Pure and Applied Geophysics

, Volume 176, Issue 1, pp 357–370 | Cite as

TRMM-Based Optical and Microphysical Features of Precipitating Clouds in Summer Over the Yangtze–Huaihe River Valley, China

  • Yuan-Jian Yang
  • Hong Wang
  • Fengjiao Chen
  • Xiaoyi Zheng
  • Yunfei Fu
  • Shuxue ZhouEmail author


The optical and microphysical features of precipitating clouds are key information for studying the satellite-based precipitation estimation, cloud radiative effects, aerosol–cloud–precipitation interactions, cloud and precipitation parameterization in weather and climate models. In this study, 15-year synchronous spectral and radar observations from the TRMM satellite were used to statistically explore the optical and microphysical features of precipitating clouds (PCs), including cloud effective radius (CER), cloud optical thickness (COT), cloud water path (CWP), thermal infrared brightness temperature at channel 4 (TB4) of cloud top, and storm top height (STH) and their relationships with surface rain rates in summer over Yangtze–Huaihe River Valley (YHRV). Results show that the optical and microphysical features of PCs/stratiform PCs/convective PCs vary with geographical locations in summer over YHRV, due to the different ambient meteorological and topographical conditions. Higher CER/COT/CWP/STH and lower TB4 mainly locate at areas of bigger rain rates. For PCs, their spatial distribution of CER is mainly dominated by stratiform PCs, while their spatial distribution of COT/CWP is mainly dominated by convective PCs. Moreover, stratiform precipitation is the dominant form in summer over YHRV and, thus, most PCs present vertical structures of optical and microphysical features as stratiform PCs. Stratiform PCs are usually thicker and contain more water vapor with bigger cloud particles than convective PCs (including deep and shallow convective PCs). In addition, existing shallow convective PCs are associated with lower storm heights and warmer cloud tops. Finally, surface rain rates of PCs (convective/stratiform PCs) increase gradually with the increment of CER/COT/CWP/STH, especially under 5 (15/5) mm/h. Similar relationship between surface rain rates and COT/CWP for shallow convective PCs is also found under 0.75 mm/h. Surface rain rate of PCs (convective/stratiform PCs) with cold cloud tops (TB4 < 247 K) obviously increases as TB4 decreases. Differently, for shallow convective PCs with warmer cloud tops (TB4 > 264 K), surface rain rate usually increases as CER decreases, which suggests that aerosol indirect effects are dominant in lower PCs, because over pollution regions abundant aerosols enter into lower clouds more easily and then suppress the development of shallow convective PCs.


Optical feature microphysical feature precipitating clouds TRMM Yangtze–Huaihe River Valley 



We appreciate the comments and suggestions of the editors and anonymous reviewers. Many thanks are extended to Japan Aerospace Exploration Agency and Goddard Space Flight Center for providing PR 2A25 and VIRS data. This work is jointly supported by the National Key Projects of Ministry of Science and Technology of China (2016YFA0602100 and 2017YFC1501402), the National Natural Science Foundation of China (41675009, 41675041, 41601550, 41620104009, 41230419 and 41505004), the Startup Foundation for Introducing Talent of NUIST, the Startup Foundation for Anhui Meteorological Bureau (RC201703), Jiangsu Provincial Natural Science Fund Project (BK20150910), Natural Science Foundation of Anhui Province (1808085MD99), Huaihe river basin meteorological open fund (HRM201507), the Open Project Program (KLME1508) of the Key Laboratory of Meteorological Disaster of Ministry of Education at Nanjing University of Information Science and Technology, and The Startup Foundation for Introducing Talent of NUIST.


  1. Arkin, P. A., & Meisner, B. N. (1987). The relationship between large-scale convective rainfall and cold cloud over the western hemisphere during 1982–84. Monthly Weather Review, 115(1), 51–74.CrossRefGoogle Scholar
  2. Awaka, J., Iguchi, T., & Okamoto, K. (2009). TRMM PR standard algorithm 2A23 and its performance on bright band detection. Journal of the Meteorological Society of Japan, 87A, 31–52.CrossRefGoogle Scholar
  3. Ba, M. B., & Gruber, A. (2001). GOES Multispectral Rainfall Algorithm (GMSRA). Journal of Applied Meteorology, 40(8), 1500–1514.CrossRefGoogle Scholar
  4. Chen, Y., & Fu, Y. (2017). Characteristics of VIRS signals within pixels of TRMM PR for warm rain in the tropics and subtropics. Journal of Applied Meteorology & Climatology, 56(3), 789–801.CrossRefGoogle Scholar
  5. Chen, Y., & Fu, Y. (2018). Tropical echo-top height for precipitating clouds observed by multiple active instruments aboard satellites. Atmospheric Research, 199, 54–61.CrossRefGoogle Scholar
  6. Chen, F., Fu, Y., Liu, P., et al. (2016). Seasonal variability of storm top altitudes in the tropics and subtropics observed by TRMM PR. Atmospheric Research, 169(2016), 113–126.CrossRefGoogle Scholar
  7. Guo, J., Deng, M., Lee, S. S., et al. (2016). Delaying precipitation and lightning by air pollution over the Pearl River Delta. Part I: Observational analyses. Journal of Geophysical Research-Atmosphere, 121, 6472–6488.CrossRefGoogle Scholar
  8. Ding, Y. H. (2007). The variability of the Asian summer monsoon. Journal of the Meteorological Society of Japan, 85, 21–54.CrossRefGoogle Scholar
  9. Fu Y., F. Chen, G., Liu, et al. (2016). Recent trends of summer convective and stratiform precipitation in Mid-Eastern China, Scientific Reports, 6, 33044.
  10. Fu, Y. (2014). Cloud parameters retrieved by the bispectral reflectance algorithm and associated applications. Journal of Meteorological Research, 28(5), 965–982.CrossRefGoogle Scholar
  11. Fu, Y., Feng, J., Zhu, H., et al. (2005). Structures of a thermal convective precipitation system happened in controlling of the western subtropical pacific high. Acta Meteorologica Sinica (in Chinese), 63(5), 750–761. Scholar
  12. Fu, Y. F., Lin, Y. H., Liu, G. S., et al. (2003). Seasonal characteristics of precipitation in 1998 over East Asia as derived from TRMM PR. Advances in Atmospheric Sciences, 20, 511–529.CrossRefGoogle Scholar
  13. Fu, Y., Pan, X., Yang, Y., et al. (2017). Climatological characteristics of summer precipitation over East Asia measured by TRMM PR: A review. Journal Meteorological Research, 31(1), 142–159. Scholar
  14. Gadgil, S., & Kumar, K. R. (2006). The Asian monsoon-agriculture and economy. In B. Wang (Ed.), The Asian Monsoon (pp. 651–682). Chichester: Praxis Publishing Ltd.CrossRefGoogle Scholar
  15. Griffith, C. G., Woodley, W. L., Grube, P. G., et al. (1978). Rain estimation from geosynchronous satellite imagery—visible and infrared studies. Monthly Weather Review, 106(8), 1153–1171.CrossRefGoogle Scholar
  16. Guo J. P., Deng, M. J., Fan, J. et al. (2014). Precipitation and air pollution at mountain and plain stations in northern China: Insights gained from observations and modeling, Journal of Geophysical Research-Atmosphere, 119 (8), 4793–4807. 10.10022013JD021161.Google Scholar
  17. Hobbs, P. V. (1989). Research on clouds and precipitation: past, present, and future. The Bulletin of the American Meteorological Society, 70, 282–285.CrossRefGoogle Scholar
  18. Hu, L., Li, Y. D., Yang, S., et al. (2011). Seasonal variability in tropical and subtropical convective and stratiform precipitation of the East Asian monsoon. Science China Earth Science, 54, 1595–1603. Scholar
  19. Iguchi, T., Kozu, T., Kwiatkowski, J., Meneghini, R., Awaka, J., & Okamoto, K. (2009). Uncertainties in the rain profiling algorithm for the TRMM Precipitation Radar. Journal of the Meteorological Society of Japan, 87A, 1–30.CrossRefGoogle Scholar
  20. Iguchi, T., Kozu, T., Meneghini, R., Awaka, J., & Okamoto, K. (2000). Rain-profiling algorithm for the TRMM precipitation radar. Journal of Applied Meteorology, 39, 2038–2052.CrossRefGoogle Scholar
  21. Iguchi, T., & Meneghini, R. (1994). Intercomparison of single-frequency methods for retrieving a vertical rain profile from airborne or spaceborne radar data. Journal of Atmospheric and Oceanic Technology, 11, 1507–1516.CrossRefGoogle Scholar
  22. Awaka J., Iguchi, T., Okamoto, K. (1998). Early results on rain type classification by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar. Proc 8th URSI Commission F Open Symp, Aveiro, Portugal, 143–146.Google Scholar
  23. Inoue, T., & Aonashi, K. (2000). A comparison of cloud and rainfall information from instantaneous visible and infrared scanner and precipitation radar observations over a frontal zone in East Asia during June 1998. Journal of Applied Meteorology, 39, 2292–2301.CrossRefGoogle Scholar
  24. Kummerow, C., Barnes, W., Kozu, T., et al. (1998). The tropical rainfall measuring mission (TRMM) sensor package. Journal of Atmospheric and Oceanic Technology, 15, 809–817.CrossRefGoogle Scholar
  25. Lau, K. M. (1992). East Asian summer monsoon rainfall variability and climate teleconnection. Journal of the Meteorological Society of Japan, 70, 211–242.CrossRefGoogle Scholar
  26. Lensky, I. M., & Rosenfeld, D. (1997). Estimation of precipitation area and rain intensity based on the microphysical properties retrieved from NOAA AVHRR Data. Journal of Applied Meteorology, 36, 234–242.CrossRefGoogle Scholar
  27. Liu, G. S., & Fu, Y. F. (2001). The characteristics of Tropical precipitation profiles as inferred from satellite radar measurements. Journal of the Meteorological Society of Japan, 79, 131–143.CrossRefGoogle Scholar
  28. Liu, Q., & Fu, Y. (2010). Comparison of radiative signals between precipitating and non-precipitating clouds in frontal and typhoon domains over East Asia. Atmosphere Research., 96, 436–446. Scholar
  29. Liu, Q., Fu, Y., Yu, R., et al. (2008). A new satellite-based census of precipitating and non-precipitating clouds over the tropics and subtropics. Geophysical Research Letters, 35, L07816. Scholar
  30. Liu, Y., Xi, D. G., Li, Z. L., et al. (2014). Analysis and application of the relationship between cumulonimbus (Cb) cloud features and precipitation based on FY-2C image. Atmosphere, 5(2), 211–229.CrossRefGoogle Scholar
  31. Lu, D., Yang, Y., & Fu, Y. (2016). Interannual variability of summer monsoon convective and stratiform precipitation in East Asia during 1998–2013. International Journal of Climatology, 36(10), 3507–3520. Scholar
  32. Min, Q., Li, R., Lin, B., et al. (2009). Evidence of mineral dust altering cloud microphysics and precipitation. Atmospheric Chemistry and Physics, 9(9), 3223–3231.CrossRefGoogle Scholar
  33. Nakajima, T. Y., & Nakajima, T. (1995). Wide-area determination of cloud microphysical properties from NOAA AVHRR measurements for FIRE and ASTEX regions. Journal of Atmospheric Science, 52, 4043–4059.CrossRefGoogle Scholar
  34. Nauss, T., & Kokhanovsky, A. A. (2006). Discriminating raining from non-raining clouds at mid-latitudes using multispectral satellite data. Atmospheric Chemistry and Physics, 6(12), 5031–5036.CrossRefGoogle Scholar
  35. Qin, F., & Fu, Y. (2016). TRMM-observed summer warm rain over the tropical and subtropical Pacific Ocean: Characteristics and regional differences. Journal of Meteorological Research, 30(3), 371–385.CrossRefGoogle Scholar
  36. Rosenfeld, D. (2000). Suppression of rain and snow by urban and industrial air pollution. Science, 287(5459), 1793–1796.CrossRefGoogle Scholar
  37. Rosenfeld, D., Woodley, W. L., Khain, A., et al. (2012). Aerosol effects on microstructure and intensity of tropical cyclones. The Bulletin of the American Meteorological Society, 93(7), 987–1001.CrossRefGoogle Scholar
  38. Song, H. J., & Sohn, B. J. (2015). Two heavy rainfall types over the Korean peninsula in the humid East Asian summer environment: a satellite observation study. Monthly Weather Review, 143, 363–382. Scholar
  39. Wang, F., Guo, J., Zhang, J., et al. (2015). Multi-sensor quantification of aerosol-induced variability in warm cloud properties over eastern China. Atmospheric Environment, 113, 1–9.CrossRefGoogle Scholar
  40. Xu, W. X. (2013). Precipitation and convective characteristics of summer deep convection over East Asia observed by TRMM. Monthly Weather Review, 141, 1577–1592. Scholar
  41. Yamamoto, M. K., Furuzawa, F. A., Higuchi, A., et al. (2008). Comparison of diurnal variations in precipitation systems observed by TRMM PR, TMI, and VIRS. Journal of Climate, 21, 4011–4028. Scholar
  42. Yang, Y. J., Fu, Y. F., Chen, F. J. et al. (2014). Spectral characteristics of precipitating clouds during the Meiyu over the Yangtze-Huaihe River Valley from merged TRMM precipitation radar and visible/infrared scanner data. Proceedings of the SPIE 9259, Remote Sensing of the Atmosphere, Clouds, and Precipitation V, Beijing, China, 13 October, SPIE, 92591 K,
  43. Yang, Y., Fu, Y., Qin, F., et al. (2017). Radiative forcing of the tropical thick anvils evaluated by combining TRMM with atmospheric radiative transfer model. Atmospheric Science. Letters., 18(5), 222–229.CrossRefGoogle Scholar
  44. Yang, Y.-J., Lu, D.-R., Fu, Y.-F., et al. (2015). Spectral characteristics of tropical anvils obtained by combining TRMM precipitation radar with visible and infrared scanner data. Pure and Applied Geophysics, 172(6), 1717–1733.CrossRefGoogle Scholar
  45. Yuter, S. E., & Houze, R. A., Jr. (1995). Three dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Monthly Weather Review, 123, 1941–1963.CrossRefGoogle Scholar
  46. Zipser, E. J., & Lutz, K. R. (1994). The vertical profile of radar reflectivity of convective cells: A strong indicator of storm intensity and lightning probability? Monthly Weather Review, 122, 1751–1759.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Meteorological Disaster of Ministry of Education, School of Atmospheric PhysicsNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China
  2. 2.Anhui Meteorological Information CenterHefeiPeople’s Republic of China
  3. 3.Key Laboratory of Atmospheric Sciences and Satellite Remote Sensing of Anhui ProvinceAnhui Weather Modification OfficeHefeiPeople’s Republic of China
  4. 4.School of Earth and Space SciencesUniversity of Science and Technology of ChinaHefeiPeople’s Republic of China

Personalised recommendations