Sea Ice Data for Shipping Routes

Abstract

We provide waypoints changing spatially in time for optimal shipping routes through sea ice. For this, we morph sea ice type classification results derived from synthetic aperture radar (SAR) images with sea ice drift forecast data to produce a sea ice type forecast in near real‑time (NRT).

The joint use of remote sensing observations in combination with sea ice drift forecast model data creates an added-value NRT-product for shipping routes and offshore applications. Our method combines Sentinel-1 SAR satellite data from the Arctic with the sea ice drift forecast data produced by the TOPAZ4 and the neXtSIM sea ice (and ocean) forecast models by the Copernicus Marine Environment Monitoring Service (CMEMS). The SAR scenes are first classified into distinct sea ice types with a sea ice classification algorithm based on convolutional neural networks. Then, the corners of polygons derived from the classification result are used to generate a Voronoi diagram. The nodes of the Voronoi diagram are used as waypoints and are spatially propagated with forecast model data, using a vector‑model‑based Lagrangian tracking algorithm based on an inverse distance weighting variant of Runge-Kutta 4th-order. The ice class information is therewith propagated forward in time.

We evaluate the sea ice dynamics forecasted in the two models with SAR-based ice drift measurements. In addition, we validate our products with buoy data in the Beaufort Sea