1 citations · 2 across the 6 of their papers we have counts for
6 papers
AI Emulation of Stochastic Sudden Stratospheric Warming with Interpretable Latent Structure
C. Daniel Boscu, Daniel Hernandez, Fabio Alvarez Ventura +4
Rare weather regime transitions pose a challenge for data-driven modeling due to class imbalance. In this study, we develop a probabilistic deep learning emulator for a prototypica…
Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?
Pedram Hassanzadeh, Weidong Li, Y. Qiang Sun +4
AI weather prediction (AIWP) models rival physics-based models, yet the sources of their unexpected forecast accuracy and the degree of their physical fidelity remain unclear. Here…
Routes to rare events with optimally timed perturbations: a Tent Map is all you need
Justin Finkel
Extreme weather events are difficult to understand for the same reason that they are dangerous: they happen rarely, catching victims unprepared when they do occur and scientists un…
Rare event sampling for moving targets: extremes of temperature and daily precipitation in a general circulation model
Justin Finkel, Paul A. O'Gorman
Extreme weather events epitomize high cost: to society through their physical impacts, and to computer servers that simulate them to assess risk and advance physical understanding.…
Boosting Ensembles for Statistics of Tails at Conditionally Optimal Advance Split Times
Justin Finkel, Paul A. O'Gorman
Climate science needs more efficient ways to study high-impact, low-probability extreme events. Ensemble boosting, a form of rare event sampling, offers a novel strategy to extract…
Bringing statistics to storylines: rare event sampling for sudden, transient extreme events
Justin Finkel, Paul A. O'Gorman
A leading goal for climate science and weather risk management is to accurately model both the physics and statistics of extreme events. These two goals are fundamentally at odds:…