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Radar Quantitative Precipitation Inversion and Its Applicationto Areal Rainfall Estimation in the NortheasternMarginal Areas of the Tibetan Plateau

ZHANG Zhi-xian1,2,5,ZHANG Qiang1,2,4,ZHAO Qing-yun3,ZHANG Li-yang2,CAI Yun-teng6(1.Gansu Key Laboratory of Arid Climatic Change and Reducing Disaster,Key Open Laboratory of Arid Climatic Change and Disaster Reduction of China Meteorological Administration,Institute of Arid Meteorology,China Meteorological Administration,Lanzhou Gansu 730020,China;2.College of Atmospheric Sciences,Lanzhou University,Lanzhou Gansu 730000,China;3.Center of Lanzhou Meteorological Observatory,Lanzhou Gansu 730020,China;4.Gansu Meteorology Administration,Lanzhou Gansu 730020,China;5.Unit of 93808 of People's Liberation Army,Yuzhong Gansu 730109,China;6.Meteorological Observatory of Lanzhou Zhongchuan Airport,Civil Aviation Administration of China,Lanzhou Gansu 730087,China)  
Using radar-rain gauge to estimate areal rainfall is one of the major ways to improve the application of radar,which has great advantages in both the wide coverage of radar scanning and high single-point precision of rain-gauge.Based on the correlation with radar echo and rainfall in northeastern marginal areas of the Tibetan Plateau,a regional heavy rain case on May 10,2012,is compared and spatial calibrated by using average calibration,optimal interpolation and variational-Kalman filter.It is found that the Z-I relationship with terrain is very different.Compared with precipitation observation,one can see that the local Z-I relationship by method of optimization is better than the others in diverse bands or districts,which effectively changes the situation of estimating lowly.In the mean time,the variational-Kalman filter method has the advantage of radar areal scan,which nicely reflected the spatial distribution of rainfall,able to calibrate well.In addition,it will be more effective in spatial precipitation estimation if take correct mathematical measures through the equations with multi-elevation angles and multi variable.
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