Abstract
Representation, representativity, representativeness error, forward interpolation error, forward model error, observation‐operator error, aggregation error and sampling error are all terms used to refer to components of observation error in the context of data assimilation. This article is an attempt to consolidate the terminology that has been used in the earth sciences literature and was suggested at a European Space Agency workshop held in Reading in April 2014. We review the state of the art and, through examples, motivate the terminology. In addition to a theoretical framework, examples from application areas of satellite data assimilation, ocean reanalysis and atmospheric chemistry data assimilation are provided. Diagnosing representation‐error statistics as well as their use in state‐of‐the‐art data assimilation systems is discussed within a consistent framework.
Highlights
At its core, data assimilation relies on comparing each available observation of a variable with a prior estimate of the variable, generally taken from a discrete dynamical model, to deduce a revised estimate on the model grid
The aim of this article is to review some of the literature that has grown around representation error in recent years and, in so doing, to explain and consolidate the terminology that has evolved in different disciplines
Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society
Summary
Data assimilation relies on comparing each available observation of a variable with a prior estimate of the variable, generally taken from a discrete dynamical model, to deduce a revised estimate on the model grid. Errors associated with imperfections in these procedures that depend on the geophysical model, observation operator or data assimilation algorithm, here called pre-processing or quality-control errors, will be considered as part of the representation error. In this article we will focus on representation error in the data assimilation context, there is a wide body of literature on representation error in other contexts, such as when two different types of observations are intercompared, or in the context of forecast verification Common to these applications is the idea that two quantities are compared that represent different scales or processes, e.g. different sampling volumes of two instruments.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
