Zobrazeno 1 - 10
of 168
pro vyhledávání: '"Ionel Michael Navon"'
A Study of Coupling Parameter Estimation Implemented by 4D-Var and EnKF with a Simple Coupled System
Publikováno v:
Advances in Meteorology, Vol 2015 (2015)
Coupling parameter estimation (CPE) that uses observations to estimate the parameters in a coupled model through error covariance between variables residing in different media may increase the consistency of estimated parameters in an air-sea coupled
Externí odkaz:
https://doaj.org/article/4d3e058d274946589ef9743e0512c3f0
Accurate prediction of spatiotemporal ozone concentration is of great significance to effectively establish advanced early warning systems and regulate air pollution control. However, the comprehensive assessment of uncertainty and heterogeneity in s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd94e29655b511d3f516e512ca25a5be
http://hdl.handle.net/10044/1/104926
http://hdl.handle.net/10044/1/104926
Autor:
Rossella Arcucci, Dunhui Xiao, Fangxin Fang, Ionel Michael Navon, Pin Wu, Christopher C. Pain, Yi-Ke Guo
Publikováno v:
Computers & Fluids. 257:105862
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f592068c499991cca0f123b07fc72dfd
https://doi.org/10.1016/j.compfluid.2023.105862
https://doi.org/10.1016/j.compfluid.2023.105862
Publikováno v:
Engineering with Computers. 38:2245-2268
This paper deals with developing a fast and robust numerical formulation to simulate a system of fractional PDEs. At the first stage, the time variable is approximated by a finite difference method with first-order accuracy. At the second stage, the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::143069c9e46da9a062703aa7a0c38ba6
https://doi.org/10.1007/s00366-020-01195-5
https://doi.org/10.1007/s00366-020-01195-5
Publikováno v:
International Journal for Numerical Methods in Fluids. 92:1415-1436
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0cd019e7d6797f8450fd83f2ab7ef1ec
https://doi.org/10.1002/fld.4834
https://doi.org/10.1002/fld.4834
Publikováno v:
Meteorology and Atmospheric Physics. 132:703-719
This paper investigates sparse grids on a hexagonal cell structure using a Local-Galerkin method (LGM) or generalized spectral element method (SEM). Such methods allow sparse grids to be used, known as serendipity grids in square cells. This means th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b80517a0632dde0257b8601870401025
https://doi.org/10.1007/s00703-019-00718-0
https://doi.org/10.1007/s00703-019-00718-0
Real-time flood forecasting is crucial for supporting emergency responses to inundation-prone regions. Due to uncertainties in the future (e.g., meteorological conditions and model parameter inputs), it is challenging to make accurate forecasts of sp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f381eed3e2f14a422ef983063b0f9e1
http://hdl.handle.net/10044/1/104958
http://hdl.handle.net/10044/1/104958
Publikováno v:
GEM-International Journal on Geomathematics
This work presents a hybrid modeling approach to data-driven learning and representation of unknown physical processes and closure parameterizations. These hybrid models are suitable for situations where the mechanistic description of dynamics of som
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b68893db00b925419315ea076c99ac70
http://arxiv.org/abs/2104.00114
http://arxiv.org/abs/2104.00114
Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows
Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows but often require a set of ensemble forward simulations to estimate forecast covariance. On the other hand, predictor–corrector type nudging approach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1164c33ef845fd0dd9834524ed57dd59
http://arxiv.org/abs/2005.11296
http://arxiv.org/abs/2005.11296
Deep learning techniques for improving fluid flow modelling have gained significant attention in recent years. Advanced deep learning techniques achieve great progress in rapidly predicting fluid flows without prior knowledge of the underlying physic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76e19b782c4b1a6043a2d599460a05b1
http://arxiv.org/abs/2004.00707
http://arxiv.org/abs/2004.00707