Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Peter Jan van Leeuwen"'
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 75, Iss 1, Pp 108–128-108–128 (2023)
Numerical weather prediction systems contain model errors related to missing and simplified physical processes, and limited model resolution. While it has been widely recognized that these model errors need to be included in the data assimilation for
Externí odkaz:
https://doaj.org/article/769299795db4462e846ca845c6073efa
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:
Physical Review Research, Vol 4, Iss 1, p 013036 (2022)
Measuring time lags between time series or light curves at different wavelengths from a variable or transient source in astronomy is an essential probe of physical mechanisms causing multiwavelength variability. Time lags are typically quantified usi
Externí odkaz:
https://doaj.org/article/156bfd6ef3a644b1beac215259424793
Autor:
Sanita Vetra-Carvalho, Peter Jan van Leeuwen, Lars Nerger, Alexander Barth, M. Umer Altaf, Pierre Brasseur, Paul Kirchgessner, Jean-Marie Beckers
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 70, Iss 1, Pp 1-43 (2018)
This paper compares several commonly used state-of-the-art ensemble-based data assimilation methods in a coherent mathematical notation. The study encompasses different methods that are applicable to high-dimensional geophysical systems, like ocean a
Externí odkaz:
https://doaj.org/article/5465d29af7f642e5b91553c94ed17128
Autor:
Peter Jan van Leeuwen
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 5 (2019)
Non-local observations are observations that cannot be allocated one specific spatial location. Examples are observations that are spatial averages of linear or non-linear functions of system variables. In conventional data assimilation, such as (ens
Externí odkaz:
https://doaj.org/article/e4513ec5ed8d4bf19ff0b706ca3d987f
Autor:
Christine Gommenginger, Bertrand Chapron, Andy Hogg, Christian Buckingham, Baylor Fox-Kemper, Leif Eriksson, Francois Soulat, Clément Ubelmann, Francisco Ocampo-Torres, Bruno Buongiorno Nardelli, David Griffin, Paco Lopez-Dekker, Per Knudsen, Ole Andersen, Lars Stenseng, Neil Stapleton, William Perrie, Nelson Violante-Carvalho, Johannes Schulz-Stellenfleth, David Woolf, Jordi Isern-Fontanet, Fabrice Ardhuin, Patrice Klein, Alexis Mouche, Ananda Pascual, Xavier Capet, Daniele Hauser, Ad Stoffelen, Rosemary Morrow, Lotfi Aouf, Øyvind Breivik, Lee-Lueng Fu, Johnny A. Johannessen, Yevgeny Aksenov, Lucy Bricheno, Joel Hirschi, Adrien C. H. Martin, Adrian P. Martin, George Nurser, Jeff Polton, Judith Wolf, Harald Johnsen, Alexander Soloviev, Gregg A. Jacobs, Fabrice Collard, Steve Groom, Vladimir Kudryavtsev, John Wilkin, Victor Navarro, Alex Babanin, Matthew Martin, John Siddorn, Andrew Saulter, Tom Rippeth, Bill Emery, Nikolai Maximenko, Roland Romeiser, Hans Graber, Aida Alvera Azcarate, Chris W. Hughes, Doug Vandemark, Jose da Silva, Peter Jan Van Leeuwen, Alberto Naveira-Garabato, Johannes Gemmrich, Amala Mahadevan, Jose Marquez, Yvonne Munro, Sam Doody, Geoff Burbidge
Publikováno v:
Frontiers in Marine Science, Vol 6 (2019)
High-resolution satellite images of ocean color and sea surface temperature reveal an abundance of ocean fronts, vortices and filaments at scales below 10 km but measurements of ocean surface dynamics at these scales are rare. There is increasing rec
Externí odkaz:
https://doaj.org/article/cebc3e6db1fb4a44bcc45f8d13940dff
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 68, Iss 0, Pp 1-10 (2016)
In numerical weather prediction, parameterisations are used to simulate missing physics in the model. These can be due to a lack of scientific understanding or a lack of computing power available to address all the known physical processes. Parameter
Externí odkaz:
https://doaj.org/article/eb8f0c193bfa44fa8020841520b6fbf0
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 67, Iss 0, Pp 1-13 (2015)
We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this perfo
Externí odkaz:
https://doaj.org/article/5e4af77f98d1493c8f540e184043fce7
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 69, Iss 1 (2017)
4DEnsembleVar is a hybrid data assimilation method which purpose is not only to use ensemble flow-dependent covariance information in a variational setting, but to altogether avoid the computation of tangent linear and adjoint models. This formulatio
Externí odkaz:
https://doaj.org/article/8298b4917b2d4e8e86fb848dc517e5fd
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 69, Iss 1 (2017)
In recent years, hybrid data-assimilation methods which avoid computation of tangent linear and adjoint models by using ensemble 4-dimensional cross-time covariances have become a popular topic in Numerical Weather Prediction. 4DEnsembleVar is one su
Externí odkaz:
https://doaj.org/article/a3fd298ef92d45feaa83fd8f476a7bde