The Efficient Identification of Meandering and other Low-Frequency Phenomena in Raw Ultrasonic Anemometer Data

Abstract

This short conference paper shows a new experimental method for the detection and identification of meandering and other low-frequency components 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 delay-free recursive filtering, both described in recent published works. The results of the application of the described method to a sample of hourly raw data files are also shown. 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 field.