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Discussion papers | Copyright
https://doi.org/10.5194/hess-2017-689
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 Jan 2018

Research article | 02 Jan 2018

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

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 from the Doppler weather radar and the Global Telecommunication System (GTS) 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 in this 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 (3D-Var) data assimilation method. Eleven data assimilation modes are designed for assimilating different combinations of observations in the two nested domains of the WRF model. Results show that the assimilation can largely improve the WRF rainfall products especially the accumulative process of rainfall, which is of great importance for hydrologic applications through the rainfall-runoff transformation process. Both radar reflectivity and GTS data are good choices for assimilation in improving the rainfall products, whereas special attentions should be paid for assimilating radial velocity where unsatisfactory results are always found. Simultaneously assimilating GTS and radar data always perform better than assimilating radar data alone. The inclusion of GTS data in the nested domains when radar reflectivity and radial velocity are assimilated in the innermost domain show the best results among all the 11 assimilation modes. 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. 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 also made.

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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 perform 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...
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