Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year 4.819
  • CiteScore value: 4.10 CiteScore 4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H index value: 99 Scimago H index 99
Volume 22, issue 8 | Copyright
Hydrol. Earth Syst. Sci., 22, 4329-4348, 2018
https://doi.org/10.5194/hess-22-4329-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 16 Aug 2018

Research article | 16 Aug 2018

Evaluation of Doppler radar and GTS data assimilation for NWP rainfall prediction of an extreme summer storm in northern China: from the hydrological perspective

Jia Liu1, Jiyang Tian1, Denghua Yan1, Chuanzhe Li1, Fuliang Yu1, and Feifei Shen2 Jia Liu et al.
  • 1State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China
  • 2Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing, 210044, China

Abstract. Data assimilation is an effective tool in improving high-resolution rainfall of the numerical weather prediction (NWP) systems which always fails in providing satisfactory rainfall products for hydrological use. The aim of this study is to explore the potential effects of assimilating different sources of observations, i.e., the Doppler weather radar and the Global Telecommunication System (GTS) data, in improving the mesoscale NWP rainfall products. A 24h summer storm occurring over the Beijing–Tianjin–Hebei region of northern China on 21 July 2012 is selected as a case study. The Weather Research and Forecasting (WRF) Model is used to obtain 3km rainfall forecasts, and the observations are assimilated using the three-dimensional variational (3DVar) data assimilation method. Eleven data assimilation modes are designed for assimilating different combinations of observations in the two nested domains of the WRF model. Both the rainfall accumulative amount and its distribution in space and time are examined for the forecasting results with and without data assimilation. The results show that data assimilation can effectively help improve the WRF rainfall forecasts, which is of great importance for hydrologic applications through the rainfall–runoff transformation process. Both the radar reflectivity and the GTS data are good choices for assimilation in improving the rainfall products, whereas special attention should be paid to assimilating radial velocity where unsatisfactory results are always found. The assimilation of the GTS data in the coarser domain has positive effects on the radar data assimilation in the finer domain, which can make the rainfall forecasts more accurate than assimilating the radar data alone. It is also found that the assimilation of more observations cannot guarantee further improvement of the rainfall products, whereas the effective information contained in the assimilated data is of more importance than the data quantity. Potential improvements of data assimilation in improving the NWP rainfall products are discussed and suggestions are further made.

Publications Copernicus
Download
Short summary
Both radar reflectivity and GTS data are good choices for assimilation in improving high-resolution rainfall of the NWP systems, which always fails in providing satisfactory rainfall products for hydrological use. Simultaneously assimilating GTS and radar data always performs better than assimilating radar data alone. The assimilation efficiency of the GTS data is higher than both radar reflectivity and radial velocity considering the number of data assimilated and its effect.
Both radar reflectivity and GTS data are good choices for assimilation in improving...
Citation
Share