Combining Hybrid and One-Step-Ahead Smoothing for Efficient Short-Range Storm Surge Forecasting with an Ensemble Kalman Filter

Raboudi, Naila Mohammed Fathi; Ait‐El‐Fquih, Boujemaa; Dawson, Clint; Hoteit, Ibrahim

doi:10.1175/mwr-d-18-0410.1

Cited by 7 publications

(7 citation statements)

References 62 publications

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“…Coastal marine forecast systems are in use or development in a number of regions worldwide (e.g., Lellouche et al., 2018; Mel & Lionello, 2014; Moore et al., 2011; Pinardi & Coppini, 2010; Raboudi et al., 2019; Wilkin et al., 2018). As each region is unique, the length of forecast window and relative levels of forced to internal variability differ among these systems.…”

Section: Introductionmentioning

confidence: 99%

Consistent Predictability of the Ocean State Ocean Model Using Information Theory and Flushing Timescales

et al. 2021

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The Ocean State Ocean Model (OSOM) is an application of the Regional Ocean Modeling System spanning the Rhode Island waterways, including Narragansett Bay, Mt. Hope Bay, larger rivers, and the Block Island Shelf circulation from Long Island to Nantucket. This study discusses the physical aspects of the estuary (Narragansett and Mount Hope Bays and larger rivers) to evaluate physical circulation predictability. This estimate is intended to help decide if a forecast and prediction system is warranted, to prepare for coupling with biogeochemistry and fisheries models with widely disparate timescales, and to find the spin‐up time needed to establish the climatological circulation of the region. Perturbed initial condition ensemble simulations are combined with metrics from information theory to quantify the predictability of the OSOM forecast system–i.e., how long anomalies from different initial conditions persist. The predictability timescale in this model agrees with readily estimable timescales such as the freshwater flushing timescale evaluated using the total exchange flow (TEF) framework, indicating that the estuarine dynamics rather than chaotic transport is the dominant model behavior limiting predictions. The predictability of the OSOM is ∼7–40 days, varying with parameters, region, and season.

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Section: Introductionmentioning

confidence: 99%

Consistent Predictability of the Ocean State Ocean Model Using Information Theory and Flushing Timescales

et al. 2021

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“…Replacing the EnKF‐like update of the deterministic parameters by a PF‐like update, as in, for example, Santitissadeekorn and Jones (2015) and Ait‐El‐Fquih and Hoteit (2018), which should be more efficient in tackling strong inconsistency issues, would be an important direction for future work. We will also consider introducing deterministic variants of the stochastic EnKF‐OSAS update of the state, following, for example, (Raboudi et al ., 2018; Raboudi et al ., 2019), which would be more suitable for large‐scale models. Further work will be conducted to derive Gaussian mixture variants of the latter, to tackle strongly nonlinear models, and to test them with realistic climate, weather, and ocean models.…”

Section: Summary and Discussionmentioning

confidence: 99%

A variational Bayesian approach for ensemble filtering of stochastically parametrized systems

Ait‐El‐Fquih

Subramanian

Hoteit

2023

Quart J Royal Meteoro Soc

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Modern climate models use both deterministic and stochastic parametrization schemes to represent uncertainties in their physics and inputs. This work considers the problem of estimating the involved parameters of such systems simultaneously with their state through data assimilation. Standard state‐parameter filtering schemes cannot be applied to such systems, owing to the posterior dependence between the stochastic parameters and the “dynamical” augmented state, defined as the state augmented by the deterministic parameters. We resort to the variational Bayesian (VB) approach to break this dependence, by approximating the joint posterior probability density function (pdf) of the augmented state and the stochastic parameters with a separable product of two marginal pdfs that minimizes the Kullback–Leibler divergence. The resulting marginal pdf of the augmented state is then sampled using a one‐step‐ahead smoothing‐based ensemble Kalman filter (EnKF‐OSAS), whereas a closed form is derived for the marginal pdf of the stochastic parameters. The proposed approach combines the effectiveness of the OSAS filtering approach to mitigate inconsistency issues that often arise with the joint ensemble Kalman filter (EnKF), with the advantage of obtaining a full posterior pdf for the stochastic parameters, which is not possible with the traditional maximum‐likelihood method. We demonstrate the relevance of the proposed approach through extensive numerical experiments with a one‐scale Lorenz‐96 model, which includes a stochastic parametrization representing subgrid‐scale effects.

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“…The linear coefficients combining the static and the dynamic covariance are called the "hybridization coefficients," which optimally balance the superior but noisy sample covariance with that of less noisy but static covariance. To achieve optimal performance, it is crucial to tune these coefficients Gharamti et al, 2014;Raboudi et al, 2019;X. Wang et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Covariance Hybridization for the Assimilation of SST Observations Within a Coupled Earth System Reanalysis

Barthélémy,

Counillon,

Wang

2024

J Adv Model Earth Syst

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Ensemble data assimilation methods, such as the Ensemble Kalman Filter (EnKF), are well suited for climate reanalysis because they feature flow‐dependent covariance. However, because Earth System Models are heavy computationally, the method uses a few tens of members. Sampling error in the covariance matrix can introduce biases in the deep ocean, which may cause a drift in the reanalysis and in the predictions. Here, we assess the potential of the hybrid covariance approach (EnKF‐OI) to counteract sampling error. The EnKF‐OI combines the flow‐dependent covariance computed from a dynamical ensemble with another covariance matrix that is static but less prone to sampling error. We test the method within the Norwegian Climate Prediction Model, which combines the Norwegian Earth System Model and the EnKF. We test the performance of the reanalyzes in an idealized twin experiment, where we assimilate synthetic sea surface temperature observations monthly over 1980–2010. The dynamical and static ensembles consist respectively of 30 members and 315 seasonal members sampled from a pre‐industrial run. We compare the performance of the EnKF to an EnKF‐OI with a global hybrid coefficient, referred to as standard hybrid, and an EnKF‐OI with adaptive hybrid coefficients estimated in space and time. Both hybrid covariance methods cure the bias introduced by the EnKF at intermediate and deep water. The adaptive EnKF‐OI performs best overall by addressing sampling noise and rank deficiencies issues and can sustain low analysis errors by doing smaller updates than the standard hybrid version.

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Combining Hybrid and One-Step-Ahead Smoothing for Efficient Short-Range Storm Surge Forecasting with an Ensemble Kalman Filter

Cited by 7 publications

References 62 publications

Consistent Predictability of the Ocean State Ocean Model Using Information Theory and Flushing Timescales

Consistent Predictability of the Ocean State Ocean Model Using Information Theory and Flushing Timescales

A variational Bayesian approach for ensemble filtering of stochastically parametrized systems

Adaptive Covariance Hybridization for the Assimilation of SST Observations Within a Coupled Earth System Reanalysis

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