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

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

doi:10.1016/j.ecss.2021.107389

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

Journal article

Peer reviewed

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

Mohammadreza Khanarmuei, Neda Mardani, Kabir Suara, Julius Sumihar, Roy Sidle, Adrian McCallum and Richard J Brown

Estuarine, Coastal and Shelf Science, Vol.257, pp.1-12

2021

DOI: https://doi.org/10.1016/j.ecss.2021.107389

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Abstract

Estuary

Hydrodynamic modelling

Data assimilation

OSSE

Twin experiment

Observation locations

Data assimilation (DA) is an essential element for the next generation of operational forecast systems for estuaries , to improve estuarine management. With limited resources and prohibitive cost to collect observations for such system, sensor choice and location is of prime importance in improving hydrodynamic model performance. In this study, we examine an optimal ensemble-based DA platform for improving the hydrodynamic modelling of a shallow estuary. Using an ensemble Kalman filter (EnKF), a set of synthetic (twin) experiments was conducted to test different DA scenarios covering observation types (i.e. water level and velocity) and noise modelling. We also evaluated the impact of the observation location on the DA performance by performing an observing system simulation experiment (OSSE). Results revealed that the assimilation of a single variable can significantly enhance the accuracy of the variable being assimilated, while the level of improvement for another variable is smaller. However, the best model estimates were obtained via a multivariate EnKF (i.e. both observations are assimilated). EnKF was robust to under and overestimation of the model errors, although overestimation led to slightly greater improvements. Our analysis showed that model performance is more sensitive to velocity observation location, rather than water level. These findings suggest that locations with strong velocity gradients are the locations where the hydrodynamic model needs to be enhanced, and accordingly, they are the preferable locations to deploy a velocity sensor.

Details

Title: Assessment of an ensemble-based data assimilation system for a shallow estuary
Authors: Mohammadreza Khanarmuei (Author) - Queensland University of Technology
Neda Mardani (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Kabir Suara (Author) - Queensland University of Technology
Julius Sumihar (Author) - Deltares
Roy Sidle (Author) - University of the Sunshine Coast
Adrian McCallum (Author) - University of the Sunshine Coast, Queensland, Indigenous and Transcultural Research Centre - Legacy
Richard J Brown (Author) - Queensland University of Technology
Publication details: Estuarine, Coastal and Shelf Science, Vol.257, pp.1-12
Publisher: Academic Press
Date published: 2021
DOI: 10.1016/j.ecss.2021.107389
ISSN: 1096-0015
Grants: Innovative Global Navigation Satellite Systems (GNSS) - enabled technology for monitoring regular and flooded flows in Australian Waters., LP150101172, Australian Research Council (Australia, Canberra) - ARC
Understanding pollutant transport in estuaries and coastal rivers, DP190103379, Australian Research Council (Australia, Canberra) - ARC
Organisation Unit: Indigenous and Transcultural Research Centre; Cancer Research Cluster; School of Science, Technology and Engineering; Sustainability Research Cluster
Language: English
Record Identifier: 99533808102621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Marine & Freshwater Biology; Oceanography

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