The concept of a digital twin of Earth envisages the convergence of Big Earth Data with
physics-based models in an interactive computational framework that enables monitoring …

Computational science is crucial for delivering reliable weather and climate predictions.
However, despite decades of high-performance computing experience, there is serious …

Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation in: Monthly
Weather Review Volume 144 Issue 12 (2016) Jump to Content Jump to Main Navigation …

This paper presents a new data assimilation approach based on the particle filter (PF) that
has potential for nonlinear/non-Gaussian applications in geoscience. Particle filters provide …

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 …

Ensemble forecasts are the method of choice in numerical weather prediction (NWP) to
generate probabilistic forecasts. The number of members in an ensemble is an important …

Following the invention of the telegraph, electronic computer, and remote sensing,“big data”
is bringing another revolution to weather prediction. As sensor and computer technologies …

Particle filters are well known in statistics. They have a long tradition in the framework of
ensemble data assimilation (EDA) as well as Markov chain Monte Carlo (MCMC) methods …

This study presents the first convective‐scale 1,000‐member ensemble simulation over
central Europe, which provides a unique data set for various applications. A comparison with …

We present the first ever real‐time numerical weather prediction system with 30‐s update
cycles at a 500‐m grid spacing for the prediction of convective precipitation in the …