Preprints
https://doi.org/10.5194/egusphere-2026-4972
https://doi.org/10.5194/egusphere-2026-4972
25 Aug 2026
 | 25 Aug 2026
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Solar-tidal phase and amplitude in middle-atmosphere winds investigated from an adaptive rational neural operator, two reanalyses and meteor-radar observations

Yang Wu, Qixiang Liao, Zhihui Li, and Zheng Sheng

Abstract. Winds between 20 and 80 km couple the lower and upper atmosphere through solar tides, planetary waves and gravity-wave forcing, and they set the operating envelope for near-space flight. Observations thin rapidly above the lower stratosphere, so reanalysis is in practice the only systematic source for studying and predicting this layer. Reanalyses reproduce the canonical solar-tidal harmonics here, and satellite comparisons of temperature indicate that their tidal amplitudes weaken too rapidly above the upper stratosphere. How much tidal wind amplitude survives near the mesopause has not been measured against ground-based observations, and the consequences for reanalysis-trained prediction have not been drawn. Winds from three meteor radars spanning 23° of latitude are compared with collocated ERA5, and an adaptive Takenaka–Malmquist neural operator is trained on seven-level ERA5 vector winds over eastern Asia and evaluated against eleven capacity-matched architectures, MERRA-2 and the radar observations. ERA5 retains 15 to 36 % of the observed semidiurnal and 30 to 82 % of the observed diurnal wind amplitude at 80 km. The operator attains the lowest wind-speed and direction errors evaluated, 2.215 ms−1 and 5.2°, and improves on three-hour ERA5 persistence at all three radar sites. Without frequency supervision its learned poles converge on the semidiurnal and terdiurnal harmonics, vary by only 4 to 11 % across the domain, and reappear after retraining on MERRA-2. The amplitude deficit constrains what any reanalysis- trained model of this layer can reproduce, and the recovered frequencies give a representation that can be audited directly against observations.

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Yang Wu, Qixiang Liao, Zhihui Li, and Zheng Sheng

Status: open (until 06 Oct 2026)

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Yang Wu, Qixiang Liao, Zhihui Li, and Zheng Sheng
Yang Wu, Qixiang Liao, Zhihui Li, and Zheng Sheng
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Latest update: 26 Aug 2026
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Short summary
Winds in the upper atmosphere influence the environment where many space-based technologies operate, but these winds are difficult to observe and predict. We combined atmospheric observations, global datasets and a new learning approach to study repeating wind patterns. Our results show that current atmospheric datasets miss part of these regular variations, providing new guidance for improving predictions of the middle atmosphere.
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