Description and assessment of the OSTIA reanalysis. - Met Office

y a factor which increases linearly from 1 at 1200 UTC on the analysis day to 1.5 at plus/minus
36 hours. The magnitude of the scaling required was estimated by assuming that observation
errors propagate with ocean features, thus the temporal correlations in the observation error can be
obtained by calculating the temporal correlations in the SST’s or in SST anomalies (which removes
the seasonal cycle from the correlations). A linear fit to the SST and SST anomaly correlations
resulted in scaling factors of 1.3 and 1.7 respectively. Analysis of the impact of the different scaling
factors on the analysis increments and observation minus background fields resulted in the mid-way
value of 1.5 being chosen.
3.6 Error estimation
Each SST analysis is accompanied by an uncertainty estimate in the form of the estimated error
standard deviation of the analysed SST. Computing the analysis error within the optimal interpolation
scheme requires much more computational resources than the analysis itself so a simpler
approach is used to estimate the analysis error. This is carried out in the OSTIA system using an
analysis quality (AQ) optimal interpolation, as described in (Donlon et al., 2011).
The analysis error is calculated by carrying out a second optimal interpolation identical to that
described in section 3.5 except all observations are given a value of 1.0, the background is set to 0
and the background and observation errors are the same as those used in the SST analysis. New
observations add information to the AQ field and observation gaps result in the field decaying. The
analysis error field provides information on the data coverage and observation errors that have been
input into a given analysis plus information on the analysis background errors.
© c Crown Copyright 2011 35