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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.12775 |
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Table of Contents:
- 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.