www.Autodiff.org - Publication: Which data assimilation method to use and when: unlocking the potential of observations in shoreline modelling

Which data assimilation method to use and when: unlocking the potential of observations in shoreline modelling

- Article in a journal -

Area
Data Assimilation

Author(s)
M. Alvarez-Cuesta , A. Toimil , I. J. Losada

Published in
Environmental Research Letters

Year
2024

Publisher
IOP Publishing

Abstract
Shoreline predictions are essential for coastal management. In this era of increasing amounts of data from different sources, it is imperative to use observations to ensure the reliability of shoreline forecasts. Data assimilation has emerged as a powerful tool to bridge the gap between episodic and imprecise spatiotemporal observations and the incomplete mathematical equations describing the physics of coastal dynamics. This research seeks to maximize this potential by assessing the effectiveness of different data assimilation algorithms considering different observational data characteristics and initial system knowledge to guide shoreline models towards delivering results as close as possible to the real world. Two statistical algorithms (stochastic ensemble and extended Kalman filters) and one variational algorithm (4D-Var) are incorporated into an equilibrium cross-shore model and a one-line longshore model. A twin experimental procedure is conducted to determine the observation requirements for these assimilation algorithms in terms of accuracy, length of the data collection campaign and sampling frequency. Similarly, the initial system knowledge needed and the ability of the assimilation methods to track the system nonstationarity are evaluated under synthetic scenarios. The results indicate that with noisy observations, the Kalman filter variants outperform 4D-Var. However, 4D-Var is less restrictive in terms of initial system knowledge and tracks nonstationary parametrizations more accurately for cross-shore processes. The findings are demonstrated at two real beaches governed by different processes with different data sources used for calibration. In this contribution, the coastal processes assimilated thus far in shoreline modelling are extended, the 4D-Var algorithm is applied for the first time in the field of shoreline modelling, and guidelines on which assimilation method can be most beneficial in terms of the available observational data and system knowledge are provided.

AD Tools
ADiMat

BibTeX
@ARTICLE{
         Alvarez-Cuesta2024Wda,
       author = "M. Alvarez-Cuesta and A. Toimil and I. J. Losada",
       title = "Which data assimilation method to use and when: unlocking the potential of
         observations in shoreline modelling",
       journal = "Environmental Research Letters",
       doi = "10.1088/1748-9326/ad3143",
       url = "https://doi.org/10.1088/1748-9326/ad3143",
       year = "2024",
       publisher = "IOP Publishing",
       volume = "19",
       number = "4",
       pages = "044023",
       abstract = "Shoreline predictions are essential for coastal management. In this era of
         increasing amounts of data from different sources, it is imperative to use observations to ensure
         the reliability of shoreline forecasts. Data assimilation has emerged as a powerful tool to bridge
         the gap between episodic and imprecise spatiotemporal observations and the incomplete mathematical
         equations describing the physics of coastal dynamics. This research seeks to maximize this potential
         by assessing the effectiveness of different data assimilation algorithms considering different
         observational data characteristics and initial system knowledge to guide shoreline models towards
         delivering results as close as possible to the real world. Two statistical algorithms (stochastic
         ensemble and extended Kalman filters) and one variational algorithm (4D-Var) are incorporated into
         an equilibrium cross-shore model and a one-line longshore model. A twin experimental procedure is
         conducted to determine the observation requirements for these assimilation algorithms in terms of
         accuracy, length of the data collection campaign and sampling frequency. Similarly, the initial
         system knowledge needed and the ability of the assimilation methods to track the system
         nonstationarity are evaluated under synthetic scenarios. The results indicate that with noisy
         observations, the Kalman filter variants outperform 4D-Var. However, 4D-Var is less restrictive in
         terms of initial system knowledge and tracks nonstationary parametrizations more accurately for
         cross-shore processes. The findings are demonstrated at two real beaches governed by different
         processes with different data sources used for calibration. In this contribution, the coastal
         processes assimilated thus far in shoreline modelling are extended, the 4D-Var algorithm is applied
         for the first time in the field of shoreline modelling, and guidelines on which assimilation method
         can be most beneficial in terms of the available observational data and system knowledge are
         provided.",
       ad_area = "Data Assimilation",
       ad_tools = "ADiMat"
}

back

autodiff.org
Username:
Password: