Timothy B. Higgins

Generating a 1000-Member Ensemble of Integrated Water Vapor Transport Forecasts with Diffusion

Physics-Based Versus AI Weather Prediction Models: A Comparative Performance Assessment of Atmospheric River Prediction

Machine learning (ML) poses a potential paradigm shift in weather forecasting, but critical questions arise regarding its ability to predict high-impact weather events. This study …

Subseasonal Potential Predictability of Horizontal Water Vapor Transport and Precipitation Extremes in the North Pacific

Using Deep Learning for an Analysis of Atmospheric Rivers in a High-Resolution Large Ensemble Climate Data Set

There is currently large uncertainty over the impacts of climate change on precipitation trends over the US west coast. Atmospheric rivers (ARs) are a significant source of US …

Improved Forecast Skill Through the Assimilation of Dropsonde Observations From the Atmospheric River Reconnaissance Program

Landfalling atmospheric rivers (ARs) over the western US are responsible for similar to 30%-50 % of the annual precipitation, and their accurate forecasts are essential for …