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Experiment of assimilating Doppler radar data in Typhoon Saomai based on the different initial conditions
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Feifei Shen1, 2, 3, Chao Tang1, 4, Dongmei Xu1, 2, 3, *, Hong Li5, Ruixia Liu6
Haiyang Xuebao | 2021, 43(1) : 69 - 81
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Haiyang Xuebao | 2021, 43(1): 69-81
• Article •
Experiment of assimilating Doppler radar data in Typhoon Saomai based on the different initial conditions
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Feifei Shen1, 2, 3, Chao Tang1, 4, Dongmei Xu1, 2, 3, *, Hong Li5, Ruixia Liu6
Affiliations
  • 1Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
  • 2Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610225, China
  • 3The Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China
  • 4Weixi Meteorological Bureau, Weixi 674600, China
  • 5Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China
  • 6National Satellite Meteorological Center, Beijing 100081, China
Published: 2021-01-25 doi: 10.12284/hyxb2021075
Outline
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Based on the WRF (Weather Research and Forecasting Model) and its three-dimensional variational data assimilation system, the numerical simulation and Doppler radar data assimilation are conducted with the data of GFS (Global Forecasting System) and JMA (Japan Meteorological Agency) reanalyses as the initial conditions respectively. The impact of assimilation radar data in different background fields on the initial typhoon field, internal structure and forecast were investigated based on the super typhoon case Saomai in 2006. The results show that, both experiments with GFS and JMA data are able to enhance the typhoon initial winds field at 700 hPa and geopotential height field at 500 hPa after assimilating radar observations. The improvements in terms of the root-mean-square error during the 3 h during the data assimilation cycling, the minimum sea level pressure, and the thermal and dynamic structure from the JMA tests are more significant than that with GFS data. The forecast skills for the precipitation, the typhoon track, and the intensity are also noticeable with JMA data by correctly predicting the precipitation location in the front of typhoon.

initial conditions  /  data assimilation  /  WRF model  /  radar radial velocity
Feifei Shen, Chao Tang, Dongmei Xu, Hong Li, Ruixia Liu. Experiment of assimilating Doppler radar data in Typhoon Saomai based on the different initial conditions[J]. Haiyang Xuebao, 2021 , 43 (1) : 69 -81 . DOI: 10.12284/hyxb2021075
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Article Info
doi: 10.12284/hyxb2021075
  • Receive Date：2019-10-23
  • Online Date：2026-02-26
  • Published：2021-01-25
Article Data
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History
  • Received：2019-10-23
  • Revised：2019-12-06
Funding
Affiliations
    1Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
    2Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610225, China
    3The Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China
    4Weixi Meteorological Bureau, Weixi 674600, China
    5Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China
    6National Satellite Meteorological Center, Beijing 100081, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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