This data publication contains airborne wind and eddy covariance data files, that were recorded with the ASK-16, a motorized glider owned by the FU Berlin, Germany. These data files include a large range of meteorological variables (wind speed, direction, temperature, humidity, etc.), positioning information, but also information on atmospheric chemistry (mainly methane concentration, carbon dioxide concentration, water vapor concentration) and turbulent matter (CH4 and CO2) and energy fluxes (latent heat flux) is available.
Measurements were recorded between 2017 and 2022 to:
(1) obtain three-dimensional wind vectors in within the atmospheric boundary layer
(2) calibrate of wind measurements
(3) record turbulent energy and matter fluxes
A lot of these data files have been used in the publication “The ASK-16 Motorized Glider: An Airborne Eddy Covariance Platform to measure Turbulence, Energy and Matter Fluxes (to be published in atmospheric measurement techniques)” by Wiekenkamp et al., 2024a. This publication also provides a lot of additional details on the measurement system, the data handling and processing.
In the last years, a whole series of codes has been developed to process airborne wind data. Initially, the PyWingpod package was mainly build to handle data from the Wingpod of the ASK-16 motorized glider of the FU Berlin. However, due to the modular buildup of the package, functions within the different libraries can also be used to process data from other airborne platforms.
Functions and scripts within PyWingpod have been developed to:
a. load and process airborne five hole probe and meteo data, this includes
(1) 5 hole probe pressure sensor data (static pressure, dynamic pressure and the differential alpha and beta pressure),
(2) INS-GNSS data,
(3) Temperature and humidity data and (4) any auxillary data that you want to add to the time series/ data frame.
b. calibrate pressure sensor data from the five hole probe (mainly to correct for any effect of aircraft movement)
c. calculate a reliable wind vector based on the available data that are specified in a. and the calibration parameters, which are obtained in step b.