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Showing 1–6 of 6 results
Advanced filters: Author: Matthew Chantry Clear advanced filters

  • Aardvark Weather, an end-to-end machine learning model, replaces the entire numerical weather prediction pipeline with a machine learning model, by producing accurate global and local forecasts without relying on numerical solvers, revolutionizing weather prediction with improved speed, accuracy and customization capabilities.

    • Anna Allen
    • Stratis Markou
    • Richard E. Turner
    ResearchOpen Access
    Nature
    Volume: 641, P: 1172-1179

  • This report summarises the main outcomes of the 4th edition of the workshop on Machine Learning (ML) for Earth System Observation and Prediction (ESOP / ML4ESOP) co-organised by the European Space Agency (ESA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The 4-day workshop was held on 7-10 May 2024 in a hybrid format at the ESA Frascati site with an interactive online component, featuring over 46 expert talks with a record number of submissions and about 800 registrations. The workshop offered leading experts a platform to exchange on the current opportunities, challenges and future directions for applying ML methodology to ESOP. To structure the presentations and discussions, the workshop featured five main thematic areas covering key topics and emerging trends. The most promising research directions and significant outcomes were identified by each thematic area’s Working Group and are the focus of this document.

    • Patrick Ebel
    • Rochelle Schneider
    • Marcin Chrust
    Comments & OpinionOpen Access
    npj Climate and Atmospheric Science
    Volume: 7, P: 1-5

  • GenCast, a probabilistic weather model using artificial intelligence for weather forecasting, has greater skill and speed than the top operational medium-range weather forecast in the world and provides probabilistic, rather than deterministic, forecasts.

    • Ilan Price
    • Alvaro Sanchez-Gonzalez
    • Matthew Willson
    ResearchOpen Access
    Nature
    Volume: 637, P: 84-90

  • The rapid emergence of deep learning is attracting growing private interest in the traditionally public enterprise of numerical weather and climate prediction. A public–private partnership would be a pioneering step to bridge between physics- and data-based methods, and necessary to effectively address future societal challenges.

    • Peter Bauer
    • Peter Dueben
    • Bjorn Stevens
    Comments & Opinion
    Nature Reviews Earth & Environment
    Volume: 4, P: 507-509

  • This report provides a summary of the main outcomes of the 3rd 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.

    • Massimo Bonavita
    • Rochelle Schneider
    • Claudia Vitolo
    News & ViewsOpen Access
    npj Climate and Atmospheric Science
    Volume: 6, P: 1-5