Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Matthew Chantry"'
Autor:
Patrick Ebel, Rochelle Schneider, Massimo Bonavita, Mariana Clare, Anna Jungbluth, Maryam Pourshamsi, Matthew Chantry, Mihai Alexe, Alessandro Sebastianelli, Marcin Chrust
Publikováno v:
npj Climate and Atmospheric Science, Vol 7, Iss 1, Pp 1-5 (2024)
This report summarises the main outcomes of the 4th edition of the workshop on Machine Learning (ML) for Earth System Observation and Prediction (ESOP / ML4ESOP) co-organised by the European Space Agency (ESA) and the European Centre for Medium-Range
Externí odkaz:
https://doaj.org/article/f9698820f5f049c8b3e5b894704ce101
Autor:
Stephan Rasp, Stephan Hoyer, Alexander Merose, Ian Langmore, Peter Battaglia, Tyler Russell, Alvaro Sanchez‐Gonzalez, Vivian Yang, Rob Carver, Shreya Agrawal, Matthew Chantry, Zied Ben Bouallegue, Peter Dueben, Carla Bromberg, Jared Sisk, Luke Barrington, Aaron Bell, Fei Sha
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 16, Iss 6, Pp n/a-n/a (2024)
Abstract WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting benchmark proposed by (Rasp et al., 2020, https://doi.org/10.1029/2020ms002203), designed with the aim to accelerate progress in data‐driven weathe
Externí odkaz:
https://doaj.org/article/26fd487748a44dd98181f82522f88510
Autor:
Massimo Bonavita, Rochelle Schneider, Rossella Arcucci, Matthew Chantry, Marcin Chrust, Alan Geer, Bertrand Le Saux, Claudia Vitolo
Publikováno v:
npj Climate and Atmospheric Science, Vol 6, Iss 1, Pp 1-5 (2023)
This report provides a summary of the main outcomes of the 3rd edition of the workshop on Machine Learning (ML) for Earth System Observation and Prediction (ESOP/ML4ESOP) co-organised by the European Centre for Medium-Range Weather Forecasts (ECMWF)
Externí odkaz:
https://doaj.org/article/b109714b89904dab833c91a2b2c399f5
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 13, Iss 9, Pp n/a-n/a (2021)
Abstract We assess the ability of neural network emulators of physical parametrization schemes in numerical weather prediction models to aid in the construction of linearized models required by four‐dimensional variational (4D‐Var) data assimilat
Externí odkaz:
https://doaj.org/article/541ee394ec4045ef8f348f8ed98f6e2e
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 13, Iss 7, Pp n/a-n/a (2021)
Abstract We assess the value of machine learning as an accelerator for the parameterization schemes of operational weather forecasting systems, specifically the parameterization of nonorographic gravity wave drag. Emulators of this scheme can be trai
Externí odkaz:
https://doaj.org/article/b1af4566ff9648f8a6f5330b50b4a3aa
Autor:
Zied Ben Bouallègue, Fenwick Cooper, Matthew Chantry, Peter Düben, Peter Bechtold, Irina Sandu
Publikováno v:
Monthly Weather Review. 151:897-911
Based on the principle “learn from past errors to correct current forecasts,” statistical postprocessing consists of optimizing forecasts generated by numerical weather prediction (NWP) models. In this context, machine learning (ML) offers state-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::315306f4e1844c5cdcf93cf27542e624
https://doi.org/10.1175/mwr-d-22-0107.1
https://doi.org/10.1175/mwr-d-22-0107.1
Publikováno v:
Quarterly Journal of the Royal Meteorological Society. 149:843-855
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b783e084b56a8cd57d249d1260bd0af6
https://doi.org/10.1002/qj.4435
https://doi.org/10.1002/qj.4435
The large data volumes available in weather forecasting make post-processing an attractive field for applying machine learning. In turn, novel statistical machine learning methods that can be used to generate uncertainty information from a determinis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ff8e98d2417b38d805d0f2ef4cce8d2
https://doi.org/10.5194/egusphere-egu23-946
https://doi.org/10.5194/egusphere-egu23-946
Autor:
Margarita Choulga, Tom Kimpson, Matthew Chantry, Gianpaolo Balsamo, Souhail Boussetta, Peter Dueben, Tim Palmer
Ever increasing computing capabilities and crave for high-resolution numerical weather prediction and climate information are specially interesting for the representation of Earth surfaces. Knowledge of accurate and up-to-date surface state for ecosy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1294660f2ccf0b28a8560ebcc1037add
https://doi.org/10.5194/egusphere-egu23-8777
https://doi.org/10.5194/egusphere-egu23-8777
Machine learning, and particularly neural networks, have been touted as a valuable accelerator for physical processes. By training on data generated from an existing algorithm a network may theoretically learn a more efficient representation and acce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::99782786f82c197b042925e576192755
https://doi.org/10.5194/egusphere-egu23-3256
https://doi.org/10.5194/egusphere-egu23-3256