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The benefits of correlated observation errors for small scales
Author(s) -
Rainwater Sabrina,
Bishop Craig H.,
Campbell William F.
Publication year - 2015
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.2582
Subject(s) - uncorrelated , covariance , data assimilation , observational error , variance (accounting) , scale (ratio) , random error , statistics , mathematics , systematic error , computer science , meteorology , physics , accounting , quantum mechanics , business
In operational data assimilation, observation errors are generally assumed to be uncorrelated, though some observations, such as satellite data, have correlated errors. We show that, if observation‐error correlations are correctly accounted for, an observing instrument with spatially correlated errors is better able to resolve small scales than an instrument with the same error variance and uncorrelated errors. We explore the disadvantages of falsely assuming uncorrelated observation errors, investigating two methods of compensating for such mis‐specification by either observation‐error inflation or data thinning. We identify scenarios in which correctly specifying the covariance reduces small‐scale error by over 99%.