The efficient identification of horizontal meandering in raw ultrasonic anemometer data

This paper shows a new experimental method for the detection and identification of sub-mesoscale low-frequency components, in particular horizontal meandering, in raw data from three-axial ultrasonic anemometers and other high resolution, high sampling-rate three-dimensional wind sensors. The proposed method is a combination of autocorrelation-based detection and FFT-based filtering, well known in literature. The results of the application of the described method to a sample of hourly raw data files are shown as well. The method can be used as a building block for eddy covariance and other data processing procedures as well as in all the situations where very short time scales (about 10s) are relevant, such as in odour or toxic chemical dispersion.