Computer Simulation, Measurement, and Data Assimilation

Abstract

This article explores some of the roles of computer simulation in measurement. A model-based view of measurement is adopted and three types of measurement—direct, derived, and complex—are distinguished. It is argued that while computer simulations on their own are not measurement processes, in principle they can be embedded in direct, derived, and complex measurement practices in such a way that simulation results constitute measurement outcomes. Atmospheric data assimilation is then considered as a case study. This practice, which involves combining information from conventional observations and simulation-based forecasts, is characterized as a complex measuring practice that is still under development. The case study reveals challenges that are likely to resurface in other measuring practices that embed computer simulation. It is also noted that some practices that embed simulation are difficult to classify; they suggest a fuzzy boundary between measurement and non-measurement. 1 Introduction 2 A Contemporary View of Measurement 3 Three Types of Measurement 4 Can Computer Simulations Measure Real-World Target Systems? 5 Case Study: Atmospheric Data Assimilation   5.1 Why data assimilation?   5.2 A complex measuring practice under development   5.3 Epistemic iteration 6 The Boundaries of Measurement 7 Epistemology, Not Terminology