A new stochastic ocean physics package and its application to hybrid‐covariance data assimilation

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We have developed a global ocean and sea‐ice ensemble forecasting system based on the operational forecasting ocean assimilation model (FOAM) system run at the Met Office. The ocean model Nucleus for European Modelling of the Ocean (NEMO) and the community ice code (CICE) sea‐ice model are run at 1/4∘$$ {}^{\circ } $$ resolution and the system assimilates data using a three‐dimensional variational assimilation (3DVar) version of NEMOVAR. This data assimilation (DA) system can perform hybrid ensemble/variational assimilation. A 36‐member ensemble of hybrid ensemble variational assimilation systems with perturbed observations (values and locations) has been set up, with each member forced at the surface by a separate member of the Met Office Global–Regional Ensemble Prediction System (MOGREPS‐G). The unperturbed member is forced by atmospheric fields from the Met Office operational numerical weather prediction (NWP) deterministic system. The system includes stochastic model perturbations and a relaxation to prior spread (RTPS) inflation scheme. A control run of the system using an ensemble of 3DVars is shown to be generally reliable for Sea‐Level Anomaly (SLA), temperature, and salinity (the ensemble spread being a good representation of the uncertainty in the ensemble mean), although the ensemble is underspread in eddying regions. The ensemble mean gives a 4% reduction in error in SLA compared with the deterministic 3DVar system currently used operationally. The system was tested with different weights for the ensemble component of the hybrid background‐error covariance matrix and different inflation factors. The best results, in terms of short‐range forecast error and ensemble reliability statistics, were obtained with hybrid three‐dimensional ensemble variational DA (3DEnVar). The RTPS inflation scheme is shown to be beneficial in producing an appropriate ensemble spread in response to hybrid DA. 3DEnVar with an ensemble hybrid weight of 0.8 leads to a reduction of 20% (5%) in the ensemble mean error for SLA (profile temperature and salinity) compared with an ensemble of standard 3DVars.

Section: Summary and Discussionmentioning

confidence: 99%

Section: Ensemble System Descriptionmentioning

confidence: 99%

Section: Stochastic Model Perturbationsmentioning

confidence: 99%

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A new global ocean ensemble system at the Met Office: Assessing the impact of hybrid data assimilation and inflation settings

Lea

While

Martin

et al. 2022

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Section: The Coupled Nwp Systemmentioning

confidence: 99%

“…A mix of stochastic parametrisation schemes are used, and the strengths of these schemes are tuned so that the ensemble is reliable in most regions (based on our assumptions about the observation-error statistics). Details of the stochastic parametrisation are described in Storto and Andriopoulos (2021) and Lea et al (2022). The ocean surface is forced by an atmospheric and land-surface ensemble (the global version of the Met Office Global and Regional Ensemble Prediction System, MOGREPS-G: Bowler et al, 2008), which itself features stochastic model perturbations and an ensemble of data assimilations with perturbed observations.…”

Section: Nemo-cice and Nemovarmentioning

confidence: 99%

The impact of hybrid oceanic data assimilation in a coupled model: A case study of a tropical cyclone

Leung

Lawless

Nichols

et al. 2022

Tropical cyclones tend to result in distinctive spatial and temporal characteristics in the upper ocean, which suggests that traditional, parametrisation‐based background‐error covariances in oceanic data assimilation (DA) may not be suitable. Using the case study of Cyclone Titli, which affected the Bay of Bengal in October 2018, we explore hybrid methods that combine the traditional covariance modelling strategy used in variational methods with flow‐dependent estimates of the ocean's error covariance structures based on a short‐range ensemble forecast. This hybrid approach is investigated in the UK Met Office's state‐of‐the‐art system. Single‐observation experiments in the ocean reveal that the hybrid approach is capable of producing analysis increments that are time‐varying, more anisotropic and vertically less uniform. When the hybrid oceanic covariances are incorporated into a weakly coupled DA system, the sea‐surface temperature (SST) in the path of the cyclone is changed, not only through the different specifications of background‐error covariances used in the SST assimilation, but also through the propagation of subsurface temperature differences to the surface as a result of vertical mixing associated with the cyclone's strong winds. The coupling with the atmosphere then leads to a discrepancy in the cyclone's central pressure, which brings forth further SST differences due to the different representations of the cyclone's emerging cold wake.

Assessment of short‐range forecast error atmosphere–ocean cross‐correlations from the Met Office coupled numerical weather prediction system

Wright,

Lawless,

Nichols

et al. 2024

Operational data assimilation systems for coupled atmosphere–ocean prediction are usually “weakly coupled”, in which there is no explicit interaction between the atmosphere and ocean within the data assimilation step. Explicitly allowing for cross‐correlations between the ocean and the atmosphere may have potential benefits in improving the consistency of atmosphere and ocean analyses, as well as allowing a better use of observations at the interface. To understand whether such correlations are significant on the time‐scales of numerical weather prediction, we investigate the atmosphere–ocean cross‐correlations of short‐term forecast errors from the Met Office coupled prediction system, considering their temporal and spatial variability. We find that significant correlations exist between atmosphere and ocean forecast errors on these time‐scales, and that these vary diurnally, from day to day, spatially and synoptically. For correlations between errors in the atmospheric wind and ocean temperature, positive correlations in the North Atlantic region are found to be synoptically dependent, with correlation structures extending into the ocean throughout the deep mixed layer, beyond a depth of 100 m. In contrast, negative correlations over the Indian Ocean are very shallow and are associated with the diurnal cycle of solar radiation. The significance and variability of cross‐correlations indicates that there should be a benefit from including them in data assimilation systems, but it will be important to allow for some flow‐dependence in the correlations. Furthermore, the differing vertical extents of the cross‐correlations in different regions implies the need for situation‐dependent localisation of ensemble correlations when including them in coupled data assimilation systems.