Predicting two-dimensional turbulence

Prediction is a fundamental objective of science. It is more difficult for chaotic and complex systems like turbulence. Here we use information theory to quantify spatial prediction using experimental data from a turbulent soap film. At high Reynolds number, Re, where a cascade exists, turbulence becomes easier to predict as the inertial range broadens. The development of a cascade at low Re is also detected.