An extended cVAE with generator decoder and CRPS loss produces better-calibrated, sharper bias-adjusted ensembles for Arctic sea ice forecasts than raw output or standard cVAE.
URL https://arxiv.org/abs/2506.10772, 2506.10772
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Probabilistic bias adjustment of seasonal forecasts using generative machine learning: A case study of Arctic sea ice predictions
An extended cVAE with generator decoder and CRPS loss produces better-calibrated, sharper bias-adjusted ensembles for Arctic sea ice forecasts than raw output or standard cVAE.