A Study On Data Matching Between Spaceborne Radar And Ground-based Weather Radar And Radar Echo Intensity Correction

The weather radar has a strong ability of monitoring and early warning for small and medium-scale disastrous weather,thus it is helpful to analyze the internal structure of the small and medium scale convective systems and understand the dynamics and thermodynamics processes inside the precipitation.The single-site ground-based radar affected by such factors as the attenuation of electromagnetic waves and the interference of ground objects,has some limitations on the detection and data quality.In order to expand the detection area of weather radar,multiple weather radar networks are needed for joint detection.However,there is no unified calibration between the various radars in the network,which affects the consistency of radar network data,the networking mosaic,and the application in numerical model assimilation.In this paper,the accurately calibrated Precipitation Radar(PR)data products,collected by the Tropical Rainfall Measuring Mission(TRMM)satellite which has steadily worked for many years,are used as a standard reference source to compare with the reflectivity factor of ground-based radars in Jiangsu province.The consistency calibration theory and method of ground-based radar network observation based on spaceborne radar is proposed.By comparing the radar reflectivity factors,the inconsistency of the measured data between the adjacent ground radars is found,and a certain method is adopted to deal with the systematic observation difference of radar network,so as to improve the quality of the precipitation product measured by radar network.In this paper,the geometry-matching method is used to match up the reflectivity factor between TRMM PR and six ground-based radars in Jiangsu Province.During the period from January 2008 to September 2014,there are 265151,230854,453448,322941,480423 and 467240 valid matching points,respectively,between the effective matching time of six ground-based radars(Nanjing,Changzhou,Lianyungang,Nantong,Xuzhou,Yancheng)and PR.The results show that single site ground-based radar and PR have good spatial consistency.The effective matching points are mainly distributed below 5km height,mostly in the range of 15-30dBZ.This paper proposes the Fourier interpolation method based on the principle of spectrum analysis.By comparing with the results of bilinear interpolation,this method can better highlight the structure characteristics of the echo region under the condition of strong convective weather,compared with the bilinear interpolation.In order to reduce the inconsistency of the observed values between PR and GR,the Available Best Comparable Dataset(ABCD)method is presented in this paper.The ABCD method includes seven steps.Through first six steps:PR and GR data match-up,GR data azimuth adjustment,GR data terrain-blocking analysis,GR data radial-distance selection,vertical-height selection,and NUBF analysis,the uncertain data of TRMM PR and GR are eliminated and an available best comparable dataset between PR and GR is selected.Ultimately,based on the dataset,the deviation between GR and PR reflectivity factor is calculated,and the reflectivity factors of ground-based radar are corrected.To assess the correction effect,this paper analyzed the ABCD method from four aspects:(1)the variation of the statistic of each step of the ABCD method,(2)the comparative analysis between the AWS precipitation,(3)the collation between GRs,(4)radar mosaic effect testing.The results show that the ABCD method can effectively correct the deviation of GR reflectivity factor,so that the observed value of GRs is consistent.Based on the ABCD method,the reflectivity factor of Ku-Band Precipitation Radar(KuPR)onboard the Global Precipitation Measurement(GPM)core satellite is used as the reference standard.Selecting two precipitation processes in 2016,the differences between GPM KuPR and three ground-based radars(Nanjing,Changzhou and Hefei)in the lower reaches of the Yangtze River are analyzed.From the GR and KuPR all-matched data,the best comparable data is filtered to correct the calibration error of the multi radars,and finally a reflectivity factor and precipitation estimation product with spatial continuity and accurate observation values are obtained.The difference of Z and QPE in the adjacent GRs overlapped areas is decreased by 65%and 92%respectively after the correction,and the discontinuity of the discontinuity is basically disappearing.The revised MGR-QPE is consistent with rain gauge observations.Finally,the mesoscale model ARPS(the Advanced Regional Prediction System)and ARPSDAS(ARPS Data Assimilation System)are used to assimilate the radar reflectivity factor before and after correction,in order to study radar data assimilation scheme and application of mesoscale rainstorm weather forecast.The results show that using the NCEP reanalysis data as the background field under the single layer grid setting,radar reflectivity factor assimilation in Cloud Analysis System can affect the meteorological element field and precipitation simulation,in which the initial humidity field and the precipitation prediction field are improved obviously.The correction of reflectivity factor can further improve the effect of simulated precipitation by reflectivity factor assimilation.