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PT CHAP
TI Comparison of Stochastic Parametrization Schemes Using Data Assimilation on Triad Models
BT Chapron, B., Crisan, D., Holm, D., Mémin, E., Radomska, A. (eds) Stochastic Transport in Upper Ocean Dynamics II. STUOD 2022. Part of the Mathematics of Planet Earth book series (MPE,volume 11). Springer, Cham. Print ISBN 978-3-031-40093-3 Online ISBN 978-3-031-40094-0. https://doi.org/10.1007/978-3-031-40094-0_7. pp.159-191
AF Lobbe, Alexander
   Crisan, Dan
   Holm, Darryl
   Mémin, Etienne
   Lang, Oana
   Chapron, Bertrand
AS 1:1;2:1;3:1;4:2;5:1;6:3;
FF 1:;2:;3:;4:;5:;6:PDG-ODE-LOPS-SIAM;
C1 Imperial College London, Mathematics, London, UK
   Campus Universitaire de Beaulieu, Inria - Institut National de Recherche en Sciences et Technologies du Numérique, Rennes, France
   Ifremer – Institut Français de Recherche pour l’Exploitation de la Mer, Plouzané, France
C2 IMPERIAL COLL LONDON, UK
   INRIA, FRANCE
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
UR https://archimer.ifremer.fr/doc/00856/96755/105303.pdf
LA English
DT Book section
AB In recent years, stochastic parametrizations have been ubiquitous in modelling uncertainty in fluid dynamics models. One source of model uncertainty comes from the coarse graining of the fine-scale data and is in common usage in computational simulations at coarser scales. In this paper, we look at two such stochastic parametrizations: the Stochastic Advection by Lie Transport (SALT) parametrization introduced by Holm (Proc A 471(2176):20140963, 19, 2015) and the Location Uncertainty (LU) parametrization introduced by Mémin (Geophys Astrophys Fluid Dyn 108(2):119–146, 2014). Whilst both parametrizations are available for full-scale models, we study their reduced order versions obtained by projecting them on a complex vector Fourier mode triad of eigenfunctions of the curl. Remarkably, these two parametrizations lead to the same reduced order model, which we term the helicity-preserving stochastic triad (HST). This reduced order model is then compared with an alternative model which preserves the energy of the system, and which is termed the energy preserving stochastic triad (EST). These low-dimensional models are ideal benchmark models for testing new Data Assimilation algorithms: they are easy to implement, exhibit diverse behaviours depending on the choice of the coefficients and come with natural physical properties such as the conservation of energy and helicity. 
PY 2024
DI 10.1007/978-3-031-40094-0_7
ID 96755
ER

EF