A multiple length scale correlation operator for ocean data assimilation

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.

Section: Overviewmentioning

confidence: 99%

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

Fiedler

Mao

Good

et al. 2019

“…() give a description of the scheme, as implemented in an earlier FOAM version. The main updates to the scheme since then (of relevance to this work) include the implementation of a multiple length‐scale background error covariance (Mirouze et al, ) and the use of a two‐dimensional implicit diffusion operator for modelling the background error covariances (Weaver et al, ). A variational observation bias correction scheme is also used, as described for SST by While and Martin (), and has been adapted for use in bias correcting satellite SSS observations, as described below.…”

Section: Model Data Assimilation and Bias Correctionmentioning

confidence: 99%

Assimilating satellite sea‐surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system

Martin

King

While

et al. 2019

Self Cite

Funding informationESA.Measuring Sea-Surface Salinity (SSS) from space is a relatively recent technique that relies on L-band radiometry that has evolved to a point where useful information is provided every few days. The impact of assimilating satellite SSS data is investigated using the global FOAM ocean forecasting system. This system assimilates daily satellite SSS products from the ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius and Soil Moisture Active Passive (SMAP) missions equatorward of 40 • N/S, in addition to other observing systems. The data are assimilated using a 3D-Var scheme that includes an observation bias correction scheme to estimate spatially and temporally varying bias estimates for each SSS satellite. The SSS assimilation is tested over 2 years and 3 months covering the 2015/2016 El Niño with assessment focussed on the tropical regions, particularly in the Pacific. Consistent reductions in root-mean-square errors are found in all tropical regions from each of the satellite SSS datasets. The largest improvements of up to about 8% are found in the tropical Pacific from assimilating both the SMOS and SMAP datasets together. The largest impact is in the intertropical convergence zone (ITCZ) in the central Pacific during 2015 and early 2016 where the surface salinity is reduced by about 0.03 pss on average, correcting for too little precipitation. A smaller magnitude, large-scale reduction in the SSS is also seen in the tropical Pacific that increases the modelled surface stratification and leads to reduced vertical mixing. Changes to the SSS in the ITCZ lead to changes in the meridional gradients of SSS that affects the sea surface height and surface currents, both of which are improved in the SMOS assimilation experiment compared to externally produced observation-based datasets. The results suggest that assimilation of SMOS and SMAP satellite data is now of an appropriate quality for operational implementation. 1Ocean Assimilation Model (FOAM) system (Blockley et al., 2014) produces daily analyses and 7-day forecasts of the three-dimensional ocean and sea-ice state, including temperature, salinity and velocity. The same system is also used to produce reanalyses over the satellite altimeter era in order to calibrate seasonal forecasts (MacLachlan et al., 2015).The observing systems routinely assimilated in FOAM include in situ and satellite sea-surface temperature (SST) This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.

“…) and the use of multiple horizontal length‐scales (Mirouze et al . ). In each analysis window NEMOVAR calculates increments to the model by assimilating observations of temperature, salinity, sea‐ice concentration, and sea‐surface height.…”

Section: Bias Correction In a Full Ocean Reanalysis Systemmentioning

confidence: 97%

Variational bias correction of satellite sea‐surface temperature data incorporating observations of the bias

While

Martin

2019

Self Cite

We describe a variational bias-correction system for satellite sea-surface temperature (SST) data that includes the use of "observations-of-bias". The bias-correction scheme is designed to work in the historical period, when good quality low-bias reference data were scarce, but can also take advantage of reference data when they are available. In testing with a simple Lorenz 63 model, our new scheme outperformed traditional variational bias correction. When compared with an offline bias-correction method, the new scheme showed superior performance both when the bias was large and when reference observations were sparse. The bias-correction scheme has also been tested using a three-year assimilative run (2008-2010) of the Nucleus for European Modeling of the Ocean (NEMO) ocean general circulation model, with reference data from the Advanced Along Track Scanning Radiometer (AATSR) instrument withheld in 2009. In these tests, the new scheme was found to be more robust to missing reference observations than an offline scheme. Against AATSR data, the new bias-correction method had lower biases and root-mean-square (RMS) errors than an offline scheme, but was degraded relative to a pure variational technique. However, in comparisons with drifting buoys, the new scheme outperformed both offline and pure variational methods. K E Y W O R D S bias correction, SST, variational methods 1 This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.