Generative Modelling of Stochastic Rotating Shallow Water Noise
<|reference_start|>Generative Modelling of Stochastic Rotating Shallow Water Noise: In recent work, the authors have developed a generic methodology for calibrating the noise in fluid dynamics stochastic partial differential equations where the stochasticity was introduced to parametrize subgrid-scale processes. The stochastic parameterization of sub-grid scale processes is required in the estimation of uncertainty in weather and climate predictions, to represent systematic model errors arising from subgrid-scale fluctuations. The previous methodology used a principal component analysis (PCA) technique based on the ansatz that the increments of the stochastic parametrization are normally distributed.   In this paper, the PCA technique is replaced by a generative model technique. This enables us to avoid imposing additional constraints on the increments. The methodology is tested on a stochastic rotating shallow water model with the elevation variable of the model used as input data. The numerical simulations show that the noise is indeed non-Gaussian. The generative modelling technology gives good RMSE, CRPS score and forecast rank histogram results.<|reference_end|>
@article{crisan2024generative,
    title={Generative Modelling of Stochastic Rotating Shallow Water Noise},
    author={Dan Crisan, Oana Lang, Alexander Lobbe},
    journal={arXiv preprint arXiv:2403.10578},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10578},
    primaryClass={stat.ML cs.LG cs.NA math.DS math.NA physics.flu-dyn}
}