Reduction of uncertainties through Data Model Integration (DMI)

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1. Introduction

Application of techniques for data model integration (DMI) are increasingly used in many fields of science, finance, economics, etc. Every day examples are improvement of geophysical model descriptions (flows, water levels, waves), improvements and optimization of daily weather forecasts, detection of errors in data series, on-line identification of stolen credit card use, detection of malfunctioning components in manufacturing processes. The one common element is the prior knowledge of the behaviour of a process in the form of an explicit model description, or a set of characteristic data. The second common element is a set of independent or new data. Neither the description of the behaviour and the data are 100% certain – they have uncertainties associated with them. If one has information on the (statistical) nature of these uncertainties, smart mathematical techniques can be used to combine these two information sources and generate new or improved information. As the examples show, this may be an improved model description (less uncertain), an improved forecast, detection of significant deviation from established patterns (faulty component, credit card use,…). In case of the former, we often speak of model calibration and calibration or parameter estimation techniques; in the latter, we speak of (sequential) data assimilation and data assimilation techniques.