Assessment of an ensemble-based data assimilation system for a shallow estuary

Khanarmuei, Mohammadreza; Mardani, Neda; Suara, Kabir Adewale; Sumihar, Julius; Sidle, Roy C.; McCallum, Adrian; Brown, Richard J.

doi:10.1016/j.ecss.2021.107389

Cited by 6 publications

(3 citation statements)

References 55 publications

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“…To the best of our knowledge, few works were carried out in this area. However, a recent interest is emerging (Mohan Das et al, 2017;Iglesias et al, 2019b;Taeb and Weaver, 2019;Dinápoli et al, 2021;Khanarmuei et al, 2021).…”

Section: The Ensemble Techniquementioning

confidence: 99%

Improving Estuarine Hydrodynamic Forecasts Through Numerical Model Ensembles

et al. 2022

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Numerical models are essential tools for the study and analysis of the hydrodynamics of estuarine systems. However, the model results contain uncertainties, which need to be minimized to increase the accuracy of predictions. In this work, the ensemble technique is proposed as a solution to improve hydrodynamic forecasts for estuarine regions. Two numerical models, openTELEMAC-MASCARET and Delft3D, were considered for the application of this technique to two Portuguese estuaries. Superensembles for three scenarios (summer, winter, and extreme event) were built to assess the effectiveness of the technique in improving water level prediction. Various weighing techniques were tested in the construction of the ensembles. Weighing techniques that consider the previous performance of each model alone outperformed other techniques. This was observed for all scenarios considered, at all sampling points and in both studied estuaries. The effect of the ensemble size was also analyzed. It was found that the size of the set is directly related to the prediction accuracy, with the best results provided by the superensembles with the highest number of elements. It is concluded that the combined use of several hydrodynamic models reduces the uncertainty of the results and increases the reliability and consistency of predictions for estuarine regions.

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Section: The Ensemble Techniquementioning

confidence: 99%

Improving Estuarine Hydrodynamic Forecasts Through Numerical Model Ensembles

et al. 2022

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“…Furthermore, Khanarmuei et al. (2021) conducted twin experiments for the shallow estuary of Currimundi Lake, Australia. They perturbed the lateral boundary condition of water level and river discharge, and the synthetic observed values of water level and current velocity were assimilated.…”

Section: Introductionmentioning

confidence: 99%

“…Although they did not add perturbations to lateral boundary conditions and river discharge forcing, they noted it may be necessary to add perturbations to lateral boundary conditions and river discharge forcing for generating ensembles when data assimilation is conducted using real observation data. Furthermore, Khanarmuei et al (2021) conducted twin experiments for the shallow estuary of Currimundi Lake, Australia. They perturbed the lateral boundary condition of water level and river discharge, and the synthetic observed values of water level and current velocity were assimilated.…”

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confidence: 99%

Perturbation of Boundary Conditions to Create Appropriate Ensembles for Regional Data Assimilation in Coastal Estuary Modeling

Matsuzaki

Inoue

2022

JGR Oceans

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Regional data assimilation is conducted for a coastal estuary using the ensemble Kalman filter, real observation data from Ise Bay, Japan, and a simulation model called the Ise Bay Simulator. The applicability and robustness of the method are then examined. We also analyze the relationship between the boundary conditions, which add perturbations and the data assimilation results of water temperature and salinity. A method of creating an ensemble by perturbing three boundary conditions (atmospheric forcing, lateral boundary conditions [open boundary conditions], and river discharge forcing) is then proposed. In situ water temperature and salinity profiles observed at fixed points are assimilated daily. The proposed assimilation method provides stable data assimilation without unnatural values for water temperature and salinity throughout the year. Further, applying a perturbation to the three boundary conditions does not lead to filter divergence, thus indicating good applicability and robustness, that is, applying a perturbation to the three boundary conditions does not degenerate the ensemble spread. According to a sensitivity experiment, perturbing the atmospheric boundary conditions of both air temperature and wind speed increases the ensemble spread of water temperature, especially near the surface layer. Wind speed has the greatest influence on the magnitude of the salinity ensemble spread, and its dominance depends on location. Perturbation of lateral boundary conditions increases the ensemble spread of water temperature and salinity at all water depths near the bay mouth, and the observations are effectively assimilated. Perturbation of river discharge forcing successfully assimilates water temperature and salinity near the estuary.

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Data assimilation in a regional high-resolution ocean model by using Ensemble Adjustment Kalman Filter and its application during 2020 cold spell event over Asia-Pacific region

Wang

Zhang

et al. 2022

Applied Ocean Research

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Assessment of an ensemble-based data assimilation system for a shallow estuary

Cited by 6 publications

References 55 publications

Improving Estuarine Hydrodynamic Forecasts Through Numerical Model Ensembles

Improving Estuarine Hydrodynamic Forecasts Through Numerical Model Ensembles

Perturbation of Boundary Conditions to Create Appropriate Ensembles for Regional Data Assimilation in Coastal Estuary Modeling

Data assimilation in a regional high-resolution ocean model by using Ensemble Adjustment Kalman Filter and its application during 2020 cold spell event over Asia-Pacific region

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