Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Peter D Dueben"'
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 14, Iss 3, Pp n/a-n/a (2022)
Abstract The treatment of cloud structure in numerical weather and climate models is often greatly simplified to make them computationally affordable. Here we propose to correct the European Centre for Medium‐Range Weather Forecasts 1D radiation sc
Externí odkaz:
https://doaj.org/article/589cf09b30a942fa895802fcc243be07
Autor:
Stephan Rasp, Peter D. Dueben, Sebastian Scher, Jonathan A. Weyn, Soukayna Mouatadid, Nils Thuerey
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 11, Pp n/a-n/a (2020)
Abstract Data‐driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data‐driven methods could also be used to predict global weather patterns days in adv
Externí odkaz:
https://doaj.org/article/db7463c2769d4534a00fa07937b96c7a
Publikováno v:
Environmental Research Letters, Vol 16, Iss 7, p 073008 (2021)
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of
Externí odkaz:
https://doaj.org/article/ffbea563a55f4d5ebfd710d8e54c94e7
Semi-implicit time-stepping schemes for atmosphere and ocean models require elliptic solvers that work efficiently on modern supercomputers. This paper reports our study of the potential computational savings when using mixed precision arithmetic in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::732090a75888057bcfd4039127bd0e0e
https://doi.org/10.1002/essoar.10511194.1
https://doi.org/10.1002/essoar.10511194.1
Autor:
Peter D. Dueben, Peter Bauer
Publikováno v:
Geoscientific Model Development, Vol 11, Pp 3999-4009 (2018)
Geoscientific Model Development
Geoscientific Model Development
Can models that are based on deep learning and trained on atmospheric data compete with weather and climate models that are based on physical principles and the basic equations of motion? This question has been asked often recently due to the boom of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::837e28a7553648598759ece6c3530acf
https://doi.org/10.5194/gmd-11-3999-2018
https://doi.org/10.5194/gmd-11-3999-2018
Autor:
Peter D. Dueben, Martin G. Schultz, Matthew Chantry, David John Gagne, David Matthew Hall, Amy McGovern
Publikováno v:
Artificial Intelligence for the Earth Systems
Benchmark datasets and benchmark problems have been a key aspect for the success of modern machine learning applications in many scientific domains. Consequently, an active discussion about benchmarks for applications of machine learning has also sta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19624b38e4fc1cf5152768c5f6cd2aad
Publikováno v:
Journal of Advances in Modeling Earth Systems. 14(10)
Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and int
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73b318b8b6743c96d3f4a4156162f4b4
Autor:
Harris, Lucy1 (AUTHOR) lucy.harris2@physics.ox.ac.uk, McRae, Andrew T. T.1 (AUTHOR), Chantry, Matthew2 (AUTHOR), Dueben, Peter D.2 (AUTHOR), Palmer, Tim N.1 (AUTHOR)
Publikováno v:
Journal of Advances in Modeling Earth Systems. Oct2022, Vol. 14 Issue 10, p1-27. 27p.
Autor:
Ackmann, Jan1 (AUTHOR), Dueben, Peter D.2 (AUTHOR) peter.dueben@ecmwf.int, Palmer, Tim1 (AUTHOR), Smolarkiewicz, Piotr K.3 (AUTHOR)
Publikováno v:
Journal of Advances in Modeling Earth Systems. Sep2022, Vol. 14 Issue 9, p1-26. 26p.