Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data
Understanding extreme events and their probability is key for the study of climate change
impacts, risk assessment, adaptation, and the protection of living beings. Extreme …
impacts, risk assessment, adaptation, and the protection of living beings. Extreme …
[HTML][HTML] Transition paths of marine debris and the stability of the garbage patches
We used transition path theory (TPT) to infer “reactive” pathways of floating marine debris
trajectories. The TPT analysis was applied on a pollution-aware time-homogeneous Markov …
trajectories. The TPT analysis was applied on a pollution-aware time-homogeneous Markov …
[HTML][HTML] Learning forecasts of rare stratospheric transitions from short simulations
Rare events arising in nonlinear atmospheric dynamics remain hard to predict and attribute.
We address the problem of forecasting rare events in a prototypical example, sudden …
We address the problem of forecasting rare events in a prototypical example, sudden …
Coupling rare event algorithms with data-based learned committor functions using the analogue Markov chain
Rare events play a crucial role in many physics, chemistry, and biology phenomena, when
they change the structure of the system, for instance in the case of multistability, or when …
they change the structure of the system, for instance in the case of multistability, or when …
Revealing the statistics of extreme events hidden in short weather forecast data
Extreme weather events have significant consequences, dominating the impact of climate on
society. While high‐resolution weather models can forecast many types of extreme events …
society. While high‐resolution weather models can forecast many types of extreme events …
Predicting rare events using neural networks and short-trajectory data
Estimating the likelihood, timing, and nature of events is a major goal of modeling stochastic
dynamical systems. When the event is rare in comparison with the timescales of simulation …
dynamical systems. When the event is rare in comparison with the timescales of simulation …
Committor functions for climate phenomena at the predictability margin: The example of El Niño–Southern Oscillation in the Jin and Timmermann model
Many atmosphere and climate phenomena lie in the gray zone between weather and
climate: they are not amenable to deterministic forecast, but they still depend on the initial …
climate: they are not amenable to deterministic forecast, but they still depend on the initial …
Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction
Understanding dynamics in complex systems is challenging because there are many
degrees of freedom, and those that are most important for describing events of interest are …
degrees of freedom, and those that are most important for describing events of interest are …
Statistical analysis of tipping pathways in agent-based models
Agent-based models are a natural choice for modeling complex social systems. In such
models simple stochastic interaction rules for a large population of individuals on the …
models simple stochastic interaction rules for a large population of individuals on the …
[HTML][HTML] Dynamical geography and transition paths of Sargassum in the tropical Atlantic
By analyzing a time-homogeneous Markov chain constructed using trajectories of
undrogued drifting buoys from the NOAA Global Drifter Program, we find that probability …
undrogued drifting buoys from the NOAA Global Drifter Program, we find that probability …