Luca Delle Monache

Advancing Atmospheric River Science and Inspiring Future Development of the Atmospheric River Reconnaissance Program

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 …

Subseasonal prediction of impactful California weather in a hybrid dynamical-statistical framework

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from …

Subseasonal prediction of impactful California weather in a hybrid dynamical-statistical framework

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from …

Multi-Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated With Atmospheric Rivers Over the Western US

Subseasonal-to-seasonal (S2S) forecasts of atmospheric rivers (ARs) are in high demand in the water supply management and flood control communities. This study focuses on a new …

Development of a Statistical Subseasonal Forecast Tool to Predict California Atmospheric Rivers and Precipitation Based on MJO and QBO Activity

This paper examines the empirical relationship between the Madden-Julian oscillation (MJO), the quasi-biennial oscillation (QBO), and atmospheric river (AR) activity and …

Winter wet-dry weather patterns driving atmospheric rivers and Santa Ana winds provide evidence for increasing wildfire hazard in California

Floods caused by atmospheric rivers and wildfires fanned by Santa Ana winds are common occurrences in California with devastating societal impacts. In this work, we show that …

Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning

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

Atmospheric River Reconnaissance Workshop Promotes Research and Operations Partnership

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 …