Effect On The Simulation Of Precipitation In Middle And Lower Reaches Of The Yangtze River Made By Assimilating Of Doppler Radar Data

Doppler Weather Radar(DWR) can provide very high temporal and spatial resolution observations which conventional observations are short on. Data assimilation plays an important role in providing an accurate initial condition for numerical weather prediction(NWP). As we will complete the DWR network in China, how to make full use of the radar data to improve the model initial field is one of the of research fields in data assimilation. Through assimilating all the 46 SA DWRs with Weather Research and Forecasting(WRF) and Gridpoint Statistical Interpolation(GSI), the impact of DWR on the simulation of precipitation in middle and lower reaches of the Yangtze River is examined.DWR data is edited with a threshold and a quality control algorithm of DWR,which was developed by Center for Analysis and Prediction of Storms(CAPS),all the DWR radial velocity were edited by the 88d2 GSI to conduct the data quality control. The noise was removed effectively by the improved algorithm.The DWR reflectivity was quality control and combined by the CINRAD 3D Digital Mosaic System developed by State Key Laboratory of Severe Weather-Chinese Academy of Meteorological Sciences. The generated regional three-dimensional mosaic reflectivity can reflect the DWR locations and the precipitation fields. All the data was used effectively in GSI.All the 46 SA DWR data was assimilated in batch experiments. DWR can detect more data when there is precipitation around it and, meantime, can improve the simulation result more effectively when assimilated. The evaluations of the simulation fields were performed against conventional surface data and radiosonde observations. In general, no significant influences were found against the conventional surface data, but there was more influence on radiosonde observations than on conventional surface observations. It was found that the influence of DWR data assimilation on geopotential height associated with the intensity of precipitation. The impact was found up to 200 h Pa when there was heavy rainfall happed. Besides, 6 DWRs were also found abnormal when compared with background.A rainfall case in the middle and lower reaches of the Yangtze River was studied after batch experiments. The DWR data after quality control was used about 90 percent and improved the quantitative precipitation forecasting skills in the middle and lower reaches of the Yangtze River. Assimilating DWR radial velocity can enhance the information of mesoscale weather system in initial field and the simulated field, and improve the water vapor transmission. DWR reflectivity data is used mainly in cloud analysis which can adjust the amount of hydrometers and moisture. Besides, Assimilating radial velocity and reflectivity can get more precipitation than assimilation one of them.