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TC-Next is a multimodal deep learning model that leverages foundation model forecasts and satellite imagery for zero-shot tropical cyclone track and intensity forecasting, showing significant error reduction over conventional trackers.
This paper introduces EddyFlow, a deep learning framework for kilometer-scale sea surface temperature downscaling that balances predictive accuracy, scale-dependent structure, and regional generalization. It achieves strong zero-shot performance and near-ideal spectral fidelity across multiple ocean regions.
Njord is a probabilistic graph neural network for ensemble ocean forecasting that provides uncertainty estimates and achieves state-of-the-art performance on global and regional benchmarks, improving surface temperature prediction.
Introduces Q-srdrn, a multi-quantile super-resolution network using pinball loss to improve extreme precipitation downscaling, achieving dramatic detection rate gains for heavy rainfall events while maintaining overall accuracy.