Mesoscale convective systems (MCSs) describe organized groupings of thunderstorms in
the tropics and mid-latitudes that span thousands of square kilometres. While recognized for …

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

Data‐driven approaches, most prominently deep learning, have become powerful tools for
prediction in many domains. A natural question to ask is whether data‐driven methods could …

Making the Black Box More Transparent: Understanding the Physical Implications of Machine
Learning in: Bulletin of the American Meteorological Society Volume 100 Issue 11 (2019) Jump …

Ensemble weather predictions require statistical postprocessing of systematic errors to
obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with …

Abadi, M., and Coauthors, 2016: Tensorflow: A system for large-scale machine learning.
Proc. 12th USENIX Symp. on Operating Systems Design and Implementation (OSDI'16) …

Recently, the use of machine learning in meteorology has increased greatly. While many
machine learning methods are not new, university classes on machine learning are largely …

Lightning discharges in the atmosphere owe their existence to the combination of complex
dynamic and microphysical processes. Knowledge discovery and data mining methods can …

A deep learning objective forecasting solution for severe convective weather (SCW)
including short-duration heavy rain (HR), hail, convective gusts (CG), and thunderstorms …

Atmospheric extreme events cause severe damage to human societies and ecosystems.
The frequency and intensity of extremes and other associated events are continuously …