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A scale model of the Akatsuki Venus Climate Orbiter spacecraft on a black stand, with gold-colored body, dark solar panels and a central antenna, displayed in a glass case.

Institutional development · Research, innovation and assessment · Japan

Japanese team reconstructs Venus's super-rotating winds in 3D from Akatsuki images

Researchers from Japanese universities, JAXA and RIKEN used the ALEDAS-V data assimilation system to reproduce long-term wind changes in Venus's atmosphere, including at altitudes with no observations.

Photo: 江戸村のとくぞう / Wikimedia Commons, CC BY-SA 4.0

Why it matters: The study offers Venus atmosphere specialists estimates of the planet's three-dimensional atmospheric structure, including unobserved altitudes, for five periods between 2017 and 2022.

According to Kyoto University, a team spanning Keio, Kobe, Kyoto Sangyo, Tokyo, Hokkaido and Kyoto universities, JAXA (Japan's space agency) and RIKEN used the Venus atmospheric data assimilation system ALEDAS-V to reproduce long-term wind-speed changes in the planet's super-rotation, in which the atmosphere circles faster than the planet spins.

The team fed cloud-top horizontal winds, derived from ultraviolet camera images taken by the Akatsuki Venus probe, into a numerical model for five periods between 2017 and 2022 with differing wind speeds, reproducing observed features including north-south differences.

The university said the estimated north-south and vertical circulation, known as meridional circulation, also differed between periods, and that the study estimated the atmosphere's three-dimensional structure, including altitudes without observations.

The findings were published online in the Nature Portfolio journal npj Space Exploration on 23 October 2026.

Editor’s view

Scott James, Research and Innovation Editor

Professor Takeshi Enomoto of Kyoto University's Disaster Prevention Research Institute said that stitching limited observations into a long-term dataset has begun to reveal super-rotation's variations and three-dimensional flow, and that he expects the dataset still contains various phenomena that have yet to be named.

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