The benefits of correlated observation errors for small scales

Autor:	William F. Campbell, Craig H. Bishop, Sabrina Rainwater
Rok vydání:	2015
Předmět:	High resolution analysis Atmospheric Science Data assimilation Error variance Satellite data Statistics Covariance Non-sampling error Uncorrelated Mathematics
Zdroj:	Quarterly Journal of the Royal Meteorological Society. 141:3439-3445
ISSN:	1477-870X 0035-9009
DOI:	10.1002/qj.2582
Popis:	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%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::db2e38eb21ea8e79a5fa3d2fda06226d https://doi.org/10.1002/qj.2582 Zobrazit plný text záznamu Plný text