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)

Yamazaki, Yosuke

doi:https://doi.org/10.5194/gmd-16-4749-2023

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

Data sets

Simulation data from GAIA (Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy) for the September 2019 sudden stratospheric warming event Yosuke Yamazaki and Miyoshi Yasunobu https://doi.org/10.5880/GFZ.2.3.2020.004

WACCM-X simulations - 2009 SSW Tarique Siddiqui https://doi.org/10.17632/47pnw8pgmk.1

SD WACCM-X v2.1 Federico Gasperini https://doi.org/10.26024/5b58-nc53

Model code and software

Matlab and Python software to compute Fourier-wavelet spectra (fourierwavelet v1.1) using longitude-time data for studying global-scale atmospheric waves Yosuke Yamazaki https://doi.org/10.5281/zenodo.8033686

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.