Papers Weather Forecasting
“Weather Forecasting” 태그가 달린 논문 579편 · 필터 해제
Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography
We present Varda-single-1.0, a medium-range data-driven weather prediction system built for the Alpine domain. It provides hourly deterministic regional forecasts on a mesh of 1 km resolution and global forecasts on a 31…
Weather ForecastingRecursive self-improvement of AI research agents
AI agents are beginning to automate research and development across the AI stack, from improving training efficiency to optimizing inference. A natural next step is to improve the research efficiency of the agents themse…
Weather ForecastingAsyncCouple-Flow: Asynchronous Cross-Modal Coupling and Flow Matching for Spatio-Temporal Forecasting
Multi-modal spatio-temporal forecasting (MM-STF) supports weather nowcasting, traffic prediction, and earth-system modeling by combining heterogeneous sources such as physical fields, satellite imagery, and in-situ senso…
Semantic SimilarityWeather ForecastingTraffic PredictionUncertainty-Aware End-to-End AI Weather Forecasting: Disentangling Observation and Model Contributions
End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the numerical weather prediction pipeline, including data assimilation, at a fr…
Weather ForecastingBridging short- and medium-range weather forecasting with machine learning
The National Oceanic and Atmospheric Administration (NOAA) employs independent prediction systems for distinct forecast products. While some separation is practical, we argue that combining short- and medium-range weathe…
Weather ForecastingTimestep-Conditioned Transformers for Global Weather Forecasting
Existing machine-learning weather forecasting models rely on predetermined and fixed autoregressive timesteps. The choice of model timestep involves a fundamental trade-off: shorter timesteps (e.g. 1 to 6 hours) finely r…
Weather ForecastingAIFS-TC: A simple correction competitive with the operational frontier for tropical cyclone intensity forecasting
AI weather models are in the process of revolutionising weather forecasting. While these models have been shown to achieve superior performance to physics-based NWP in forecasting tropical cyclone (TC) tracks, they tend …
Weather ForecastingNipping the Butterfly Effect in the Bud: Self-Output Fine-Tuning for Autoregressive Weather Prediction
Long-horizon weather forecasting is a fundamental challenge in atmospheric science, for which autoregressive Deep Learning Weather Prediction (DLWP) has emerged as the primary paradigm. Although the autoregressive pipeli…
Weather ForecastingToward Mechanistic Interpretability of an AI Foundation Model Fine-Tuned for Atmospheric Chemistry
Weather forecasting foundation models (FMs) are increasingly fine-tuned to predict air quality, offering fast global pollution forecasts at lower computational cost than conventional chemical transport models. These FMs …
Weather ForecastingFrom Observation to Insight: Mechanistic World Models and the Quest for Autonomous Discovery
Recent advances in foundation models have transformed AI for Science, enabling remarkably accurate predictive performance across domains ranging from protein folding to weather forecasting. Yet prediction alone does not …
Representation LearningWeather ForecastingLess Tokens, Better Forecasts: Sparse Residual Routing for Efficient Weather Prediction
Existing ViT-based weather forecasting models apply uniform computation across all spatial tokens, even though nearby atmospheric grid points often contain similar values and large regions evolve smoothly over time. This…
Weather ForecastingDiffusion Fine-tuning with Rewarded Moment Matching Distillation
Distillation and Reinforcement Learning (RL) fine-tuning are the primary pillars of diffusion post-training. While traditionally studied in isolation, the interaction between these phases remains poorly understood, and i…
Reinforcement LearningWeather ForecastingSampling sea state using a diffusion model
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…
Weather ForecastingOtter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting
State-of-the-art medium-range AI weather models can outperform traditional Numerical Weather Prediction (NWP) but require massive training budgets. This restricts usage for under-resourced groups and severely limits fast…
Weather ForecastingARCO-Mars: A Unified Cloud-Optimized Archive of Mars Atmosphere Reanalysis
Long-term records of the Martian atmosphere based on general circulation models and reanalysis of atmospheric state variables are important to understand the diurnal, seasonal, and climatological changes of the planet. A…
Weather ForecastingRigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
Probabilistic weather forecasting is undergoing rapid transformation with artificial intelligence (AI). In traditional numerical weather prediction, computing power can limit how well ensemble forecasts approximate the u…
Weather ForecastingStarter-Iterator Neural Operator: A Unified Architecture for High-Fidelity Forward and Inverse PDE Problems
Operator learning is an emerging interdisciplinary field that integrates machine learning with scientific computing. By mapping infinite-dimensional function spaces, this approach provides an efficient surrogate modeling…
Weather ForecastingPhysics-Constrained Neural Networks for Improved Short-Term Weather Forecasting: A Case Study over the South Pacific
This study introduces enhancements to physics-constrained neural networks (PCNNs) that improve the accuracy and stability of hybrid short-term weather forecasting models. Building on the WeatherGFT architecture, three in…
Weather ForecastingCan Deep Neural Networks Improve Compression of Very Large Scientific Data?
Error-bounded lossy compression is a fundamental technique for managing the rapidly growing volumes of scientific data produced by modern simulations and observational instruments. Most state-of-the-art-compressors follo…
Graph Neural NetworkWeather ForecastingPractical Quantum Advantage before Fault Tolerance via Quantum-Informed Machine Learning
Early quantum devices can deliver a practical advantage before fault tolerance. The role we identify is a statistical module within a classical scientific workflow: a compressed memory with a collective two-copy readout,…
Weather Forecasting