Low-level atmospheric turbulence dataset in China generated by combining radar wind profiler and radiosonde observations
<p>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...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-08-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/4023/2025/essd-17-4023-2025.pdf |
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| Summary: | <p>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 (<span class="inline-formula"><i>N</i><sup>2</sup></span>), turbulent dissipation rate (<span class="inline-formula"><i>ε</i></span>), vertical eddy diffusivity (<span class="inline-formula"><i>K</i></span>), inner scale (<span class="inline-formula"><i>l</i><sub>0</sub></span>), and buoyancy length scale (<span class="inline-formula"><i>L</i><sub>B</sub></span>), 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 <span class="inline-formula"><i>ε</i></span>, <span class="inline-formula"><i>K</i></span>, and <span class="inline-formula"><i>L</i><sub>B</sub></span> are higher in northwest and north China compared to south China, while <span class="inline-formula"><i>N</i><sup>2</sup></span> and <span class="inline-formula"><i>l</i><sub>0</sub></span> 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: <span class="inline-formula"><i>ε</i></span>, <span class="inline-formula"><i>L</i><sub>B</sub></span>, and <span class="inline-formula"><i>K</i></span> exhibit progressive attenuation with altitude, while <span class="inline-formula"><i>N</i><sup>2</sup></span> and <span class="inline-formula"><i>l</i><sub>0</sub></span> increase with altitude. Statistical analysis indicates that <span class="inline-formula"><i>ε</i></span> and <span class="inline-formula"><i>K</i></span> follow log-normal distributions, whereas <span class="inline-formula"><i>l</i><sub>0</sub></span> and <span class="inline-formula"><i>L</i><sub>B</sub></span> align with Gamma distributions. This dataset is publicly accessible at <a href="https://doi.org/10.5281/zenodo.14959025">https://doi.org/10.5281/zenodo.14959025</a> (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.</p> |
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| ISSN: | 1866-3508 1866-3516 |