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Application Research of Big Data in Heavy Rainfall Forecast Model in Meiyu Season

  • Shan YinEmail author
  • Jie MaEmail author
  • Ronghua Jin
  • Ningfang Zhou
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)

Abstract

In this paper, 33 classic Meiyu precipitation processes in recent 30 years are selected by using historical observation big data, and the inter-annual variation characteristics of heavy rainfall location are analyzed by descriptive big data analysis method. Besides, it is verified that there is a close connection between rainfall location and 500 hPa 5840 geopotential meter isoline. However, serious errors appeared in the forecast model during the medium-range precipitation forecast (4–10 days) from June 30th to July 4th, 2016. Therefore, in this paper, the causes of errors are analyzed by diagnostic big data analysis method using European Centre for Medium-range Weather Forecasts (ECMWF) ensemble forecast data. The results show that the premise for an accurate forecast by the classic forecast model is that, the heavy precipitation process must be accompanied by a southward-moving cold air. As the precipitation was a warm area rainfall in the monsoon region, errors were caused by the lack of high-level cold air participation. On one hand, this study proves the important impact of southward-moving cold air on the accuracy of rain belt location forecast. On the other, it will undoubtedly serve as an important reference for the subjective correction of the rain belt location in the forecast operation.

Keywords

Big data Rain belt during Meiyu season 5840 geopotential meter isoline Monsoon Jet 

Notes

Acknowledgement

This work was supported by grants of the National Natural Science Foundation of China (41575066) and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2015BAC03B04, 2015BAC03B06 & 2015BAC03B07).

References

  1. 1.
    Si, D., Ding, Y.H., Liu, Y.J.: Decadal northward shift of the Meiyu belt and the possible cause. Chin. Sci. Bull. 55(1), 68–73 (2010)Google Scholar
  2. 2.
    Xu, W.G., Jiang, J.: Characteristics of the rain belt of Meiyu between inter-annual and inter-decadal climate variations. J. Nanjing Univ. 40(3), 292–303 (2004)Google Scholar
  3. 3.
    Ding, Y.H., Liu, J.J., Sun, Y., et al.: A Study of the Synoptic-Climatology of the Meiyu System in East Asia. Chin. J. Atmos. Sci. 31(6), 1082–1101 (2007)Google Scholar
  4. 4.
    Oh, J.H., Kwon, W.T., Ryoo, S.B., et al.: Review of the research on Changma and future observational study (KORMEX). Adv. Atmos. Sci. 14(2), 207–222 (1997)CrossRefGoogle Scholar
  5. 5.
    Wei, F.Y., Xie, Y.: Interannual and interdecadal oscillations of meiyu over the middle-lower reaches of the changjiang river for 1885–2000. Q. J. Appl. Meteorol. 16(4), 492–499 (2005)Google Scholar
  6. 6.
    Zong, H.F., Zhang, Q.Y., Chen, L.T.: Temporal and spatial variations of precipitation in Eastern China during the Meiyu period and their relationships with circulation and sea surface temperature. Chin. J. Atmos. Sci. 30(6), 1189–1197 (2006)Google Scholar
  7. 7.
    Su, T.H., Xue, F.: The intraseasonal variation of summer monsoon circulation and rainfall in East Asia. Chin. J. Atmos. Sci. 34(3), 611–628 (2010)Google Scholar
  8. 8.
    Sampe, T., Xie, S.P.: Large-scale dynamics of the Meiyu-baiu rainband: environmental forcing by the westerly jet. J. Clim. 23, 113–134 (2010)CrossRefGoogle Scholar
  9. 9.
    Sugimoto, S., Hirakuchi H.: Simulation of precipitation caused by a Baiu front: an evaluation study with radar data. In: Weather Radar Information and Distributed Hydrological Modelling, vol. 282, pp. 51–58. IAHS Publication (2003)Google Scholar
  10. 10.
    Kalnay, E., Kanamitsu, M., Kistler, R., et al.: The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.National Meteorological CenterBeijingChina

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