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

Demands to manage the risks of artificial intelligence (AI) are growing. These demands and
the government standards arising from them both call for trustworthy AI. In response, we …

With increasing interest in explaining machine learning (ML) models, this paper synthesizes
many topics related to ML explainability. We distinguish explainability from interpretability …

In 2009, advancements in NWP and computing power inspired a vision to advance
hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This …

As artificial intelligence (AI) methods are increasingly used to develop new guidance
intended for operational use by forecasters, it is critical to evaluate whether forecasters …

Statistical post-processing of systematic errors is required for numerical weather predictions
to obtain accurate and credible forecasts. Traditionally, this is accomplished separately with …

Convective initiation (CI) nowcasting is crucial for reducing loss of human life and property
caused by severe convective weather. A novel deep learning method based on the U-Net …

Thunderstorms are recognized as one of the most disastrous weather threats in Canada
because of their power to cause substantial damage to human-made structures and even …

Hail, a highly destructive weather phenomenon, necessitates critical identification and
forecasting for the protection of human lives and properties. The identification and …

This research seeks to understand simulated supercell precipitation characteristics across
the conterminous United States (CONUS) using high-resolution, convection-permitting …