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Journal Articles Nonlinear Processes in Geophysics Year : 2020

Data-driven versus self-similar parameterizations for Stochastic Advection by Lie Transport and Location Uncertainty

Valentin Resseguier
Wei Pan
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Abstract

Stochastic subgrid parameterizations enable ensemble forecasts of fluid dynamics systems and ultimately accurate data assimilation. Stochastic Advection by Lie Transport (SALT) and models under Location Uncertainty (LU) are recent and similar physically-based stochastic schemes. SALT dynamics conserve helicity whereas LU models conserve kinetic energy. After highlighting general similarities between LU and SALT frameworks, this paper focuses on their common challenge: the parameterization choice. We compare uncertainty quantification skills of a stationary heterogeneous data-driven parameteriza-5 tion and a non-stationary homogeneous self-similar parameterization. For stationary, homogeneous Surface Quasi-Geostrophic (SQG) turbulence, both parameterizations lead to high quality ensemble forecasts. This paper also discusses a heterogeneous adaptation of the homogeneous parameterization targeted at better simulation of strong straight buoyancy fronts.
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Dates and versions

hal-02371789 , version 1 (20-11-2019)
hal-02371789 , version 2 (09-02-2020)
hal-02371789 , version 3 (29-05-2020)

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Valentin Resseguier, Wei Pan, Baylor Fox-Kemper. Data-driven versus self-similar parameterizations for Stochastic Advection by Lie Transport and Location Uncertainty. Nonlinear Processes in Geophysics, 2020, 27 (2), pp.209-234. ⟨10.5194/npg-27-209-2020⟩. ⟨hal-02371789v3⟩

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