TY - JOUR

T1 - Bayesian inference of subglacial topography using mass conservation

AU - Brinkerhoff, Douglas J.

AU - Aschwanden, Andy

AU - Truffer, Martin

N1 - Publisher Copyright:
© 2016 Brinkerhoff, Aschwanden and Truffer.

PY - 2016/2/5

Y1 - 2016/2/5

N2 - 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, Storglaciären, and Jakobshavn Isbræ. 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.

AB - 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, Storglaciären, and Jakobshavn Isbræ. 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.

KW - Bayesian inference

KW - Inverse methods

KW - Subglacial topography

UR - http://www.scopus.com/inward/record.url?scp=84994632750&partnerID=8YFLogxK

U2 - 10.3389/feart.2016.00008

DO - 10.3389/feart.2016.00008

M3 - Article

AN - SCOPUS:84994632750

SN - 2296-6463

VL - 4

JO - Frontiers in Earth Science

JF - Frontiers in Earth Science

M1 - 8

ER -