Bayesian neural networks (BNNs) have been demonstrated to be effective in accurate retrieval of sea ice concentration (SIC) from multi-source data, while providing estimates of uncertainty, which are essential for downstream services. However, uncertainty obtained by BNNs are intrinsically uncalibrated, which indicates that it may not correlate well with model error. To address this issue, we investigate a new approach that combines an auxiliary prediction interval (PI) estimator with the BNN-based SIC mean estimator to develop a well-calibrated SIC retrieval model that is both accurate and reliable. We adopt a training strategy called “uncertainty matching" to train the model, which ensures that the estimated uncertainties match the estimated PIs. We use a subset of AMSR2 brightness temperature data and ERA5 atmospheric data collected from 2014 to 2015 in the Baffin Bay area as input features of the model. Comparison between model inference and SIC labels obtained from the enhanced NASA Team (NT2) algorithm shows that the proposed approach is able to produce well-calibrated uncertainty with more accurate predictions in marginal ice zones.
Chen et al. (2023) studied this question.