Evaluation of a Neural Network on Sea Ice Concentration Estimation in MIZ Using Passive Microwave Data

Abstract

In this paper, sea ice concentration along the east coast of Canada is predicted using a multilayer perceptron regression model, which has the AMSR-E passive microwave 36.5 GHz brightness temperatures and numerical weather prediction data as its inputs. Derived sea ice concentrations are evaluated against those from the NT2 algorithm, ASI algorithm, ESA algorithm, and CIS ice chart. Results show that the multilayer perceptron regression model estimates sea ice concentration in the marginal ice zones with a reasonably low bias in comparison to CIS ice charts and, in general, its capability of estimating the SIC is comparable to that of the other models.