2. Guo, X., Guo, J.*, Chen, T., Li, N., Zhang, F., and Sun, Y. (2024) Revisiting the evolution of downhill thunderstorms over Beijing: A new perspective from radar wind profiler mesonet, Atmos. Chem. Phys. In press.

3. Meng, D., Guo, J.*, Guo, X., Wang, Y., Li, N., Sun, Y., Zhang, Z., Tang, N., Li, H., Zhang, F., Tong, B., Xu, H., and Chen, T. 2024. Elucidating the boundary layer turbulence dissipation rate using high-resolution measurements from a radar wind profiler network over the Tibetan Plateau, Atmos. Chem. Phys. In press.

4. Li, S., J. Guo*, X. Zhang, B. Tong, T. Su, J. Wei, Z. Li* (2024). Preference of afternoon precipitation over dry soil in the North China Plain during warm seasons. Journal of Geophysical Research: Atmospheres, 129, e2023JD040641. https://doi.org/10.1029/2023JD040641.

5. Wu, J., J. Guo*, Y. Yun, R. Yang, X. Guo, D. Meng, Y. Sun, Z. Zhang, H. Xu, and T. Chen (2024). Can ERA5 reanalysis data characterize the pre-storm environment? Atmospheric Research, 297, 107108, https://doi.org/10.1016/j.atmosres.2023.107108.

6. Shang, M., L. Cao*, J. Guo*, Z. Guo, L. Liu and S. Zhong (2024). Influence of pure sea breeze on urban heat island in Tianjin, China: A perspective from multiple meteorological observations. Atmospheric Research, 304: 107408.https://doi.org/10.1016/j.atmosres.2024.107408.

7. Liu, B., Ma, X., Guo, J.*, Wen, R., Li, H., Jin, S., Ma, Y., Guo, X., and Gong, W. (2024). Extending the wind profile beyond the surface layer by combining physical and machine learning approaches, Atmos. Chem. Phys., 24, 4047–4063, https://doi.org/10.5194/acp-24-4047-2024.

8. Tan, Z., J. Wang*, J. Guo*, C. Liu, M. Zhang, and S. Ma, (2024). Improving satellite-retrieved cloud base height with ground-based cloud radar measurements. Advances in Atmospheric Sciences. doi:10.1007/s00376-024-4052-7.

9. Guo, X., Guo, J.*, Zhang, D-L., & Yun, X. (2023). Vertical divergence profiles as detected by two wind profiler mesonets over East China: implications for nowcasting convective storms, Quarterly Journal of the Royal Meteorological Society, 149(754), 1629-1649, https://doi.org/10.1002/qj.4474

10. Xu, H., Guo, J.*, Tong, B., Zhang, J., Chen, T., Guo, X., Zhang, J., and Chen, W. (2023). Characterizing the near-global cloud vertical structures over land using high-resolution radiosonde measurements, Atmos. Chem. Phys., 23, 15011–15038, https://doi.org/10.5194/acp-23-15011-2023.

11. Xian, T., Guo, J.*, Zhao, R., Su, T., & Li, Z.* (2023). The impact of urbanization on mesoscale convective systems in the Yangtze River Delta region of China: Insights gained from observations and modeling. Journal of Geophysical Research: Atmospheres, 128, e2022JD037709. https://doi.org/10.1029/2022JD037709

12. Liu, B., Ma, X., Guo, J.*, Li, H., Jin, S., Ma, Y., and Gong, W. (2023). Estimating hub-height wind speed based on a machine learning algorithm: implications for wind energy assessment. Atmos. Chem. Phys., 23, 3181–3193, https://doi.org/10.5194/acp-23-3181-2023.

13. Solanki, R., J. Guo*, Y. Lv, J. Zhang, J. Wu, B. Tong, and J. Li, 2022. Elucidating the atmospheric boundary layer turbulence by combining UHF Radar wind profiler and radiosonde measurements over urban area of Beijing. Urban Climate, 43, 101151, doi: 10.1016/j.uclim.2022.101151.

14. Zhang, J., Guo, J.*, Li, J., Shao, J., Tong, B., and Zhang, S. (2022). The prestorm environment and prediction for local-scale and nonlocal precipitation: Insights gained from high-resolution radiosonde measurements across China. Journal of Geophysical Research: Atmospheres, 127, e2021JD036395. https://doi.org/10.1029/2021JD036395

15. Tong, B., J. Guo*, Y. Wang, J., Li, Y. Yun, R. Solanki, N. Hu, H. Yang, H. Li, J. Su, Q. He, Y. Zhou, K. Zhang, and Y. Zhang, 2022. The near-surface turbulent kinetic energy characteristics under the different convection regimes at four towers with contrasting underlying surfaces. Atmospheric Research, 106073,doi:10.1016/j.atmosres.2022.106073

16. Chen, T., J. Guo*, B. Tong, J. Cohen, X. Chen, Y. Yun, M. Lv, X. Guo, S. S. Lee, 2022. Elucidating the impact of high- and low-pressure systems on temperature inversion from nine years of radiosonde observations in Beijing, Atmospheric Research, https://doi.org/10.1016/j.atmosres.2022.106115.

17. Zhang, J., J. Guo*, S. Zhang, J. Shao, 2022. Inertia-gravity wave energy and instability drive turbulence: Evidence from a near-global high-resolution radiosonde dataset. Climate Dynamics, 58, 2927–2939. https://doi.org/10.1007/s00382-021-06075-2.

18. Bai, K., K. Li, J. Guo*, W. Cheng*, and X. Xu, 2022. Do more frequent temperature inversions aggravate haze pollution in China? GeophysicalResearch Letters, 49(4), e2021GL096458,https://doi.org/10.1029/2021GL096458

19. Feng, X.*, S. Wang, J. Guo*, 2022. Temperature inversions in the lower troposphere over the Sichuan Basin, China: Seasonal feature and relation with regional atmospheric circulations, Atmospheric Research. 271,10697.https://doi.org/10.1016/j.atmosres.2022.106097

20. Guo, J., Liu, B.*, Gong, W., Shi, L., Zhang, Y., Ma, Y., Zhang, J., Chen, T., Bai, K., Stoffelen, A., de Leeuw, G., and Xu, X., 2021. Technical note: First comparison of wind observations from ESA's satellite mission Aeolus and ground-based radar wind profiler network of China. Atmos. Chem. Phys., 21, 2945–2958, https://doi.org/10.5194/acp-21-2945-2021.

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25. Han, Y., J. Guo*, Y. Yun, J. Li, X. Guo, Y. Lv, D. Wang, L. Li and Y. Zhang, 2021. Regional variability of summertime raindrop size distribution from a network of disdrometers in Beijing. Atmospheric Research, 257: 105591. doi:10.1016/j.atmosres.2021.105591.

26. Xu, H., J. Guo*, J. Li, L. Liu, T. Chen, X. Guo, Y. Lv, D. Wang, Y. Han, Q. Chen, Y. Zhang, 2021. Significant Role of Radiosonde-measured Cloud-base Height in Estimating Cloud Radiative Forcing. Adv. Atmos. Sci., 38 (9): 1552–1565. https://doi.org/10.1007/s00376-021-0431-5.

27. Xu, Z., Chen, H.*, Guo, J.*, and Zhang, W. (2021). Contrasting effect of soil moisture on the daytime boundary layer under different thermodynamic conditions in summer over China. Geophysical Research Letters, 48, e2020GL090989. https://doi. org/10.1029/2020GL090989.

28. Lv, Y., Guo, J.*, Li, J., Han, Y., Xu, H., Guo, X., et al. (2021). Increased turbulence in the Eurasian upper‐level jet stream in winter: past and future. Earth and Space Science, 8(2), e2020EA001556, doi:10.1029/2020EA001556(AGU EOS Editor’s highlight)

29. Guo, J.*, X. Chen, T. Su, L. Liu, Y. Zheng, D. Chen, J. Li, H. Xu, Y. Lv, B. He, Y. Li, X. Hu, A. Ding, and P. Zhai, 2020. The climatology of lower tropospheric temperature inversions in China from radiosonde measurements: roles of black carbon, local meteorology, and large-scale subsidence. Journal of Climate, 33 (21): 9327–9350,doi: 10.1175/JCLI-D-19-0278.1

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31. Wang, D., J. Guo*, A. Chen*, L. Bian, M. Ding, L. Liu, Y. Lv, J. Li, X. Guo, and Y. Han, 2020. Temperature inversion and clouds over the Arctic Ocean observed by the 5^th Chinese national Arctic research expedition, Journal of Geophysical Research: Atmospheres, 125, e2019JD032136.doi: 10.1029/2019JD032136.

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其它情況

瞄準現代氣象觀測業務體系的國家戰略需求🫰，聚焦邊界層和雲降水組網觀測及局地熱對流演變機製，取得了如下創新性成果👨‍🌾：1)基於星地組網觀測發展了大氣邊界層高度和湍流關鍵參數反演算法🧚‍♂️🥗，構建了全球陸地高分辨率探空大氣邊界層高度數據集，突破了從邊界層到自由大氣層的大氣湍流無縫觀測技術瓶頸；2)獲得了多尺度大氣邊界層時空演變特征，並從氣溶膠、雲💂🏼‍♀️、土壤濕度和多尺度環流等角度揭示了有雲邊界層演變機製；3)從對流邊界層具有地氣耦合特性視角，提出了氣溶膠-局地熱對流降水相互作用概念模型🧑🏿‍🔧，獲得了高汙染區雲降水物理演變規律的新認知。邊界層相關產品已成功應用到國家級氣象業務單位和國防建設，並被廣泛用於人類活動對環境🔖、天氣和氣候系統影響等相關研究中，相關創新性成果被Nature Climate Change, Nature Communications等期刊正面引用，具有重要國際影響力。

目前已在Nature Communications, PNAS, Review of Geophysics, National Science Review, Atmospheric Chemistry and Physics, Journal of the Atmospheric Sciences, Environmental Pollution, Journal of Geophysical Research, Journal of Climate, Atmospheric Environment, Atmospheric Research 等雜誌發表SCI收錄論文200余篇🔂，SCI引用1.1萬余次，H指數56🙎🏽‍♂️。22篇論文入選ESI全球TOP 1%高被引論文（其中5篇入選ESI全球TOP 0.1%熱點論文）。邊界層氣象和湍流相關成果有力支撐了我國氣象監測預警業務、重大活動氣象服務保障和國防氣象科技事業。

  #以上信息由本人提供，更新時間：2024/09/27