{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T04:29:42Z","timestamp":1781929782469,"version":"3.54.5"},"reference-count":81,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T00:00:00Z","timestamp":1628812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heterogeneous data and discover and exploit complex and intricate relations among these data, without recourse to an explicit analytical treatment of those relations. These AI techniques are unavoidable to make sense of the rapidly increasing data deluge and to respond to the challenging new demands in Weather Forecast (WF), Climate Monitoring (CM) and Decadal Prediction (DP). The use of AI techniques can lead simultaneously to: (1) a reduction of human development effort, (2) a more efficient use of computing resources and (3) an increased forecast quality. To realise this potential, a new generation of scientists combining atmospheric science domain knowledge and state-of-the-art AI skills needs to be trained. AI should become a cornerstone of future weather and climate observation and modelling systems.<\/jats:p>","DOI":"10.3390\/rs13163209","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T09:22:38Z","timestamp":1628846558000},"page":"3209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":118,"title":["Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction"],"prefix":"10.3390","volume":"13","author":[{"given":"Steven","family":"Dewitte","sequence":"first","affiliation":[{"name":"Royal Meteorological Institute of Belgium, B-1180 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1180-1968","authenticated-orcid":false,"given":"Jan P.","family":"Cornelis","sequence":"additional","affiliation":[{"name":"Faculty of Applied Sciences, Vrije Universiteit Brussel, B-1050 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0376-0393","authenticated-orcid":false,"given":"Richard","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Deutscher Wetterdienst, D-63067 Offenbach, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian","family":"Munteanu","sequence":"additional","affiliation":[{"name":"Faculty of Applied Sciences, Vrije Universiteit Brussel, B-1050 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/nature14956","article-title":"The quiet revolution of numerical weather prediction","volume":"525","author":"Bauer","year":"2015","journal-title":"Nature"},{"key":"ref_2","unstructured":"Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., and Eickemeier, P. 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