3 citations · 5 across the 5 of their papers we have counts for
5 papers · 1 filter
AI-boosted rare event sampling to characterize extreme weather
Amaury Lancelin, Alex Wikner, Laurent Dubus +5
Weather extremes pose major societal risks, especially in a changing climate, but due to their rarity, they are difficult to study using limited observations or complex climate mod…
Using a rare event sampling technique to quantify extreme El Niño event statistics
Sarah Packman, Justin Finkel, Dorian S. Abbot +1
Extreme El Niño events, such as occurred in 1997--1998, can induce severe weather on a global scale, with significant socioeconomic impacts that motivate efforts to understand the…
Can AI weather models predict out-of-distribution gray swan tropical cyclones?
Y. Qiang Sun, Pedram Hassanzadeh, Mohsen Zand +3
Predicting gray swan weather extremes, which are possible but so rare that they are absent from the training dataset, is a major concern for AI weather models and long-term climate…
Predator and Prey: A Minimum Recipe for the Transition from Steady to Oscillating Precipitation in Hothouse Climates
Da Yang, Dorian S. Abbot, Seth Seidel
In the present tropical atmosphere, precipitation typically exhibits noisy, small-amplitude fluctuations about an average. However, recent cloud-resolving simulations show that in…
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational data
Huan Zhang, Justin Finkel, Dorian S. Abbot +2
Blocking events are an important cause of extreme weather, especially long-lasting blocking events that trap weather systems in place. The duration of blocking events is, however,…