Study On Precipitation Estimation And Nowcasting Based On Weather Radar

Precipitation is the most fundamental and active part in hydrological cycle, andplays an important role in hydrology. As the result of the process of climate changeand urbanization in China, extreme heavy rainfall events become more and morefrequent and threaten the cities and people. Against the backdrop of this, how to getinformation and nowcasting about rainfall events with a high temporal and spatialresolution has become a focus of study. Weather radar can provide a broader range ofrainfall distribution but less accurate, therefore it’s a need for combination of radarand rain gauges to reduce bias between radar and gauges for further application.Five of representative rainfall events occurring between2007and2010wereselected for calculating and analysis, using data from weather radar and automaticweather stations both located in Beijing area. Types of precipitation were identifiedby weather radar echo image while optimization algorithm and Kalman filter methodwere applied to reduce the bias between gauges and weather radar. Gini Index wasintroduced to assess the inhomogeneity of rainfall distribution, while crosscorrelation technique and nowcasting conception model were used together withvertically integrated liquid (VIL) for precipitation nowcasting.It shows that the relationship between radar’s reflectivity factor and rainfall rate(Z-R relation) varies with different types of rainfall. The underestimation made byweather radar with a default Z-R relation could be improved after using a new Z-Rrelation determined by optimization algorithm, and the precision of gauges and radarpairs could be increased. Kalman filter method could reduce the bias between gaugesand radar pairs and the dispersion of the two types of data, and removenon-precipitation echo’s influence from the weather radar observation of rainfall fora better estimation. Gini Index could illustrate the homogeneity of rainfalldistribution, and gauges have less accurate index for the sparse distribution.Movement and trail of rainfall center could be forecasted by both cross correlationtechnique and nowcasting conception model in a short time, while as the increase of time length, both results of nowcasting become less credible. Nowcasting conceptionmodel may have a brighter future for its ability to predict the amount ofprecipitation.