Klaus Hasselmann's revolutionary intuition in climate science was to use the stochasticity
associated with fast weather processes to probe the slow dynamics of the climate system …

Reduced order models (ROMs) have achieved a lot of success in reducing the
computational cost of traditional numerical methods across many disciplines. For convection …

The use of multitaper estimates for spectral proper orthogonal decomposition (SPOD) is
explored. Multitaper and multitaper-Welch estimators that use discrete prolate spheroidal …

Modelling geophysical processes as low-dimensional dynamical systems and regressing
their vector field from data is a promising approach for learning emulators of such systems …

Data assimilation (DA) in geophysical sciences remains the cornerstone of robust forecasts
from numerical models. Indeed, DA plays a crucial role in the quality of numerical weather …

The simulation of atmospheric flows by means of traditional discretization methods remains
computationally intensive, hindering the achievement of high forecasting accuracy in short …

Abstract The Madden‐Julian Oscillation (MJO) is the leading mode of intraseasonal climate
variability, having profound impacts on a wide range of weather and climate phenomena …

We aim to reconstruct the latent space dynamics of high dimensional, quasi-stationary
systems using model order reduction via the spectral proper orthogonal decomposition …

This paper describes the numerical implementation in a high-performance computing
environment of an open-source library for model order reduction in fluid dynamics. This …

Large unstructured datasets may contain complex coherent patterns that evolve in time and
space, and that the human eye cannot grasp. These patterns are frequently essential to …