Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth
and predicting how it might change in the future under ongoing anthropogenic forcing. In …

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

This open-access textbook's significant contribution is the unified derivation of data-
assimilation techniques from a common fundamental and optimal starting point, namely …

State estimation is widely used in various automated systems, including IoT systems,
unmanned systems, robots, etc. In traditional state estimation, measurement data are …

This paper provides a unifying mean field based framework for the derivation and analysis of
ensemble Kalman methods. Both state estimation and parameter estimation problems are …

We consider filtering in high-dimensional non-Gaussian state-space models with intractable
transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in …

The beginning of the 21st century is marked by a rapid growth of land surface satellite data
and model sophistication. This offers new opportunities to estimate multiple components of …

The task of quantifying spatial and temporal variations in terrestrial water, energy, and
vegetation conditions is challenging due to the significant complexity and heterogeneity of …

Floods are among the most severe and impacting natural disasters. Their occurrence rate
and intensity have been significantly increasing worldwide in the last years due to climate …

Data assimilation (DA) in physically-based hydrodynamic models is conditioned by the
difference in temporal and spatial scales of the observed data and the resolution of the …