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

The benefits of dynamical atmosphere-ocean-wave coupling in probabilistic weather forecasts generated using convective-scale ensemble prediction systems are to date unknown. We investigate the respective impacts of atmosphere-ocean-wave coupling, and initial condition (IC), lateral boundary condition (LBC), and stochastic physics perturbations within a convective-scale ensemble coupled system for an extratropical cyclone case study. Towards this aim, we developed the first 18-member, 2.2 km grid spacing ensemble regional coupled system (Ensemble-RCS) with domain covering the British Isles and surrounding seas. Ensemble-RCS coupled and uncoupled simulations of cyclone Ciara (February 2020) were performed. Adding stochastic perturbations to the model physics parametrizations enhances the ensemble spread of the uncoupled atmosphere-only ensemble driven by IC and LBC perturbations, while slightly reducing (by up to 0.5 m⋅s −1 ) the median of the ensemble 95th percentile 10-m wind speeds from its value of about 24 m⋅s −1 at peak time. A substantial proportion of this impact is attributable to Charnock parameter perturbations alone. By coupling the atmosphere-only ensemble, with stochastic physics, to the ocean, the ensemble median and spread is mainly unaffected. However, additional coupling to waves reduces the median wind speed by 1 m⋅s −1 , which leads to reductions of up to 70% in strong wind strike probability, and halving of the spatial coverage of high values (>50%) of this probability. Finally, we demonstrate the usefulness of two metrics originally developed for precipitation verification -the neighbourhood-based Fraction Skill Score (FSS) and the object-based Structure, Amplitude, Location (SAL) -for examining the spread in convective-scale ensemble forecasts. It is concluded that coupling has a consistent impact across the ensemble members. Remarkably, the impact of coupling to waves is found to be comparable in size to that of adding IC, LBC andThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Section: Discussionmentioning

confidence: 99%

The sensitivity of probabilistic convective‐scale forecasts of an extratropical cyclone to atmosphere–ocean–wave coupling

Gentile

Gray

Lewis

2021

“…The top level is approximately 1 m thick and the vertical resolution decreases with depth. An ensemble version of FOAM has recently been developed (Lea et al ., 2022), consisting of 37 ensemble members. To account for model uncertainty, stochastic parametrisation is applied on all ensemble members except one (the control member).…”

Section: The Coupled Nwp Systemmentioning

confidence: 99%

“…As in Lea et al . (2022), univariate localisation is used in this study, meaning that localisation is only applied to individual control variables and does not change the prescribed balance relationships. The ensemble correlation information between different control variables is therefore discarded, and the localisation matrix becomes block‐diagonal.…”

Section: The Coupled Nwp Systemmentioning

confidence: 99%

“…Lea et al . (2022) show that this particular combination of

\alpha

and the inflation factor produces the best results globally, and it is on this basis that an oceanic ensemble with these parameters is chosen to construct

{\mathbf{B}}_{\mathrm{ens}}

for the oceanic component of the coupled DA system used here. It should be noted that this offline ensemble runs on daily assimilation cycles and is thus only available to be incorporated into the coupled DA system once a day.…”

Section: Impacts Of Hybrid Oceanic Covariances In the Coupled Systemmentioning

confidence: 99%

“…The matrix B ens is computed by taking the elementwise product between a localisation matrix and the empirical covariance matrix, but in practice this is achieved through an equivalent sequence of matrix-vector multiplications (Weaver et al, 2018). As in Lea et al (2022), univariate localisation is used in this study, meaning that localisation is only applied to individual control variables and does not change the prescribed balance relationships. The ensemble correlation information between different control variables is therefore discarded, and the localisation matrix becomes block-diagonal.…”

Section: Nemo-cice and Nemovarmentioning

confidence: 99%

See 2 more Smart Citations

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

Leung

Lawless

Nichols

et al. 2022

Self Cite

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

Self Cite

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.