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On the diagnosis of model error statistics using weak‐constraint data assimilation
Author(s) -
Bowler N. E.
Publication year - 2017
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3051
Subject(s) - data assimilation , constraint (computer aided design) , statistics , computer science , errors in variables models , assimilation (phonology) , observational error , data set , uncorrelated , context (archaeology) , algorithm , mathematics , meteorology , philosophy , paleontology , physics , biology , linguistics , geometry
Outputs from a data assimilation system may be used to diagnose observation‐ and background‐error statistics, as has been demonstrated by previous researchers. In this study, that technique is extended to diagnose model error statistics using a weak‐constraint data assimilation. It deals with a set of observations over a time window and uses the temporal distribution to separate model errors from errors in the background forecast. In a set of idealized tests, this method is shown to be able to distinguish successfully between model and background errors. The success of this method depends on the prior assumptions included in the weak‐constraint data assimilation and how well these describe the true nature of the system being modelled. Other authors have demonstrated that it can be challenging to separate observation and background errors in diagnosis schemes. Some separation of these errors is possible within the context of four‐dimensional assimilation, provided that observation errors are known to be uncorrelated in time.