The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme.

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<p>During the past decades, aquaculture industry has developed rapidly and, due to a continuously growing market, a high demand for new installations, both near and off-shore, is already observed. Various studies show direct correlations between fish growth parameters and water quality variables, among which the most important are temperature, salinity and dissolved oxygen concentration. Those variables can directly impact planning and farming operations such as location, height and depth of cages, stocking density, or fish feeding rate. Moreover, for a sustainable seafood production, necessary management practices should be in place aiming to reduce food waste and spread of diseases.</p><p>At present, large section of the farming sector depends on ad-hoc measurements of water quality without a forecast mechanism. With availability of ocean hydrodynamic and water quality data from various sources such as Copernicus Marine Environmental Monitoring Service (CMEMS), atmospheric data from European Centre for Medium-Range Weather Forecasts (ECMWF), water quality variables can be simulated and forecasted well in advance with the use of numerical modelling tools.</p><p>Within the framework of the EU H2020 funded HiSea Project, a new high-resolution coastal 3D hydrodynamic model aiming at describing the vertical gradients of temperature and salinity and their seasonal variations is developed for southern Aegean Sea of Greece. The Delft3D Flexible Mesh modelling tool is used which allows for computationally economic grid development. Data from CMEMS are utilized to setup the model boundary conditions. A complex heat flux model of temperature computations is employed, which means that the model needs to be provided with several atmospheric forcing data such as wind speed, air temperature, dew point temperature, and mean sea level pressure. These data are derived from ERA5 single level reanalysis data of ECMWF. The output variables show a seasonal trend due to changes in atmospheric forces. Therefore, the developed model simulates seasonal water quality conditions and gives important insights into the vertical gradient of temperature and salinity. Validation of the model outputs is carried out at multiple levels. The water level simulation is verified against Intergovernmental Oceanographic Commission (IOC) mean sea level measurements while the simulated temperature at the two aquaculture sites is verified against the daily in-situ measurements.  </p><p>The uncertainties in the model outputs (temperature and salinity) are estimated through ensemble simulation using different atmospheric forcing from ERA5 and perturbed model process parameters as source of uncertainty. The application of ensemble simulations to understand the vertical gradients of the water quality parameters is a unique approach. Moreover, the application of the numerical model simulations to optimize the aquaculture planning and operation is innovative. The research could be replicated for other marine sectors where water quality variables are of paramount importance.</p><p><em>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 821934.</em></p>