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An hourly updated WRF-3DVar weather radar data assimilation system and its application for rainfall-runoff prediction in North China

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

  • Email：melodieyu@163.com
  • Introduction：刘昱辰，研究方向为数值大气模拟、流域水文预报。E-mail： melodieyu@163.com
LIU Yuchen1,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

  • Email：jia.liu@iwhr.com
  • Introduction：刘佳，研究方向为水文模拟与预报、中尺度数值大气模拟、天气雷达与数据同化。E-mail： jia.liu@iwhr.com
LIU Jia1*,  
  • Affiliation：

    State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

LI Chuanzhe1,  
  • Affiliation：

    State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

    Power China Chengdu Engineering Corporation Limited, Chengdu 610072, China

WANG Wei12,  
  • Affiliation：

    State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

    Research Center on Flood & Drought Reduction of the Ministry of Water Resources, Beijing 100038, China

TIAN Jiyang13

resumen

The frequency of extreme rainfall and flooding in North China has increased because of the influence of climate change and human activities. Convective and strong precipitation processes occur in summer. Under the influence of the mixed flow generation mechanism in semihumid and semiarid areas, the flood burst is strong and difficult to forecast. Based on the Weather Research Forecast (WRF) model, namely, coupled WRF-Hydro, this study uses three-dimensional variational data assimilation (3DVAR) in constructing the WRF-3DVAR assimilation system for a rapid hourly update to assimilate high spatial and temporal resolution radar reflectivity data with the traditional meteorological observed data from the Global Telecommunication System (GTS). The study of rainfall-runoff prediction based on the land-atmosphere coupling is conducted by taking the typical rainfall processes of the north and south branches of the Daqinghe River Basin as the research object. Moreover, the performance of the rainfall-runoff prediction method in North China is further verified. The research results have some theoretical and practical values for constructing the data assimilation system of the atmospheric model and flood forecast practice in northern China.We employ three nested domains and adopt the GFS data for driving the WRF model. This study evaluates the improvement effect of WRF on forecasting rainfall and WRF-Hydro forecasting runoff by assimilating radar reflectivity and GTS data. The GTS data are released every 6 h. Thus, in the hourly assimilation scheme, GTS is only assimilated at the 6th, 12th, 18th, and 24th h from the start of the storm. However, radar reflectivity is set to assimilate once every hour. The rainfall evaluation indexes include Root Mean Square Error (RMSE), Mean Bias Error (MBE), and Critical Success Index (CSI). CSI/RMSE is a comprehensive index for evaluating rainfall forecast results. RMSE, MBE, and Nash (Nash-Sutcliffe efficiency coefficient) are used to evaluate runoff.The results show that the precipitation forecasted by the WRF model is always lower than the observed rainfall. However, assimilation systems can increase rainfall. The improved initial conditions in the WRF-3DVAR system via radar data assimilation and GTS data achieve good short-term and convectively strong precipitation. The high assimilation frequency significantly helps trigger and maintain the convective activities in the 3DVAR framework and the storm case applied. The assimilation weather radar combined with the traditional meteorological observed data can effectively improve the rainfall prediction accuracy of the WRF model, particularly for the rainfall with uniform spatial and temporal distribution. The CSI/RMSE index of the forecast rainfall after assimilation is increased by 23.24%—50.00%. Whether data assimilation is carried out or not, the CSI index results show different degrees of rainfall false alarm frequency. In the runoff forecast, the accurate rainfall forecast after data assimilation also improves the runoff forecast results to a certain extent. The peak flow error is reduced by 15.05%, 38.07%, 18.53% and 6.99%, the flood volume error is reduced by 25.99%, 29.32%, 26.02% and 23.95%, and the Nash efficiency coefficient is increased by 0.25, 0.25, 0.29 and 0.48, respectively. However, the forecast results of flood peak discharge and the peak occurrence time for the rainfall with uneven spatial and temporal distribution, large magnitude, and slow water retreat are still not ideal. Moreover, subsequent improvements should be made in terms of accurate calibration of hydrological parameters and real-time correction of forecast errors. The accuracy of the WRF-Hydro runoff forecast in the mixed runoff generation areas of northern China mainly depends on two aspects. One is the accuracy of the rainfall forecast of WRF model, which is related to the driving data and rainfall distribution type. For the rainfall with uneven spatial-temporal distribution, the poor rainfall forecast indirectly affects the runoff forecast effect. On the contrary, it is related to different runoff characteristics, such as the complexity of the runoff process, the magnitude of runoff, the presence or absence of base flow in the early stage, and the soil water content. Data assimilation improves rainfall forecast. Thus, the runoff forecast results of WRF-Hydro are improved to a certain extent by reasonably using a basic flow module, improving land surface initial conditions, such as soil water content, and combining with effective real-time correction technology.

palabra clave

remote sensing;data assimilation;Doppler Weather Radar;quick cycle update;WRF-Hydro;rainfall-runoff forecasting

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