Ensemble transform Kalman–Bucy filters

Amezcua, Javier; Ide, Kayo; Kalnay, Eugenia; Reich, Sebastian

doi:10.1002/qj.2186

Cited by 33 publications

(52 citation statements)

References 38 publications

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“…Therefore, LETKF offers outstanding scalability properties. SPEEDY-LETKF is an open-source software which has already been used for several DA studies (Li et al, 2009;Miyoshi, 2010;Lien et al, 2013;Ruiz et al, 2013;Amezcua et al, 2014). Here, SPEEDY-LETKF was extended to allow the assimilation of pseudo-TRW observations by means of either online or off-line time-averaged EnKF methods.…”

Section: Da Techniquementioning

confidence: 99%

Assimilation of pseudo-tree-ring-width observations into an atmospheric general circulation model

et al. 2017

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Abstract. Paleoclimate data assimilation (DA) is a promising technique to systematically combine the information from climate model simulations and proxy records. Here, we investigate the assimilation of tree-ring-width (TRW) chronologies into an atmospheric global climate model using ensemble Kalman filter (EnKF) techniques and a processbased tree-growth forward model as an observation operator. Our results, within a perfect-model experiment setting, indicate that the "online DA" approach did not outperform the "off-line" one, despite its considerable additional implementation complexity. On the other hand, it was observed that the nonlinear response of tree growth to surface temperature and soil moisture does deteriorate the operation of the timeaveraged EnKF methodology. Moreover, for the first time we show that this skill loss appears significantly sensitive to the structure of the growth rate function, used to represent the principle of limiting factors (PLF) within the forward model. In general, our experiments showed that the error reduction achieved by assimilating pseudo-TRW chronologies is modulated by the magnitude of the yearly internal variability in the model. This result might help the dendrochronology community to optimize their sampling efforts.

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Section: Da Techniquementioning

confidence: 99%

Assimilation of pseudo-tree-ring-width observations into an atmospheric general circulation model

et al. 2017

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“…The model dynamics act as a filter that supplies additional information about the unobserved states of the model, which are required to construct an accurate estimate of the state of the true system. This idea of using the model as a filter is well established and is the core idea behind algorithms like the Kalman-Bucy filter [21], as well as its various extensions. In those algorithms, the coupling term g(t) is dynamical and chosen to minimize an estimate of the error covariance [12].…”

Section: Assimilating Data Into Models Of Observed Processesmentioning

confidence: 99%

“…(21) for L c in terms of the average number of unstable directions in the dynamics and to acknowledge the apparent connection with observability. These ideas have had some mention in the data assimilation literature.…”

Section: Direct Estimation Of L Cmentioning

confidence: 99%

Using waveform information in nonlinear data assimilation

et al. 2014

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Information in measurements of a nonlinear dynamical system can be transferred to a quantitative model of the observed system to establish its fixed parameters and unobserved state variables. After this learning period is complete, one may predict the model response to new forces and, when successful, these predictions will match additional observations. This adjustment process encounters problems when the model is nonlinear and chaotic because dynamical instability impedes the transfer of information from the data to the model when the number of measurements at each observation time is insufficient. We discuss the use of information in the waveform of the data, realized through a time delayed collection of measurements, to provide additional stability and accuracy to this search procedure. Several examples are explored, including a few familiar nonlinear dynamical systems and small networks of Colpitts oscillators.

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“…We consider the Lorenz-63 model [13] dx [1] dt = − σ(x [1] − x [2] ), dx [2] dt = − x [1] x [3] + rx [1] − x [2] , dx [3] dt =x [1] x [2] − bx [3] ,…”

Section: 2mentioning

confidence: 99%

On the consistency of ensemble transform filter formulations

Reich¹,

Shin²

2014

Journal of Computational Dynamics

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In this paper, we consider the data assimilation problem for perfect differential equation models without model error and for either continuous or intermittent observational data. The focus will be on the popular class of ensemble Kalman filters which rely on a Gaussian approximation in the data assimilation step. We discuss the impact of this approximation on the temporal evolution of the ensemble mean and covariance matrix. We also discuss options for reducing arising inconsistencies, which are found to be more severe for the intermittent data assimilation problem. Inconsistencies can, however, not be completely eliminated due to the classic moment closure problem. It is also found for the Lorenz-63 model that the proposed corrections only improve the filter performance for relatively large ensemble sizes.2010 Mathematics Subject Classification. Primary: 93E11, 60G35, 65C05; Secondary: 62M20, 62F15, 65C35.

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Ensemble transform Kalman–Bucy filters

Cited by 33 publications

References 38 publications

Assimilation of pseudo-tree-ring-width observations into an atmospheric general circulation model

Assimilation of pseudo-tree-ring-width observations into an atmospheric general circulation model

Using waveform information in nonlinear data assimilation

On the consistency of ensemble transform filter formulations

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