Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …

Climate modelling and analysis are facing new demands to enhance projections and
climate information. Here we argue that now is the time to push the frontiers of machine …

In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …

Tropical cyclones have always been a concern of meteorologists, and there are many
studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting …

The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …

The rapid urbanization process in the last century has deeply changed the way we live and
interact with each other. As most people now live in urban areas, cities are experiencing …

Deep neural networks (DNNs) have achieved tremendous success in making accurate
predictions for computer vision, natural language processing, as well as science and …

The forecast of tropical cyclone trajectories is crucial for the protection of people and
property. Although forecast dynamical models can provide high-precision short-term …

Advances of the analytical, numerical, experimental and field-measurement approaches in
wind engineering offers unprecedented volume of data that, together with rapidly evolving …

This paper presents a viewpoint from computer vision to the radar echo extrapolation task in
the precipitation nowcasting domain. Inspired by the success of some convolutional …