Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Yvonne Ruckstuhl"'
Machine learning represents a potential method to cope with the gray zone problem of representing motions in dynamical systems on scales comparable to the model resolution. Here we explore the possibility of using a neural network to directly learn t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cdefc597c62aa27856722e8dd1605542
https://doi.org/10.5194/egusphere-egu23-5523
https://doi.org/10.5194/egusphere-egu23-5523
Parametrization of microphysics as well as parametrization of processes in the surface and boundary layers typically contain several tunable parameters. The parameters are not observed and are only crudely known. Traditionally, the numerical values o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bddf66b98c7bf086aa766b355c1d3aa0
https://doi.org/10.5194/egusphere-egu23-7868
https://doi.org/10.5194/egusphere-egu23-7868
The intertropical convergence zone (ITCZ) is a key circulation and precipitation feature in the tropics. There has been a large spread in the representation of the ITCZ in global weather and climate models for a long time, the reasons for which remai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e3af21eafcf91f80d9853ae855bf7d6
https://doi.org/10.5194/wcd-2023-7
https://doi.org/10.5194/wcd-2023-7
Publikováno v:
Quarterly Journal of the Royal Meteorological Society. 147:1949-1963
Autor:
Tijana Janjic, Yvonne Ruckstuhl
Publikováno v:
Monthly Weather Review. 148:1607-1628
We investigate the feasibility of addressing model error by perturbing and estimating uncertain static model parameters using the localized ensemble transform Kalman filter. In particular we use the augmented state approach, where parameters are upda
Machine learning represents a potential method to cope with the gray zone problem of representing motions in dynamical systems on scales comparable to the model resolution. Here we explore the possibility of using a neural network to directly learn t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7322f94a48b315062b663772c8b8c177
https://npg.copernicus.org/preprints/npg-2021-20/
https://npg.copernicus.org/preprints/npg-2021-20/
Quantification of evolving uncertainties is required for both probabilistic forecasting and data assimilation in weather prediction. In current practice, the ensemble of model simulations is often used as primary tool to describe the required uncerta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c81c1f8a5ccb66c2a25aa6abb5ae2509
https://doi.org/10.5194/egusphere-egu21-13077
https://doi.org/10.5194/egusphere-egu21-13077