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.
Hybrid NARX-LLM for Greenland Iceberg Discharge: Prompt-Driven Residual Correction
arXiv:2606. 15288v1 Announce Type: cross Abstract: Greenland iceberg discharge exhibits complex nonlinear dynamics with limited observability, challenging traditional predictive models.
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arXiv Machine Learning
Jun 18
Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting
arXiv:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.
arXiv Machine Learning
Jun 9
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.
arXiv AI
Jul 24
Knowledge-Guided Time-Varying Causal Inference for Arctic Sea Ice Dynamics
arXiv:2601. 17647v3 Announce Type: replace-cross Abstract: Quantifying the causal relationship between sea ice thickness and sea surface height (SSH) is essential for understanding the mechanisms driving polar climate dynamics.
arXiv AI
Jun 2
Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction
arXiv:2601. 17074v4 Announce Type: replace-cross Abstract: Accurate estimation in time-varying inverse problems under limited and sparse observations remains a fundamental challenge across scientific domains.
arXiv AI
Jul 28
Physics-Encoded Inverse Modeling for Arctic Snow Depth Estimation
arXiv:2601. 17074v5 Announce Type: replace-cross Abstract: Accurate estimation of unobserved quantities in time-varying inverse problems remains challenging when observations are sparse and only indirectly related to the target variable.
arXiv Machine Learning
Jun 30
NIVA: A Multimodal Foundation Model for Actionable Earth System Intelligence
arXiv:2606. 28546v1 Announce Type: new Abstract: Recent advances in AI-driven weather and climate modeling have improved forecast skill while reducing computational cost.
arXiv AI
Jul 7
Evaluating Skill and Stability of ArchesWeather and ArchesWeatherGen under Multi-Decadal Climate Simulations
arXiv:2605. 29976v2 Announce Type: replace-cross Abstract: We evaluate the climate simulation capabilities of ArchesWeather and ArchesWeatherGen, two machine learning models originally trained for weather forecasting and evaluated up to a 10-day lead time.
arXiv Machine Learning
Jun 10
COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting
arXiv:2606. 11162v1 Announce Type: new Abstract: In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular geospatial meshes.
arXiv AI
Jul 10
PARA-PV: Physics-Aware Retrieval-Augmented PV Prediction Based on Frozen Foundation Model and Distribution Shift Correction
arXiv:2607. 08079v1 Announce Type: new Abstract: Accurate photovoltaic (PV) power forecasting is essential for reliable grid dispatch and renewable energy integration, yet it remains challenging because PV generation is jointly shaped by weather variability, day-night transitions, regime-dependent dynamics, and strict physical constraints.
arXiv AI
Jun 3
PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting
arXiv:2605. 08935v3 Announce Type: replace Abstract: Coupled spatiotemporal forecasting is important for predicting the future evolution of multiple interacting dynamical systems, such as in climate models.
arXiv Machine Learning
Jul 21
Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting
arXiv:2607. 17511v1 Announce Type: new Abstract: Large \emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains.