Stochastic Longshore Current Dynamics

TL;DR

This paper introduces a stochastic parametrization for longshore current dynamics that combines deterministic physics-based models with stochastic elements, effectively capturing observed statistical behaviors and improving data assimilation predictions.

Contribution

It develops a novel stochastic parametrization method for longshore currents that is not directly tuned to observations but still accurately reproduces their statistical properties.

Findings

Stochastic parametrization captures the statistical distribution of observed currents.

The model improves data assimilation by matching unseen observational statistics.

Introduces a new measure called 'consistency of sensitivity' for stochastic models.

Abstract

We develop a stochastic parametrization, based on a `simple' deterministic model for the dynamics of steady longshore currents, that produces ensembles that are statistically consistent with field observations of these currents. Unlike deterministic models, stochastic parameterization incorporates randomness and hence can only match the observations in a statistical sense. Unlike statistical emulators, in which the model is tuned to the statistical structure of the observation, stochastic parametrization are not directly tuned to match the statistics of the observations. Rather, stochastic parameterization combines deterministic, i.e physics based models with stochastic models for the "missing physics" to create hybrid models, that are stochastic, but yet can be used for making predictions, especially in the context of data assimilation. We introduce a novel measure of the utility of stochastic models of complex processes, that we call {\em consistency of sensitivity}. We show, in the context of data assimilation, the stochastic parametrization of longshore currents achieves good results in capturing the statistics of observation {\em that were not used} in tuning the model.