On the representation error in data assimilation. (11th October 2017)

• Record Type: Journal Article
• Authors: Janjić, T.; Bormann, N.; Bocquet, M.; Carton, J. A; Cohn, S. E.; Dance, S. L.; Losa, S. N.; Nichols, N. K.; Potthast, R.; Waller, J. A.; Weston, P.
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 144:Number 713(2018)
• Page Start: 1257
• Page End: 1278
• Publication Date: 2017-10-11
• Subjects: aggregation error -- forward interpolation error -- observation error -- observation‐ operator error -- representativity error -- representativeness error; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3130
• Languages: English
• ISSNs: 0035-9009

Abstract

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. 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.