Estimation of Anisotropic Viscosities for the Stochastic Primitive Equations

Fuente: arXiv
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Autori principali: Cialenco, Igor, Hu, Ruimeng, Lin, Quyuan
Natura: Preprint
Pubblicazione: 2023
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author Cialenco, Igor
Hu, Ruimeng
Lin, Quyuan
author_facet Cialenco, Igor
Hu, Ruimeng
Lin, Quyuan
contents The viscosity parameters play a fundamental role in applications involving stochastic primitive equations (SPE), such as accurate weather predictions, climate modeling, and ocean current simulations. In this paper, we develop several novel estimators for the anisotropic viscosities in the SPE, using a finite number of Fourier modes of a single sample path observed within a finite time interval. The focus is on analyzing the consistency and asymptotic normality of these estimators. We consider a torus domain and treat strong, pathwise solutions in the presence of additive white noise (in time). Notably, the analysis for estimating horizontal and vertical viscosities differs due to the unique structure of the SPE and the fact that both parameters of interest are adjacent to the highest-order derivative. To the best of our knowledge, this is the first work addressing the estimation of anisotropic viscosities, with the potential applicability of the developed methodology to other models.
format Preprint
id arxiv_https___arxiv_org_abs_2309_06952
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Estimation of Anisotropic Viscosities for the Stochastic Primitive Equations
Cialenco, Igor
Hu, Ruimeng
Lin, Quyuan
Probability
Analysis of PDEs
The viscosity parameters play a fundamental role in applications involving stochastic primitive equations (SPE), such as accurate weather predictions, climate modeling, and ocean current simulations. In this paper, we develop several novel estimators for the anisotropic viscosities in the SPE, using a finite number of Fourier modes of a single sample path observed within a finite time interval. The focus is on analyzing the consistency and asymptotic normality of these estimators. We consider a torus domain and treat strong, pathwise solutions in the presence of additive white noise (in time). Notably, the analysis for estimating horizontal and vertical viscosities differs due to the unique structure of the SPE and the fact that both parameters of interest are adjacent to the highest-order derivative. To the best of our knowledge, this is the first work addressing the estimation of anisotropic viscosities, with the potential applicability of the developed methodology to other models.
title Estimation of Anisotropic Viscosities for the Stochastic Primitive Equations
topic Probability
Analysis of PDEs
url https://arxiv.org/abs/2309.06952