Zobrazeno 1 - 10
of 161
pro vyhledávání: '"Roland Potthast"'
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 8 (2022)
In this work, we use a tempering-based adaptive particle filter to infer from a partially observed stochastic rotating shallow water (SRSW) model which has been derived using the Stochastic Advection by Lie Transport (SALT) approach. The methodology
Externí odkaz:
https://doaj.org/article/4d0006cee76348f58a6a676f2dcf6dbf
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 8 (2022)
Nonlinear data assimilation methods like particle filters aim to improve the numerical weather prediction (NWP) in non-Gaussian setting. In this manuscript, two recent versions of particle filters, namely the Localized Adaptive Particle Filter (LAPF)
Externí odkaz:
https://doaj.org/article/d378e0c895c246a5b1327edca7f9f323
Autor:
Frederik Kurzrock, Sylvain Cros, Fabrice Chane Ming, Jason A. Otkin, Axel Hutt, Laurent Linguet, Gilles Lajoie, Roland Potthast
Publikováno v:
Meteorologische Zeitschrift, Vol 27, Iss 4, Pp 277-298 (2018)
Many research and societal applications such as surface solar irradiance assessment and forecasting require accurate short-term cloudiness forecasts at kilometre and hourly scales. Today limited-area numerical weather prediction models have the poten
Externí odkaz:
https://doaj.org/article/a7bd935d2af84cf9b6872a7e5b6cfa3f
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 5 (2019)
Externí odkaz:
https://doaj.org/article/f82e408146d74fa98ab5e67cc8872bee
Autor:
Axel Hutt, Roland Potthast
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 4 (2018)
Data assimilation permits to compute optimal forecasts in high-dimensional systems as, e.g., in weather forecasting. Typically such forecasts are spatially distributed time series of system variables. We hypothesize that such forecasts are not optima
Externí odkaz:
https://doaj.org/article/7140342747eb4fbaaf62c837706040fb
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 4 (2018)
The goal of this work is to analyse and study an ultra-rapid data assimilation (URDA) method for adapting a given ensemble forecast for some particular variable of a dynamical system to given observation data which become available after the standard
Externí odkaz:
https://doaj.org/article/f00a92b35731431dadfb4e69225555ff
Autor:
Roland Potthast, Peter Beim Graben
Publikováno v:
Frontiers in Computational Neuroscience, Vol 3 (2009)
Inverse problems in computational neuroscience comprise the determination of synaptic weight matrices or kernels for neural networks or neural fields respectively. Here, we reduce multi-dimensional inverse problems to inverse problems in lower dimens
Externí odkaz:
https://doaj.org/article/ad4e798268ab4c79b0f7a3e52e2701fe
Publikováno v:
eISSN
This study presents an extension to the method for fast satellite image synthesis (MFASIS) to allow simulating reflectances for the 1.6 μm near-infrared channel based on a computationally efficient neural network with improved accuracy. Such a fast
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b44e1989e0d832c730ee8fe9e7fa5c27
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-353/
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-353/
Publikováno v:
Artificial Intelligence for the Earth Systems. 2
This paper presents an innovational way of assimilating observations of clouds into the icosahedral nonhydrostatic weather forecasting model for regional scale (ICON-D2), which is operated by the German Weather Service (Deutscher Wetterdienst) (DWD).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::54f5b7d6eb7bfe22065936d075ab86f8
https://doi.org/10.1175/aies-d-22-0025.1
https://doi.org/10.1175/aies-d-22-0025.1
Within the SINFONY project at DWD, we have developed a framework for assimilating convective objects from the cell-identification and -tracking scheme KONRAD3D into NWP forecasts using ICON-LAM. The main idea behind this approach is to additionally c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c14d9346543c84c9497146f7b4040654
https://doi.org/10.5194/ecss2023-27
https://doi.org/10.5194/ecss2023-27