Therefore, ice drift is often considered in models of ice coverĬharacteristics, overall sea ice mass throughout the Arctic, and globalĪl., 2010 Kimura and Wakatsuchi, 2000 Kwok et al., 2013). Throughout the Arctic (Rampal et al., 2009). It also drives the rate of sea ice export, which affects ice extent (Hutchings and Rigor, 2012 Mahoney et al.,Ģ019). Rampal et al., 2009), and can determine spatial distribution andĬonfiguration of different ice ages and thicknesses Important thermodynamic processes through the formation of polynyas and Sea ice drift is a fundamentalĬontributor to the dynamism of the Arctic ecosystem. Sea ice studies often rely on remotely sensed data due to the remote, vast,Īnd dynamic nature of the environment. Particularly in remote areas that are difficult to ground truth. However, assessing these errors is challenging, Products (Cressie et al., 2009) and is important for dataĪssimilation and the development of new products Quantifying error in remotely sensed data can be used to improve these data System being modelled, it could lead to spurious results The degree of measurement error is greater than the variability of the Lead to large inaccuracies (Reichle, 2008). However, measurement errors and assimilation biases can Satellite sensors, ice charts, and historic records (TitchnerĪnd Rayner, 2014). Which combines data from numerous sources including active and passive Often combined using modelling and interpolation techniques to create anĪccessible gridded product (Reichle, 2008)– forĮxample, the Hadley Centre Sea Ice and Sea Surface Temperature dataset, The raw data from various remote sensing sources are Many research fields increasingly depend on remote sensing to collectĮnvironmental data. Particularly in under-examined areas without in situ data. Further investigation is required into the sources of error, These drift data should consider integrating these biases into theirĪnalyses, particularly where absolute ground speed or direction is Unbiased however, it was less precise at lower drift speeds. Of drift, particularly at high ice speeds. Consequently, the NSIDC model underestimated magnitude To underestimate the horizontal and vertical (i.e., u and v)Ĭomponents of drift. Our results showed that the NSIDC model tended Validate a widely used sea ice drift dataset produced by the National SnowĪnd Ice Data Center (NSIDC). Originally deployed on polar bears, Ursus maritimus, in western Hudson Bay, Canada, to Motion of 20 passively drifting high-accuracy GPS telemetry collars Is often prohibitively expensive or practically unfeasible. However, obtaining reference data for validation These dataĪre often associated with errors and biases that must be considered when Sensing has been used to obtain large-scale longitudinal data. Due to the challenges of accessing the Arctic, remote Its effects on the ice cover, thermodynamics, and energetics of northern Sea ice drift plays a central role in the Arctic climate and ecology through
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