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Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

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Abstract

To improve the accuracy of short-term (0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System (HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) three-dimensional variational data assimilation (3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station (AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting (QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score (FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.

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Authors and Affiliations

  1. Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

    Tuanjie Hou, Hengchi Lei & Zhaoxia Hu

  2. Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, 73072, USA

    Tuanjie Hou, Fanyou Kong & Xunlai Chen

  3. Shenzhen Key Laboratory of Severe Weather in South China, Shenzhen, 518040, China

    Xunlai Chen

  4. Shenzhen Meteorological Bureau, Shenzhen, 518040, China

    Xunlai Chen

  5. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Hengchi Lei

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  1. Tuanjie Hou
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  2. Fanyou Kong
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  4. Hengchi Lei
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  5. Zhaoxia Hu
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Correspondence to Tuanjie Hou.

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Hou, T., Kong, F., Chen, X. et al. Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China. Adv. Atmos. Sci. 32, 967–978 (2015). https://doi.org/10.1007/s00376-014-4155-7

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  • DOI: https://doi.org/10.1007/s00376-014-4155-7

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