One of the key needs of the aquaculture industry is the implementation of effective management methods to ensure the sustainability, economic viability and minimization of negative impacts on

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An operational model for an area of the northeast Atlantic that encompasses all of Ireland’s territorial waters has been developed. The model is an implementation of the Regional Ocean Modelling System (ROMS) and uses operationally available atmospheric and boundary forcing, and a global tide solution for tidal forcing. River forcing is provided by climatological daily discharge rates for 29 rivers across Ireland, west Britain, and west France. It is run in an operational framework to produce 7-day hindcasts once a week, and daily 3-day forecasts which are published in a number of formats. We evaluated the model skill by comparing with measured data and calculating statistics such as mean error, root mean square error (RMSE), and correlation coefficient. The observations consist of satellite Sea Surface Temperature (SST), total surface velocity fields from satellite, water level time series from around the Irish coast, and temperature and salinity data from Array for Real-Time Geostrophic Oceanography (ARGO) and Conductivity Temperature Depth (CTD) profiles. The validation period is from 1 January 2016 until 31 December 2019. The correlation coefficient between the model and satellite SST is 0.97 and recorded in March and April 2018. The model error is about 5% of the total M2 amplitude in the Celtic Sea recorded at Dunmore East tide gauge station. The maximum RMSE between the model and the CTD temperature profiles is 0.8 °C while it is 0.17 PSU for salinity. The model correctly defines the shelf water masses around Ireland. In 2019 the Irish Coastal Current (ICC) was very strong and well defined along most of the western Irish coast. The model results have well reproduced the ICC front for the whole simulation period.

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Abstract. ENSURF (Ensemble SURge Forecast) is a multimodel application for sea level forecast that makes use of several storm surge or circulation models and near-real time tide gauge data in the region, with the following main goals:1. providing easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool;2. generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average technique (BMA).The Bayesian Model Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the Bayesian likelihood that a model will give the correct forecast and are continuously updated based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. The system was implemented for the European Atlantic facade (IBIROOS region) and Western Mediterranean coast based on the MA-TROOS visualization tool developed by Deltares. Results of validation of the different models and BMA implementation for the main harbours are presented for these regions where this kind of activity is performed for the first time. The system is currently operational at Puertos del Estado and has proved to be useful in the detection of calibration problems in some of the circulation models, in the identification of the systematic differences between baroclinic and barotropic models for sea level forecasts and to demonstrate the feasibility of providing an overall probabilistic forecast, based on the BMA method.

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ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of existing storm surge or circulation models today operational in Europe, as well as near-real time tide gauge data in the region, with the following main goals: <br><br> &ndash; providing an easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool <br> &ndash; generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average Technique (BMA) <br><br> The system was developed and implemented within ECOOP (C.No. 036355) European Project for the NOOS and the IBIROOS regions, based on MATROOS visualization tool developed by Deltares. Both systems are today operational at Deltares and Puertos del Estado respectively. The Bayesian Modelling Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the probability that a model will give the correct forecast PDF and are determined and updated operationally based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. Results of validation of the different models and BMA implementation for the main harbours will be presented for the IBIROOS and Western Mediterranean regions, where this kind of activity is performed for the first time. The work has proved to be useful to detect problems in some of the circulation models not previously well calibrated with sea level data, to identify the differences on baroclinic and barotropic models for sea level applications and to confirm the general improvement of the BMA forecasts

Monitoring from ships of opportunity allows a wide range of parameters to be measured, thereby extending the coverage of operational oceanographic studies. Observation of dissolved oxygen using new sensors offers an effective way of monitoring changes in biological production. The limits of the application were tested following the transition from winter storms to the spring bloom (2007). Calculation of dissolved nitrogen enables changes in gas saturation due to physical and biological processes to be separated. By combining these measurements with data from numerical models and Argo floats the critical role of subsurface processes in determining rates of change at the surface can be assessed AUTHORS' BIOGRAPHIES David Hydes is a chemist and joined the IOS (Institute of Oceanographic Sciences) now National Oceanography Centre (NOC), Southampton in 1980. He headed the nutrient studies in the NERC North Sea Project, which lead to his involvement with EuroGOOS and the EU-FerryBox project. He is keen to promote the increased use of commercial ships for scientific research. Mark Hartman is an engineer and joined IOS in 1990. He has wide experience working with data buoys and ships of opportunity (SOO). He is responsible for the calibration quality control and scientific processing of data from NOC's SOOs. Charlene Bargeron is a chemist and PhD student in the School of Ocean and Earth Science at NOC. The work she did for her MSc project established the use of oxygen measurements from SOOs as an effective way of monitoring primary production.