# Improving solution accuracy and convergence for stochastic physics parameterizations with colored noise

## TL;DR

This paper extends the Itô correction for stochastic differential equations to include colored noise, demonstrating improved accuracy and convergence in numerical weather prediction models with faster, more efficient simulations.

## Contribution

A generalized Itô correction for colored noise is derived and applied, enhancing solution accuracy and convergence in stochastic PDEs used in climate modeling.

## Key findings

- Reduces time integration error with colored noise.
- Enables longer time steps for same accuracy.
- Improves convergence rate in stochastic simulations.

## Abstract

Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properties. Convergence issues can arise when time integration methods originally developed for deterministic differential equations are applied naively to stochastic problems. (Hodyss et al 2013, 2014) demonstrated that a correction term to various deterministic numerical schemes, known in stochastic analysis as the It\^o correction, can help improve solution accuracy and ensure convergence to the physically relevant solution without substantial computational overhead. The usual formulation of the It\^o correction is valid only when the stochasticity is represented by {\it white} noise. In this study, a generalized formulation of the It\^o correction is derived for noises of any color. The formulation is applied to a test problem described by an advection-diffusion equation forced with a spectrum of fast processes. We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized It\^o correction helps to substantially reduce time integration error and significantly improve the convergence rate of the numerical solutions when the forcing term in the governing equation is rough (fast varying); alternatively, for the same target accuracy, the generalized It\^o correction allows for the use of significantly longer time steps and hence helps to reduce the computational cost of the numerical simulation.