RealBench is a benchmark for data-driven weather forecasting that enforces operational conditions via a 2025 OOD test set, operational analysis, in-situ observations, and event-specific extreme metrics to expose gaps versus reanalysis-based evaluation.
arXiv preprint arXiv:2402.00059 (2024)
5 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Kilometer-scale modeling of global atmosphere dynamics enables fine-grained weather forecasting and decreases the risk of disastrous weather and climate activity. Therefore, building a kilometer-scale global forecast model is a persistent pursuit in the meteorology domain. Active international efforts have been made in past decades to improve the spatial resolution of numerical weather models. Nonetheless, developing the higher resolution numerical model remains a long-standing challenge due to the substantial consumption of computational resources. Recent advances in data-driven global weather forecasting models utilize reanalysis data for model training and have demonstrated comparable or even higher forecasting skills than numerical models. However, they are all limited by the resolution of reanalysis data and incapable of generating higher-resolution forecasts. This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$^{\circ}$ horizontal resolution. FengWu-GHR introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a pretrained low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES. Furthermore, evaluations on station observations and case studies of extreme events support the competitive operational forecasting skill of FengWu-GHR at the high resolution.
representative citing papers
Earth-o1 learns continuous atmospheric dynamics from ungridded observations and matches operational IFS forecast skill in hindcasts.
A generative 3D Gaussian splatting model with scale-aware attention enables unified arbitrary-resolution forecasting and downscaling of 87 atmospheric variables.
STARQ uses a SegFormer-based multi-scale transformer with Gaussian-kernel pseudo-label propagation from sparse OpenAQ stations to downscale CAMS PM2.5 forecasts from 0.4° to 0.01° (~1 km) across Europe, achieving MAE 5.87 and R² 0.24 on held-out stations.
STCast introduces Spatial-Aligned Attention and Temporal Mixture-of-Experts modules to adaptively refine regional boundaries in data-driven weather forecasting and reports better performance than prior methods on global, regional, extreme-event, and ensemble tasks.
citing papers explorer
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RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges
RealBench is a benchmark for data-driven weather forecasting that enforces operational conditions via a 2025 OOD test set, operational analysis, in-situ observations, and event-specific extreme metrics to expose gaps versus reanalysis-based evaluation.
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Earth-o1: A Grid-free Observation-native Atmospheric World Model
Earth-o1 learns continuous atmospheric dynamics from ungridded observations and matches operational IFS forecast skill in hindcasts.
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Generative 3D Gaussian Splatting for Arbitrary-ResolutionAtmospheric Downscaling and Forecasting
A generative 3D Gaussian splatting model with scale-aware attention enables unified arbitrary-resolution forecasting and downscaling of 87 atmospheric variables.
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Air Quality Downscaling with Station-Guided Pseudo-Supervision
STARQ uses a SegFormer-based multi-scale transformer with Gaussian-kernel pseudo-label propagation from sparse OpenAQ stations to downscale CAMS PM2.5 forecasts from 0.4° to 0.01° (~1 km) across Europe, achieving MAE 5.87 and R² 0.24 on held-out stations.
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STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting
STCast introduces Spatial-Aligned Attention and Temporal Mixture-of-Experts modules to adaptively refine regional boundaries in data-driven weather forecasting and reports better performance than prior methods on global, regional, extreme-event, and ensemble tasks.