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. To address this, we introduce WEATHER-5K, a large-scale observational weather dataset that better reflects real-world conditions, supporting improved model training and evaluation. While recent TSF methods perform well on benchmarks, they lag behind operational Numerical Weather Prediction systems i
The proliferation of advanced AI models and the increasing computational power make sophisticated physics-informed time-series models for weather forecasting more feasible and effective now.
Improved global weather forecasting through AI can have significant economic and humanitarian impacts, directly influencing agriculture, disaster preparedness, logistics, and energy markets.
The introduction of a large-scale observational dataset like WEATHER-5K and benchmarks for physics-informed models accelerates the progress in AI-driven weather prediction, potentially challenging traditional Numerical Weather Prediction systems.
- · AI/ML researchers
- · Agricultural sector
- · Logistics and supply chain
- · Renewable energy
- · Traditional Numerical Weather Prediction (NWP) systems (if slow to adapt)
- · Sectors reliant on less accurate forecasting
More accurate and localized weather predictions become widely available, enhancing operational efficiencies across numerous industries.
Better climate modeling and early warning systems for extreme weather events become possible, reducing economic losses and saving lives.
The application of physics-informed AI models extends to other complex Earth systems, revolutionizing environmental monitoring and resource management globally.
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