A trajectory-based diagnostic framework built on a topology-preserving state space for visually and quantitatively comparing forecast behavior across different AI weather forecast models and shows that the proposed representation provides an intuitive way to compare model-dependent forecast evolution, identify regions of relatively good or poor trajectory behavior, and examine state-or season-dependent differences that are not readily summarized by variable-wise RMSE curves alone.
Results demonstrate that observation-space forecasting can recover large-scale atmospheric structure and provide meaningful short-range skill without reanalysis supervision and substantially outperforms persistence at longer lead times for 2-m temperature and 10-m wind components.
A. Gholoubi, R. McLaren, Mu-Chieh Ko et al.· 1 citation
It is demonstrated that HourGlass effectively bridges the gap between 6-hourly data-driven forecasts and the hourly products required for operational regional and global forecasting.
Magnus Sikora Ingstad, Mariana Clare, Olav Ersland et al.· 3 citations
It is demonstrated that Earth-trained AI weather models can be adapted to simulate Martian atmospheric dynamics, providing a pathway toward rapid planetary weather prediction to support mission operations, dust storm risk mitigation, and future human exploration.
M. Carroll, J. Li, S. Guzewich et al.· 0 citations
It is shown that iterated forecasts over many time steps deviate from the ground truth along the unstable manifold of the target point, in both directions, so that, if forecast errors were independent and had zero mean, the arithmetic mean forecast should approach the true target like $1/\sqrt{\nens}$ where $\nens$ is...
Daniel Estevez Moya, Francesco Martinuzzi, E. R. dos Santos et al.· 0 citations
Global weather forecast models are vital tools with numerous applications, including public safety, agriculture, and transportation. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown the potential to enhance weather forecasting accuracy and speed. In this study, we developed a shor...
Hoang Tran, Hao Li, Vinh Ngoc Tran et al.· Journal of Geophysical Resea...· 0 citations
It is argued that machine learning-based climate models should be designed to pass generalization tests to prevent overfitting on present-day regional climate.
Maren Höver, Milan Klöwer, Christian Schroeder de Witt et al.· 0 citations
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