Estimation of Anisotropic Viscosities for the Stochastic Primitive Equations

TL;DR

This paper introduces novel estimators for anisotropic viscosities in stochastic primitive equations, analyzing their consistency and asymptotic properties, with potential applications in climate and ocean modeling.

Contribution

First to develop and analyze estimators for anisotropic viscosities in stochastic primitive equations, addressing unique challenges of the model's structure.

Findings

Proposed estimators are consistent and asymptotically normal.

Analysis distinguishes between horizontal and vertical viscosity estimation.

Methodology applicable to other models with similar structure.

Abstract

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.