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A short exploration of structural noise
Author(s) -
Doherty John,
Welter David
Publication year - 2010
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2009wr008377
Subject(s) - noise (video) , weighting , computer science , calibration , process (computing) , filter (signal processing) , parameterized complexity , covariance , covariance matrix , value noise , basis (linear algebra) , term (time) , algorithm , noise measurement , statistical physics , mathematics , acoustics , statistics , noise reduction , artificial intelligence , physics , computer vision , image (mathematics) , noise floor , geometry , quantum mechanics , operating system
“Structural noise” is a term often used to describe model‐to‐measurement misfit that cannot be ascribed to measurement noise and therefore must be ascribed to the imperfect nature of a numerical model as a simulator of reality. As such, it is often the dominant contributor to model‐to‐measurement misfit. As the name “structural noise” implies, this type of misfit is often treated as an additive term to measurement noise when assessing model parameter and predictive uncertainty. This paper inquires into the nature of defect‐induced model‐to‐measurement misfit and provides a conceptual basis for accommodating it. It is shown that inasmuch as defect‐induced model‐to‐measurement misfit can be characterized as “noise,” this noise is likely to show a high degree of spatial and temporal correlation; furthermore, its covariance matrix may approach singularity. However, the deleterious impact of structural noise on the model calibration process may be mitigated in a variety of ways. These include adoption of a highly parameterized approach to model construction and calibration (including the strategic use of compensatory parameters where appropriate), processing of observations and their model‐generated counterparts in ways that are able to filter out structural noise prior to fitting one to the other, and/or through implementation of a weighting strategy that gives prominence to observations that most resemble predictions required of a model.