Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4749-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4749-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A method to derive Fourier–wavelet spectra for the characterization of global-scale waves in the mesosphere and lower thermosphere and its MATLAB and Python software (fourierwavelet v1.1)
Leibniz Institute of Atmospheric Physics, University of Rostock, Schlossstraße 6, 18225 Kühlungsborn, Germany
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The E-region of the Earth’s ionosphere plays an important role in atmospheric energy balance and High Frequency radio propagation. In this paper, we compare predictions from two recently developed ionospheric models to observations by ionospheric sounders (ionosondes). Overall, the models show reasonable agreement with the observations. However, there are several areas for improvement in the models as well as questions about the accuracy of the automatically processed ionosonde dataset.
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To study long-term trends in the mesosphere and lower thermosphere (70–100 km), we established two summer length definitions and analyzed the variability over the years (2004–2020). After the analysis, we found significant trends in the summer beginning of one definition. Furthermore, we were able to extend one of the time series up to 31 years and obtained evidence of non-uniform trends and periodicities similar to those known for the quasi-biennial oscillation and El Niño–Southern Oscillation.
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Short summary
The Earth's atmosphere can support various types of global-scale waves. Some waves propagate eastward and others westward, and they can have different zonal wavenumbers. The Fourier–wavelet analysis is a useful technique for identifying different components of global-scale waves and their temporal variability. This paper introduces an easy-to-implement method to derive Fourier–wavelet spectra from 2-D space–time data. Application examples are presented using atmospheric models.
The Earth's atmosphere can support various types of global-scale waves. Some waves propagate...