arctic-sea-ice

The paper introduces PIHIM, a physics-informed hybrid neural model for predicting Arctic sea ice concentration evolution, combining deep learning with physical equations for improved accuracy and short-range prediction.

This paper presents a probabilistic post-processing framework using conditional variational autoencoders (cVAEs) to bias-adjust seasonal forecasts of Arctic sea ice, improving calibration, sharpness, and spectral power over standard methods.