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Found 4 papers, 1 papers with code
Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere
The cubed-sphere remapping minimizes the distortion on the cube faces on which convolution operations are performed and provides natural boundary conditions for padding in the CNN.
Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models
Current forecast systems have low skill in predicting one- or 2-week-average weather patterns at S2S time scales.
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh
We present a parsimonious deep learning weather prediction model on the Hierarchical Equal Area isoLatitude Pixelization (HEALPix) to forecast seven atmospheric variables for arbitrarily long lead times on a global approximately 110 km mesh at 3h time resolution.
A Practical Probabilistic Benchmark for AI Weather Models
We also reveal how multiple time-step loss functions, which many data-driven weather models have employed, are counter-productive: they improve deterministic metrics at the cost of increased dissipation, deteriorating probabilistic skill.
