Low-level atmospheric turbulence dataset in China generated by combining radar wind profiler and radiosonde observations

Abstract. Low-level atmospheric turbulence plays a critical role in cloud dynamics and aviation safety. Nevertheless, altitude-resolved turbulence profiles remain scarce, largely owing to observational challenges. By leveraging collocated radar wind profiler (RWP) and radiosonde observations from 29 stations across China in 2023, a high vertical resolution dataset of low-level turbulence-related parameters is generated based on the spectral width method. This dataset includes squared Brunt–Väisälä frequency (N2), turbulent dissipation rate (ε), vertical eddy diffusivity (K), inner scale (l0), and buoyancy length scale (LB), which are provided twice daily at 00:00 and 12:00 UTC with a vertical resolution of 120 m, covering altitudes from 0.12 to 3.0 km above ground level (a.g.l.). Spatial analysis reveals significant regional disparities in turbulence-related parameters across China, where ε, K, and LB are higher in northwest and north China compared to south China, while N2 and l0 display an inverse spatial pattern. These contrasting geographical distributions suggest distinct atmospheric instability across China. In terms of seasonality, turbulence-related variables showed maxima during spring and summer. Vertical profile characteristics show distinct altitudinal dependencies: ε, LB, and K exhibit progressive attenuation with altitude, while N2 and l0 increase with altitude. Statistical analysis indicates that ε and K follow log-normal distributions, whereas l0 and LB align with Gamma distributions. This dataset is publicly accessible at https://doi.org/10.5281/zenodo.14959025 (Meng and Guo, 2025) and provides crucial insights into the fine-scale structural evolution of low-level turbulence. The preliminary findings based on the dataset have great implications for improving our understanding of the pre-storm environment, conducting scientific planning, and guiding low-level flight routes in the emerging low-altitude economy in China.

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Publication Details

Journal
Earth system science data
Published
2025-08-21
DOI
https://doi.org/10.5194/essd-17-4023-2025
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
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article

Low-level atmospheric turbulence dataset in China generated by combining radar wind profiler and radiosonde observations

Rongfang Yang, Deli Meng, Tianmeng Chen, Jianping Guo et al.
Earth system science data
Meteorological Phenomena and Simulations
article

Low-level atmospheric turbulence dataset in China generated by combining radar wind profiler and radiosonde observations

Rongfang Yang, Deli Meng, Tianmeng Chen, Jianping Guo, Jiajia Hua, Hui Xu, Yuping Sun, Juan Chen, Zhen Zhang, Xiaoran Guo, Na Tang, Ning Li
article en

Abstract

Abstract. Low-level atmospheric turbulence plays a critical role in cloud dynamics and aviation safety. Nevertheless, altitude-resolved turbulence profiles remain scarce, largely owing to observational challenges. By leveraging collocated radar wind profiler (RWP) and radiosonde observations from 29 stations across China in 2023, a high vertical resolution dataset of low-level turbulence-related parameters is generated based on the spectral width method. This dataset includes squared Brunt–Väisälä frequency (N2), turbulent dissipation rate (ε), vertical eddy diffusivity (K), inner scale (l0), and buoyancy length scale (LB), which are provided twice daily at 00:00 and 12:00 UTC with a vertical resolution of 120 m, covering altitudes from 0.12 to 3.0 km above ground level (a.g.l.). Spatial analysis reveals significant regional disparities in turbulence-related parameters across China, where ε, K, and LB are higher in northwest and north China compared to south China, while N2 and l0 display an inverse spatial pattern. These contrasting geographical distributions suggest distinct atmospheric instability across China. In terms of seasonality, turbulence-related variables showed maxima during spring and summer. Vertical profile characteristics show distinct altitudinal dependencies: ε, LB, and K exhibit progressive attenuation with altitude, while N2 and l0 increase with altitude. Statistical analysis indicates that ε and K follow log-normal distributions, whereas l0 and LB align with Gamma distributions. This dataset is publicly accessible at https://doi.org/10.5281/zenodo.14959025 (Meng and Guo, 2025) and provides crucial insights into the fine-scale structural evolution of low-level turbulence. The preliminary findings based on the dataset have great implications for improving our understanding of the pre-storm environment, conducting scientific planning, and guiding low-level flight routes in the emerging low-altitude economy in China.

Earth system science dataVol. 17(8)
National Natural Science Foundation of China, Chinese Academy of Meteorological Sciences
Openalex Percentile: Top 73%
Meteorological Phenomena and Simulations
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