arXiv AI
Jun 4
Physics-Informed Machine Learning for Short-Term Flood Prediction
arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.
Real-time probabilistic tsunami forecasting via generative AI
arXiv:2608. 04327v1 Announce Type: new Abstract: Explicit onshore tsunami inundation forecasting can improve public risk awareness, but deterministically predicted inundation boundaries under highly uncertain conditions, such as near-field tsunamis generated by megathrust earthquakes, may falsely imply safety outside the boundaries.
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arXiv AI
Jul 28
Context-Aware Concept Distillation for Trustworthy Flood Prediction
arXiv:2607. 23237v1 Announce Type: cross Abstract: Effective flood risk management relies on accurate forecasting, yet the "black box" nature of stateof-the-art Deep Learning models creates a barrier to trust and accountability in high-stakes public safety decisions.
arXiv AI
Jun 26
Sampling sea state using a diffusion model
arXiv:2606. 26389v1 Announce Type: cross Abstract: Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain computationally prohibitive for many use cases, including online coupling to climate simulations and making probabilistic (ensemble-based) predictions.
arXiv Machine Learning
Jun 10
Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting
arXiv:2509. 25017v2 Announce Type: replace Abstract: Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems.
arXiv Machine Learning
Jun 29
Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps
arXiv:2603. 11229v2 Announce Type: replace-cross Abstract: Machine learning forecast systems are moving beyond point predictions to full predictive distributions for future outcomes y conditional on complex inputs x.
arXiv AI
Jun 3
AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret
arXiv:2606. 02663v1 Announce Type: cross Abstract: Recent advances in machine learning have produced probabilistic weather forecasting models comparable to state-of-the-art numerical weather predictors.
arXiv Machine Learning
Jul 13
Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction
arXiv:2604. 16238v2 Announce Type: replace Abstract: Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes.
arXiv AI
Aug 11
Do AI Forecast Ensembles Sample the Correct Conditional Distribution?
arXiv:2608. 08954v1 Announce Type: cross Abstract: Ensemble forecasting aims to sample the conditional distribution of outcomes; whether AI forecast ensembles do this correctly in a joint sense remains largely untested.
arXiv Machine Learning
Jul 14
Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles
arXiv:2602. 13010v2 Announce Type: replace Abstract: Accurate production forecasts are essential for the integration of renewable energy sources into the power grid.
arXiv Machine Learning
Jul 2
TRIE: An Evaluation Framework for Stochastic PDE Surrogates
arXiv:2607. 00196v1 Announce Type: new Abstract: Many scientific systems exhibit uncertainty from stochastic forcing, unresolved degrees of freedom, or imperfect observations, making reliable surrogate forecasting fundamentally distributional rather than pointwise.
arXiv AI
Jun 15
Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?
arXiv:2606. 14570v1 Announce Type: cross Abstract: Emulators provide a cost-effective alternative to regional climate models (RCMs) by capturing their dynamical downscaling function.
arXiv Machine Learning
Jul 7
Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics
arXiv:2603. 07108v2 Announce Type: replace-cross Abstract: Accurate and reliable forecasting of epidemic incidences is critical for public health preparedness, yet it remains a challenging task due to complex nonlinear temporal dependencies and heterogeneous spatial interactions.