Application Research Of GPS/PWV Data In Meiyu Onset Analysis And Precipitation Forecasts

Water vapor makes up an essential part of atmosphere. Preciptiable Water Vapor, as an indicator of water vapor content, is extremely important for the forecast of precipitation. For PWV detection, GPS remote sensing is nowadays widely applicated since compared with conventional detection methods, its advantages are obvious:high temporal and spatial resolution, high detection precision, all_weather observation, no affection by aerosol, cloud and precipitation, low expenses and easy deployment. To learn more about PWV data, its application is studied in this paper with the data derived from GPS/MET net of Jiangsu province.Firstly, the precision of GPS/PWV data of Jiangsu province is tested by comparison between Liuhe, Gaochun, Xuzhou GPS stations and Nanjing, Xuzhou sounding stations from 2008 to 2010.The result shows that the root mean square error is from 4.83 to 6.56mm, the same as the domestic research result. But when the PWV value is high, GPS/PWV will be much smaller than Radiosonde/PWV. The correction equation of GPS/PWV is PWV=1.1×GRS_PWV-2.74 by using the least square method. Also due to the serious lack of GPS/PWV data, PWV are linear fitted with ground vapor pressure to access a simple interpolation scheme PWV(e)=-0.4544+1.7193 ×e in Nanjing when lack of precipitable water vapor.Secondly, this paper explores the feasibility of diagnosis of Meiyu onset data with a single variable of PWV. When the original method failed in that diagnosis of Nanjing in 2011, it summarizes a new method of diagnosis of Meiyu onset data by using the K line graph which comes from stock market.It is very effective to diagnose Meiyu onset datas of Nanjing from 2009 to 2011 with the GPS/PWV data by using the K line graph method. Then, it is proved significantly effective in almost all cases of Nanjing station from 2003 to 2008 with the Radiosonde/PWV. So the single variable of PWV could effectively diagnose Meiyu onset data.Making use of its advantages of near real-time and high temporal and spatial resolution, the paper then explores GPS/PWV’s application in prediction of impending Meiyu front rainstorm. Meiyu front has barotropic structure with a weak temperature gradient but a strong mositure gradient. So Meiyu front is essentially moisture front. On the basis of Meiyu large-scale condition and vertical structure moisture front ideal test in WRF model is designed. The test result illustrates that the stationary moisture front can stimulate a convective rain belt which moves southward. High moisture condition and strong mositure gradient should be the necessary conditions that cause the rainstorm. The develpment of Meiyu front rainstorm is due to the couple of gravity wave, the convective unstable condition at low level and the condensation heating feedback. The establishment of ground temperature front forms the double-front structure.The baroclinity and steady stream of water vapor transfer would be the key to maintain Meiyu front rainstorm. Deep study shows that when PWV_max reaches the threshold of 58mm in the wet area of PWV>50mm, the gradient of PWV front reaches 7-10mm/50km, and that the moisture configuration is wet in low and dry in high, it will easily cause the mesoscale convective system (MCS) in the high area of PWV value close to the south border of moisture front. According to the result of ideal experiment combined with GPS/PWV distribution graph, it is analysed the Meiyu front rainstorm on June 18,2011. It can indicate a certain role of the impending storm forecast.Finally, several rain simulate experiments of Meiyu front rainstorm on June 18, 2011 are carried out with the FNL data, GTS data, GPS/PWV data in the mesoscale meteorological model WRFV3.4.1 version and its three-demensional variational data assimilation system 3DVAR. Firstly, according to susceptibility tests of parameterization schemes, we select Lin_KF、WSM3_KF and Lin_GD three groups of microphysics and cumulus convection parameterization schemes which the simulated results of precipitation patterns are better. Secondly, we make a statistics of background error covariance with the NMC method, and then select the appropriate Var_scaling=l and Len_scaling=0.3, which are the variable scale adjustment factor and the characteristic length scale adjustment factor. Then, we design four tests, including controlled trial with non-assimilation observation,3 hours cycling assimilation with GTS data,3 hours cycling assimilation with GTS data and GPS/PWV data, and 3 hour cycling assimilation with GTS data and revised GPS/PWV data. All of them simulate 12 hours. Finally, we assess the last 6 hours precipitation with the TS method.The results show that:the capacity of precipitation forecast is significantly increased with the assimilation of the revised GPS/PWV data, especially in heavy rain. The TS scores increase 0.1-0.3 comparing with control tests in the precipation magnitude of 25mm/6h. RMSE of precipitation are reduced and correlation coefficient are increased when they compare with control tests.In the most significant test, RMSE drops from 19.1mm to 12.6mm,and the correlation coefficient increases from 0.45 to 0.74. The TS scores are improved in the magnitudes from middle rain to heavy rain with the background error covariance by using the statistic of NMC method, while TS scores are improved significantly in the magnitude of more than big rain by using the default background error covariance.