Traditionally, the nowcasting of precipitation was conducted to a large extent by means of
extrapolation of observations, especially of radar ref lectivity. In recent years, the blending of …

We present an overview of recent work on using artificial intelligence (AI)/machine learning
(ML) techniques for forecasting convective weather and its associated hazards, including …

This paper describes the development of convolutional neural networks (CNN), a type of
deep-learning method, to predict next-hour tornado occurrence. Predictors are a storm …

Idealized, dry simulations are used to investigate the roles of environmental vertical wind
shear and baroclinic vorticity generation in the development of near-surface vortices in …

Precise and timely lightning nowcasting is still a great challenge for meteorologists. In this
study, a new semantic segmentation deep learning network for cloud-to-ground (CG) …

Artificial intelligence (AI) techniques have had significant recent successes in multiple fields.
These fields and the fields of satellite remote sensing and NWP share the same fundamental …

This first part of a two-part study on storm-scale radar and satellite data assimilation provides
an overview of a multicase study conducted as part of the NOAA Warn-on-Forecast (WoF) …

An object-based verification methodology for the NSSL Experimental Warn-on-Forecast
System for ensembles (NEWS-e) has been developed and applied to 32 cases between …

The increasing maturity of the Warn-on-Forecast System (WoFS) coupled with the now
operational GOES-16 satellite allows for the first time a comprehensive analysis of the …

This research represents the second part of a two-part series describing the development of
a prototype ensemble data assimilation system for the Warn-on-Forecast (WoF) project …