Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application

• Published in: Journal of Geophysical Research: Oceans
• Main Authors: Fritzner, Sindre Markus, Graversen, Rune, Christensen, Kai Håkon
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union (AGU) 2020
• Subjects: VDP::Mathematics and natural science: 400::Physics: 430; VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430; Arctic; Sea ice
• Online Access: https://hdl.handle.net/10037/19736; https://doi.org/10.1029/2020JC016277

In this study, the potential for sea ice concentration prediction using machine‐learning methods is investigated. Three different sea ice prediction models are compared: one high‐resolution dynamical assimilative model and two statistical machine‐learning models. The properties of all three models are explored, and the quality of their forecasts is compared. The dynamical model is a state‐of‐the‐art coupled ocean and sea ice ensemble‐prediction system with assimilation. The observations assimilated are high‐resolution sea ice concentration from synthetic aperture radar (SAR) and sea surface temperature from infrared instruments. The machine‐learning prediction models are a fully convolutional network and a k ‐nearest neighbors method. These methods use several variables as input for the prediction: sea ice concentration, sea surface temperature, and 2‐m air temperature. Earlier studies have applied machine‐learning approaches primarily for seasonal ice forecast. Here we focus on short‐term predictions with a length of 1–4 weeks, which are of high interest for marine operations. The goal is to predict the future state of the sea ice using the same categories as traditional ice charts. The machine‐learning forecasts were compared to persistence, which is the assumption that the sea ice does not change over the forecasting period. The machine‐learning forecasts were found to improve upon persistence in periods of substantial change. In addition, compared to the dynamical model, the k ‐nearest neighbor algorithm was found to improve upon the 7‐day forecast during a period of small sea ice variations. The fully convolutional network provided similar quality as the dynamical forecast. The study shows that there is a potential for sea ice predictions using machine‐learning methods.