Technical University of Denmark

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Global Wind Atlas v3 (orography and bathymetry)

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posted on 2021-04-09, 08:42 authored by Neil DavisNeil Davis, Søren Krohn, Duncan Heathfield, Marko Onninen
This data combines the GEBCO bathymetric dataset with the SRTMv3 and Viewfinder 3 arc-second orography data. The data is interpolated to 250m, and all land locations are given positive values, while water surfaces are given negative values.

Orographic height over land

Void-filled SRTM data was used between 60°N and 60°S, with a few exceptions where the void filling process introduced artifacts that were not present in the Viewfinder Panoramas Digital Elevation Model. This was particularly apparent in the Pacific islands.

North of 60°N the Viewfinder Panoramas DEM was used, allowing us to include model results for large parts of Scandinavia, Russia, and Canada. For these areas, the main sources to the Viewfinder Panoramas DEM are height contours from Russian military maps and Canadian Digital Elevation Data. The Viewfinder Panoramas DEM had several void regions which were filled using a cubic interpolation in the y-dimension of the raster. This was chosen after investigating many of the void regions and finding that they tended to run east-west.

Both DEM datasets were provided as 1° by 1° tiles in the WGS 1984 coordinate system (EPSG: 4326). To support the UTM projection used in the modeling, the data was re-projected to a 150m grid spacing using cubic interpolation. The interpolation was done using the Geospatial Data Abstraction Library (GDAL) tool gdalwarp. The 150m resolution was selected as it corresponds to the effective resolution of the SRTM data.

The data was then re-projected again in the WAsP model to the 250m using the approach of the European Wind Atlas.

Bathymetry data (Oceans)

Negative elevation data for seabed bathymetry was obtained from GEBCO’s gridded bathymetric data set, the GEBCO_2020 grid, which is a global terrain model for ocean and land at 15 arc-second intervals. The GEBCO_2020 grid was prepared for use in the GWA by the World Bank Group.

Data set attribution: GEBCO Compilation Group (2020) GEBCO 2020 Grid (doi:10.5285/a29c5465-b138-234d-e053-6c86abc040b9).

Data combination

The datasets were combined by first masking each using the fine resolution of the GSHHG coastline dataset, to ensure that all land values were positive and all water values were negative, with no data have a value of 0.


Primarily funded by the Energy Sector Management Assistance Program (ESMAP) in the World Bank



  • Other


  • Not applicable


  • Modeling

External conditions

  • Not applicable

Data category

  • Topographic data


Jake Badger, Brian Ohrbeck Hansen, Niels Gylling Mortensen, Bjarke Tobias Olsen