Atmospheric rivers (ARs) generate most of the economic losses associated with flooding in
the western United States and are projected to increase in intensity with climate change …

Deep-learning (DL) postprocessing methods are examined to obtain reliable and accurate
probabilistic forecasts from single-member numerical weather predictions of integrated …

Aberson, SD, 2011: The impact of dropwindsonde data from the THORPEX–Pacific area
regional campaign and the NOAA hurricane field program on tropical cyclone forecasts in …

In the warming climate, flood risk is likely to increase over much of the globe. We present
projections of changes of flood losses in China for a range of global warming scenarios …

Reliably quantifying uncertainty in precipitation forecasts remains a critical challenge. This
work examines the application of a deep learning (DL) architecture, Unet, for postprocessing …

The global navigation satellite system (GNSS) has the capacity for remote sensing of water
vapor content in the atmosphere. Post-processing of GNSS data can provide integrated …

Observational networks enhance real‐time situational awareness for emergency and water
resource management during extreme weather events. We present examples of how a …

During a 6-day intensive observing period in January 2021, Atmospheric River
Reconnaissance (AR Recon) aircraft sampled a series of atmospheric rivers (ARs) over the …

Conventional observations of atmospheric rivers (ARs) over the northeastern Pacific Ocean
are sparse. Satellite radiances are affected by the presence of clouds and heavy …

This study presents a high-resolution regional reforecast based on the Weather Research
and Forecasting (WRF) Model, tailored for the prediction of extreme hydrometeorological …