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Analysis on Operational Forecast of Typhoon Rumiba(1818)Affecting Liaodong Peninsula
LIANG Jun, FENG Chengcheng, ZHANG Shengjun, LIU Xiaochu, ZHANG Lihong, LI Tingting
Journal of Arid Meteorology    2020, 38 (2): 280-289.   DOI: 10.11755/j.issn.1006-7639(2020)-02-0280
Abstract408)      PDF(pc) (4841KB)(1953)       Save
Based on CMA Tropical Cyclone Yearbook,FY-2G temperature of brightness blackbody (TBB) product (0.1°×0.1°), hourly rainfall observations from automatic weather stations, conventional observation and ERA-Interim reanalysis data(0.125°×0.125°), the heavy precipitation process in the Liaodong Peninsula caused by typhoon Rumiba (1818) was analyzed preliminarily. The major conclusions are as follows: (1) The difficulty of “Rumiba” prediction was to predict the turning point and the track after landing. The eastward movement of westerly trough and continental high blocked the westward movement of “Rumiba”. The northward position of the subtropical high caused by northwesterly movement of typhoon “Soulik” and the continental high in northeast China were favorable for the northward movement of “Rumiba”. (2) The low-level southeast jet forming between typhoon “Rumiba”, “Soulik” and the subtropical high provided continuous water vapor and energy, which was conducive to maintaining the intensity of “Rumiba” and inducing continuous occurrence of the heavy precipitation in the Liaodong Peninsula. (3) The interaction of “Rumiba” in transformation process with westerly trough and low-level jet was beneficial to the development of spiraling cloud system on its north side. The heavy precipitation area was closely related to the cold and warm advection in the convergence zone of the north side of low-level typhoon circulation. And the energy front zone appearing at the intersection of cold and warm advection was a good indication for heavy rainfall. (4) There were great differences in the prediction of turning point and path after turning from numerical prediction models. In addition to referring to ensemble prediction products, the similarity prediction method can also be used to compare and analyze the large-scale environmental field of the similar individual cases, and the correction can be carried out with the help of the numerical prediction products and the similar individual cases.
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