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Assimilation of oceanographic observations with estimates of vertical background‐error covariances by a Bayesian hierarchical model
Author(s) -
Dobricic Srdjan,
Wikle Christopher K.,
Milliff Ralph F.,
Pinardi Nadia,
Berliner L. Mark
Publication year - 2014
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.2348
Subject(s) - data assimilation , covariance , bayesian probability , statistics , mathematics , errors in variables models , covariance matrix , mean squared error , environmental science , climatology , meteorology , geology , geography
A new method to estimate the vertical part of the background‐error covariance matrix for an ocean variational data assimilation system is presented and tested in the Mediterranean operational daily analysis system. The operational, seasonally varying error covariances are compared with high‐frequency estimates from a Bayesian Hierarchical Model (BHM) which estimates distributions for the vertical error covariances from two data‐stage inputs: model anomalies and differences between model background and observations, i.e. so‐called misfits. It is found that the posterior mean BHM‐error covariance estimates that vary on 5‐day time‐scales reduce the misfits root mean square of the analysis vertical profiles of temperature and salinity by 10–20% versus analyses arising from covariances that vary on seasonal time‐scales or those from the BHM given only model anomalies as data stage inputs.