Improving the initialisation of the Met Office operational shelf-seas model. (October 2018)

• Record Type: Journal Article
• Authors: King, Robert R.; While, James; Martin, Matthew J.; Lea, Daniel J.; Lemieux-Dudon, Benedicte; Waters, Jennifer; O'Dea, Enda
• Is Part Of: Ocean modelling. Volume 130(2018)
• Page Start: 1
• Page End: 14
• Publication Date: 2018-10
• Subjects: Data assimilation -- Operational -- 3DVAR -- Shelf seas -- Regional modelling -- Altimetry; Oceanography -- Periodicals; Océanographie -- Périodiques; Oceanography; Periodicals; 551.46
• DOI: 10.1016/j.ocemod.2018.07.004
• Languages: English
• ISSNs: 1463-5003

Abstract

Highlights: Profile and altimeter observations are now assimilated in our shelf-seas model. In our new system, temperature and salinity RMS errors are reduced by > 25%. More realistic eddying structures are introduced by the altimeter assimilation. Abstract: The quality of a short-term ocean forecast relies on its initialisation. However, operational shelf-seas forecasting systems tend to assimilate fewer observation types than non-tidal global systems. For shelf-seas systems, the challenge is to incorporate observations into the 3D ocean state within a model with complex vertical coordinates and large-amplitude variations in the sea surface height due to tides. In this paper, we describe the first use of altimeter and in situ profile observations to improve the initialisation of an operational shelf-seas forecasting system. FOAM-Shelf v9 is a 7 km horizontal resolution ocean model covering the European North-West Shelf (NWS) seas. We have adapted our assimilation scheme in this system to account for spatially- and temporally-varying vertical coordinates. Throughout the domain, there is now assimilation of in situ profile measurements of temperature and salinity and satellite and in situ sea surface temperature observations. Additionally, in deep water regions (>700 m) southward of 60°N, the system assimilates newly-available altimeter observations tailored for use in coastal models. With this new system, gross biases are significantly reduced, the sub-surface root-mean-square (RMS) temperature and salinity errors are reduced by >25%, and there is an increase in the number of eddying structures providing a better qualitative match to observation-derived surface current products.