Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Matthias Morzfeld"'
Autor:
David Vishny, Matthias Morzfeld, Kyle Gwirtz, Eviatar Bach, Oliver R. A. Dunbar, Daniel Hodyss
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 16, Iss 9, Pp n/a-n/a (2024)
Abstract We synthesize knowledge from numerical weather prediction, inverse theory, and statistics to address the problem of estimating a high‐dimensional covariance matrix from a small number of samples. This problem is fundamental in statistics,
Externí odkaz:
https://doaj.org/article/4c73d169c15e4e87bb074793909dc293
Publikováno v:
Geochemistry, Geophysics, Geosystems, Vol 24, Iss 6, Pp n/a-n/a (2023)
Abstract Some rocks contain multiple remanence “components,” each of which preserves a record of a different magnetic field. The temperature ranges over which these remanence components unblock can overlap, making it difficult to determine their
Externí odkaz:
https://doaj.org/article/158aa402c22e49ea8487900a68411a5c
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 73, Iss 1, Pp 1-18 (2021)
We present a new multiscale covariance localisation method for ensemble data assimilation that is based on the estimation of eigenvectors and subsequent projections, together with traditional spatial localisation applied with a range of localisation
Externí odkaz:
https://doaj.org/article/3ec824684f74427ea6acc3bcc9395ff4
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 73, Iss 1, Pp 1-16 (2021)
Global Bayesian optimization (GBO) is a derivative-free optimization method that is used widely in the tech-industry to optimize objective functions that are expensive to evaluate, numerically or otherwise. We discuss the use of GBO in ensemble data
Externí odkaz:
https://doaj.org/article/028de1b4d61b4d8c8fb124ca91f85a41
Publikováno v:
Geochemistry, Geophysics, Geosystems, Vol 22, Iss 8, Pp n/a-n/a (2021)
Abstract The assumptions of paleointensity experiments are violated in many natural and archeological materials, leading to Arai plots which do not appear linear and yield inaccurate paleointensity estimates, leading to bias in the result. Recently,
Externí odkaz:
https://doaj.org/article/dc834258a19842e9816898b27ee175fa
Autor:
Matthias Morzfeld, Daniel Hodyss
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 71, Iss 1 (2019)
We present mathematical arguments and experimental evidence that suggest that Gaussian approximations of posterior distributions are appropriate even if the physical system under consideration is nonlinear. The reason for this is a regularizing effec
Externí odkaz:
https://doaj.org/article/bfd650d5d4064c2cb0711c7f3a52baae
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 68, Iss 0, Pp 1-17 (2016)
Recently there has been a surge in interest in coupling ensemble-based data assimilation methods with variational methods (commonly referred to as 4DVar). Here we discuss a number of important differences between ensemble-based and variational method
Externí odkaz:
https://doaj.org/article/7b3d72add30b4219ab6991eeb5fcbcc9
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 69, Iss 1 (2017)
The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorological problems. On the other hand, the EnKF can be interpreted as a particle filter, and particle filters (PF) collapse in high-dimensional problems.
Externí odkaz:
https://doaj.org/article/a091d72fd295456ca5ff0e4349ed252e
Autor:
Matthias Morzfeld, Daniel Hodyss
Publikováno v:
Monthly Weather Review. 151:717-736
Covariance localization has been the key to the success of ensemble data assimilation in high dimensional problems, especially in global numerical weather prediction. We review and synthesize optimal and adaptive localization methods that are rooted
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9a6837e0c1c401ed70cee4cc89472ad
https://doi.org/10.1175/mwr-d-22-0255.1
https://doi.org/10.1175/mwr-d-22-0255.1
Publikováno v:
Geophysical Journal International. 231:1075-1095
SUMMARY This paper is Part II of a two-part series on a mathematical and computational framework for computing a meaningful uncertainty quantification (UQ) for regularized inversions of electromagnetic data. In Part I, we explained the theory behind
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::946b1fa1a737e0d7575950a15817894b
https://doi.org/10.1093/gji/ggac242
https://doi.org/10.1093/gji/ggac242