Creation of a global tide analysis dataset: Application of NEMO and an offline objective analysis scheme

Byrne, David; Polton, Jeff ORCID: https://orcid.org/0000-0003-0131-5250; Bell, Colin. 2021 Creation of a global tide analysis dataset: Application of NEMO and an offline objective analysis scheme. Journal of Operational Oceanography. 1-14. 10.1080/1755876X.2021.2000249

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Official URL: http://dx.doi.org/10.1080/1755876X.2021.2000249

Abstract/Summary

The accurate prediction of tides is vital for the operation of many industries, early warning of coastal flooding and scientific understanding of ocean processes. In this paper, we describe the creation method of a global dataset of tidal harmonics using NEMO (Nucleus for European Modelling of the Ocean) for the first time and an offline objective analysis scheme. Data are assimilated as part of a post-processing step, reducing the computational resources required. A reduced ensemble of tidal harmonics is generated, where each member is run for a shorter period of time than a central background state. This ensemble is used to estimate a single background covariance state, which is used for analysis. Output is validated using an ensemble of objective analyses. For each ensemble member, random selections of observations are omitted and validation is performed at these locations. Improvements in both Mean Absolute Error (MAE) and correlation coefficients (R2) are seen across all 6 of the largest diurnal and semi-diurnal constituents. MAEs in amplitude and phase are reduced by up to 78% and 89%, respectively, and correlations by as much as 0.14. In addition, the majority of locations (between 70 and 80%) see significant improvement.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1080/1755876X.2021.2000249
ISSN:	1755-876X
Date made live:	19 Oct 2022 12:48 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533381

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1080/1755876X.2021.2000249

ISSN:

1755-876X

Date made live:

19 Oct 2022 12:48 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/533381