During the era of global warming and highly urbanized development, extreme and high
impact weather as well as air pollution incidents influence everyday life and might even …

Recent years have seen a surge in deep learning approaches to accelerate numerical
solvers, which provide faithful but computationally intensive simulations of the physical …

With the computing power of High-Performance Computing (HPC) systems having stepped
into the exascale era, more complex problems can be solved with scientific applications on a …

Predicting the spatial distribution of maximum inundation depth (depth-MAP) is important for
the mitigation of hydrological disasters induced by extreme precipitation. However, physics …

Atmospheric data assimilation is essential for numerical weather prediction. Ensemble data
assimilation connects multiple instances of an atmospheric model through a Kalman filter …

Large-scale ensemble runs typically consist of executing thousands of physical simulation
instances according to a range of different input parameters. These ensemble runs enable …

A particle-based cloud model was developed for meter-to submeter-scale-resolution
simulations of warm clouds. Simplified cloud microphysics schemes have already made …

We investigate the applicability of the data assimilation (DA) to large eddy simulations based
on the lattice Boltzmann method (LBM). We carry out the observing system simulation …

Prediction of chaotic systems relies on a floating fusion of sensor data (observations) with a
numerical model to decide on a good system trajectory and to compensate non-linear …

Over the long journey of understanding the micro-world, cryo-EM has become an effective
technique for biomolec-ular structure determinations. However, due to the complex …