Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 16
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.
https://doi.org/10.5194/gmd-16-4749-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 16
Methods for assessment of models
 | 
23 Aug 2023
Methods for assessment of models |  | 23 Aug 2023

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)

Yosuke Yamazaki

Viewed

Total article views: 2,031 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,512 464 55 2,031 45 38
  • HTML: 1,512
  • PDF: 464
  • XML: 55
  • Total: 2,031
  • BibTeX: 45
  • EndNote: 38
Views and downloads (calculated since 22 Dec 2022)
Cumulative views and downloads (calculated since 22 Dec 2022)

Viewed (geographical distribution)

Total article views: 2,031 (including HTML, PDF, and XML) Thereof 2,002 with geography defined and 29 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Jan 2025
Download
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.
Read more