Relevance of Detail in Basal Topography for Basal Slipperiness Inversions: A Case Study on Pine Island Glacier, Antarctica

Kyrke-Smith, Teresa M.; Gudmundsson, G. Hilmar; Farrell, Patrick E.

doi:10.3389/feart.2018.00033

Cited by 24 publications

(38 citation statements)

References 59 publications

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“…If the basal topography is unperturbed at the same x coordinate as the observation point such that δb * = 0, then the contributions of δb and δC cannot be separated since they are both multiplied by the same weight except for a different scaling factor. This is in agreement with numerical investigations in Kyrke-Smith et al (2018). It is shown in Cheng and Lötstedt (2019) that the perturbation in δu is proportional to the wavelength of δC.…”

Section: Ssasupporting

confidence: 92%

“…2 increasing towards the grounding line. Such an increased sensitivity is also noted in Kyrke-Smith et al (2018) and Leguy et al (2014). However, in the steady- .…”

Section: Ssamentioning

confidence: 66%

“…Perturbations at the ice base with short wavelength are propagated to the surface with a weaker effect on the height and velocity compared to long wavelengths in Gudmundsson (2003Gudmundsson ( , 2008. These are the conclusions in calculations with FS in Kyrke- Smith et al (2018), where it is difficult to separate the contribution from the friction and the bed topography from each other. These effects are confirmed in our analysis.…”

Section: G Cheng and P Lötstedt: Sensitivity Analysis Of Ice Sheet mentioning

confidence: 94%

“…Fast temporal variations in the meltwater under the ice drive the ice flow in the analysis in Schoof (2010). The spatial and temporal variations of the basal conditions are inferred from satellite data in Larour et al (2014) with an inverse method for SSA and automatic differentiation. Based on observations, the conclusion in Sole et al (2011) is also that the annual change in the water drainage under the ice affects the sliding and the acceleration and deceleration of the ice.…”

Section: G Cheng and P Lötstedt: Sensitivity Analysis Of Ice Sheet mentioning

confidence: 99%

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Parameter sensitivity analysis of dynamic ice sheet models – numerical computations

Cheng

Lötstedt

2020

The Cryosphere

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Abstract. The friction coefficient and the base topography of a stationary and a dynamic ice sheet are perturbed in two models for the ice: the full Stokes equations and the shallow shelf approximation. The sensitivity to the perturbations of the velocity and the height at the surface is quantified by solving the adjoint equations of the stress and the height equations providing weights for the perturbed data. The adjoint equations are solved numerically and the sensitivity is computed in several examples in two dimensions. A transfer matrix couples the perturbations at the base with the perturbations at the top. Comparisons are made with analytical solutions to simplified problems. The sensitivity to perturbations depends on their wavelengths and the distance to the grounding line. A perturbation in the topography has a direct effect at the ice surface above it, while a change in the friction coefficient is less visible there.

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Section: Ssasupporting

confidence: 92%

“…2 increasing towards the grounding line. Such an increased sensitivity is also noted in Kyrke-Smith et al (2018) and Leguy et al (2014). However, in the steady- .…”

Section: Ssamentioning

confidence: 66%

Section: G Cheng and P Lötstedt: Sensitivity Analysis Of Ice Sheet mentioning

confidence: 94%

Section: G Cheng and P Lötstedt: Sensitivity Analysis Of Ice Sheet mentioning

confidence: 99%

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Parameter sensitivity analysis of dynamic ice sheet models – numerical computations

Cheng

Lötstedt

2020

The Cryosphere

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“…The roughness values for BedMachine Antarctica are consistently lower throughout the transect, a consequence of the mass conservation technique using regularization parameters that yields smooth results. Recent studies have stressed the importance of form drag 280 (basal drag due to bed topography) over skin drag (or basal friction) on the basal traction of Pine Island Glacier (Bingham et al, 2017;Kyrke-Smith et al, 2018), and the DeepBedMap super-resolution work here shows strong potential in meeting that demand as a realistic high resolution bed topography dataset for ice sheet modelling studies.…”

Section: Discussionmentioning

confidence: 83%

DeepBedMap: Using a deep neural network to better resolve the bed topography of Antarctica

Leong¹,

Horgan²

2020

Preprint

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Abstract. To better resolve the bed elevation of Antarctica, we present DeepBedMap – a novel machine learning method that produces realistic Antarctic bed topography from multiple remote sensing data inputs. Our super-resolution deep convolutional neural network model is trained on scattered regions in Antarctica where high resolution (250 m) groundtruth bed elevation grids are available. The model is then used to generate high resolution bed topography in less well surveyed areas. DeepBedMap improves on previous interpolation methods by not restricting itself to a low spatial resolution (1000 m) BEDMAP2 raster image as its prior. It takes in additional high spatial resolution datasets, such as ice surface elevation, velocity and snow accumulation to better inform the bed topography even in the absence of ice-thickness data from direct ice-penetrating radar surveys. Our DeepBedMap model is based on an adapted Enhanced Super Resolution Generative Adversarial Network architecture, chosen to minimize per-pixel elevation errors while producing realistic topography. The final product is a four times upsampled (250 m) bed elevation model of Antarctica that can be used by glaciologists interested in the subglacial terrain, and by ice sheet modellers wanting to run catchment or continent-scale ice sheet model simulations. We show that DeepBedMap offers a more realistic topographic roughness profile compared to a standard bicubic interpolated BEDMAP2 and BedMachine Antarctica, and envision it to be used where a high resolution bed elevation model is required.

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DeepBedMap: Resolving the bed topography of Antarctica with a deep neural network

Leong

Horgan

2021

Preprint

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To resolve the bed elevation of Antarctica, we present DeepBedMap -a novel machine learning method that can produce Antarctic bed topography with adequate surface roughness from multiple remote sensing data inputs. The super-resolution deep convolutional neural network model is trained on scattered regions in Antarctica where high-resolution (250 m) ground-truth bed elevation grids are available. This model is then used to generate high-resolution bed topography in less surveyed areas. DeepBedMap improves on previous interpolation methods by not restricting itself to a low-spatial-resolution (1000 m) BEDMAP2 raster image as its prior image. It takes in additional high-spatialresolution datasets, such as ice surface elevation, velocity and snow accumulation, to better inform the bed topography even in the absence of ice thickness data from direct icepenetrating-radar surveys. The DeepBedMap model is based on an adapted architecture of the Enhanced Super-Resolution Generative Adversarial Network, chosen to minimize perpixel elevation errors while producing realistic topography. The final product is a four-times-upsampled (250 m) bed elevation model of Antarctica that can be used by glaciologists interested in the subglacial terrain and by ice sheet modellers wanting to run catchment-or continent-scale ice sheet model simulations. We show that DeepBedMap offers a rougher topographic profile compared to the standard bicubically interpolated BEDMAP2 and BedMachine Antarctica and envision it being used where a high-resolution bed elevation model is required.

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Relevance of Detail in Basal Topography for Basal Slipperiness Inversions: A Case Study on Pine Island Glacier, Antarctica

Cited by 24 publications

References 59 publications

Parameter sensitivity analysis of dynamic ice sheet models – numerical computations

Parameter sensitivity analysis of dynamic ice sheet models – numerical computations

DeepBedMap: Using a deep neural network to better resolve the bed topography of Antarctica

DeepBedMap: Resolving the bed topography of Antarctica with a deep neural network

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