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

Dierking, Wolfgang; Stern, Harry L.; Hutchings, Jennifer K.

doi:10.5194/tc-14-2999-2020

Cited by 21 publications

(27 citation statements)

References 29 publications

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“…Following Lindsay and Stern (2003) and Dierking et al. (2020), we also consider σ geo =0 in the present study for RGPS, but we have verified that the results presented in section 4 are robust to considering a nonzero geolocation error within uncertainty (since the added error uniformly affects all deformation estimates). With the assumption of a zero geolocation error, the strain rate errors reduce to

σ_{{\overset{ϵ}{true}}_{11}}^{2} = true \sum_{i = 1}^{4} ()(, \frac{{false(y_{i + 1} - y_{i - 1} false)}^{2}}{4 A^{2} T^{2}}) σ_{t r a c k}^{2},

σ_{{\overset{ϵ}{true}}_{12}}^{2} = true \sum_{i = 1}^{4} ()(, \frac{{false(x_{i + 1} - x_{i - 1} false)}^{2}}{4 A^{2} T^{2}}) σ_{t r a c k}^{2},

σ_{{\overset{ϵ}{true}}_{21}}^{2} = true \sum_{i = 1}^{4} ()(, \frac{{false(y_{i + 1} - y_{i - 1} false)}^{2}}{4 A^{2} T^{2}}) σ_{t r a c k}^{2},

σ

…”

Section: Methodssupporting

confidence: 62%

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

Bouchat

Tremblay

2020

JGR Oceans

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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.

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σ_{{\overset{ϵ}{true}}_{11}}^{2} = true \sum_{i = 1}^{4} ()(, \frac{{false(y_{i + 1} - y_{i - 1} false)}^{2}}{4 A^{2} T^{2}}) σ_{t r a c k}^{2},

σ_{{\overset{ϵ}{true}}_{12}}^{2} = true \sum_{i = 1}^{4} ()(, \frac{{false(x_{i + 1} - x_{i - 1} false)}^{2}}{4 A^{2} T^{2}}) σ_{t r a c k}^{2},

σ_{{\overset{ϵ}{true}}_{21}}^{2} = true \sum_{i = 1}^{4} ()(, \frac{{false(y_{i + 1} - y_{i - 1} false)}^{2}}{4 A^{2} T^{2}}) σ_{t r a c k}^{2},

σ

…”

Section: Methodssupporting

confidence: 62%

“…Dierking et al. (2020) also suggested that the geolocation errors should be treated as biases affecting equally all points in a given SAR image given that variations in the orbit of the satellite is uniform across a given pass, such that σ geo =0. Following Lindsay and Stern (2003) and Dierking et al.…”

Section: Methodsmentioning

confidence: 99%

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

Bouchat

Tremblay

2020

JGR Oceans

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“…where y is the grid spacing in the y direction, i.e., 700 m. For a regularly spaced grid this calculation to derive deformation is identical to the commonly used equations for a combination of 2 × 2 adjacent velocity grid cells, which are based on Green's theorem. (see Supplement and, e.g., Kwok et al, 2003Kwok et al, , 2008Dierking et al, 2020). The velocity gradients were evaluated every 700 m, corresponding to a step width of one grid cell.…”

Section: Sea Ice Drift and Deformation From Sequential Sar Imagesmentioning

confidence: 99%

“…Convergent motion results in the closing of leads and then rafting and ridging of young and old ice. Ridging of thick ice forms pressure ridges that are many times thicker than the initial thickness (Strub-Klein and Sudom, 2012;Duncan et al, 2020). Ridging and rafting shape the ITD predominantly by redistributing thin ice to thicker ice categories (e.g., Thorndike et al, 1975;Wadhams, 1994;Rabenstein et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations

2021

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Abstract. An unusual, large, latent-heat polynya opened and then closed by freezing and convergence north of Greenland's coast in late winter 2018. The closing presented a natural but well-constrained full-scale ice deformation experiment. We observed the closing of and deformation within the polynya with satellite synthetic-aperture radar (SAR) imagery and measured the accumulated effects of dynamic and thermodynamic ice growth with an airborne electromagnetic (AEM) ice thickness survey 1 month after the closing began. During that time, strong ice convergence decreased the area of the refrozen polynya by a factor of 2.5. The AEM survey showed mean and modal thicknesses of the 1-month-old ice of 1.96 ± 1.5 m and 1.1 m, respectively. We show that this is in close agreement with modeled thermodynamic growth and with the dynamic thickening expected from the polynya area decrease during that time. We found significant differences in the shapes of ice thickness distributions (ITDs) in different regions of the refrozen polynya. These closely corresponded to different deformation histories of the surveyed ice that we reconstructed from Lagrangian ice drift trajectories in reverse chronological order. We constructed the ice drift trajectories from regularly gridded, high-resolution drift fields calculated from SAR imagery and extracted deformation derived from the drift fields along the trajectories. Results show a linear proportionality between convergence and thickness change that agrees well with the ice thickness redistribution theory. We found a proportionality between the e folding of the ITDs' tails and the total deformation experienced by the ice. Lastly, we developed a simple, volume-conserving model to derive dynamic ice thickness change from the combination of Lagrangian trajectories and high-resolution SAR drift and deformation fields. The model has a spatial resolution of 1.4 km and reconstructs thickness profiles in reasonable agreement with the AEM observations. The modeled ITD resembles the main characteristics of the observed ITD, including mode, e folding, and full width at half maximum. Thus, we demonstrate that high-resolution SAR deformation observations are capable of producing realistic ice thickness distributions.

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“…We discretized the curve applying the trapezoid method that linearly interpolates velocity between the vertices ( ) of the grid cells (e.g. Kwok et al, 2008;Dierking et al, 2020). The spatial derivatives are thus given by:…”

Section: Sea Ice Deformation Derived From Drift Fieldsmentioning

confidence: 99%

Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations

Albedyll

Haas

Dierking

2020

Preprint

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Abstract. An unusual, large polynya opened and then closed by freezing and convergence north of the coast of Greenland in late winter 2018. The closing corresponded to a natural, but well-constrained, full-scale ice deformation experiment. We have observed the closing of and deformation within the polynya with satellite synthetic-aperture radar (SAR) imagery, and measured the accumulated effects of dynamic and thermodynamic ice growth 5 with an airborne electromagnetic (AEM) ice thickness survey one month after the closing began. During that time strong ice convergence decreased the area of the former polynya by a factor of 2.5. The AEM survey showed mean and modal thicknesses of the one-month old ice of 1.96 ± 1.5 m and 0.95 m, respectively.We show that this is in close agreement with the modeled thermodynamic growth and with the dynamic thickening expected from the polynya area decrease during that time. In addition,we found characteristic differences in the shapes of ice thickness distributions in different regions of the closing polynya. These closely corresponded to different deformation histories of the surveyed ice that were derived from the high-resolution SAR imagery by drift tracking along Lagrangian backward trajectories. Results show a linear proportionality between convergence and thickness change that agrees well with ice thickness redistribution theory. In addition, the e-folding of the tails of the different ice thickness distributions is proportional to the magnitude of the total deformation experienced by the ice. Lastly, we developed a simple volume-conserving model to derive dynamic ice thickness change from high-resolution SAR deformation tracking. The model has a spatial resolution of 1.4 km and reconstructs thickness profiles in reasonable agreement with the AEM observations. The computed ice thickness distribution resembles main characteristics like mode, e-folding, and width of the observed distribution. This demonstrates that high-resolution SAR deformation observations are capable of producing realistic ice thickness distributions. The MYI surrounding the polynya had a mean and modal total thickness (snow + ice) of 2.1 ± 1.4 m and 2.0 m, respectively. The similar first- and multi-year ice mean thicknesses elude to the large amount of deformation experienced by the closing polynya.

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

Cited by 21 publications

References 29 publications

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

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

Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations

Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations

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