Year: 2025
Type: preprint
Abstract: This paper studies whether numerically preserving monotonic properties can offer modelling advantages in data assimilation, particularly when the signal or data is a realization of a stochastic partial differential equation (SPDE) or partial differential equation (PDE) with a monotonic property. We investigate the combination of stochastic Strong Stability Preserving (SSP) time-stepping, nonlinear solving strategies and data assimilation. Experimental results indicate that a particle filter whose ensemble members are solved monotonically can increase forecast skill when the reference data (not necessarily observations) also has a monotone property. Additionally, more advanced techniques used to avoid the degeneracy of the filter (tempering-jittering) are shown to be compatible with a conservative monotone approach.
Source: arXiv (Cornell University)
Author: Woodfield, James
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4407759694