CoMo: Coastal Monitoring and Forecasting for Nautical Activities.

Research Methodology

The four main features of the proposed methodology include: a) Statistical Analysis, b)Dynamic Downscaling Modeling, c)CFD Modeling and d)Localized Sea State Forecast.

The statistical data analysis methodology is based on classical spatiotemporal methods. These methods are able to quantify and minimize the estimation error of the investigated parameters, thus improving on the overall spatially varying predictions.
Sea-surface and near sea-surface experimental data for statistical analysis and downscaling computational techniques will originate from the EMODNET Portal and the NOAA ISD/ISH METAR data. The Satellite data and model data for use for WRF input, will originate from the open-source Copernicus Marine Environment Monitoring Service (CMEMS), from the mesoscale GISS GGCM Model 2 results and geospatial data from the USGS GMTED2010 database and other sources if required.

Climatic data downscaling techniques are incorporated, in order to further improve on the accuracy of the results in near-coast regions of interest and to include long-term historic satellite observational climatic data. The proposed computational algorithm (WRF-ARW) may combine data from different sources (e.g. satellite data, field simulations and forecasting data). Generally, dynamic downscaling models are very flexible and allow for easy input data integration from various resources.

The downscaled climatic data will be used as initial and boundary conditions for the OpenFOAM CFD model for solving the incompressible RANS equations for high resolution wind speed and direction estimates. The model will output the required wind field results for comparison purpuses. The quality of the results will be cross-validated with the existing observational and satellite data as well as with the results obtained from the computational methods.

The CoMo program aims to evaluate the localized sea state by estimating, the spatial distribution of wind/wave distribution in time, as well as its arising seasonal variations. The wind/wave distribution will be estimated by i) the dynamic downscaling method coupled with wave modelling ii) the CFD method, or iii) a combination of both methods. The results will be compared with the statistical model output, to test the validity of both methods (geospatial and computational).