Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi‐Geostrophic Coupled Model

Penny, Stephen G.; Bach, Eviatar; Bhargava, Kriti; Chang, Chia-Chuan; Da, Chaojiu; Sun, Luyu; Yoshida, T.

doi:10.1029/2019ms001652

Cited by 69 publications

(83 citation statements)

References 77 publications

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“…The above is in overall agreement with previous works that, while having shown benefit in both directions, have also indicated atmospheric data to be more effective in constraining the ocean than the opposite (see e.g. [48] and references therein). This includes studies (see e.g.…”

Section: Resultssupporting

confidence: 92%

“…52st and 56wk, even for N > n 0 . This behaviour was already observed by [48] and conjectured to be due to the extended spectrum of nearzero LEs in the coupled system. In fact, these quasi-neutral asymptotic LEs have high probability to be instantaneously positive.…”

Section: Coupled Ensemble Kalman Filter With Maooamsupporting

confidence: 74%

“…We provide here a succinct survey of CDA efforts in the geosciences that is functional to our discussion. Recent reviews of CDA can be found in [49,47,48]. In particular [48] provides a detailed comparison of different DA methods using the same low dimensional coupled model used here (see Sect.…”

Section: Coupled Data Assimilation In the Geosciences: Brief Surveymentioning

confidence: 99%

“…Recent reviews of CDA can be found in [49,47,48]. In particular [48] provides a detailed comparison of different DA methods using the same low dimensional coupled model used here (see Sect. 3) Early attempts include a Kalman filter type approach used to assimilate sparse data in a system with various spatio-temporal scales [26], and the EnKF in a two-scale low dimensional system [3].…”

Section: Coupled Data Assimilation In the Geosciences: Brief Surveymentioning

confidence: 99%

“…This would introduce "representation error" (see e.g. [31]) and degrade the performance of data assimilation as shown by [48] using MAOOAM. Figure 6 displays the correlation matrices of the EnKF after 1 year of assimilation.…”

Section: Coupled Ensemble Kalman Filter With Maooammentioning

confidence: 99%

See 4 more Smart Citations

On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter

Tondeur¹,

Carrassi

Vannitsem

et al. 2020

J Stat Phys

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Data assimilation for systems possessing many scales of motions is a substantial methodological and technological challenge. Systems with these features are found in many areas of computational physics and are becoming common thanks to increased computational power allowing to resolve finer scales and to couple together several sub-components. Coupled data assimilation (CDA) distinctively appears as a main concern in numerical weather and climate prediction with major efforts put forward by meteo services worldwide. The core issue is the scale separation acting as a barrier that hampers the propagation of the information across model components (e.g. ocean and atmosphere).We provide a brief survey of CDA, and then focus on CDA using the ensemble Kalman filter (EnKF), a widely used Monte Carlo Gaussian method. Our goal is to elucidate the mechanisms behind information propagation across model components. We consider first a coupled system of equations with temporal scale difference, and deduce that: (i) cross components effects are strong from the slow to the fast scale, but, (ii) intra-component effects are much(1) Paris

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Section: Resultssupporting

confidence: 92%

Section: Coupled Ensemble Kalman Filter With Maooamsupporting

confidence: 74%

Section: Coupled Data Assimilation In the Geosciences: Brief Surveymentioning

confidence: 99%

Section: Coupled Data Assimilation In the Geosciences: Brief Surveymentioning

confidence: 99%

Section: Coupled Ensemble Kalman Filter With Maooammentioning

confidence: 99%

See 3 more Smart Citations

On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter

Tondeur¹,

Carrassi

Vannitsem

et al. 2020

J Stat Phys

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Routes to long‐term atmospheric predictability in reduced‐order coupled ocean–atmosphere systems: Impact of the ocean basin boundary conditions

Vannitsem

Solé-Pomies

Cruz

2019

Quart J Royal Meteoro Soc

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The predictability of the atmosphere at short and long time-scales associated with coupling to the ocean is explored in a new version of the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM), based on a two-layer quasigeostrophic atmosphere and a one-layer reduced-gravity quasigeostrophic ocean. This version features a new ocean basin geometry with periodic boundary conditions in the zonal direction. The analysis presented in this article considers a low-order version of the model with 40 dynamical variables. First, the increase of surface friction (and the associated heat flux) due to the ocean can either induce chaos when the aspect ratio between the meridional and zonal directions of the domain of integration is small or suppress chaos when it is large. This reflects the potentially counterintuitive role that the ocean can play in the coupled dynamics. Second, and perhaps more importantly, the emergence of long-term predictability within the atmosphere for specific values of the friction coefficient occurs through intermittent excursions in the vicinity of a (long-period) unstable periodic solution. Once close to this solution, the system is predictable for long times, i.e. a few years. The intermittent transition close to this orbit is, however, erratic and probably hard to predict. This new route to long-term predictability contrasts with the one found in the closed ocean-basin low-order version of MAOOAM, in which the chaotic solution is permanently wandering in the vicinity of an unstable periodic orbit for specific values of the friction coefficient. The model solution is thus influenced by the unstable periodic orbit at any time, and inherits from its long-term predictability. K E Y W O R D Schaos, low-order models, ocean-atmosphere coupling, predictability, surface friction 1 Q J R Meteorol Soc. 2019;145:2791-2805.wileyonlinelibrary.com/journal/qj

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The role of cross‐domain error correlations in strongly coupled 4D‐Var atmosphere–ocean data assimilation

Smith

Lawless

Nichols

2020

Quart J Royal Meteoro Soc

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Strongly coupled atmosphere-ocean data assimilation offers the ability to improve information exchange across the modelled air-sea interface by enabling observations in one domain to have a direct influence on the analysis in the other. For incremental 4D-Var assimilation a strongly coupled approach enables both domains to be updated at the beginning of the assimilation window, whether they are observed or not, and is hence more likely to produce consistent initial model states. This is made possible by the explicit inclusion of cross-domain forecast error covariance information in the coupled forecast error covariance matrix. In this study we use an idealised 1D single-column coupled atmosphere-ocean model to examine the extent to which explicit cross-domain forecast error covariances play a role in shaping the coupled analysis increments compared to those implicitly generated in the inner-loop of the incremental formulation of the 4D-Var algorithm. This is done via a set of single-observation experiments with and without initial cross-domain forecast error covariances prescribed. Using single observations allows us to obtain explicit expressions for the atmosphere and ocean analysis updates, separating out the individual effects of the explicitly prescribed and implicitly generated cross-domain covariances. Our experiments show that when only one domain is observed, including explicit cross-domain error covariances allows more consistent adjustment of the unobserved domain. Neglecting the cross-domain terms and relying solely on the covariances implicitly generated by the coupled tangent linear and adjoint models restricts the ability of the covariance matrix to impose balance between the two domains. In this case the coupling is essentially one-way; the update to the observed domain is independent of the unobserved domain and so is likely to produce atmosphere and ocean updates that are inconsistent with one another.As we show, this has important consequences for the balance of the coupled analysis state. K E Y W O R D S 4D-Var, coupled data assimilation, cross-correlations, error covariancesThis 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.

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Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi‐Geostrophic Coupled Model

Cited by 69 publications

References 77 publications

On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter

On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter

Routes to long‐term atmospheric predictability in reduced‐order coupled ocean–atmosphere systems: Impact of the ocean basin boundary conditions

The role of cross‐domain error correlations in strongly coupled 4D‐Var atmosphere–ocean data assimilation

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