2023 article
Objective identification of pressure wave events from networks of 1-Hz, high-precision sensors
Allen, L. R., Yuter, S. E., Miller, M. A., & Tomkins, L. M. (2023, September 7).
open access via amt.copernicus.org (publisher PDF)
Source: ORCID
Added: November 22, 2024
Abstract. Mesoscale pressure waves including atmospheric gravity waves, outflow and frontal passages, and wake lows are outputs of and can potentially modify clouds and precipitation. A wavelet-based method for identifying and tracking these types of wave signals in time series data from networks of low-cost, high-precision (0.8-Pa noise floor, 1-Hz recording frequency) pressure sensors is demonstrated. Strong wavelet signals are identified using a wave period-dependent (i.e., frequency-dependent) threshold, then those signals are extracted by inverting the wavelet transform. Wave periods between 1 minute and 120 minutes were analyzed, a range which would include several types of mesoscale disturbances in the troposphere. After extracting the signals from a network of pressure sensors, the cross-correlation function is used to estimate the time difference between the wave passage at each pressure sensor. From those time differences, the wave phase velocity vector is calculated using a least-squares fit. If the fitting error is sufficiently small (thresholds of RMSE < 90 s and NRMSE < 0.1 were used), then a wave event is considered robust and trackable.