Journal
FRONTIERS IN EARTH SCIENCE
Volume 4, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/feart.2016.00008
Keywords
inverse methods; Bayesian inference; subglacial topography
Categories
Funding
- NSF Graduate Research Fellowship [DGE1242789]
- NASA [NNX13AM16G, NNX13AK27G]
- NSF [PLR 1107491]
- Directorate For Geosciences
- Office of Polar Programs (OPP) [1107491] Funding Source: National Science Foundation
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We develop a Bayesian model for estimating ice thickness given sparse observations coupled with estimates of surface mass balance, surface elevation change, and surface velocity. These fields are related through mass conservation. We use the Metropolis-Hastings algorithm to sample from the posterior probability distribution of ice thickness for three cases: a synthetic mountain glacier, Storglaciaren, and Jakobshavn Isbre. Use of continuity in interpolation improves thickness estimates where relative velocity and surface mass balance errors are small, a condition difficult to maintain in regions of slow flow and surface mass balance near zero. Estimates of thickness uncertainty depend sensitively on spatial correlation. When this structure is known, we suggest a thickness measurement spacing of one to two times the correlation length to take best advantage of continuity based interpolation techniques. To determine ideal measurement spacing, the structure of spatial correlation must be better quantified.
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