Uncertainty-Incorporated Arctic Sea Ice Concentration Estimation Using Heteroscedastic Bayesian Neural Networks

This paper presents an investigation into the use of a heteroscedastic Bayesian neural network (HBNN) for predicting sea ice concentration (SIC) using both passive microwave (PM) and atmospheric data. The primary objective is to provide accurate estimates for downstream services that require uncertainty estimates. To achieve this, HBNNs are implemented using a multilayer perceptron (MLP) architecture with methods for uncertainty quantification based on the Bayes by backprop (BBB) algorithm and a heteroscedastic loss function. The models are trained and tested using data collected from the Eastern Arctic regions. The results of numerical analysis demonstrate that the HBNNs are able to significantly reduce estimation error compared to deterministic NNs. The study also investigates the spatial and seasonal variation of uncertainty in detail.