Relationship between unpredictability and intermittency in shell models of turbulence and experiments

TL;DR

This study investigates how unpredictability and intermittency in turbulence are related by analyzing shell models and experimental data, revealing that small-scale extreme events cause predictability loss.

Contribution

It establishes a statistical link between predictability loss and intermittency across scales using shell models and experimental turbulence data.

Findings

Predictability decreases from large to small scales.

Extreme small-scale velocity events lead to higher uncertainty.

Shell models help relate intermittency to forecast performance.

Abstract

We study the predictability of turbulent velocity signals using probabilistic analog-forecasting. Here, predictability is defined by the accuracy of forecasts and the associated uncertainties. We study the Gledzer--Ohkitani--Yamada (GOY) shell model of turbulence as well as experimental measurements from a fully developed turbulent flow. In both cases, we identify the extreme values of velocity at small scales as localized unpredictable events that lead to a loss of predictability: worse mean predictions and increase of their uncertainties. The GOY model, with its explicit scale separation, allows to evaluate the prediction performance at individual scales, and so to better relate the intensity of extreme events and the loss of forecast performance. Results show that predictability decreases systematically from large to small scales. These findings establish a statistical connection between predictability loss across scales and intermittency in turbulent flows.