A Probabilistic Automated Isochrone Picking Routine to Derive Annual Surface Mass Balance From Radar Echograms

Keeler, D. G.; Rupper, S.; Forster, R. R.; Miège, C.

doi:10.1109/tgrs.2020.2989102

Cited by 5 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We apply approaches from Keeler et al (2020) to estimate surface mass balance in central West Antarctica over recent decades using estimates of snow density to 40 meters below the surface. This approach consists of computer vision algorithms, principally based on Radon transforms and peak finding, to pick annual snow layers in radar images to estimate annual SMB.…”

Section: Snow Density: Physics and Datamentioning

confidence: 99%

“…We estimate snow density down to 40 meters because, due to radar signal attenuation, these are the only densities useful for estimating SMB. Based on these snow density estimates along the flight line, we apply approaches from Keeler et al (2020), briefly discussed in Section 2.1, to estimate SMB (with standard error) over recent decades. We focus on three locations without drilled snow cores, labeled "A," "B" and "C" in Figure 1, to illustrate how estimated snow densities can be leveraged to estimate SMB (see Figure 6).…”

Section: Interpolation and Extension Of Snow Density Curvesmentioning

confidence: 99%

“…To account for these patterns in variability, we consider models for the variance that account for the length of the core used for the measurement and allow model uncertainty to depend on the group that took the measurement. Lastly, we apply the approach of Keeler et al (2020) to estimate SMB as a function of time, using snow density curve predictions at locations with airplane acquired radar measurements and providing a much richer recent history of water accumulation over the Antarctic ice sheets.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Hierarchical integrated spatial process modeling of monotone West Antarctic snow density curves

2021

Self Cite

View full text Add to dashboard Cite

Snow density estimates below the surface, used with airplane-acquired ice-penetrating radar measurements, give a site-specific history of snow water accumulation. Because it is infeasible to drill snow cores across all of Antarctica to measure snow density and because it is critical to understand how climatic changes are affecting the world's largest freshwater reservoir, we develop methods that enable snow density estimation with uncertainty in regions where snow cores have not been drilled.In inland West Antarctica, snow density increases monotonically as a function of depth, except for possible microscale variability or measurement error, and it cannot exceed the density of ice. We present a novel class of integrated spatial process models that allow interpolation of monotone snow density curves. For computational feasibility we construct the space-depth process through kernel convolutions of log-Gaussian spatial processes. We discuss model comparison, model fitting and prediction. Using this model, we extend estimates of snow density beyond the depth of the original core and estimate snow density curves where snow cores have not been drilled. Along flight lines with ice-penetrating radar, we use interpolated snow density curves to estimate recent water accumulation and find predominantly decreasing water accumulation over recent decades.

show abstract

Section: Snow Density: Physics and Datamentioning

confidence: 99%

Section: Interpolation and Extension Of Snow Density Curvesmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Hierarchical integrated spatial process modeling of monotone West Antarctic snow density curves

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…We select these locations because they intersect with flight lines where icepenetrating radar measurements were taken. Thus, based on these snow density estimates along the flight line, we apply approaches from Keeler et al (2020), discussed in Section 2.1, to estimate SMB over recent decades.…”

Section: Interpolation and Extension Of Snow Density Curvesmentioning

confidence: 99%

“…We apply approaches from Keeler et al (2020) to estimate surface mass balance in central West Antarctica over recent decades using estimates of snow density to 30 meters below the surface. This approach consists of computer vision algorithms, principally based on Radon transforms and peak finding, to pick annual snow layers in radar images to estimate annual SMB.…”

mentioning

confidence: 99%

Hierarchical Integrated Spatial Process Modeling of Monotone West Antarctic Snow Density Curves

White,

Keeler,

Rupper

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Snow density estimates below the surface, used with airplaneacquired ice-penetrating radar measurements, give a site-specific history of snow water accumulation. Because it is infeasible to drill snow cores across all of Antarctica to measure snow density and because it is critical to understand how climatic changes are affecting the world's largest reservoir of fresh water, we develop methods that enable snow density estimation with uncertainty in regions where firn cores have not been drilled.Snow density increases monotonically as a function of depth, except for possible micro-scale variability or measurement error, and it cannot exceed the density of ice. We present two novel models that enable monotone spatial interpolation of snow density and show that spatial monotone spline models offer huge out-of-sample prediction advantages over our more traditional spatial model. We discuss model comparison, model fitting, and prediction for our monotone spatial spline model. Using this model, we extend estimates of snow density beyond the depth of the original core and estimate snow density curves at locations where firn cores have not been drilled. At four illustrative locations, we show how estimated snow density curves can be combined with radar measurements to estimate recent water accumulation.

show abstract

Improving piecewise linear snow density models through hierarchical spatial and orthogonal functional smoothing

et al. 2022

Self Cite

View full text Add to dashboard Cite

Snow density estimates as a function of depth are used for understanding climate processes, evaluating water accumulation trends in polar regions, and estimating glacier mass balances. The common and interpretable physically derived differential equation models for snow density are piecewise linear as a function of depth (on a transformed scale); thus, they can fail to capture important data features. Moreover, the differential equation parameters show strong spatial autocorrelation. To address these issues, we allow the parameters of the physical model, including random change points over depth, to vary spatially.We also develop a framework for functionally smoothing the physically motivated model. To preserve inference on the interpretable physical model, we project the smoothing function into the physical model's spatially varying null space. The proposed spatially and functionally smoothed snow density model better fits the data while preserving inference on physical parameters. Using this model, we find significant spatial variation in the parameters that govern snow densification.

show abstract

A Probabilistic Automated Isochrone Picking Routine to Derive Annual Surface Mass Balance From Radar Echograms

Cited by 5 publications

References 36 publications

Hierarchical integrated spatial process modeling of monotone West Antarctic snow density curves

Hierarchical integrated spatial process modeling of monotone West Antarctic snow density curves

Hierarchical Integrated Spatial Process Modeling of Monotone West Antarctic Snow Density Curves

Improving piecewise linear snow density models through hierarchical spatial and orthogonal functional smoothing

Contact Info

Product

Resources

About