Initial Results from SAR-Based Validation of Sea Ice Drift Forecast Models Introduction • Optimal shipping routes through drifting sea ice increasingly important for navigation in polar regions • Sea ice drift information obtained from Synthetic Aperture Radar (SAR) [5] • Evaluation of usability of sea ice drift forecast models for multi-day sea ice analysis • Improvements for high-resolution forecast Predictive Ice Image application (PRIIMA) [2] • Forecast model trajectories derived with Lagrangian Tracking • Sea ice drift vector fields obtained from successive SAR-scene pairs • Comparison of forecast model trajectories and SAR-based drift vector fields Initial Results Martin Bathmann*1, Stefan Wiehle1, Anja Frost1, Lasse Rabenstein2, Gunnar Spreen3 1German Aerospace Center (DLR), Maritime Safety and Security Lab Bremen, Germany; 2Drift+Noise Polar Services, Bremen, Germany, 3University of Bremen, Institute of Environmental Physics, Germany *martin.bathmann@dlr.de Further Research • How is the measured sea ice deformation represented in the forecast models? • How can the influence of the sea ice rheology be derived by evaluating the forecast model input data (e.g. winds and ocean currents)? • Which other solutions for sea ice analysis can be put into practice with the available OOP approach? • Divergence maps derived with Sobel kernels [1] Data and Methodology • Historical TOPAZ4 [4] and neXtSIM [3] forecasts from December 2021 • 7 Sentinel-1 SAR-scene pairs from 2 regions of interest (ROIs) in the Lincoln Sea • Vector model for data processing • high flexibility • floating-point number resolution • Object-oriented-programming (OOP), topology, hashing and spatial indexing • Trajectories and measurements both calculated starting from a regular grid • Forecast model sea ice drift interpolated in every grid point with cubic splines • Runge-Kutta 4th-order (RK4) [6] combined with Inverse Distance Weighting (IDW) to a refined approach (RK4-IDW) Lagrangian Tracking Performing Lagrangian Tracking with RK4-IDW RK4: 𝑋𝐼𝐷𝑊𝑘+1 = 𝑋𝐼𝐷𝑊𝑘+ 𝑓𝐼𝐷𝑊𝑘+1∆𝑡 𝑓𝐼𝐷𝑊𝑘+1 = 1 6 𝑓𝐼𝐷𝑊1 + 2𝑓𝐼𝐷𝑊2 + 2𝑓𝐼𝐷𝑊3 + 𝑓𝐼𝐷𝑊4 𝑓𝐼𝐷𝑊1 = 𝑓𝑋𝐼𝐷𝑊𝑘, 𝑡𝑘 𝑓𝐼𝐷𝑊2 = 𝑓𝑋𝐼𝐷𝑊𝑘+ 𝑓𝐼𝐷𝑊1 Τ ∆𝑡2 , 𝑡𝑘+ Τ ∆𝑡2 𝑓𝐼𝐷𝑊3 = 𝑓𝑋𝐼𝐷𝑊𝑘+ 𝑓𝐼𝐷𝑊2 Τ ∆𝑡2 , 𝑡𝑘+ Τ ∆𝑡2 𝑓𝐼𝐷𝑊4 = 𝑓𝑋𝐼𝐷𝑊𝑘+ 𝑓𝐼𝐷𝑊3∆𝑡, 𝑡𝑘+ ∆𝑡 IDW: 𝑑𝑝,𝑡𝑗= 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑏𝑒𝑡𝑤𝑒𝑒𝑛𝑔𝑟𝑖𝑑𝑝𝑜𝑖𝑛𝑡 𝑎𝑛𝑑𝑐𝑢𝑟𝑟𝑒𝑛𝑡𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝑢𝑠,𝑡𝑗= 𝑠𝑒𝑎𝑖𝑐𝑒𝑑𝑟𝑖𝑓𝑡𝑎𝑡𝑡𝑖𝑚𝑒𝑡𝑗 𝑓𝐼𝐷𝑊𝑖= σ𝑖=1 3 𝑢𝑖,𝑡𝑗 𝑑𝑖,𝑡𝑗 σ𝑖=1 3 1 𝑑𝑖,𝑡𝑗 forecast model grid point one hour drift vector 𝑠𝑖= 𝑠𝑒𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑠𝑖 𝑅𝑀𝑆𝑠𝑒𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒= σ𝑖=1 𝑛 𝑠𝑖2 𝑛 forecast model trajectorySAR-based measurement 𝑑1,𝑡𝑗 𝑑4,𝑡𝑗 𝑑3,𝑡𝑗 𝑓𝐼𝐷𝑊𝑖 𝑢1,𝑡𝑗 𝑢4,𝑡𝑗 𝑢3,𝑡𝑗 𝑑2,𝑡𝑗 𝑢2,𝑡𝑗 𝑓𝐼𝐷𝑊4 𝑓𝐼𝐷𝑊3 𝑓𝐼𝐷𝑊2 𝑓𝐼𝐷𝑊1 0 5 10 15 20 one two three RMS separation distance [km] timeframe [days] RMS separation distance measured values vs quality information (QI) TOPAZ4 IDW TOPAZ4 RK4 TOPAZ QI neXtSIM IDW neXtSIM RK4 neXtSIM QI neXtSIM 6th – 8th December 2021 48h TOPAZ4 6th – 8th December 2021 48h ROIs Conclusions • Trajectories • RK4-IDW yields smoother trajectories • Small difference (ca. 200 m) between IDW and RK4-IDW • TOPAZ4: viscous-plastic rheology • Rheology without brittleness of sea ice, but good overall drift • Difficult to derive deformation fields • neXtSIM: brittle rheology • Problems of low drift near land • Divergence field is promising • Only small case study so far • Overall RMS separation distance of TOPAZ4 and neXtSIM between 3 and 5 ൗ 𝑘𝑚𝑑𝑎𝑦 References [1] Albedyll, L. von: Sea ice deformation and sea ice thickness change, Dissertation, Universität Bremen, 2022. [2] Drift+Noise: PRIIMA - Predictive Ice Image, https://business.esa.int/projects /priima, last access: 31 January 2023, 2019. [3] European Union - Copernicus Marine Service: neXtSIM: Arctic Ocean Sea Ice Analysis and Forecast, https://doi.org/10.48670/moi-00004, 2020. [4] European Union - Copernicus Marine Service: TOPAZ4: Arctic Ocean Physics Analysis and Forecast, https://doi.org/10.48670/moi-00001, 2015. [5] Frost, A., Wiehle, S., Singha, S., and Krause, D.: Sea Ice Motion Tracking from Near Real Time Sar Data