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Journal of Meteorological Research

, Volume 33, Issue 3, pp 433–445 | Cite as

Microphysical Properties of Convective Clouds in Summer over the Tibetan Plateau from SNPP/VIIRS Satellite Data

  • Zhiguo Yue
  • Xing YuEmail author
  • Guihua Liu
  • Jin Dai
  • Yannian Zhu
  • Xiaohong Xu
  • Ying Hui
  • Chuang Chen
Special Collection on the Third Tibetan Plateau Atmospheric Science Experiment (TIPEX-III)
  • 242 Downloads

Abstract

The Tibetan Plateau (TP) plays an important role in formation and development of the East Asian atmospheric circulation, climate variability, and disastrous weathers in China. Among the many topics on TP meteorology, it is critical to understand the microphysical characteristics of clouds over the TP; however, observations of the cloud micro-physics in this area are insufficient mainly due to sparse stations and limited cloud physical data. The Visible Infrared Imaging Radiometer Suite (VIIRS), onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite, has an improved imaging spectroradiometer with 17 channels of 750-m moderate resolution and 5 channels of 375-m image resolution. The high-resolution instrument has an advantage for observing the small or initial convective clouds. Based on the methodologies that we proposed before for retrieving cloud microphysical properties from SNPP, an automated mapping software package named Automatic Mapping of Convective Clouds (AMCC) has been developed at the scale of satellite swath. The properties of convective clouds are retrieved by AMCC and their values are averaged over 0.33° × 0.33° grids based on the SNPP/VIIRS satellite data over the TP during the summers of 2013–17. The results show that: (1) the temperature of lifting condensation level (TLCL) at Naqu meteorological station and the cloud base temperature (Tb) retrieved from VIIRS are linearly correlated, with a correlation coefficient of 0.87 and standard deviation (STD) of 3.0°C; (2) convective clouds over the TP have the following macro- and micro-physical properties. First, the cloud base temperature (Tb) is about −5°C, the cloud base height above the ground (Hb) ranges between 1800 and 2200 m, and the cloud water content is low. Second, the cloud condensation nuclei concentration (NCCN) is between 200 and 400 mg−1 with 0.7% in maximum supersaturation (Smax); consequently, the condensation growth of water cloud droplet with less NCCN and higher Smax is fast. Third, because the precipitation initiation depth (D14) varies within 1500–2000 m and 500–1000 m at the Yarlung Zangbo River basin and southern Tibet, respectively, the clouds over these areas are more prone to precipitation. Fourth, mean height of the cloud top above sea level (Htop) is between 10 and 13 km, but the cloud depth (Dcld) is rather small, which is about 5000 m in southern TP and gradually reduces to 2500 m in northern TP. Fifth, the glaciation temperature (Tg) ranges from −30°C in central and southern TP to −25°C in northern TP, which, combined with the warmer Tg and the Tb less than 0°C, leads to the domination of ice process in the clouds; (3) the macro- and microphysical properties of convective clouds over the TP explain why rainfall there is frequent and lasts over a short time with small amount and large rain drops.

Key words

Tibetan Plateau Visible Infrared Imaging Radiometer Suite (VIIRS) retrieval of cloud microphysical properties convective cloud cloud base temperature cloud condensation nuclei 

Notes

Acknowledgment

We acknowledge usage of the NOAA CLASS VIIRS LIB data, the NCEP FNL analysis data, and the intensive observational data from the TIPEX-III organized by the China Meteorological Administration.

References

  1. Andreae, M. O., D. Rosenfeld, P. Artaxo, et al., 2004: Smoking rain clouds over the Amazon. Science, 303, 1337–1342, DOI:  https://doi.org/10.1126/science.1092779.CrossRefGoogle Scholar
  2. Braga, R. C., D. Rosenfeld, R. Weigel, et al., 2017: Further evidence for CCN aerosol concentrations determining the height of warm rain and ice initiation in convective clouds over the Amazon basin. Atmos. Chem. Phys., 17, 14433–14456, DOI:  https://doi.org/10.5194/acp-17-14433-2017.CrossRefGoogle Scholar
  3. Brenguier, J. L., F. Burnet, and O. Geoffroy, 2011: Cloud optical thickness and liquid water path—does the k coefficient vary with droplet concentration? Atmos. Chem. Phys., 11, 9771–9786, DOI:  https://doi.org/10.5194/acp-11-9771-2011.CrossRefGoogle Scholar
  4. Chang, Y., and X. L. Guo, 2016: Characteristics of convective cloud and precipitation during summer time at Naqu over Tibetan Plateau. Chinese Sci. Bull, 61, 1706–1720, DOI:  https://doi.org/10.1360/N972015-01292. (in Chinese)Google Scholar
  5. Chen, B. J., J. Yang, and J. P. Pu, 2013: Statistical characteristics of raindrop size distribution in the Meiyu season observed in eastern China. J. Meteor. Soc. Japan, 91, 215–227, DOI:  https://doi.org/10.2151/jmsj.2013-208.CrossRefGoogle Scholar
  6. Dai, J., X. Yu, G. H. Liu, et al., 2010: Analyses of satellite retrieved microphysical properties of a rainstorm in the northern part of Shaanxi. Acta Meteor. Sinica, 68, 387–397, DOI:  https://doi.org/10.11676/qxxb2010.038. (in Chinese)Google Scholar
  7. Freud, E., D. Rosenfeld, and J. R. Kulkarni, 2011: Resolving both entrainment-mixing and number of activated CCN in deep convective clouds. Atmos. Chem. Phys., 11, 12, 887–12,900, DOI:  https://doi.org/10.5194/acp-11-12887-2011.Google Scholar
  8. Freud, E., J. StrÖM, D. Rosenfeld, et al., 2008: Anthropogenic aerosol effects on convective cloud microphysical properties in southern Sweden. Tellus. B, 60, 286–297, DOI:  https://doi.org/10.1111/j.1600-0889.2007.00337.X.CrossRefGoogle Scholar
  9. Fu, Y. F., X. Pan, G. S. Liu, et al., 2016: Characteristics of precipitation based on cloud brightness temperatures and storm tops in summer over the Tibetan Plateau. Chinese J. Atmos. Sci, 40, 102–120, DOI:  https://doi.org/10.3878/j.issn.l006-9895.1507.15165. (in Chinese)Google Scholar
  10. Hillger, D., T. Kopp, T. Lee, et al., 2013: First-light imagery from Suomi NPP VIIRS. Bull. Amer. Meteor. Soc., 94, 1019–1029, DOI:  https://doi.org/10.1175/bams-d-12-00097.1.CrossRefGoogle Scholar
  11. Huang, G., S. R. Li, Deligeer, et al., 2002: Observational analysis of content of condensation nuclei in the atmosphere in the upper reaches of Yellow River. Meteor. Mon., 28, 45–49. (in Chinese)Google Scholar
  12. Lensky, I. M., and D. Rosenfeld, 1997: Estimation of precipitation area and rain intensity based on the microphysical properties retrieved from NOAA AVHRR data. J. Appl. Meteorol, 36, 234–242, DOI:  https://doi.org/10.1175/1520-0450(1997)036<0234:EOPAAR> 2.0.CO;2.CrossRefGoogle Scholar
  13. Liu, G. H., X. Yu, C. X. Shi, et al., 2011a: Comparisons of micro-physical property of cloud retrieved from FY-3A/VIRR. and TERRA/MODIS. Plateau Meteor., 30, 461–170. (in Chinese)Google Scholar
  14. Liu, G. H., X. Yu, J. Dai, et al., 2011b: A case study of the conditions for topographic cloud seeding based on the retrieval of satellite measurements. Acta Meteor. Sinica, 69, 363–369, DOI:  https://doi.org/10.11676/qxxb2011.031. (in Chinese)Google Scholar
  15. Liu, J. J., and B. D. Chen, 2017: Cloud occurrence frequency and structure over the Qinghai-Tibetan Plateau from CloudSat observation. Plateau Meteor., 36, 632–642. (in Chinese)Google Scholar
  16. Liu, L. P., J. F. Zheng, Z. Ruan, et al., 2015: Comprehensive radar observations of clouds and precipitation over the Tibetan Plateau and preliminary analysis of cloud properties. J. Meteor. Res., 29, 546–561, DOI:  https://doi.org/10.1007/sl3351-015-4208-6.CrossRefGoogle Scholar
  17. Merk, D., H. Deneke, B. Pospichal, et al., 2016: Investigation of the adiabatic assumption for estimating cloud micro- and macrophysical properties from satellite and ground observations. Atmos. Chem. Phys., 16, 933–952, DOI:  https://doi.org/10.5194/acp-16-933-2016.CrossRefGoogle Scholar
  18. Miller, D. J., Z. B. Zhang, A. S. Ackerman, et al., 2016: The impact of cloud vertical profile on liquid water path retrieval based on the bispectral method: A theoretical study based on large-eddy simulations of shallow marine boundary layer clouds. J. Geophys. Res. Atmos., 121, 4122–4141, DOI:  https://doi.org/10.1002/2015JD024322.CrossRefGoogle Scholar
  19. Min, Q., E. Joseph, Y. Lin, et al., 2012: Comparison of MODIS cloud microphysical properties with in-situ measurements over the Southeast Pacific. Atmos. Chem. Phys., 12, 11261–11273, DOI:  https://doi.org/10.5194/acp-12-11261-2012.CrossRefGoogle Scholar
  20. Nakajima, T., and M. D. King, 1990: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory. J. Atmos. Sci., 47, 1878–1893, DOI:  https://doi.org/10.1175/1520-0469(1990)047<1878:Dotota>2.0.Co;2.CrossRefGoogle Scholar
  21. Pinsky, M., A. Khain, I. Mazin, et al., 2012: Analytical estimation of droplet concentration at cloud base. J. Geophys. Res. Atmos., 117, D18211, DOI:  https://doi.org/10.1029/2012JD017753.CrossRefGoogle Scholar
  22. Prabha, T. V., A. Khain, R. S. Maheshkumar, et al., 2011: Micro-physics of premonsoon and monsoon clouds as seen from in situ measurements during the cloud aerosol interaction and precipitation enhancement experiment (CAIPEEX). J. Atmos. Sci., 68, 1882–1901, DOI:  https://doi.org/10.1175/2011jas3707.1.CrossRefGoogle Scholar
  23. Rosenfeld, D., and I. M. Lensky, 1998: Satellite-based insights into precipitation formation processes in continental and maritime convective clouds. Bull. Amer. Meteor. Soc., 79, 2457–2476, doi:  https://doi.org/10.1175/1520-0477(1998)079<2457:sbiipf>2.0.co;2.CrossRefGoogle Scholar
  24. Rosenfeld, D., and W. L. Woodley, 2000: Deep convective clouds with sustained supercooled liquid water down to −37.5°C. Nature, 405, 440–442, DOI:  https://doi.org/10.1038/35013030.CrossRefGoogle Scholar
  25. Rosenfeld, D., W. L. Woodley, A. Lerner, et al., 2008: Satellite detection of severe convective storms by their retrieved vertical profiles of cloud particle effective radius and thermodynamic phase. J. Geophys. Res. Atmos., 113, D04208, DOI:  https://doi.org/10.1029/2007JD008600.Google Scholar
  26. Rosenfeld, D., X. Yu, G. H. Liu, et al., 2011: Glaciation temperatures of convective clouds ingesting desert dust, air pollution and smoke from forest fires. Geophys. Res. Lett., 38, L21804, DOI:  https://doi.org/10.1029/2011gl049423.CrossRefGoogle Scholar
  27. Rosenfeld, D., B. Fischman, Y. T. Zheng, et al., 2014a: Combined satellite and radar retrievals of drop concentration and CCN at convective cloud base. Geophys. Res. Lett., 41, 3259–3265, DOI:  https://doi.org/10.1002/2014gl059453.CrossRefGoogle Scholar
  28. Rosenfeld, D., G. H. Liu, X. Yu, et al., 2014b: High resolution (375 m) cloud microstructure as seen from the NPP/VIIRS satellite imager. Atmos. Chem. Phys., 14, 2479–2496, DOI:  https://doi.org/10.5194/acp-14-2479-2014.CrossRefGoogle Scholar
  29. Rosenfeld, D., Y. T. Zheng, E. Hashimshoni, et al., 2016: Satellite retrieval of cloud condensation nuclei concentrations by using clouds as CCN chambers. Proc. Natl. Acad. Sci. USA, 113, 5828–5834, DOI:  https://doi.org/10.1073/pnas.1514044113.CrossRefGoogle Scholar
  30. Wallace, J. M., and P. V. Hobbs, 2006: Chapter 6: Cloud Micro-physics. Atmospheric Science: An Introductory Survey (Second Edition), J. M. Wallace, and P. V. Hobbs, Eds., Academic Press, San Diego, 209–269, 483 pp, DOI:  https://doi.org/10.1016/B978-0-12-732951-2.50011-9.CrossRefGoogle Scholar
  31. Wan, B. C., Z. Q. Gao, F. Chen, et al., 2017: Impact of Tibetan Plateau surface heating on persistent extreme precipitation events in southeastern China. Mon. Wea. Rev., 145, 3485–3505, DOI:  https://doi.org/10.1175/mwr-d-17-0061.1.CrossRefGoogle Scholar
  32. Wang, B. J., Y. X. Huang, D. Wei, et al., 2017: Structure analysis of heavy precipitation over the eastern slope of the Tibetan Plateau based on TRMM data. Acta Meteor. Sinica, 75, 966–980, DOI:  https://doi.org/10.11676/qxxb2017.062. (in Chinese)Google Scholar
  33. Wang, H., H. Lei, Deligeer, et al., 2002: A numerical simulation of characteristics of convective cloud at the upper reaches of the Yellow River. Climatic Environ. Res., 7, 397–408. (in Chinese)Google Scholar
  34. Wang, H., Y. L. Luo, and R. H. Zhang, 2011: Analyzing seasonal variation of clouds over the Asian monsoon regions and the Tibetan Plateau region using CloudSat/CALIPSO data. Chinese J. Atmos. Sci., 35, 1117–1131, DOI:  https://doi.org/10.3878/j.issn.1006-9895.2011.06.11. (in Chinese)Google Scholar
  35. Wang, L. J., Y. Yin, Z. Y. Yao, et al., 2013: Microphysical responses to catalysis during a stratocumulus aircraft seeding experiment over the Sanjiangyuan region of China. J. Meteor. Res., 27, 849–867, DOI:  https://doi.org/10.1007/sl3351-013-0609-6.Google Scholar
  36. Wu, C., L. P. Liu, and X. C. Zhai, 2017: The comparison of cloud base observations with Ka-band solid-state transmitter-based millimeter wave cloud radar and ceilometer in summer over Tibetan Plateau. Chinese J. Atmos. Sci., 41, 659–672, DOI:  https://doi.org/10.3878/j.issn.1006-9895.1701.16170. (in Chinese)Google Scholar
  37. Xia, X., R. C. Ren, G. X. Wu, et al., 2016: An analysis on the spatiotemporal variations and dynamic effects of the tropopause and the related stratosphere-troposhpere coupling surrounding the Tibetan Plateau area. Acta Meteor. Sinica, 74, 525–541, DOI:  https://doi.org/10.11676/qxxb2016.036. (in Chinese)Google Scholar
  38. Xu, X. D., and L. X. Chen, 2006: Advances of the study from Tibetan Plateau experiment on atmospheric sciences. J. Appl. Meteor. Sci., 17, 756–772. (in Chinese)Google Scholar
  39. Xu, X. D., T. L. Zhao, X. H. Shi, et al., 2015: A study of the role of the Tibetan Plateau’s thermal forcing in modulating rain-band and moisture transport in eastern China. Acta Meteor. Sinica, 73, 20–35, DOI:  https://doi.org/10.11676/qxxb2014.051. (in Chinese)Google Scholar
  40. Yue, Z. G., D. Rosenfeld, G. H. Liu, et al., 2019: Automated mapping of convective clouds (AMCC) thermodynamical, micro-physical and CCN properties from SNPP/VIIRS satellite data. J. Appl. Meteor. Climatol., 58, 887–902, DOI:  https://doi.org/10.1175/jamcd-18-0144.1.CrossRefGoogle Scholar
  41. Zhang, H. F., S. G. Guo, Y. J. Zhang, et al., 2003: Distribution characteristics of severe convective thunderstorm clouds over Qinghai-Xizang Plateau. Plateau Meteor., 22, 558–564. (in Chinese)Google Scholar
  42. Zhang, Q., X. Q. Song, J. T. Liu, et al., 2016: Observation of cloud base height with ceilometer in Tibetan Planteau during summer. J. Optoelectronics Laser, 27, 406–412, DOI:  https://doi.org/10.16136/j.joel.2016.04.0753. (in Chinese)Google Scholar
  43. Zhang, X., K. Q. Duan, and P. H. Shi, 2015: Cloud vertical profiles from CloudSat data over the eastern Tibetan Plateau during summer. Chinese J. Atmos. Sci., 39, 1073–1080, DOI:  https://doi.org/10.3878/j.issn.l006-9895.1502.14196. (in Chinese)Google Scholar
  44. Zheng, Y. T., and D. Rosenfeld, 2015: Linear relation between convective cloud base height and updrafts and application to satellite retrievals. Geophys. Res. Lett., 42, 6485–6491, DOI:  https://doi.org/10.1002/2015gl064809.CrossRefGoogle Scholar
  45. Zheng, Y. T., D. Rosenfeld, and Z. Q. Li, 2015: Satellite inference of thermals and cloud-base updraft speeds based on retrieved surface and cloud-base temperatures. J. Atmos. Sci., 72, 2411–2428, DOI:  https://doi.org/10.1175/jas-d-14-0283.1.CrossRefGoogle Scholar
  46. Zhu, Y. N., D. Rosenfeld, X. Yu, et al., 2014: Satellite retrieval of convective cloud base temperature based on the NPP/VIIRS Imager. Geophys. Res. Lett., 41, 1308–1313, doi: https://doi.org/10.1002/2013gl058970.CrossRefGoogle Scholar
  47. Zhu, Y. N., D. Rosenfeld, X. Yu, et al., 2015a: Separating aerosol microphysical effects and satellite measurement artifacts of the relationships between warm rain onset height and aerosol optical depth. J. Geophys. Res. Atmos., 120, 7726–7736, DOI:  https://doi.org/10.1002/2015jd023547.CrossRefGoogle Scholar
  48. Zhu, Y. N., X. Yu, X. H. Xu, et al., 2015b: Glaciation and ice multiplication of convective clouds and their dependence on aerosol investigated by satellites. Plateau Meteor., 34, 1758–1764. (in Chinese)Google Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019

Authors and Affiliations

  • Zhiguo Yue
    • 1
    • 2
  • Xing Yu
    • 2
    Email author
  • Guihua Liu
    • 2
  • Jin Dai
    • 2
  • Yannian Zhu
    • 2
  • Xiaohong Xu
    • 2
  • Ying Hui
    • 2
  • Chuang Chen
    • 2
  1. 1.Meteorological Institute of Shaanxi ProvinceXi’anChina
  2. 2.Office of Weather Modification of Shaanxi ProvinceXi’anChina

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