Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …

We discuss the emerging advances and opportunities at the intersection of machine
learning (ML) and climate physics, highlighting the use of ML techniques, including …

In this paper, we performed an analysis of the 500 most relevant scientific articles published
since 2018, concerning machine learning methods in the field of climate and numerical …

Data Assimilation (DA) is the approximation of the true state of some physical system by
combining observations with a dynamic model. DA incorporates observational data into a …

Progress within physical oceanography has been concurrent with the increasing
sophistication of tools available for its study. The incorporation of machine learning (ML) …

Data assimilation, in its most comprehensive form, addresses the Bayesian inverse problem
of identifying plausible state trajectories that explain noisy or incomplete observations of …

The idea of using machine learning (ML) methods to reconstruct the dynamics of a system is
the topic of recent studies in the geosciences, in which the key output is a surrogate model …

Earth system models have been continously improved over the past decades, but systematic
errors compared with observations and uncertainties in climate projections remain. This is …

The development of data-informed predictive models for dynamical systems is of
widespread interest in many disciplines. We present a unifying framework for blending …

Data-driven prediction and physics-agnostic machine-learning methods have attracted
increased interest in recent years achieving forecast horizons going well beyond those to be …