ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction
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 Novembe...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-06-01
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| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-022-00269-z |
| _version_ | 1811334274206400512 |
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| author | Rochelle Schneider Massimo Bonavita Alan Geer Rossella Arcucci Peter Dueben Claudia Vitolo Bertrand Le Saux Begüm Demir Pierre-Philippe Mathieu |
| author_facet | Rochelle Schneider Massimo Bonavita Alan Geer Rossella Arcucci Peter Dueben Claudia Vitolo Bertrand Le Saux Begüm Demir Pierre-Philippe Mathieu |
| author_sort | Rochelle Schneider |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T17:05:20Z |
| format | Article |
| id | doaj.art-9e2191ce556d42fcaa85a05299937592 |
| institution | Directory Open Access Journal |
| issn | 2397-3722 |
| language | English |
| last_indexed | 2024-04-13T17:05:20Z |
| publishDate | 2022-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Climate and Atmospheric Science |
| spelling | doaj.art-9e2191ce556d42fcaa85a052999375922022-12-22T02:38:29ZengNature Portfolionpj Climate and Atmospheric Science2397-37222022-06-01511510.1038/s41612-022-00269-zESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and predictionRochelle Schneider0Massimo Bonavita1Alan Geer2Rossella Arcucci3Peter Dueben4Claudia Vitolo5Bertrand Le Saux6Begüm Demir7Pierre-Philippe Mathieu8European Space AgencyEuropean Centre for Medium-Range Weather ForecastEuropean Centre for Medium-Range Weather ForecastImperial College LondonEuropean Centre for Medium-Range Weather ForecastEuropean Space AgencyEuropean Space AgencyTechnische Universität BerlinEuropean Space AgencyAbstract 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.https://doi.org/10.1038/s41612-022-00269-z |
| spellingShingle | Rochelle Schneider Massimo Bonavita Alan Geer Rossella Arcucci Peter Dueben Claudia Vitolo Bertrand Le Saux Begüm Demir Pierre-Philippe Mathieu ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction npj Climate and Atmospheric Science |
| title | ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction |
| title_full | ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction |
| title_fullStr | ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction |
| title_full_unstemmed | ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction |
| title_short | ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction |
| title_sort | esa ecmwf report on recent progress and research directions in machine learning for earth system observation and prediction |
| url | https://doi.org/10.1038/s41612-022-00269-z |
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