Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …

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

While global warming has generally increased the occurrence of extreme precipitation, the
physical mechanisms by which climate change alters regional and local precipitation …

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 …

Climate variability and weather phenomena can cause extremes and pose significant risk to
society and ecosystems, making continued advances in our physical understanding of such …

Previous studies have identified environmental characteristics that skillfully discriminate
between severe and significant-severe weather events, but they have largely been limited …

Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …

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

Abstract The National Severe Storm Laboratory's Warn-on-Forecast System (WoFS) is a
convection-allowing ensemble with rapidly cycled data assimilation (DA) of various satellite …

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 …