From AI Weather Prediction to Infrastructure Resilience: A Real-Time Correction-Downscaling Framework for Tropical Cyclone Impact Forecasting

AIFS-TC: A simple correction competitive with the operational frontier for tropical cyclone intensity forecasting

arXiv:2608. 09959v1 Announce Type: cross Abstract: AI weather models are in the process of revolutionising weather forecasting.

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS

arXiv:2602. 16579v2 Announce Type: replace-cross Abstract: Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products.

Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts

arXiv:2605. 30184v2 Announce Type: replace Abstract: While AI weather models excel at short-to-medium range forecasts (up to 15 days), they frequently suffer from ill-defined "instabilities" when rolled out over longer horizons.

A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)

arXiv:2605. 20494v2 Announce Type: replace Abstract: Reliable assessment of tropical cyclone risk is limited by the short and spatially uneven historical record, especially for rare, high-intensity landfalls that dominate insured loss.

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.

SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland

arXiv:2605. 16163v2 Announce Type: replace-cross Abstract: Skillful medium-range precipitation forecasting at kilometer scale remains challenging over complex terrain because precipitation arises from multiscale nonlinear processes that global models cannot explicitly resolve at affordable cost.

WeatherNext: AI model achieves breakthrough in forecasting cyclones

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.

From Global to Local: Efficient Regional Weather Downscaling with Global Weather Foundation Model

arXiv:2607. 03279v1 Announce Type: new Abstract: Accurate regional weather prediction requires resolving fine-scale structure while remaining consistent with global dynamics.

Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting

arXiv:2606. 08630v1 Announce Type: cross Abstract: Global wind power capacity, especially in China, is booming, with new farms spanning diverse terrains and climates.

Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction

arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.

Enhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations

arXiv:2607. 04862v1 Announce Type: new Abstract: Accurate extreme precipitation forecasting is critical for disaster mitigation but remains challenging for numerical weather prediction (NWP) models due to systemic intensity underestimation and spatial displacement.