Machine learning (ML) provides novel and powerful ways of accurately and efficiently
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …

The use of machine learning to build subgrid parametrizations for climate models is
receiving growing attention. State‐of‐the‐art strategies address the problem as a supervised …

Ensemble forecasts depend on representations of model uncertainties. Here, we introduce a
model uncertainty representation where a novel approach is taken to the established …

The atmospheric component of Energy Exascale Earth System Model version 1 has
included many new features in the physics parameterizations compared to its predecessors …

It is now acknowledged that representing model uncertainty in atmospheric simulators is
essential for the production of reliable probabilistic forecasts, and a number of different …

Stochastic parametrisations can be used in weather and climate models to improve the
representation of unpredictable unresolved processes. When compared with a deterministic …

Frozen hydrometeors are found in a huge range of shapes and sizes, with variability on
much smaller scales than those of typical model grid boxes or satellite fields of view. Neither …

HIRAS-II is the hyperspectral detector carried on FengYun-3E which is the world's first
meteorological satellite in dawn–dusk orbit. It fills the observation gaps during the dawn and …

Abstract The Department of Energy (DOE)'s Energy Exascale Earth System Model (E3SM),
including its atmosphere model (EAM), has many relatively new features. In a previous study …

The stochastically perturbed parameterizations scheme (SPP) is here implemented and
tested in HarmonEPS—the convection-permitting limited area ensemble prediction system …