Articles | Volume 17, issue 15
https://doi.org/10.5194/amt-17-4659-2024
https://doi.org/10.5194/amt-17-4659-2024
Research article
 | 
12 Aug 2024
Research article |  | 12 Aug 2024

Limitations in wavelet analysis of non-stationary atmospheric gravity wave signatures in temperature profiles

Robert Reichert, Natalie Kaifler, and Bernd Kaifler

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Alfaouri, M. and Daqrouq, K.: ECG signal denoising by wavelet transform thresholding, Am. J. Appl. Sci., 5, 276–281, 2008. a
Bauer, K., Norden, B., Ivanova, A., Stiller, M., and Krawczyk, C. M.: Wavelet transform-based seismic facies classification and modelling: application to a geothermal target horizon in the NE German Basin, Geophys. Prospect., 68, 466–482, 2020. a
Baumgarten, K., Gerding, M., and Lübken, F.-J.: Seasonal variation of gravity wave parameters using different filter methods with daylight lidar measurements at midlatitudes, J. Geophys. Res.-Atmos., 122, 2683–2695, 2017. a, b
Boix, M. and Canto, B.: Wavelet Transform application to the compression of images, Math. Comput. Modell., 52, 1265–1270, 2010. a
Bruce, L. M., Koger, C. H., and Li, J.: Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction, IEEE T. Geosci. Remote Sens., 40, 2331–2338, 2002. a
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Short summary
Imagine you want to determine how quickly the pitch of a passing ambulance’s siren changes. If the vehicle is traveling slowly, the pitch changes only slightly, but if it is traveling fast, the pitch also changes rapidly. In a similar way, the wind in the middle atmosphere modulates the wavelength of atmospheric gravity waves. We have investigated the question of how strong the maximum wind may be so that the change in wavelength can still be determined with the help of wavelet transformation.
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