A two-step method to derive combined Fourier-wavelet spectra from space-time data for studying planetary-scale waves, and its Matlab and Python software (cfw v1.0)

Yamazaki, Yosuke

doi:https://doi.org/10.5194/egusphere-2022-1275

Preprints

https://doi.org/10.5194/egusphere-2022-1275

Preprints

22 Dec 2022

| 22 Dec 2022

A two-step method to derive combined Fourier-wavelet spectra from space-time data for studying planetary-scale waves, and its Matlab and Python software (cfw v1.0)

Yosuke Yamazaki

Abstract. The combined Fourier-wavelet (CFW) transform is a useful technique to characterize planetary-scale waves, such as tides and traveling planetary waves in the Earth's atmosphere. A CFW spectrum, presented in a time versus period diagram, can be used to identify wave activity that is localized in time, similar to a wavelet spectrum. A CFW spectrum can be obtained for each of eastward- and westward-propagating wave components with different zonal wavenumbers. This paper introduces an easy-to-implement method to derive CFW spectra in two steps. In the first step, the Fourier transform is performed in space (longitude), and time series of the space Fourier coefficients are derived. In the second step, the wavelet transform is performed on these time series, and wavelet coefficients are derived. It is shown that the CFW transform can be easily derived from these wavelet coefficients. The results suggest that existing Fourier and wavelet software can be utilized to derive CFW spectra. Matlab and Python scripts are created and made available at https://igit.iap-kborn.de/yy01/cfw that compute CFW spectra using the wavelet software provided by Torrence and Compo (1998). Some application examples are presented using longitude-time data from atmospheric and geomagnetic-field models.

Received: 18 Nov 2022 – Discussion started: 22 Dec 2022

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Journal article(s) based on this preprint

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

Geosci. Model Dev., 16, 4749–4766, https://doi.org/10.5194/gmd-16-4749-2023,https://doi.org/10.5194/gmd-16-4749-2023, 2023

Short summary

Yosuke Yamazaki

Interactive discussion

Status: closed

RC1: 'Comment on egusphere-2022-1275', Jun-Ichi Yano, 30 Dec 2022

This is an interesting piece of work, potentially worthwhile for a publication: there are already extensive literature performing the continuous wavelet transform to time series in atmospheric science. However, this work is new: the continuous wavelet transform is applied to time series, that itself is nothing new, but in the wavenumber space. The author shows that this methodology can characterized certain wave activities rather well, as shown in Figs. 2-4.

*

The main problem with the present manuscript, as it stands for now, is to present it with a rather sensational fanfare, calling it specifically a "combined Fourier-wavelet spectrum". As far as I can follow, it is nothing other than just performing a continuous wavelet transform in time and a Fourier transform in space (longitude): these are two independent linear operations, that can be performed in any order. There is nothing to "combine", but just to perform two independent things in sequence. In other words, the adjective "combined" is nothing other than just a misnomer.

With an attempt of the author convincing the readers that this is "revolutionary", the author provides rather lengthy technical details of the so-called Hayashi's method (that itself is nothing other than just performing the Fourier transform in time and longitude) and the continuous wavelet in the last half of the introduction section. However, I do not see those technical details to be any importance for this method. As I just said above, what is done in practice is very simple: perform FFT in longitude, and the continuous wavelet analysis in time. We just need few more extra words on specific.

On the other hand, the presentation of the proposed methodology in Sec. 2 is rather "muddled", and difficult to get this very simple point straight.

*

A simple methodology is always a beauty. However, if the author intends to present the present manuscript as a proposal paper of a new methodology, a more careful review of the existing methodologies is required.

First of all, planetary waves can be extracted in a straightforward manner, at least in principle, by the normal-mode decomposition. A full description of this methodology is provided in Zager et al. (2015, GMD, https://gmd.copernicus.org/articles/8/1169/2015/) with a software publicly available to apply this methodology. Also please refer to a workshop report for further backgrounds: Zagar et al. (2016, BAMS, https://journals.ametsoc.org/view/journals/bams/97/6/bams-d-15-00325.1.xml).

In this respect, the introduction is slightly confused as it stands for now: its first half reviews previous works detecting "linear" planetary waves. Then, suddenly, at L55, the author decides to talk about the stratospheric sudden warming: this is clearly a nonlinear process that cannot be described by a single wave. The authors further begins to remark that the observed waves are rather "intermittent" (in own wording), and they can emerge even like bursts: that is all fine with me: these observed waves are not perfectly linear, and they are often generated by forcings as well as instabilities, and those evolution can be very nonlinear. However, after said all those (though the author does not comment on them), if one wishes to understand those phenomena as a part of the wave dynamics, an obvious way to go is to perform the normal-mode decompositions so that one can see explicitly which modes are involved in processes in which manner, etc. Those are very basic backgrounds of the atmospheric-wave dynamics, that should remind the readers.

*

Second is lack of a proper review of the wavelet method. The most important question, in this context, for me is a choice between the continuous redundant wavelet and the discrete orthogonal wavelet. Here, the author chooses the former, but without explanation. The choice is just puzzling for me considering a very fact that the latter is much more robust, with much more potential applicabilities, as my series of work suggest: see a list of reference below.

Performing a continuous wavelet analysis is like a decomposition of a finite domain data into continuous wavenumbers, when only the discrete integer wavenumbers have a meaning.

*

Finally, if detection of a planetary-wave packet is the main issue, a discrete set of wavelets can be constructed based on normal modes, in an equivalent manner as the Meyer wavelet is constructed based on the Fourier modes. Though I do not think that the author has to try this possibility in the present work, all those potential possibilities must be clearly mentioned in the manuscript.

It is obvious that the author is only taking a small first step forwards for exploring all those wider possibilities.

*

Specifics:

L79-80, the standard wavelet technique is not directly applicable to longitude-time data: false. 2D wavelet transform can easily performed in analogy with the 2D Fourier transform: refer to my publications below.

L90, parameter -> variable

Eqs. (5)-: the frequency, omega, must be discrete, as the case for the wavenumbers. please comment on this

L122: state explicitly that continuous wavelet is applied in time

Eq. (16): the actual data set only has a finite length in time. comment on this

Eq. (21): if not, this expression is puzzling: since continuous wavelet is applied here, obtained coefficients must also be continuous: why we suddenly get a discrete expression?

Sec. 2: as far as I can follow, the longitudinal dependence does not play any role in the presentation, though Eqs. (29) and (30) retain it. at least a word would be required for a clarification: otherwise, in my own reading, Sec. 2 is essentially just repeating Sec. 1.3. If not, what is a difference, except for a longitudinal dependence added in Eqs. (29) and (30)?

Eqs. (29), (30): the exponent, -t^2/2 must be replaced by -t^2/2s? if not, I do not know how to connect this expansion with (17), as invoked after Eq. (34).

Eqs. (29), (30): the given decomposition modes are only localized in time, thus it appears to me that the author essentially fails to address a question of the propagation of a wave packet, that should happen both in time and space.

Eqs. (35) and (36): Psi* here must depend on both s and omega: how do you specify them?

Fig. 5b, c: they should be better presented by standard Fourier transforms: the plots do not take any advantage of wavelet, either.

References:

J.-I. Yano, M. W. Moncrieff, X. Wu, and M. Yamada, 2001 : Wavelet Analysis of Simulated Tropical Convective Cloud Systems Part I: Basic Analysis. J. Atmos. Sci., 58, 850-867.

J.-I. Yano, M. W. Moncrieff, and X. Wu, 2001 : Wavelet Analysis of Simulated Tropical Convective Cloud Systems Part II: Decomposition of Convective-scale and Mesoscale structures. J. Atmos. Sci., 58, 868-876.

J.-I. Yano, R. Blender, Chidon Zhang, and K. Fraedrich, 2004: 1/f Noise and Pulse--like Events in the Tropical Atmospheric Surface Variabilities. Quator. J. Roy. Meteor. Soc, 300, 1697--1721.

J.-I. Yano, P. Bechtold, J.-L. Redelsperger, and F. Guichard, 2004: Wavelet--Compressed Representation of deep moist convection. Mon Wea Rev, 132, 1472--1486.

J.-I. Yano, and B. Jakubiak, 2016: Wavelet-Based Verification of the Quantitative Precipitation Forecast. DAO, 74, 14--29

Å½agar, N., A. Kasahara, K. Terasaki, J. Tribbia and H. Tanaka, 2015: Normal-mode function representation of global 3-D data sets: open-access software for the atmospheric research community. Geosci. Model Dev., 8, 1169–1195, doi:10.5194/gmd-8-1169-2015.

Zagar, N., J. Boyd, E. Kallen, A. Kasahara, J. Tribbia, H. Tanaka, J.-I. Yano, 2016: Normal modes of atmospheric variability in observations, numerical weather prediction and climate models. BAMS, 97, ES125–ES128, doi.org/10.1175/BAMS-D-15-00325.1

Citation: https://doi.org/10.5194/egusphere-2022-1275-RC1
RC2:
'Comment on egusphere-2022-1275', Anonymous Referee #2, 15 May 2023
Review comments for “A two-step method to derive combined Fourier-wavelet spectra from space-time data for studying planetary-scale waves, and its Matlab and Python software (cfw v1.0)” by Yosuke Yamazaki (2022) discussed in EGUsphere.
This paper introduces a simple method to perform combined Fourier-wavelet (CFW) transform to extract planetary-scale waves in the gridded 2-D longitude-time atmospheric data set. Although the concept of this method is not new, or the idea is not difficult to understand, the implementation of this method has always been a difficulty for the space physics or mesosphere and lower thermosphere community. After testing the programs provided by the authors, I think this method has the following advantages:
The calculation speed is fast since this method only reads the data once. This advantage is meaningful, especially to the extraction of tides, which usually requires data with a high temporal resolution and is a challenge for the memory and computing resources of a personal computer;

The output is directly the real amplitudes of the waves, which is similar to the S-transform, but this method obtains the amplitudes in the 2-D data set;

In fact, there has been no good way to accurately extract the planetary wave activity during sudden stratospheric warmings in the longitude-time satellite observations or simulations. However, the results in this manuscript show that this method does a relatively good job on this issue, which is important for related research.

Overall, the authors implemented the CFW transform in a relatively simple way with MATLAB and Python, which is very worthy of recognition and will bring a lot of convenience to the middle and upper atmosphere community. I only have two minor comments on the current manuscript:
The authors may consider comparing the results obtained by different methods (e.g., CFW transform, 2-D fast Fourier transform, and least squares fitting) to better demonstrate the superiority of the CFW transform in studying the temporal variations of planetary-scale waves.

The authors can appropriately show the results on the planetary waves or tides in the ionospheric parameters (e.g., total electron content) extracted by the CFW transform.
Citation: https://doi.org/10.5194/egusphere-2022-1275-RC2
AC1: 'Comment on egusphere-2022-1275', Yosuke Yamazaki, 13 Jun 2023

Please find my replies to Reviewers' comments.

Citation: https://doi.org/10.5194/egusphere-2022-1275-AC1

Interactive discussion

Status: closed

RC1: 'Comment on egusphere-2022-1275', Jun-Ichi Yano, 30 Dec 2022

This is an interesting piece of work, potentially worthwhile for a publication: there are already extensive literature performing the continuous wavelet transform to time series in atmospheric science. However, this work is new: the continuous wavelet transform is applied to time series, that itself is nothing new, but in the wavenumber space. The author shows that this methodology can characterized certain wave activities rather well, as shown in Figs. 2-4.

*

The main problem with the present manuscript, as it stands for now, is to present it with a rather sensational fanfare, calling it specifically a "combined Fourier-wavelet spectrum". As far as I can follow, it is nothing other than just performing a continuous wavelet transform in time and a Fourier transform in space (longitude): these are two independent linear operations, that can be performed in any order. There is nothing to "combine", but just to perform two independent things in sequence. In other words, the adjective "combined" is nothing other than just a misnomer.

With an attempt of the author convincing the readers that this is "revolutionary", the author provides rather lengthy technical details of the so-called Hayashi's method (that itself is nothing other than just performing the Fourier transform in time and longitude) and the continuous wavelet in the last half of the introduction section. However, I do not see those technical details to be any importance for this method. As I just said above, what is done in practice is very simple: perform FFT in longitude, and the continuous wavelet analysis in time. We just need few more extra words on specific.

On the other hand, the presentation of the proposed methodology in Sec. 2 is rather "muddled", and difficult to get this very simple point straight.

*

A simple methodology is always a beauty. However, if the author intends to present the present manuscript as a proposal paper of a new methodology, a more careful review of the existing methodologies is required.

First of all, planetary waves can be extracted in a straightforward manner, at least in principle, by the normal-mode decomposition. A full description of this methodology is provided in Zager et al. (2015, GMD, https://gmd.copernicus.org/articles/8/1169/2015/) with a software publicly available to apply this methodology. Also please refer to a workshop report for further backgrounds: Zagar et al. (2016, BAMS, https://journals.ametsoc.org/view/journals/bams/97/6/bams-d-15-00325.1.xml).

In this respect, the introduction is slightly confused as it stands for now: its first half reviews previous works detecting "linear" planetary waves. Then, suddenly, at L55, the author decides to talk about the stratospheric sudden warming: this is clearly a nonlinear process that cannot be described by a single wave. The authors further begins to remark that the observed waves are rather "intermittent" (in own wording), and they can emerge even like bursts: that is all fine with me: these observed waves are not perfectly linear, and they are often generated by forcings as well as instabilities, and those evolution can be very nonlinear. However, after said all those (though the author does not comment on them), if one wishes to understand those phenomena as a part of the wave dynamics, an obvious way to go is to perform the normal-mode decompositions so that one can see explicitly which modes are involved in processes in which manner, etc. Those are very basic backgrounds of the atmospheric-wave dynamics, that should remind the readers.

*

Second is lack of a proper review of the wavelet method. The most important question, in this context, for me is a choice between the continuous redundant wavelet and the discrete orthogonal wavelet. Here, the author chooses the former, but without explanation. The choice is just puzzling for me considering a very fact that the latter is much more robust, with much more potential applicabilities, as my series of work suggest: see a list of reference below.

Performing a continuous wavelet analysis is like a decomposition of a finite domain data into continuous wavenumbers, when only the discrete integer wavenumbers have a meaning.

*

Finally, if detection of a planetary-wave packet is the main issue, a discrete set of wavelets can be constructed based on normal modes, in an equivalent manner as the Meyer wavelet is constructed based on the Fourier modes. Though I do not think that the author has to try this possibility in the present work, all those potential possibilities must be clearly mentioned in the manuscript.

It is obvious that the author is only taking a small first step forwards for exploring all those wider possibilities.

*

Specifics:

L79-80, the standard wavelet technique is not directly applicable to longitude-time data: false. 2D wavelet transform can easily performed in analogy with the 2D Fourier transform: refer to my publications below.

L90, parameter -> variable

Eqs. (5)-: the frequency, omega, must be discrete, as the case for the wavenumbers. please comment on this

L122: state explicitly that continuous wavelet is applied in time

Eq. (16): the actual data set only has a finite length in time. comment on this

Eq. (21): if not, this expression is puzzling: since continuous wavelet is applied here, obtained coefficients must also be continuous: why we suddenly get a discrete expression?

Sec. 2: as far as I can follow, the longitudinal dependence does not play any role in the presentation, though Eqs. (29) and (30) retain it. at least a word would be required for a clarification: otherwise, in my own reading, Sec. 2 is essentially just repeating Sec. 1.3. If not, what is a difference, except for a longitudinal dependence added in Eqs. (29) and (30)?

Eqs. (29), (30): the exponent, -t^2/2 must be replaced by -t^2/2s? if not, I do not know how to connect this expansion with (17), as invoked after Eq. (34).

Eqs. (29), (30): the given decomposition modes are only localized in time, thus it appears to me that the author essentially fails to address a question of the propagation of a wave packet, that should happen both in time and space.

Eqs. (35) and (36): Psi* here must depend on both s and omega: how do you specify them?

Fig. 5b, c: they should be better presented by standard Fourier transforms: the plots do not take any advantage of wavelet, either.

References:

J.-I. Yano, M. W. Moncrieff, X. Wu, and M. Yamada, 2001 : Wavelet Analysis of Simulated Tropical Convective Cloud Systems Part I: Basic Analysis. J. Atmos. Sci., 58, 850-867.

J.-I. Yano, M. W. Moncrieff, and X. Wu, 2001 : Wavelet Analysis of Simulated Tropical Convective Cloud Systems Part II: Decomposition of Convective-scale and Mesoscale structures. J. Atmos. Sci., 58, 868-876.

J.-I. Yano, R. Blender, Chidon Zhang, and K. Fraedrich, 2004: 1/f Noise and Pulse--like Events in the Tropical Atmospheric Surface Variabilities. Quator. J. Roy. Meteor. Soc, 300, 1697--1721.

J.-I. Yano, P. Bechtold, J.-L. Redelsperger, and F. Guichard, 2004: Wavelet--Compressed Representation of deep moist convection. Mon Wea Rev, 132, 1472--1486.

J.-I. Yano, and B. Jakubiak, 2016: Wavelet-Based Verification of the Quantitative Precipitation Forecast. DAO, 74, 14--29

Å½agar, N., A. Kasahara, K. Terasaki, J. Tribbia and H. Tanaka, 2015: Normal-mode function representation of global 3-D data sets: open-access software for the atmospheric research community. Geosci. Model Dev., 8, 1169–1195, doi:10.5194/gmd-8-1169-2015.

Zagar, N., J. Boyd, E. Kallen, A. Kasahara, J. Tribbia, H. Tanaka, J.-I. Yano, 2016: Normal modes of atmospheric variability in observations, numerical weather prediction and climate models. BAMS, 97, ES125–ES128, doi.org/10.1175/BAMS-D-15-00325.1

Citation: https://doi.org/10.5194/egusphere-2022-1275-RC1
RC2:
'Comment on egusphere-2022-1275', Anonymous Referee #2, 15 May 2023
Review comments for “A two-step method to derive combined Fourier-wavelet spectra from space-time data for studying planetary-scale waves, and its Matlab and Python software (cfw v1.0)” by Yosuke Yamazaki (2022) discussed in EGUsphere.
This paper introduces a simple method to perform combined Fourier-wavelet (CFW) transform to extract planetary-scale waves in the gridded 2-D longitude-time atmospheric data set. Although the concept of this method is not new, or the idea is not difficult to understand, the implementation of this method has always been a difficulty for the space physics or mesosphere and lower thermosphere community. After testing the programs provided by the authors, I think this method has the following advantages:
The calculation speed is fast since this method only reads the data once. This advantage is meaningful, especially to the extraction of tides, which usually requires data with a high temporal resolution and is a challenge for the memory and computing resources of a personal computer;

The output is directly the real amplitudes of the waves, which is similar to the S-transform, but this method obtains the amplitudes in the 2-D data set;

In fact, there has been no good way to accurately extract the planetary wave activity during sudden stratospheric warmings in the longitude-time satellite observations or simulations. However, the results in this manuscript show that this method does a relatively good job on this issue, which is important for related research.

Overall, the authors implemented the CFW transform in a relatively simple way with MATLAB and Python, which is very worthy of recognition and will bring a lot of convenience to the middle and upper atmosphere community. I only have two minor comments on the current manuscript:
The authors may consider comparing the results obtained by different methods (e.g., CFW transform, 2-D fast Fourier transform, and least squares fitting) to better demonstrate the superiority of the CFW transform in studying the temporal variations of planetary-scale waves.

The authors can appropriately show the results on the planetary waves or tides in the ionospheric parameters (e.g., total electron content) extracted by the CFW transform.
Citation: https://doi.org/10.5194/egusphere-2022-1275-RC2
AC1: 'Comment on egusphere-2022-1275', Yosuke Yamazaki, 13 Jun 2023

Please find my replies to Reviewers' comments.

Citation: https://doi.org/10.5194/egusphere-2022-1275-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Yosuke Yamazaki on behalf of the Authors (13 Jun 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (16 Jun 2023) by Sylwester Arabas

RR by Jun-Ichi Yano (18 Jun 2023)

Suggestions for revision or reasons for rejection

In the original review, I stated that "This is an interesting piece of work, potentially worthwhile for a publication". My basic position on this manuscript has not changed after the revision. Though I did not say this explicitly in initial review, I do not find any serious defect in this work. In this very respect, I do not wish to block the publication of this work in any fundamental manner. The only remaining question is the quality of the publication that both the Editor and the author wishes to achieve. My following comments intend to serve for this purpose:

Most seriously, the two major points made in my initial review are practically not at all taken into account in revision:

1) review of the existing methodologies

2) proper review of the wavelet method

more specifically,

1) more specifically, I proposed to refer to Zagar et al (2015, 2016). However, the authors rejects to do so by simply stating that the normal mode approach is not relevant in the present context. Most seriously, this remark is found only the response to the reviews, and very strangely, not found in the manuscript text. As a result, as it stands for now, the text reads like claiming that this Fourier-wavelet method is an only possibility of describing the propagating waves. Of course, the readers who are familiar with those existing methodologies would just wonder why one must use this when an existing method works well enough: what is an advantage of this proposed methodology against the existing methodologies?

2) As it stands for now, there is even no proper lead sentence io introduce what the wavelet is. There is only a very short lead paragraph in Sec. 1.3, which is even misleading:

a) Torrence and Campo (1998) is just an introductory essay on wavelet. It would be misleading to state that the methodology of this paper is "described" in this essay: please cite a more proper textbook. my preference is one by Mallat.

b) As the authors states, with the continuous wavelet, a user can arbitrary select any frequency resolution, but only in a meaningless manner: this is just like trying to define the amplitude of noninteger wavenumbers over a finite domain, say, a wavenumber 1.345. Of course, one can do this, but we all know that this is meaningless. Strangely, the community simply does not realize the same with the continuous wavelet. The present author is not an exception. More formally stated, such an attempt contradicts with Heisenberg's uncertainty principle. Please refer to Mallat's text for the full discussions.

c) In short, there is no real advantage with the continuous wavelet against the discrete counterpart, apart from an easiness of using it. This very last point should be made absolutely clear in a final version with more relevant references (including a textbook by Mallat).

3) The introduction as it stands for now, mostly consists of a review of the existing studies on the plain planetary waves. The given review hardly motivates the present study.

In this context, the author totally neglects the following full paragraph from my initial review:

"In this respect, the introduction is slightly confused as it stands for now: its first half reviews previous works detecting "linear" planetary waves. Then, suddenly, at L55, the author decides to talk about the stratospheric sudden warming: this is clearly a nonlinear process that cannot be described by a single wave. The authors further begins to remark that the observed waves are rather "intermittent" (in own wording), and they can emerge even like bursts: that is all fine with me: these observed waves are not perfectly linear, and they are often generated by forcings as well as instabilities, and those evolution can be very nonlinear. However, after said all those (though the author does not comment on them), if one wishes to understand those phenomena as a part of the wave dynamics, an obvious way to go is to perform the normal-mode decompositions so that one can see explicitly which modes are involved in processes in which manner, etc. Those are very basic backgrounds of the atmospheric-wave dynamics, that should remind the readers."

An only hint for a need for the wavelet is a short phrase of "a burst of wave activity" (L57). However, without any proper elaboration, it is even not clear what the author is exactly referring to. Finally, at L73, the author states, "This is the motivation.....". However, unfortunately, I cannot identify a sentence that can be called a motivation in any earlier part: then what is this "This"? The author quickly adds a well known fact that "The wavelet analysis is useful for identifying wave activity that is localized in time" (L79): however, how often we observe such isolated waves in the atmosphere?etc The author simply fails even to provide such basic information.

The logic of the presentation is very loose at the best at many occasions. The most notable example is found in L80-81, which states: "the standard 1-D wavelet technique is not directly applicable to two-dimensional (2-D) longitude-time data...." If I understood the author's response to my comment correctly, this is nothing other than a trivial statement that any 1D transformation technique (wavelet or Fourier or else) is not directly applicable to any 2D data, because the transformation method is only 1d, and not 2D. Thus, we must adopt either 2D wavelet or 1D Fourier technique. It does not follow at all the we need to invoke Hayashi's method to overcome this difficulty. We just need to invoke any available 2D techniques.

In conclusion, I see that still substantial revisions are required before this manuscript becomes worthwhile for publication. [I click "major revision" because more than a cosmetic modification is required: the author clearly failed to note this point in then initial revision] As the very minimum, misunderstanding concerning the wavelet by the author must definitely be corrected.

More Specific:

L77: please add a reference discussing on the discrete wavelet in a more proper manner, for example: Yano et al (2001a, b)

L83, "Hayashi's method is combined with the wavelet technique": probably is would be more proper to say that "Hayashi's method is modified by adopting the wavelet in representation in time."

L130, Yano et al (2001, 2004): please also to refer to the second part (Yano 2001b) for a completeness.

Jun-Ichi Yano

Hide

ED: Reconsider after major revisions (19 Jun 2023) by Sylwester Arabas

AR by Yosuke Yamazaki on behalf of the Authors (26 Jun 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (29 Jun 2023) by Sylwester Arabas

RR by Jun-Ichi Yano (01 Jul 2023)

ED: Publish subject to minor revisions (review by editor) (01 Jul 2023) by Sylwester Arabas

AR by Yosuke Yamazaki on behalf of the Authors (04 Jul 2023) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (09 Jul 2023) by Sylwester Arabas

AR by Yosuke Yamazaki on behalf of the Authors (21 Jul 2023) Manuscript

Journal article(s) based on this preprint

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

Geosci. Model Dev., 16, 4749–4766, https://doi.org/10.5194/gmd-16-4749-2023,https://doi.org/10.5194/gmd-16-4749-2023, 2023

Short summary

Yosuke Yamazaki

Model code and software

CFW Software for Matlab and Python Yosuke Yamazaki https://igit.iap-kborn.de/yy01/cfw

Matlab and Python software for computing combined Fourier-wavelet spectra: cfw v1.0 Yosuke Yamazaki https://doi.org/10.5281/zenodo.7458051

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://zenodo.org/record/8033686

Yosuke Yamazaki

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Preprint (9153 KB)
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Short summary

The Earth's atmosphere can support various types of planetary-scale waves. Some waves propagate eastward and others westward, and they can have different zonal wavenumbers. The combined Fourier-wavelet (CFW) analysis is a useful technique for identifying different components of planetary-scale waves and their temporal variability. This paper introduces an easy-to-implement method to derive CFW spectra from 2-D space-time data. Application examples are presented using atmospheric models.


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