Machine learning (ML) provides novel and powerful ways of accurately and efficiently
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …

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

Recent progress in machine learning (ML) inspires the idea of improving (or learning) earth
system models directly from the observations. Earth sciences already use data assimilation …

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 …

In recent years, machine learning (ML) has been proposed to devise data-driven
parametrizations of unresolved processes in dynamical numerical models. In most cases …

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …

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 …

Abstract Model error is one of the main obstacles to improved accuracy and reliability in
numerical weather prediction (NWP) and climate prediction conducted with state‐of‐the‐art …

In this paper, we propose Deep Data Assimilation (DDA), an integration of Data Assimilation
(DA) with Machine Learning (ML). DA is the Bayesian approximation of the true state of …

Data assimilation is concerned with sequentially estimating a temporally evolving state. This
task, which arises in a wide range of scientific and engineering applications, is particularly …