With the rapid development of artificial intelligence, machine learning is gradually becoming
popular for predictions in all walks of life. In meteorology, it is gradually competing with …

The use of machine learning based on neural networks for cloud microphysical
parameterizations is investigated. As an example, we use the warm‐rain formation by …

Assessing diverse parameters like water quality, quantity, and occurrence of hydrological
extremes and their management is crucial to perform efficient water resource management …

Currently, most deep learning (DL)-based models for precipitation forecasting face two
conspicuous issues: the smoothing effect in the precipitation field and the degenerate effect …

Gridded precipitation data with a high spatiotemporal resolution are of great importance for
studies in hydrology, meteorology, and agronomy. Observational data from meteorological …

Numerical weather prediction (NWP) models solve a system of partial differential equations
based on physical laws to forecast the future state of the atmosphere. These models are …

Deep-learning-based radar echo extrapolation methods have achieved remarkable
progress in the precipitation nowcasting field. However, they suffer from a common notorious …

Weaknesses in process representation in chemistry–climate models lead to biases in
simulating surface ozone and to uncertainty in projections of future ozone change. We here …

Numerical weather prediction models and probabilistic extrapolation methods using radar
images have been widely used for precipitation nowcasting. Recently, machine-learning …

This paper proposes an Informer-based temperature prediction model to leverage data from
an automatic weather station (AWS) and a local data assimilation and prediction system …