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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 Dueben, Claudia Vitolo, Bertrand Le Saux, Begüm Demir, Pierre-Philippe Mathieu

2022npj Climate and Atmospheric Science27 citationsDOIOpen Access PDF

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.

Topics & Concepts

Agency (philosophy)Thematic mapEarth observationDomain (mathematical analysis)Earth observation satelliteEarth system scienceComputer scienceRange (aeronautics)Artificial intelligenceMeteorologyData scienceGeographyEngineeringAerospace engineeringCartographyGeologySatelliteMathematicsSociologySocial scienceOceanographyMathematical analysisMeteorological Phenomena and SimulationsSeismology and Earthquake StudiesClimate variability and models
ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction | Litcius