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The nightside cloud-top circulation of the atmosphere of Venus

Abstract

Although Venus is a terrestrial planet similar to Earth, its atmospheric circulation is much different and poorly characterized1. Winds at the cloud top have been measured predominantly on the dayside. Prominent poleward drifts have been observed with dayside cloud tracking and interpreted to be caused by thermal tides and a Hadley circulation2,3,4; however, the lack of nightside measurements over broad latitudes has prevented the unambiguous characterization of these components. Here we obtain cloud-tracked winds at all local times using thermal infrared images taken by the Venus orbiter Akatsuki, which is sensitive to an altitude of about 65 kilometres5. Prominent equatorward flows are found on the nightside, resulting in null meridional velocities when these are zonally averaged. The velocity structure of the thermal tides was determined without the influence of the Hadley circulation. The semidiurnal tide was found to have an amplitude large enough to contribute to the maintenance of the atmospheric superrotation. The weakness of the mean meridional flow at the cloud top implies that the poleward branch of the Hadley circulation exists above the cloud top and that the equatorward branch exists in the clouds. Our results should shed light on atmospheric superrotation in other celestial bodies.

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Fig. 1: Processing of thermal images of Venus.
Fig. 2: Latitude–local time distributions of derived velocities.
Fig. 3: Diurnal and semidiurnal components of derived velocities.
Fig. 4: Latitudinal variations of zonal-mean circulation.

Data availability

The Akatsuki LIR Level 3c data that support the findings of this study are available in Data Archives and Transmission System (DARTS) of JAXA56 at https://doi.org/10.17597/ISAS.DARTS/VCO-00019. An internal version of the data (v20190401) was used, but it is essentially the same as the publicly available version. Source data are provided with this paper.

Code availability

Cloud tracking was conducted using the Python codes developed by K.F., which are available at https://doi.org/10.5281/zenodo.4726329 and https://github.com/kiichi-f/Cloud_Tracking_using_Multiple_Images.

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Acknowledgements

The data used in this study were provided by the Akatsuki project of JAXA. We thank the science members of the project for comments. This work was supported by JSPS KAKENHI grant numbers 20H01958 and 19H05605.

Author information

Affiliations

Authors

Contributions

K.F. developed the code and led the analysis and manuscript writing. T.I. designed the study and contributed to the analysis and manuscript writing. M. Taguchi is the principal investigator of LIR. T.F. mainly developed the instrument. M.F. also developed the instrument and supported calibration. T.K. developed the data processing tool and contributed to calibration. T.Y. contributed to data processing. S.-y.M. led the development of data processing pipeline. T.M.S. contributed to observation planning and A.Y. prepared commands for observations. T.S. led the coordination of the instrument suite including LIR as the project scientist. M.N. led the whole of Akatsuki’s observation as the project manager. T.H. provided expertise on atmospheric dynamics and contributed to manuscript writing. J.P. contributed to the interpretation of the results and also helped with manuscript writing. M. Takagi contributed to the interpretation of the tidal structure.

Corresponding author

Correspondence to Takeshi Imamura.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Nikolay Ignatiev, Arianna Piccialli and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Zonal velocities obtained in first estimation.

ac, Results for template sizes of 30° × 30° (a), 20° × 20° (b) and 10° × 10° (c). Cloud-tracked vectors that satisfy the criteria described in Methods are plotted as dots. The limit of the major axis length in criterion (3) is 60 m s−1 for a, 50 m s−1 for b and 45 m s−1 for c. The blue dotted line shows the moving average with a sliding window of 20° in latitude and the red solid line shows the rigid body rotation with an equatorial velocity of −95 m s−1, which was adopted as the background zonal velocity in the second estimation

Source data.

Extended Data Fig. 2 Examples of cross-correlation coefficient map in second estimation.

The vertical axis represents the northward velocity and the horizontal axis represents the deviation of the eastward velocity from the assumed background velocity. The eastward velocities were divided by the cosine of the latitude. The crosses indicate the maxima, and the blue, green and black lines indicate the contours of the top 1%, 5% and 10% values, respectively. a, b, Two examples of accepted cases. c, Case rejected because of multiple local maxima. d, Case rejected because the major axis of the region of the top 5% is longer than 60 m s−1.

Extended Data Fig. 3 Examples of cloud tracking.

a, Local time–latitude distribution of the accepted velocity vectors on the dayside obtained from a series of images spanning from 02:00 to 23:08 on 16 November 2018. b, A pair of images in the image series showing temporal development. c, Correlation maps showing the peaks that correspond to the tracking boxes shown by squares in b. df, Same as ac, respectively, but for the dusk-nightside obtained from a series of images spanning from 23:08 on 17 December to 11:08 on 18 December. In a, d, arrows are scaled such that the distance occupied by 15° in latitude or 1 h in local time represents a speed of 50 m s−1, and red (blue) colour represents northward (southward) movement. The vertical and horizontal axes of the correlation surfaces are the northward velocity and the deviation of the eastward velocity from the assumed background velocity, respectively. The crosses indicate the maxima, and the blue, green and black lines indicate the contours of the top 1%, 5% and 10% values, respectively.

Extended Data Fig. 4 Latitude–local time distribution of measurement dispersion.

a, b, Standard deviation (a) and standard error (b) of the cloud-tracked velocity in each grid.

Extended Data Fig. 5 Changes in latitude–local time distribution of velocity associated with change in background velocity adopted in second estimation.

ac, The background velocity is the sum of a rigid body rotation with an equatorial velocity of −95 m s−1 and a constant offset of −15 m s−1 (a), 0 m s−1 (b) and 15 m s−1 (c). Velocity vectors (top) and the meridional velocity (bottom) are shown. Each velocity vector is defined by the deviation of the zonal velocity from the local-time average and the total meridional velocity. Arrows are scaled such that the distance occupied by 10° in latitude represents a meridional speed of 5 m s−1 and that occupied by 1 h in local time represents a zonal speed of 5 m s−1.

Extended Data Fig. 6 Sensitivity to template size.

af, Latitude–local time distributions of the derived velocities for template sizes of 20° × 20° (ac) and 40° × 40° (df). Zonal velocity (a, d), meridional velocity (b, e) and velocity vectors (c, f) are shown. Each velocity vector is defined by the deviation of the zonal velocity from the local-time average and the total meridional velocity. Arrows are scaled such that the distance occupied by 10° in latitude represents a meridional speed of 5 m s−1 and that occupied by 1 h in local time represents a zonal speed of 5 m s−1.

Extended Data Fig. 7 Stability of velocity field.

af, Latitude–local time distributions of the velocities obtained from the first half (from 23 December 2016 to 13 February 2018; ac) and the second half (from 25 February 2018 to 20 January 2019; df) of the dataset. Zonal velocity (a, d), meridional velocity (b, e) and velocity vectors (c, f) are shown. Each velocity vector is defined by the deviation of the zonal velocity from the local-time average and the total meridional velocity. Arrows are scaled such that the distance occupied by 10° in latitude represents a meridional speed of 5 m s−1 and that occupied by 1 h in local time represents a zonal speed of 5 m s−1.

Supplementary information

Peer Review File

Video 1 Example of convergent flow on the nightside

Data taken from 18:01, 18 August 2018 to 19:08, 19 August 2018. Animation of highpass-filtered images in a superrotating coordinate system with a duration of ~10 h, with noises being suppressed by applying a moving average in the time domain with a window length of ~10 h that corresponds to ~10 images. The red dashed line indicates noon, the black dashed line indicates midnight, and the blue solid lines indicate the terminator.

Video 2 Example of convergent flow on the nightside

Data taken from 06:01, 17 December 2018 to 11:08, 18 December 2018. Animation of highpass-filtered images in a superrotating coordinate system with a duration of ~10 h, with noises being suppressed by applying a moving average in the time domain with a window length of ~10 h that corresponds to ~10 images. The red dashed line indicates noon, the black dashed line indicates midnight, and the blue solid lines indicate the terminator.

Video 3 Example of divergent flow on the dayside

Data taken from 09:01, 23 February 2018 to 06:08, 24 February 2018. Animation of highpass-filtered images in a superrotating coordinate system with a duration of ~10 h, with noises being suppressed by applying a moving average in the time domain with a window length of ~10 h that corresponds to ~10 images. The red dashed line indicates noon, the black dashed line indicates midnight, and the blue solid lines indicate the terminator.

Video 4 Example of divergent flow on the dayside

Data taken from 02:00, 16 November 2018 to 07:08, 17 November 2018. Animation of highpass-filtered images in a superrotating coordinate system with a duration of ~10 h, with noises being suppressed by applying a moving average in the time domain with a window length of ~10 h that corresponds to ~10 images. The red dashed line indicates noon, the black dashed line indicates midnight, and the blue solid lines indicate the terminator.

Video 5 Example of the movements of tracking boxes on the dayside

Data taken from 07:00 to 20:01, 16 November 2018. Animation showing examples of the movements of tracking boxes corresponding to accepted velocity vectors. A moving average in the time domain with a window length of ~4 h was applied to be consistent with the cloud tracking procedure.

Video 6 Example of the movements of tracking boxes on the dusk-night side.

Data taken from 17:08, 17 December 2018 to 07:08, 18 December 2018. Animation showing examples of the movements of tracking boxes corresponding to accepted velocity vectors. A moving average in the time domain with a window length of ~4 h was applied to be consistent with the cloud tracking procedure.

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Fukuya, K., Imamura, T., Taguchi, M. et al. The nightside cloud-top circulation of the atmosphere of Venus. Nature 595, 511–515 (2021). https://doi.org/10.1038/s41586-021-03636-7

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