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Advances in Meteorology
Volume 2013, Article ID 129642, 17 pages
http://dx.doi.org/10.1155/2013/129642
Research Article

Impact of 3DVAR Data Assimilation on the Prediction of Heavy Rainfall over Southern China

1Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73072, USA
3Shenzhen Meteorological Bureau, Shenzhen 518040, China

Received 26 March 2013; Revised 16 June 2013; Accepted 12 July 2013

Academic Editor: Jidong Gao

Copyright © 2013 Tuanjie Hou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [9 citations]

The following is the list of published articles that have cited the current article.

  • Jae-Hyun Seo, Yong Hee Lee, and Yong-Hyuk Kim, “Feature Selection for Very Short-Term Heavy Rainfall Prediction Using Evolutionary Computation,” Advances in Meteorology, vol. 2014, pp. 1–15, 2014. View at Publisher · View at Google Scholar
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  • Qinghua Miao, Dawen Yang, Hanbo Yang, and Zhe Li, “Establishing a rainfall threshold for flash flood warnings in China’s mountainous areas based on a distributed hydrological model,” Journal of Hydrology, 2016. View at Publisher · View at Google Scholar
  • Xingchao Chen, Juanzhen Sun, Kun Zhao, Bowen Zhou, and Wen-Chau Lee, “Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case,” Advances in Atmospheric Sciences, vol. 33, no. 10, pp. 1106–1119, 2016. View at Publisher · View at Google Scholar
  • Viswanadhapalli Yesubabu, Challa Venkata Srinivas, Sabique Langodan, and Ibrahim Hoteit, “Predicting extreme rainfall events over Jeddah, Saudi Arabia: impact of data assimilation with conventional and satellite observations,” Quarterly Journal Of The Royal Meteorological Society, vol. 142, no. 694, pp. 327–348, 2016. View at Publisher · View at Google Scholar
  • Yong-Hyuk Kim, and Yourim Yoon, “Spatiotemporal Pattern Networks of Heavy Rain among Automatic Weather Stations and Very-Short-Term Heavy-Rain Prediction,” Advances in Meteorology, vol. 2016, pp. 1–13, 2016. View at Publisher · View at Google Scholar
  • Abhijit Sarkar, Devajyoti Dutta, Paromita Chakraborty, and Someshwar Das, “Numerical diagnosis of situations causing heavy rainfall over the Western Himalayas,” Modeling Earth Systems and Environment, 2017. View at Publisher · View at Google Scholar
  • Huiqin Hu, Juanzhen Sun, and Qinghong Zhang, “Assessing the Impact of Surface and Wind Profiler Data on Fog Forecasting Using WRF 3DVAR: An OSSE Study on a Dense Fog Event over North China,” Journal of Applied Meteorology and Climatology, vol. 56, no. 4, pp. 1059–1081, 2017. View at Publisher · View at Google Scholar
  • Hamid Moradkhani, Grey Nearing, Peyman Abbaszadeh, and Sahani Pathiraja, “Fundamentals of Data Assimilation and Theoretical Advances,” Handbook of Hydrometeorological Ensemble Forecasting, pp. 1–26, 2018. View at Publisher · View at Google Scholar