郭建平

    研究员，博导，国家杰青

jpguo@cma.gov.cn

010-58993189

研究兴趣

边界层气象，中尺度气象，对流触发机制，低空经济气象

教育背景

2004.09-2007.06 中国科学院大学 理学博士

2001.09-2004.06 江西理工大学  工学硕士

1997.09-2004.06 江西理工大学  工学学士

研究经历

2007.07-至今 中国气象科学研究院 助理研究员（2007），副研究员（2009），研究员（2014）

2018.06-2018.07 美国夏威夷大学，高级访问学者

2017.11-2017.12 以色列魏兹曼理工学院，高级访问学者

2015.07-2015.08 美国宇航局喷气推进实验室， 高级访问学者

2015.06-2015.07 加州理工学院， 高级访问学者

2010.01-2010.07 荷兰屯特大学，  访问学者

2010.09-2011.05 西藏高原大气环境研究所， 援藏

承担课题

1. 国家自然科学基金委杰出青年科学基金项目“大气边界层-对流云降水相互作用”（编号：42325501，400万）主持人（2024.01-2028.12）

2. 中国气象局2023年度揭榜挂帅项目“京津冀强对流天气触发短临预警产品研制” （60万），主持人（2024.01-2025.12）

3. 国家自然科学基金委气象联合基金重点支持项目“京津冀地区夏季强对流天气前期信号及适应性观测研究”（项目编号：U2142209，260万）主持人（2022.01-2025.12）

4. 科技部国家重点研发计划“重大自然灾害监测预警与防范”重点专项“气溶胶对流云降水相互作用机理研究及京津冀区域模式应用示范”（项目编号：2017YFC1501400，2044万）主持人（2018.01-2021.12）

5. 国家自然科学基金委面上项目“我国不同云的多源立体观测及云辐射效应研究”(项目编号：41771399,63万)主持人（2018.01-2021.12）

6. 科技成果转化项目“基于静止卫星数据的中尺度对流系统追踪与预警”，98万，主持人（2019.11-2020.09）

7. 国家自然科学基金委面上项目“中国雾-霾及其对暖云降水垂直分布影响的立体观测及建模研究”(项目编号：41471301，90万)主持人（2015.01-2018.12）

8. 中央级公益性科研院所基本科研业务费专项资金资助项目重点项目：“我国气溶胶对云辐射影响” (项目编号：2017Z005，108万)主持人（2017.06-2019.12）

9. 中国气象局气候变化专项“IPCC相关前沿科学问题及技术支撑” （项目编号：CCSF201926），主持人（2019.01-2019.12）.

10. 中国气象局气候变化专项“IPCC相关前沿科学问题及技术支撑”（项目编号：CCSF201732），主持人（2017.01-2017.12）.

学术兼职

Geophysical Research Letters副主编

Environmental Research Communications 编委

《气象科学》常务编委

《高原气象》常务青年编委

《干旱气象》编委

中国气象学会城市气象委员会委员

雄安、饶阳、安阳等国家气候观象台专家委员会委员

中国气象学会城市气象委员会委员

第33届中国气象学会年会青年论坛共同主席

中国科协第327次青年科学家论坛“雾霾、天气气候”共同执行主席

获奖情况

国家杰出青年科学基金获得者（2023年）

科睿唯安全球高被引科学家（2023年）

第四批国家“万人计划”青年拔尖人才计划（2019年）

新时代高层次气象科技创新领军人才计划气象领军人才（2022年）

“国家级首席科学家”称号（中国气象局）

2020年“爱思唯尔中国高被引学者（大气科学）”榜单

2021年“爱思唯尔中国高被引学者（大气科学）”榜单

2022年“爱思唯尔中国高被引学者（大气科学）”榜单

2021年“全球前2%顶尖科学家榜单” (美国斯坦福大学)

2022年“全球前2%顶尖科学家榜单” (美国斯坦福大学)

美国地球物理学会“2016 Editor's Citation for Excellence in Refereering” (2017年)

Geophysical Research Letters “Top downloaded paper 2018-2019” (2019年)

Advances in Atmospheric Sciences “优秀原创论文奖” （2020年，1/7篇）

National Science Review  “2018年度优秀论文”（2018年，地学领域唯一1篇论文获奖)

2019年度西藏自治区科学技术奖二等奖，青藏高原积雪时空变化研究及遥感积雪数据集建设（排名7/15, 证书编号：2019-JG-2-03-07）

2020年度中国测绘科技技术进步二等奖，气溶胶定量遥感反演及其在大气环境监测中的应用（排名3/10, 证书编号：2020-01-02-35）

2023年度天津市科学技术进步二等奖，渤海湾对流触发的动力微物理机理及预警关键技术（排名3/8，证书编号：2023JB-2-029-03）

中国科学院院长优秀奖，2006年

发表论文

（本人名称加粗，通讯作者加*号）

1. Guo, J., Zhang, J., Shao, J., Chen, T., Bai, K., Sun, Y., Li, N., Wu, J., Li, R., Li, J., Guo, Q., Cohen, J. B., Zhai, P., Xu, X., and Hu, F. (2024). A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS, Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024.

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.

21. Lv, Y., J. Guo*, J. Li, L. Cao, T. Chen, D. Wang, D. Chen, Y. Han, X. Guo, H. Xu, L. Liu, R. Solanki and G. Huang, 2021. Spatiotemporal characteristics of atmospheric turbulence over China estimated using operational high-resolution soundings. Environmental Research Letters, 16, 054050. https://doi.org/10.1088/1748-9326/abf461.

22. Solanki, R., J. Guo*, et al., 2021. Atmospheric boundary layer height variation over mountainous and urban sites in Beijing as derived from radar wind profiler measurements, Boundary-Layer Meteorology,181(1), 125-144. doi:10.1007/s10546-021-00639-9.

23. Li, J., J. Guo*, H. Xu*, J. Li, Y. Lv, 2021. Assessing the surface-layer stability over China using long-term wind-tower network observations, Boundary-Layer Meteorology, 180(1), 155-171. doi: 10.1007/s10546-021-00620-6.

24. Yue, M., Wang, M.*, Guo, J.*, Zhang, H., Dong, X., and Liu, Y. (2021). Long-term Trend Comparison of Planetary Boundary Layer Height in Observations and CMIP6 models over China, Journal of Climate,34(20), 8237–8256. https://doi.org/10.1175/JCLI-D-20-1000.1

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

30. Guo, J.*^#, Yan, Y.^#, Chen, D., Lv, Y., Han, Y., Guo, X., Liu, L., Miao, Y., Chen, T., Nie, J., and Zhai, P. 2020. The response of warm-season precipitation extremes in China to global warming: an observational perspective from radiosonde measurements, Climate Dynamics, 54(9), 3977-3989, doi: 10.1007/s00382-020-05216-3

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.

32. Su, T., Li, Z.*, Zheng, Y., Luan, Q., & Guo, J. (2020).  Abnormally shallow boundary layer associated with severe air pollution during the COVID‐19 lockdown in China. Geophysical Research Letters, 47, e2020GL090041. https://doi.org/10.1029/2020GL090041.

33. Guo, J.#*, T. Su#*, D. Chen, J. Wang*, Z. Li, Y. Lv, X. Guo, H. Liu, M. Cribb, P. Zhai, 2019.Declining summertime local-scale precipitation frequency over China and the United States, 1981–2012: The disparate roles of aerosols. Geophysical Research Letters, 46(22),13281-13289. doi: 10.1029/2019GL085442.

34. Guo, J., Y. Li, J. Cohen, J. Li, D. Chen, H. Xu, L. Liu, J. Yin, K. Hu, P. Zhai, 2019. Shift in the temporal trend of boundary layer height trend in China using long-term (1979–2016) radiosonde data. Geophysical Research Letters, 46 (11): 6080-6089, doi: 10.1029/2019GL082666.  (ESI热点/高被引论文；2018-2019年度Top Downloaded Paper奖）

35. Yan, Y, Y. Miao*, J Guo*, S. Liu, H. Liu, M. Lou, L. Liu, D. Chen, W. Xue, and P Zhai. 2019. Synoptic patterns and sounding-derived parameters associated with summertime heavy rainfall in Beijing. International Journal of Climatology, 39 (3): 1476–1489. doi: 10.1002/joc.5895.

36. Guo, J. *, Liu, H., Li, Z.*, Rosenfeld, D., Jiang, M., Xu, W., Jiang, J. H., He, J., Chen, D., Min, M., and Zhai, P., 2018. Aerosol-induced changes in the vertical structure of precipitation: a perspective of TRMM precipitation radar, Atmos. Chem. Phys., 18, 13329–13343. https://doi.org/10.5194/acp-18-13329-2018. (ESI高被引论文)

37. Liu, L., Guo, J.*, Chen, W., Wu, R., Wang, L., Gong, H.*, Xue, W., and Li, J., 2018. Large-scale pattern of the diurnal temperature range changes over East Asia and Australia in boreal winter: A perspective of atmospheric circulation, Journal of Climate, 31(7): 2715–2728, doi: 10.1175/JCLI-D-17-0608.1.

38. Zhang, W.#, J. Guo#*, Y. Miao, H. Liu, Y. Song, Z. Fang, J. He, M. Lou, Y. Yan, Y. Li, P. Zhai*, 2018, On the summertime planetary boundary layer with different thermodynamic stability in China: A radiosonde perspective, Journal of Climate, 31(4): 1451–1465. doi: 10.1175/JCLI-D-17-0231.1.

39. Wang, Q., Li, Z.*, Guo, J.*, Zhao, C., and Cribb, M., 2018. The climate impact of aerosols on the lightning flash rate: is it detectable from long-term measurements?, Atmos. Chem. Phys., 18, 12797–12816, https://doi.org/10.5194/acp-18-12797-2018.

40. Miao, Y., Guo, J.*, Liu, S.*, Wei, W., Zhang, G., Lin, Y. and Zhai, P., 2018. The climatology of low level jet in Beijing and Guangzhou, China, J. Geophys. Res. Atmos., 123, 2816–2830, doi: 10.1002/2017JD027321.

41. Chen, D., Guo, J.*, Wang, H.*, Li, J., Min, M., Zhao, W., and Yao, D. 2018. The cloud top distribution and diurnal variation of clouds over East Asia: preliminary results from Advanced Himawari Imager, J. Geophys. Res. Atmos., 123(7),3724–3739, doi:10.1002/2017JD028044.

42. Guo, J.*, Su, T.*, Li, Z.*, Miao, Y., Li, J., Liu, H., Xu, H., Cribb, M., and Zhai, P., 2017. Declining frequency of summertime local-scale precipitation over eastern China from 1970–2010 and its potential link to aerosols, Geophysical Research Letters, 44, 5700–5708, doi:10.1002/2017GL073533.  (ESI高被引论文)

43. Guo, J. ^#*, Miao, Y.^#, Zhang, Y., Liu, H., Li, Z.*, Zhang, W., He, J., Lou, M., Yan, Y., Bian, L., and Zhai, P.: The climatology of planetary boundary layer height in China derived from radiosonde and reanalysis data, Atmos. Chem. Phys., 16, 13309–13319, doi:10.5194/acp-16-13309-2016, 2016.(ESI热点/高被引论文)

44. Guo, J.*, H. Liu, F. Wang, J. Huang, F. Xia, M. Lou, Y. Wu, J. Jiang, T. Xie, Y. Zhaxi, and Y. Yung, 2016. Three-dimensional structure of aerosol in China: A perspective from multi-satellite observations, Atmospheric Research, 178–179: 580–589. doi: 10.1016/j.atmosres.2016.05.010. (IF = 4.676)

45. Guo, J.*, M. Deng, S. S. Lee, F. Wang, Z. Li, P. Zhai, H. Liu, W. Lv, W. Yao, and X. Li, 2016. Delaying precipitation and lightning by air pollution over the Pearl River Delta. Part I: Observational analyses,J. Geophys. Res. Atmos., 121, 6472–6488, doi:10.1002/2015JD023257.(ESI高被引论文)

46. Guo, J., He, J., Liu, H.*, Miao, Y, Liu, H., and Zhai, P., 2016. Impact of various emission control schemes on air quality using WRF-Chem during APEC China 2014, Atmos. Environ., 140: 311–319, doi:10.1016/j.atmosenv.2016.05.046.s.

47. Lee, S.-S.,J.P. Guo*, Z. Li, 2016. Delaying precipitation by air pollution over Pearl River Delta. Part 2: model simulations, J. Geophy. Res. Atmos., 121, 11,739–11,760,doi: 10.1002/2015JD024362.

48. Chen, T., J.Guo*, Z. Li, C. Zhao, H. Liu, M. Cribb, F. Wang, and J. He, 2016. A CloudSat perspective on the cloud climatology and its association with aerosol perturbation in the vertical over East China, J. Atmos. Sci., 73, 3599–3616, doi:10.1175/JAS-D-15-0309.1.

49. Zhang, W., Guo, J.*, Miao, Y., Liu, H., Li, Z., and Zhai, P.*, 2016. Planetary boundary layer height from CALIOP compared to radiosonde over China, Atmos. Chem. Phys., 16, 9951–9963, doi:10.5194/acp-16-9951-2016.

50. Xu H., J. Guo*, X. Ceamanos, J.L. Roujean, M. Min, D. Carrer, 2016. On the influence of the diurnal variations of aerosol content to estimate direct aerosol radiative forcing using MODIS data, Atmos. Environ., 141, 186–196. doi: 10.1016/j.atmosenv.2016.06.067.

51.Guo, J., Deng, M., Fan, J., Li, Z.*, Chen, Q., Zhai, P., Dai, Z., and Li. X., 2014. Precipitation and air pollution at mountain and plain stations in northern China: Insights gained from observations and modeling, Journal of Geophysical Research-Atmospheres, 119 (8), 4793–4807. doi: 10.10022013JD021161.

52. Guo J., Zhai P.*, Wu L., Cribb M., Li Z., Ma Z., Wang F., Chu D., Wang P., Zhang J., 2014, Diurnal variation and the influential factors of precipitation from surface and satellite measurements in Tibet. International Journal of Climatology. 34(9): 2940–2956. doi: 10.1002/joc.3886.

53. Wang F., Guo J.^*, Wu Y., Zhang X., Deng M., Li X., Zhang J., and Zhao J., 2014. Satellite observed aerosol-induced variability in warm cloud properties under different meteorological conditions over eastern China. Atmospheric Environment. 84(2): 122–132

其它情况

瞄准现代气象观测业务体系的国家战略需求，聚焦边界层和云降水组网观测及局地热对流演变机制，取得了如下创新性成果：1)基于星地组网观测发展了大气边界层高度和湍流关键参数反演算法，构建了全球陆地高分辨率探空大气边界层高度数据集，突破了从边界层到自由大气层的大气湍流无缝观测技术瓶颈；2)获得了多尺度大气边界层时空演变特征，并从气溶胶、云、土壤湿度和多尺度环流等角度揭示了有云边界层演变机制；3)从对流边界层具有地气耦合特性视角，提出了气溶胶-局地热对流降水相互作用概念模型，获得了高污染区云降水物理演变规律的新认知。边界层相关产品已成功应用到国家级气象业务单位和国防建设，并被广泛用于人类活动对环境、天气和气候系统影响等相关研究中，相关创新性成果被Nature Climate Change, Nature Communications等期刊正面引用，具有重要国际影响力。

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 等杂志发表SCI收录论文200余篇，SCI引用1.1万余次，H指数56。22篇论文入选ESI全球TOP 1%高被引论文（其中5篇入选ESI全球TOP 0.1%热点论文）。边界层气象和湍流相关成果有力支撑了我国气象监测预警业务、重大活动气象服务保障和国防气象科技事业。

  #以上信息由本人提供，更新时间：2024/09/27