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| Titel |
The potential of an observational data set for calibration of a computationally expensive computer model |
| VerfasserIn |
D. J. McNeall, P. G. Challenor, J. R. Gattiker, E. J. Stone |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 6, no. 5 ; Nr. 6, no. 5 (2013-10-21), S.1715-1728 |
| Datensatznummer |
250085003
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| Publikation (Nr.) |
 copernicus.org/gmd-6-1715-2013.pdf |
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| Zusammenfassung |
We measure the potential of an observational data set to constrain a set of
inputs to a complex and computationally expensive computer model. We use each
member in turn of an ensemble of output from a computationally expensive
model, corresponding to an observable part of a modelled system, as a proxy
for an observational data set. We argue that, given some assumptions, our
ability to constrain uncertain parameter inputs to a model using its own
output as data, provides a maximum bound for our ability to constrain the
model inputs using observations of the real system.
The ensemble provides a set of known parameter input and model output pairs,
which we use to build a computationally efficient statistical proxy for the
full computer model, termed an emulator. We use the emulator to find and rule
out "implausible" values for the inputs of held-out ensemble members, given
the computer model output. As we know the true values of the inputs for the
ensemble, we can compare our constraint of the model inputs with the true
value of the input for any ensemble member. Measures of the quality of
constraint have the potential to inform strategy for data collection
campaigns, before any real-world data is collected, as well as acting as an
effective sensitivity analysis.
We use an ensemble of the ice sheet model Glimmer to demonstrate our measures
of quality of constraint. The ensemble has 250 model runs with 5 uncertain
input parameters, and an output variable representing the pattern of the
thickness of ice over Greenland. We have an observation of historical ice
sheet thickness that directly matches the output variable, and offers an
opportunity to constrain the model. We show that different ways of
summarising our output variable (ice volume, ice surface area
and maximum ice thickness) offer different potential constraints on
individual input parameters. We show that combining the observational data
gives increased power to constrain the model. We investigate the impact of
uncertainty in observations or in model biases on our measures, showing that
even a modest uncertainty can seriously degrade the potential of the
observational data to constrain the model. |
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