Journal
JOURNAL OF GLACIOLOGY
Volume 61, Issue 229, Pages 947-962Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.3189/2015JoG15J050
Keywords
ice-sheet modelling
Funding
- UK Natural Environment Research Council [NE/L005212/1]
- NERC [bas0100034, NE/L005212/1] Funding Source: UKRI
- Natural Environment Research Council [NE/L005212/1, bas0100034] Funding Source: researchfish
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Ice-sheet models can be used to forecast ice losses from Antarctica and Greenland, but to fully quantify the risks associated with sea-level rise, probabilistic forecasts are needed. These require estimates of the probability density function (PDF) for various model parameters (e.g. the basal drag coefficient and ice viscosity). To infer such parameters from satellite observations it is common to use inverse methods. Two related approaches are in use: (1) minimization of a cost function that describes the misfit to the observations, often accompanied by explicit or implicit regularization, or (2) use of Bayes' theorem to update prior assumptions about the probability of parameters. Both approaches have much in common and questions of regularization often map onto implicit choices of prior probabilities that are made explicit in the Bayesian framework. In both approaches questions can arise that seem to demand subjective input. One way to specify prior PDFs more objectively is by deriving transformation group priors that are invariant to symmetries of the problem, and then maximizing relative entropy, subject to any additional constraints. Here we investigate the application of these methods to the derivation of priors for a Bayesian approach to an idealized glaciological inverse problem.
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