Storm identification and tracking based on weather radar data are essential to nowcasting and severe weather warning. A new two-dimensional storm identification method simultaneously seeking in two directions is propo...Storm identification and tracking based on weather radar data are essential to nowcasting and severe weather warning. A new two-dimensional storm identification method simultaneously seeking in two directions is proposed, and identification results are used to discuss storm tracking algorithms. Three modern optimization algorithms （simulated annealing algorithm, genetic algorithm and ant colony algorithm） are tested to match storms in successive time intervals. Preliminary results indicate that the simulated annealing algorithm and ant colony algorithm are effective and have intuitionally adjustable parameters, whereas the genetic algorithm is unsatisfaetorily constrained by the mode of genetic operations Experiments provide not only the feasibility and characteristics of storm tracking with modern optimization algorithms, but also references for studies and applications in relevant fields.展开更多
The next-generation weather radar(NEXRAD) can generally capture the spatial variability of rainfall fields,but fails to provide accurate depth measurements.A systematic strategy to evaluate the accuracy of radar data ...The next-generation weather radar(NEXRAD) can generally capture the spatial variability of rainfall fields,but fails to provide accurate depth measurements.A systematic strategy to evaluate the accuracy of radar data in depth measurement and its performance in hydrologic model is outlined.Statistical evaluation coefficients are calculated by comparing NEXRAD data with individual raingauges as well as subbasin-averaged interpolations,and point-and surface-average factors are introduced to revise radar data successively.Hydrologic simulations are then performed with a distributed hydrologic model,called basin pollution calculation center(BPCC) with both raingauge observations and revised NEXRAD estimates inputs.The BPCC model is applied to Clear Creek Watershed,IA,USA,on an hourly scale,and the calibration and validation parameters are semi-automatically optimized to improve manual calibration shortcomings.Results show that hydrographs generated from both gauge and NEXRAD are in good agreement with observed flow hydrographs.Coefficient statistics reveal that NEXRAD contributes to model performance,indicating that NEXRAD data has the potential to be used as an alternative source of precipitation data and improve the accuracy of hydrologic simulations.展开更多
基金National Natural Science Foundation of China (60674074)Natural Science Foundation of Jiangsu province (BK2009415)+5 种基金Research Fund for the Doctoral Program of Higher Education of China (20093228110002)College Graduate Student Research and Innovation Program of Jiangsu province (CX09B_227Z)Meteorology Industry Special Project of CMA (GYHY(QX)2007-6-2)National 863 Project (2007AA061901)Project of State Key Laboratory of Severe Weather of Chinese Academy of Meteorological Sciences (2008LASW-B11)Project 2009Y0006
文摘Storm identification and tracking based on weather radar data are essential to nowcasting and severe weather warning. A new two-dimensional storm identification method simultaneously seeking in two directions is proposed, and identification results are used to discuss storm tracking algorithms. Three modern optimization algorithms （simulated annealing algorithm, genetic algorithm and ant colony algorithm） are tested to match storms in successive time intervals. Preliminary results indicate that the simulated annealing algorithm and ant colony algorithm are effective and have intuitionally adjustable parameters, whereas the genetic algorithm is unsatisfaetorily constrained by the mode of genetic operations Experiments provide not only the feasibility and characteristics of storm tracking with modern optimization algorithms, but also references for studies and applications in relevant fields.
基金supported by the National Basic Research Program of China ("973" Project) (Grant No. 2007CB407202)
文摘The next-generation weather radar(NEXRAD) can generally capture the spatial variability of rainfall fields,but fails to provide accurate depth measurements.A systematic strategy to evaluate the accuracy of radar data in depth measurement and its performance in hydrologic model is outlined.Statistical evaluation coefficients are calculated by comparing NEXRAD data with individual raingauges as well as subbasin-averaged interpolations,and point-and surface-average factors are introduced to revise radar data successively.Hydrologic simulations are then performed with a distributed hydrologic model,called basin pollution calculation center(BPCC) with both raingauge observations and revised NEXRAD estimates inputs.The BPCC model is applied to Clear Creek Watershed,IA,USA,on an hourly scale,and the calibration and validation parameters are semi-automatically optimized to improve manual calibration shortcomings.Results show that hydrographs generated from both gauge and NEXRAD are in good agreement with observed flow hydrographs.Coefficient statistics reveal that NEXRAD contributes to model performance,indicating that NEXRAD data has the potential to be used as an alternative source of precipitation data and improve the accuracy of hydrologic simulations.
