keywords

-

Ask authors/readers for more resources

This report summarizes the main outcomes of the 3(rd) edition of the workshop on Machine Learning (ML) for Earth System Observation and Prediction (ESOP/ML4ESOP) co-organized by ECMWF and ESA. The workshop, which took place in hybrid format, attracted a record number of submissions and over 700 registrations. It aimed to document the current state-of-the-art and challenges in integrating ML technologies in ESOP.
This report provides a summary of the main outcomes of the 3(rd) edition of the workshop on Machine Learning (ML) for Earth System Observation and Prediction (ESOP/ML4ESOP) co-organised by the European Centre for Medium-Range Weather Forecasts (ECMWF) and European Space Agency (ESA). The 4-day workshop was held on 14-17 November 2022 in hybrid format, with an in-person component at the ECMWF Reading site and an interactive online component, attracting a record number of submissions and over 700 registrations. The workshop aimed to document the current state-of-the-art, progress and challenges in the rapidly evolving field of the integration of ML technologies in ESOP and to provide a venue for discussion and collaboration for ESOP and ML specialists. The workshop was structured along five main thematic areas covering the principal components of standard ESOP workflows. Highlights from the presentations and a discussion of the most promising development directions from the workshop Working Groups in all the different thematic areas are provided in this Report.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available