Improvements to feature resolution in the OSTIA sea surface temperature analysis using the NEMOVAR assimilation scheme

Abstract

The data assimilation scheme used in the Met Office's Operational Sea Surface Temperature and Ice Analysis (OSTIA) system has been updated from an Optimal Interpolation (OI)‐type scheme to a variational assimilation scheme. The updated system includes a dual length‐scale background error correlation operator, and a flow‐dependent component to adjust the length‐scale combination in favour of the short scale in regions of high sea surface temperature (SST) variability. The variational assimilation scheme improves both the analysis performance and the representation of SST features in the OSTIA analysis compared to the OI scheme of the original system. The results of spectral analysis, assessment of horizontal SST gradients and the response of an atmospheric model to the OSTIA SST analysis as a boundary condition indicate that the flow‐dependent formulation successfully contributes to improvements in the feature resolution capability of the analysis. Overall, using a short length‐scale of 15 km and including a flow‐dependent adjustment component produces the best results compared to using either 40 km or the first Rossby radius of deformation as the short length‐scale. The new system successfully captures realistic ocean variability without introducing noise into the analysis, allowing the feature resolution capability of the new system to out‐perform that of other comparable SST analysis products.

Use of Uncertainty Inflation in OSTIA to Account for Correlated Errors in Satellite-Retrieved Sea Surface Temperature Data

“…As described in Section 1, OSTIA represents background error correlations using a dual length scale formulation. In practice, an iterative diffusion operator is applied to approximate this within NEMOVAR [7]. The high number of iterations performed when doing this means that a Gaussian distribution is considered a reasonable approximation for the background error distribution [17].…”

“…In these equations, r is the ratio (the weighting assigned to the small and large-scale background error covariance components at the observation position), r c is the ratio condition (0.4), is the observation density (defined for each observation as the number of observations within a 100 km radius of itself), m is the maximum observation density (1000) above which the maximum inflation is applied, m is the chosen limit to the inflation factor and f* is the f found from Equation (7). The ramping between ratio values of 0.36 and 0.4 is applied in order to avoid a sudden change in OEVs in areas near the boundary of the ratio condition.…”

“…An alternative scheme (Scheme B), which did not use the observation density, was also tested. In this scheme, Equation (9) replaces Equation (7). f = m if ratio < 0.36 (i.e., 0.9 × 0.4)…”

“…OSTIA has recently moved from using an optimal interpolation scheme [2] to using the variational data assimilation scheme NEMOVAR [3,6]. NEMOVAR aims to iteratively minimise the value of the incremental cost function J by varying the ocean state x [7]:…”

“…The background error covariances are decomposed into two spatial scales, one representing errors that are correlated over small-scales and one representing errors that are correlated over large-scales. [7] describes in detail how this dual length scale formulation has been applied in OSTIA. The small and large-scale error variances, which were originally derived by [8], have associated length scales of approximately 15 km and 300 km respectively.…”