Reassessing the Quality of Sea‐Ice Deformation Estimates Derived From the RADARSAT Geophysical Processor System and Its Impact on the Spatiotemporal Scaling Statistics

Abstract

We reassess the trajectory errors inherent to sea‐ice deformation estimates with a new propagation of uncertainty derivation and show that previous formulations applied to deformation estimates from the RADARSAT Geophysical Processor System (RGPS) are either too high due to incorrect assumptions or too low due to neglected terms in certain cases. We show that when the resulting signal‐to‐noise ratios are used to discriminate the deformation estimates based on their quality, as done for buoy records, the spatiotemporal scaling exponents for the mean total deformation rate increase, especially at smaller scale, such that a space‐time coupling of the scaling—which is otherwise absent—emerges from the RGPS deformation data set, in accord with previous analyses performed with buoy observations. We also show that the preprocessing method used to reduce the effects of irregular sampling of the Lagrangian deformation fields can significantly impact the value of the deformation statistics and could possibly explain part of previous discrepancies between deformation statistics obtained with buoy records and large‐scale synthetic aperture radar (SAR) imagery. Specifically, we show that spurious lines of deformation appear when interpolating RGPS trajectories that presenttemporal sampling inconsistencies. In the context of using observed sea‐ice deformation statistics to constrain and improve the performance of sea‐ice models, high confidence in the observed deformation field statistics is necessary. Using appropriate, well‐documented, methods to derive the set of statistics to be reproduced by models therefore becomes crucial.

Estimating statistical errors in retrievals of ice velocity and deformation parameters from satellite images and buoy arrays

“…( 15) and ( 16) provided above indicate that statistical uncertainties are not only influenced by geolocation and tracking errors but also depend on the shape and size of grid cells and buoy arrays. In the following discussion we consider magnitudes of geolocation and tracking errors reported in the literature and selected squares and triangles as examples for grid cells in SAR images (Lindsay, 2002;Bouillon and Rampal, 2015) and for splitting large buoy arrays into smaller units (Hutchings et al, 2012;Itkin et al, 2017). The effect of combining several cells is investigated.…”

“…Also triangles are used for calculations of deformation parameters in SAR images (e.g., Bouillon and Rampal, 2015;Griebel and Dierking, 2018), and they form the smallest units of buoy arrays (e.g., Hutchings et al, 2011;Hutchings et al, 2012). Using the same approach as for the square above, we obtain the following for a triangle with its base a parallel to…”

“…In this technical note we focus on the estimation of statistical errors for ice velocity and deformation. The issue of error estimation was repeatedly addressed in the past, scattered in a number of publications and restricted to single aspects related to the respective analysis (e.g., Lindsay and Stern, 2003;Hollands and Dierking, 2011;Bouillon and Rampal, 2015;Hollands et al, 2015;Griebel and Dierking, 2018), and is also addressed in a more recent analysis by Bouchat and Tremblay (2020). Our motivation is to provide the mathematical background, together with examples of applications and discussions of validity, in a broader context.…”

“…Our motivation is to provide the mathematical background, together with examples of applications and discussions of validity, in a broader context. We emphasize that here we deal with statistical errors but not with boundary definition errors as described, e.g., in Lindsay and Stern (2003), Bouillon and Rampal (2015), and Griebel and Dierking (2018). Although this note is specifically focused on retrievals of parameters characterizing sea ice kinematics, the mathematical framework is also applicable to movement and deformation of ice shelves and glaciers, or for model simulations of sea ice, glacier, and ice sheet dynamics.…”

Sea Ice Rheology Experiment (SIREx): 1. Scaling and Statistical Properties of Sea‐Ice Deformation Fields

“…To ensure temporal consistency of the RGPS deformation field, we use the Weighted‐Average pre‐processing method (Bouchat & Tremblay, 2020; Hutter & Losch, 2020), which consists in keeping only trajectories forming cells that have (a) simultaneous (±3 hr) start and end times for all fours corners, (b) an average time resolution for all corners that corresponds to the nominal temporal resolution of T * = 3 days, and (c) an area corresponding to the nominal spatial resolution of L * = 10 km. We also require that all corner positions remain at least 100 km away from land for the present analysis.…”