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

Ensemble Kalman methods are widely used for state estimation in the geophysical sciences.
Their success stems from the fact that they take an underlying (possibly noisy) dynamical …

Complex dynamical systems are ubiquitous in many areas, including geoscience,
engineering, neural science, material science, etc. Modeling and predicting complex …

Data assimilation of atmospheric observations traditionally relies on variational and Kalman
filter methods. Here, an alternative neural network data assimilation (NNDA) with variational …

Using generative models from the machine learning literature to create artificial ensemble
members for use within data assimilation schemes has been introduced in Grooms (2021)[1] …

Deep learning has recently gained attention in the atmospheric and oceanic sciences for its
potential to improve the accuracy of numerical simulations or to reduce computational costs …

There is growing interest in data-driven weather prediction (DDWP), for example using
convolutional neural networks such as U-NETs that are trained on data from models or …

There is growing interest in data-driven weather prediction (DDWP), eg, using convolutional
neural networks such as U-NET that are trained on data from models or reanalysis. Here, we …

The 2-m air temperature (T2m) is an important meteorological variable and has been the
focus of meteorological forecasting. Although the numerical weather model is an important …

We propose denoising diffusion models for data-driven representation learning of dynamical
systems. In this type of generative deep learning, a neural network is trained to denoise and …