Onshore & offshore WRF generated wind data

These data sets provide the WRF [1] calculated wind data for Pritzwalk (onshore) and FINO3 (offshore) as Python dictionaries.  Additionally, the files contain k-means cluster objects derived from these profiles. These data sets were used for power assessment and design exploration of Airborne Wind Energy Systems using the awebox [2] optimization toolbox.

WRF setups are described in detail and used in publication [3,4,5].

Wind data are interpolated to fixed heights of: [10,   28,   50,   70,   90,  100,  150,  200,  250,  300,  350, 400,  450,  500,  550,  600,   700,  800,  1000, 1200] meters above ground.

Onshore wind data: 

Location lat: 53° 10.78' N; long: 12° 11.35' E

Time: 1 September 2015 - 31 August 2016

Timestep: 10 min

Offshore wind data: 

Location lat: 55° 11.7' N, long: 7° 9.5' E

Time: 1 September 2013 - 31 August 2014

Timestep: 10 min

The clusters are derived from both horizontal wind velocity components using the scikit-learn’s k-means clustering algorithm [6]. For our purposes, wind vectors were rotated such that the main wind speed always points in the same direction (u_main,u_deviation).

[1]: Weather Research and Forecasting Model

[2]: awebox

[3]: Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems

[4]: Offshore and onshore ground-generation airborne wind energy power curve characterization

[5]:Ground-generation airborne wind energy design space exploration

[6]: sklearn.cluster.KMeans