# Resampling with neural networks for stochastic parameterization in multiscale systems

arXiv: https://arxiv.org/abs/2004.01457 · HTML: https://mlsanity.com/paper/2004.01457/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Controlled abstention neural networks for identifying skillful predictions for regression problems](https://mlsanity.com/paper/2104.08236/) (2021)
- [Controlled abstention neural networks for identifying skillful predictions for classification problems](https://mlsanity.com/paper/2104.08281/) (2021)
- [Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs](https://mlsanity.com/paper/2112.01221/) (2021)
- [Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations](https://mlsanity.com/paper/2605.22242/) (2026)

## Abstract (arXiv)

In simulations of multiscale dynamical systems, not all relevant processes can be resolved explicitly. Taking the effect of the unresolved processes into account is important, which introduces the need for paramerizations. We present a machine-learning method, used for the conditional resampling of observations or reference data from a fully resolved simulation. It is based on the probabilistic classiffcation of subsets of reference data, conditioned on macroscopic variables. This method is used to formulate a parameterization that is stochastic, taking the uncertainty of the unresolved scales into account. We validate our approach on the Lorenz 96 system, using two different parameter settings which are challenging for parameterization methods.