Quality Control And Data Assimilation Experiment On Intensified Automatic Weather Station (IAWS) Observation Data

As one of the major weather disasters, rainstorm is common and multiple in China.In mid-June to early-July each year, plum rains often result in frequent and heavy rainfall in the lower-middle reaches of the Yangtze River. Storm occurrence,development and evolution is a very complex process. Using numerical simulation or forecast is one of the most common means of analysis and prediction of the heavy rains. In recent years, along with the increasingly sophisticated Numerical Weather Prediction (NWP) technology and the rapid development in computer power, the abundant types and improvement accuracy of observation data, data assimilation has been a rapid development. To adapt the demand for the meso-scale and short-time weather forecast, in addition to satellite remote sensing, aircraft observations, the surface Intensified Automatic Weather Station (IAWS) observation data is an important source of information.In this thesis, using mesoscale numerical model WRF and the global reanalysis data of NCEP, with a case of strong convective heavy rainfall occurred in the valley of middle and lower Yangtze River in early July of 2009, the simulation experiment is performed. On this basis, Jiangsu Province IAWS data is introduced and a necessary quality control (QC) been made. The experiments of comparing the IAWS data before and after QC along with the conventional radiosonde observation data are conducted with the WRF and its Four-dimensional variational assimilation (4DVAR) system, then analysis the results of each test simulation comparing with real situation. Results show that:(1) Simulation tests can simulate the precipitation process successfully and be well revealed the reasons for the rainfall and its dynamic and thermodynamic properties; (2)Applying 4DVAR to assimilate IAWS data and radiosonde data has positive impact on prediction, thereby improving accuracy of torrential rain; (3)The QC scheme for surface meteorological data can mark out the suspectable data effectively and provide promising quality assurance for the surface IAWS observation data; (4)The IAWS data after QC assimilated by 4DVAR can distinctly improve the initial field, better in simulating the drop zone of precipitation, rain trend and intensity. In short, As the quality control of IAWS data can keep real and get rid of error, to coordinated with the model better, it is important to use IAWS data effectively, result in the best simulation results.