The errors in radar quantitative precipitation estimations consist not only of systematic biases caused by random noises but also spatially nonuniform biases in radar rainfall at individual rain-gauge stations. In thi...The errors in radar quantitative precipitation estimations consist not only of systematic biases caused by random noises but also spatially nonuniform biases in radar rainfall at individual rain-gauge stations. In this study, a real-time adjustment to the radar reflectivity rainfall rates （Z R） relationship scheme and the gauge-corrected, radar-based, estimation scheme with inverse distance weighting interpolation was devel- oped. Based on the characteristics of the two schemes, the two-step correction technique of radar quantitative precipitation estimation is proposed. To minimize the errors between radar quantitative precipitation es- timations and rain gauge observations, a real-time adjustment to the Z R relationship scheme is used to remove systematic bias on the time-domain. The gauge-corrected, radar-based, estimation scheme is then used to eliminate non-uniform errors in space. Based on radar data and rain gauge observations near the Huaihe River, the two-step correction technique was evaluated using two heavy-precipitation events. The results show that the proposed scheme improved not only in the underestimation of rainfall but also reduced the root-mean-square error and the mean relative error of radar-rain gauge pairs.展开更多
Under the Watershed Allied Telemetry Experimental Research （WATER） project, a significant amount of snow size data was collected from March to April 2008. However, because of limited observation data for the Qinghai...Under the Watershed Allied Telemetry Experimental Research （WATER） project, a significant amount of snow size data was collected from March to April 2008. However, because of limited observation data for the Qinghai-Tibet Plateau, the modeling behavior was not satisfactory. This paper demonstrates characteristics of the snow drop size distribution （SSD） in this region. The experimental area is located in the northeastern part of the Qinghai-Tibet Plateau. The Heihe River Basin, which is the second largest interior river basin in China and is located on the northern slopes of the Qilian Mountains, was selected as the simulation region. This basin ranges from approximately 5,000 m to 1,000 m in elevation. A new generation Parsivel disdrometer, the OTT Parsivel, was used for measurements. Four data sets were compiled to determine the average distributions for four different snowfall rates. The characteristics of the snow particle size distribution in the mountainous area were analyzed. Similar to the raindrop distribution, there was a multi-peak structure. Most peaks appear in the D 〈 2 mm region （D： diameter of the snow drop size）. An M-P distribution and a Г distribution were developed based on the precipitation data observed in Qilian mountainous area. We found that the Г distribution has a better fit than the M-P distribution for the actual distribution. In addition, we observed that the intercept parameter （N0） and the slope parameter （Λ） correlate well with the shape parameter （μ）. The disdrometer data can also be used to model the reflectivity factor （ZH） and differential reflectivity factor （ZDR）. The radar reflectivity （ZHH, ZVV） and differential reflectivity （ZDR） were modeled in order to facilitate understanding of the connections between radar and ground measurements, and were used to support work for the improvement of rainfall estimates by polarimetric radar. Rain rate estimation using radar measurements was based on empirical models, such as the Z-R relationship and R（ZH, ZDR） in the Qilian mountainous areas. The relationship of R=0.017×100.079×ZH-0.022×ZDR is better than R=0.019×100.078×ZH for estimating R （melted snow）. The normalized errors （NE） of R（ZH） and R（ZH, ZDR） are 13.22% and 5.20%, respectively.展开更多
文摘为了降低因Z-R关系不确定导致的雷达定量降水估测(Quantitative Precipitation Estimation,简称QPE)误差,提出了基于云团的分组Z-R关系拟合方案,在风暴单体识别算法得到的不同降水云团或同一个云团内部的不同数据分组区域内,拟合并采用不同的Z-R关系反演地面降水信息。以2013年6月5—7日的梅雨锋过程为例,使用覆盖长江中下游地区的28部多普勒雷达和全国逐分钟雨量计的观测资料,对单一动态关系、简单分组Z-R关系以及基于云团的分组ZR关系反演的雷达1 h QPE进行效果对比和误差分析,结果表明:(1)基于云团的分组Z-R关系可以有效识别降水云系的局部特征,这是基于云团的分组Z-R关系优于其他两种Z-R关系方案的重要原因。(2)雷达波束部分遮挡导致的偏弱反射率因子,对雷达QPE数据场的不连续性和Z-R关系的不确定性均有影响。(3)雷达硬件或雷达标定引入的偏强(弱)的反射率因子,与简单分组Z-R关系得到的雷达QPE局部高(低)估相关,这降低了简单分组Z-R关系在大范围降水过程中的适用性,但对基于云团的分组Z-R关系的影响较小。
基金supported bythe Special Fund for Basic Research and Operation of the Chinese Academy of Meteorological Sciences (GrantNo. 2011Y004)the Research and Development Special Fund for Public Welfare Industry (Meteorology+2 种基金Grant No.GYHY201006042)the National Natural Science Foundation of China (Grant No. 40975014)the Open Research Fund for State Key Laboratory of Hydroscience and Engineering of Tsinghua University (the search of basin QPE and QPF based on new generation of weather and numerical models)
文摘The errors in radar quantitative precipitation estimations consist not only of systematic biases caused by random noises but also spatially nonuniform biases in radar rainfall at individual rain-gauge stations. In this study, a real-time adjustment to the radar reflectivity rainfall rates （Z R） relationship scheme and the gauge-corrected, radar-based, estimation scheme with inverse distance weighting interpolation was devel- oped. Based on the characteristics of the two schemes, the two-step correction technique of radar quantitative precipitation estimation is proposed. To minimize the errors between radar quantitative precipitation es- timations and rain gauge observations, a real-time adjustment to the Z R relationship scheme is used to remove systematic bias on the time-domain. The gauge-corrected, radar-based, estimation scheme is then used to eliminate non-uniform errors in space. Based on radar data and rain gauge observations near the Huaihe River, the two-step correction technique was evaluated using two heavy-precipitation events. The results show that the proposed scheme improved not only in the underestimation of rainfall but also reduced the root-mean-square error and the mean relative error of radar-rain gauge pairs.
基金supported by the CAS Action Plan for West Development Program (Grant number: KZCX2-XB2-09)Chinese State Key Basic Research Project (Grant number:2007CB714400)
文摘Under the Watershed Allied Telemetry Experimental Research （WATER） project, a significant amount of snow size data was collected from March to April 2008. However, because of limited observation data for the Qinghai-Tibet Plateau, the modeling behavior was not satisfactory. This paper demonstrates characteristics of the snow drop size distribution （SSD） in this region. The experimental area is located in the northeastern part of the Qinghai-Tibet Plateau. The Heihe River Basin, which is the second largest interior river basin in China and is located on the northern slopes of the Qilian Mountains, was selected as the simulation region. This basin ranges from approximately 5,000 m to 1,000 m in elevation. A new generation Parsivel disdrometer, the OTT Parsivel, was used for measurements. Four data sets were compiled to determine the average distributions for four different snowfall rates. The characteristics of the snow particle size distribution in the mountainous area were analyzed. Similar to the raindrop distribution, there was a multi-peak structure. Most peaks appear in the D 〈 2 mm region （D： diameter of the snow drop size）. An M-P distribution and a Г distribution were developed based on the precipitation data observed in Qilian mountainous area. We found that the Г distribution has a better fit than the M-P distribution for the actual distribution. In addition, we observed that the intercept parameter （N0） and the slope parameter （Λ） correlate well with the shape parameter （μ）. The disdrometer data can also be used to model the reflectivity factor （ZH） and differential reflectivity factor （ZDR）. The radar reflectivity （ZHH, ZVV） and differential reflectivity （ZDR） were modeled in order to facilitate understanding of the connections between radar and ground measurements, and were used to support work for the improvement of rainfall estimates by polarimetric radar. Rain rate estimation using radar measurements was based on empirical models, such as the Z-R relationship and R（ZH, ZDR） in the Qilian mountainous areas. The relationship of R=0.017×100.079×ZH-0.022×ZDR is better than R=0.019×100.078×ZH for estimating R （melted snow）. The normalized errors （NE） of R（ZH） and R（ZH, ZDR） are 13.22% and 5.20%, respectively.
