Zobrazeno 1 - 10
of 55
pro vyhledávání: '"O. Pannekoucke"'
Publikováno v:
Geoscientific Model Development, Vol 17, Pp 6657-6681 (2024)
This work proposes a hybrid approach that combines physics and artificial intelligence (AI) for cloud cover nowcasting. It addresses the limitations of traditional deep-learning methods in producing realistic and physically consistent results that ca
Externí odkaz:
https://doaj.org/article/bb25203db8fc4ee0b45a475f0fe1ae69
Publikováno v:
Nonlinear Processes in Geophysics, Vol 30, Pp 139-166 (2023)
This contribution explores a new approach to forecasting multivariate covariances for atmospheric chemistry through the use of the parametric Kalman filter (PKF). In the PKF formalism, the error covariance matrix is modellized by a covariance model r
Externí odkaz:
https://doaj.org/article/7d125af8e2354b018d29b3eec1d459d0
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 15, Iss 10, Pp n/a-n/a (2023)
Abstract This paper is a contribution to the exploration of the parametric Kalman filter (PKF), which is an approximation of the Kalman filter, where the error covariances are approximated by a covariance model. Here we focus on the covariance model
Externí odkaz:
https://doaj.org/article/4c4b415be93d45298b1760fe4cafeae3
Autor:
O. Pannekoucke, P. Arbogast
Publikováno v:
Geoscientific Model Development, Vol 14, Pp 5957-5976 (2021)
Recent research in data assimilation has led to the introduction of the parametric Kalman filter (PKF): an implementation of the Kalman filter, whereby the covariance matrices are approximated by a parameterized covariance model. In the PKF, the dyna
Externí odkaz:
https://doaj.org/article/48f1c2a1f3ff429ab63d76f59b59bf4f
Publikováno v:
Nonlinear Processes in Geophysics, Vol 28, Pp 347-370 (2021)
This paper investigates the potential of a Wasserstein generative adversarial network to produce realistic weather situations when trained from the climate of a general circulation model (GCM). To do so, a convolutional neural network architecture is
Externí odkaz:
https://doaj.org/article/2b197b82174f48cb847b62552db29439
Publikováno v:
Nonlinear Processes in Geophysics, Vol 28, Pp 1-22 (2021)
This contribution addresses the characterization of the model-error covariance matrix from the new theoretical perspective provided by the parametric Kalman filter method which approximates the covariance dynamics from the parametric evolution of a c
Externí odkaz:
https://doaj.org/article/edf5d84305d54046a8ac26966c694579
Autor:
O. Pannekoucke, R. Fablet
Publikováno v:
Geoscientific Model Development, Vol 13, Pp 3373-3382 (2020)
Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically consistent deep neural network architectures is an open issue. In the spirit of physics-informed neur
Externí odkaz:
https://doaj.org/article/268400f76adf47fb9557bebe361da0d0
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 13, Iss 10, Pp n/a-n/a (2021)
Abstract Data assimilation is a key component of operational systems and scientific studies for the understanding, modeling, forecasting and reconstruction of earth systems informed by observation data. Here, we investigate how physics‐informed dee
Externí odkaz:
https://doaj.org/article/7f97fcffce654358802fd656475f5e08
Publikováno v:
Nonlinear Processes in Geophysics, Vol 25, Pp 481-495 (2018)
The parametric Kalman filter (PKF) is a computationally efficient alternative method to the ensemble Kalman filter. The PKF relies on an approximation of the error covariance matrix by a covariance model with a space–time evolving set of parame
Externí odkaz:
https://doaj.org/article/489d892c7568433ca4d609a43acc6e9b
Autor:
E. Emili, B. Barret, S. Massart, E. Le Flochmoen, A. Piacentini, L. El Amraoui, O. Pannekoucke, D. Cariolle
Publikováno v:
Atmospheric Chemistry and Physics, Vol 14, Iss 1, Pp 177-198 (2014)
Accurate and temporally resolved fields of free-troposphere ozone are of major importance to quantify the intercontinental transport of pollution and the ozone radiative forcing. We consider a global chemical transport model (MOdèle de Chimie Atmosp
Externí odkaz:
https://doaj.org/article/3bd2360aee404e3a93b207aa63ee6718