HR: 16:30h
AN: NG34A-03 INVITED     [Abstracts]
TI: A Taxonomy of Model-Data Relationships
AU: * Malamud, B D
EM: bruce@malamud.com
AF: Environmental Monitoring and Modelling Research Group, Department of Geography, King's College London, Strand, London, WC2R 2LS United Kingdom
AU: * Malamud, B D
EM: bruce@malamud.com
AF: Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, 24--29 St.\ Giles', Oxford, OX1 3LB United Kingdom
AU: Smith, L A
EM: lenny@maths.ox.ac.uk
AF: Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, 24--29 St.\ Giles', Oxford, OX1 3LB United Kingdom
AU: Smith, L A
EM: lenny@maths.ox.ac.uk
AF: Department of Statistics, Columbia House, London School of Economics, Houghton Street, London, WC2A 2AE United Kingdom
AB: Contrasting observational data and geophysical models is a ubiquitous task in the earth sciences. The quality and quantity of available observational data sets vary across orders of magnitude, the complexity of models is similarly varied, and issues of accessibility confound analyses of both. Therefore, any examination of the relationship between models and data is of limited scope, losing rich subtleties in particular research programs. Nevertheless, there may be some value in noting similarities across the broad geosciences, with the aim of a tighter focus on and better appreciation of the goal (or goals) one hopes to obtain in any specific model-data comparison. One might suppose that these goals are obvious, but this naive view is quickly vanquished by a survey of scientists. In this paper, we propose an initial taxonomy of model-data relationships, classifying various research projects in terms of an investigator's overall goal(s) in comparing and confronting models with data. Examples are taken in the context of five loosely defined geosystems (the {\it Earth's Atmosphere}, the {\it Solid Earth}, the {\it Biosphere}, {\it Celestial Mechanics}, the {\it Research Laboratory}). Each system is thought of as the physical context within which the model is framed and the data is taken to describe or reflect. Questions posed within these systems can be classified in terms of the type of occurrence and/or temporal and spatial patterns within these systems ({\it Recurrent}, {\it Repetitive}, {\it Rare}, {\it One-Off}). Sometimes our understanding of the system itself limits which classes are appropriate to a given research project. We then illustrate, by example, a methodology for placing a specific research program within our classification. We use a four-tiered approach for writing down details about each specific research example: (a) the {\it system} considered, (b) the {\it model} constructed, (c) the {\it data} used, (d) the {\it question} asked. Finally, we give several examples of our approach taken from the broad geosciences, and considering end members of both very large and very small data sets. Clearly, any taxonomy is justified only by its function; our aim here is to recognize patterns and distinguish subtleties, as well as generate some broad suggestions of `good practice.' Ideally, this or some similar taxonomy will prove of use to the researcher both in terms of a vantage point from which to better focus their goals in model-data comparisons, and in easing communications with colleagues who may be thinking initially in terms corresponding to different entries of the taxonomy.
DE: 9820 Techniques applicable in three or more fields
DE: 3210 Modeling
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting