Using SST and land cover data from EO Missions for improved mesoscale modelling of the coastal zone

Existing wind measurements in near-shore and offshore areas are sparse and scarce, therefore simulations from state-of-the-art meso-scale models are used for wind resource predictions. In coastal and near-shore areas, models are inaccurate and uncertain, mainly because of numerical approximations, which do not resolve the large changes in local topographic features and atmospheric stability well [1].

The accuracy of modelled wind resource predictions can be improved by using local wind measurements to calibrate the models. RUNE investigated cost-effective measurement solutions for improving the wind resource modelling of coastal areas. The wind over a coastal area was measured by land-based LIDAR systems [6], an offshore LIDAR buoy and satellite radar remote sensing (SAR and scatterometers).

Simulations using the Weather Research & Forecasting (WRF) meso-scale model were performed. The aim was to evaluate the uncertainty of the modelled wind in the coastal zone and further improve it. Moreover LIDAR measurements were used to evaluate the wind speed retrieval from high resolution SAR systems (Sentinel-1 and TerraSAR-X).

The WRF model used a high-resolution satellite SST reanalysis product from the Danish Meteorological Institute (DMI), specifically developed for the North Sea and Baltic Sea region. To improve the physical description of the domain, the elevation, topography and land use, the CORINE land cover database and the SRTM elevation database are used as boundary conditions; with a spatial resolution of 100 m to 250 m, the CORINE land cover information represent a more accurate classification of land uses for the entire domain.

SST, land cover, and elevation information from Earth Observation platforms are unique due to their extended spatial coverage and resolution, such that they can be implemented in the meso-scale model to better represent the actual conditions in the study area. Such improvements are expected to strengthen the model’s ability to represent land- sea and air-sea interactions, the atmospheric stability and the local topographic features that partly affect the coastal zone