Observations & remote sensing

ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction

Rochelle Schneider, Massimo Bonavita, Alan Geer, Rossella Arcucci, Peter Dominik Dueben, Claudia Vitolo, Bertrand Le Saux, Begüm Demir, Pierre-Philippe Mathieu

npj Climate and Atmospheric Science · 21 June 2022 · ECMWF

PaperPDF

In one sentence. Workshop report on machine learning for Earth system observation and prediction, outlining opportunities and challenges across satellite and forecasting applications.

Abstract

Abstract This paper provides a short summary of the outcomes of the workshop on Machine Learning (ML) for Earth System Observation and Prediction (ESOP / ML4ESOP) organised by the European Space Agency (ESA) and the European Centre for Medium-Range Weather Forecasts (ECMWF) between 15 and 18 November 2021. The 4-days workshop had more than 30 speakers and 30 poster-presenters, attracting over 1100 registrations from 85 countries around the world. The workshop aimed to demonstrate where and how the fusion between traditional ESOP applications and ML methods has shown limitations, outstanding opportunities, and challenges based on the participant’s feedback. Future directions were also highlighted from all thematic areas that comprise the ML4ESOP domain.

Labels

Topic

Observations & remote sensing

Type

Review, survey or tutorial

Domain

Global

Data

Satellite, NWP analysis or forecasts (IFS, GFS, ...)

Status

Research

Identifiers

DOI:10.1038/s41612-022-00269-z OpenAlex:W4283207250

Added

2026-10-22

Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.