Weather Forecasting
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Benchmarks
Most implemented
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
GraphCast: Learning skillful medium-range global weather forecasting
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
WeatherBench: A benchmark dataset for data-driven weather forecasting
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
Papers
Uncertainty-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 Forecasting