Data assimilation plays a dual role in advancing the “scientific” understanding and serving
as an “engineering tool” for the Earth system sciences. Land data assimilation (LDA) has …

Decadal prediction, also known as “near-term climate prediction”, aims to forecast climate
changes in the next 1–10 years and is a new focus in the fields of climate prediction and …

A reservoir computer (RC) is a type of recurrent neural network architecture with
demonstrated success in the prediction of spatiotemporally chaotic dynamical systems. A …

The weather forecasting system is important for science and society, and significant
achievements have been made in applying artificial intelligence (AI) to medium-range …

Drawing on ergodic theory, we introduce a novel training method for machine learning
based forecasting methods for chaotic dynamical systems. The training enforces dynamical …

The outstanding breakthroughs of deep learning in computer vision and natural language
processing have been the horn of plenty for many recent developments in the climate …

There is a growing interest in developing data‐driven reduced‐order models for
atmospheric and oceanic flows that are trained on data obtained either from high‐resolution …

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 …

Data assimilation is crucial in a wide range of applications, but it often faces challenges such
as high computational costs due to data dimensionality and incomplete understanding of …

Data assimilation (DA) is a key component of many forecasting models in science and
engineering. DA allows one to estimate better initial conditions using an imperfect dynamical …