Advances in Atmospheric Sciences

, Volume 32, Issue 7, pp 967–978

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

  • Tuanjie Hou
  • Fanyou Kong
  • Xunlai Chen
  • Hengchi Lei
  • Zhaoxia Hu
Article

DOI: 10.1007/s00376-014-4155-7

Cite this article as:
Hou, T., Kong, F., Chen, X. et al. Adv. Atmos. Sci. (2015) 32: 967. doi:10.1007/s00376-014-4155-7

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.

Key words

data assimilation radar data heavy rainfall quantitative precipitation forecasting 

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Tuanjie Hou
    • 1
    • 2
  • Fanyou Kong
    • 2
  • Xunlai Chen
    • 2
    • 3
    • 4
  • Hengchi Lei
    • 1
    • 5
  • Zhaoxia Hu
    • 1
  1. 1.Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Center for Analysis and Prediction of StormsUniversity of OklahomaNormanUSA
  3. 3.Shenzhen Key Laboratory of Severe Weather in South ChinaShenzhenChina
  4. 4.Shenzhen Meteorological BureauShenzhenChina
  5. 5.Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science & TechnologyNanjingChina