设为首页
加入收藏
Identification of Non-precipitation Meteorological Echoes with Doppler Weather Radar
-
摘要: 在气象条件为晴空或有云但无降水的情况下，在雷达站附近经常可观测到大面积的非降水气象回波，这些回波对定量估测降水和雷达资料同化效果产生重要影响。为了有效识别这些非降水回波，该文发展了基于模糊逻辑识别和回波分块的非降水气象回波识别算法 (NPMDA)。该文首先利用地面和卫星资料为标准，提出了非降水回波的确定标准，并利用北京SA雷达，对非降水气象回波特性进行了统计分析，得到了隶属函数。在非降水回波识别时首先采用SCIT算法将回波组合成片，然后对整个PPI进行初步的判断。对不能初步判断为降水的PPI，采用模糊逻辑的方法计算成片回波的属性值，依据成片回波的属性值得到片内回波逐点识别时的阈值，从而实现了回波的动态阈值识别。结果表明：对大部分非降水气象回波识别效果较好，对较强降水回波误判较少，弱降水回波有时会出现一定的误判。与NCAR使用的ICADA方法相比，NPMDA方法能明显提高非降水回波的识别率，减少降水回波的误判率。Abstract: When it is clear or there are just clouds without rain, wide spread non-precipitation meteorological echoes are often observed near the radars, which have notable effect on rainfall estimation and radar data assimilation. To discriminate these echoes efficiently, a Non-Precipitation Meteorological echo Detection Algorithm (NPMDA) based on fuzzy logical and SCIT is developed. Using surface and star data, a standard to identify non-precipitation meteorological echoes is established. With data observed by the SA radar in Beijing, after analyzing the statistical characteristics of non-precipitation meteorological echo, membership functions are obtained. Echoes are assembled to pieces using SCIT. If an echo piece meets one of some special conditions, the whole PPI would be recognized as precipitation echoes. If no piece meets any of the special conditions, the threshold is set as 0.5, for the echoes which can't be assembled to pieces. If the area of an echo piece is less than 2000 km2, the echoes in the piece are also recognized with a threshold of 0.5. If the area of an echo piece reaches 2000 km2, the attribute value of this echo piece would be calculated with an algorithm based on fuzzy logical. The threshold of echoes in the piece would be calculated with the attribute value of this echo piece. If the attribute value of one piece is greater than or equal to 0.5, the echoes in the piece would be recognized with the threshold of 0.5. If the attribute value of one piece is less than 0.5, the echoes in the piece would be recognized with a threshold obtained by subtracting the attribute value of the piece from 1. The echoes would be recognized as non-precipitation meteorological echoes if their attribute values are greater than the threshold or equal to it. After using dynamic thresholds, the thresholds of most precipitation echoes would be greater than 0.5. The method can efficiently avoid this situation that the precipitation echoes are recognized as non-precipitation echoes. It should also be noticed that the thresholds of some non-precipitation echoes would also be greater than 0.5, which would decrease the identifiable accuracy for non-precipitation echoes to some extent. The algorithm does well with most of non-precipitation meteorological echoes and precipitation echoes, but it does not handle some weak precipitation echoes well. Compared with the ICADA used by NCAR, the identifiable accuracy for non-precipitation echoes can be improved remarkably with NPMDA, and the erroneous recognition for precipitation is also decreased obviously.
-
图 2 片回波WM，RA_R和RP30_A的隶属函数
Fig. 2 Membership functions of WM, RA_R and RP30_A for calculating the attributes of echo pieces
图 3 点回波的MDZ，SDBZ，DMDZ，TVE和MSW概率分布
Fig. 3 Probability distribution of MDZ, SDBZ, DMDZ, TVE and MSW
图 4 点回波的MDZ，SDBZ，DMDZ，TVE和MSW隶属函数
Fig. 4 Membership functions of MDZ, SDBZ, DMDZ, TVE and MSW
图 5 2010年10月4日23:00北京雷达PPI识别效果对比 (相邻距离圈间隔为50 km)
(a)0.4°仰角识别前反射率因子, (b)1.2°仰角识别前反射率因子, (c)0.4°仰角ICADA识别后剩余的反射率因子，(d)1.2°仰角ICADA识别后剩余的反射率因子, (e)0.4°仰角NPMDA识别后剩余的反射率因子, (f)1.2°仰角NPMDA识别后剩余的反射率因子
Fig. 5 PPI of Beijing radar at 2300 BT 4 Oct 2010(range rings at 50-km intervals)
(a) reflectivity at 0.4° elevation before echo identification, (b) reflectivity at 1.2° elevation before echo identification, (c) reflectivity remained at 0.4° elevation after echo identification with ICADA, (d) reflectivity remained at 1.2° elevation after echo identification with ICADA, (e) reflectivity remained at 0.4° elevation after echo identification with NPMDA, (f) reflectivity remained at 1.2° elevation after echo identification with NPMDA
图 6 2008年8月31日21:00北京雷达PPI识别效果分析 (相邻距离圈间隔为50 km)
(a)0.4°仰角识别前反射率因子, (b)1.3°仰角识别前反射率因子, (c)0.4°仰角ICADA识别后剩余的反射率因子，(d)1.3°仰角ICADA识别后剩余的反射率因子, (e)0.4°仰角NPMDA识别后剩余的反射率因子, (f)1.3°仰角NPMDA识别后剩余的反射率因子
Fig. 6 PPI of Beijing radar at 2100 BT 31 Aug 2008(range rings at 50-km intervals)
(a) reflectivity at 0.4° elevation before echo identification, (b) reflectivity at 1.3° elevation before echo identification, (c) reflectivity remained at 0.4° elevation after echo identification with ICADA, (d) reflectivity remained at 1.3° elevation after echo identification with ICADA, (e) reflectivity remained at 0.4° elevation after echo identification with NPMDA, (f) reflectivity remained at 1.3° elevation after echo identification with NPMDA
图 7 2008年9月24日06:24北京雷达PPI识别效果分析 (相邻距离圈间隔为50 km)
(a)0.4°仰角识别前反射率因子, (b)1.3°仰角识别前反射率因子, (c)0.4°仰角ICADA误判反射率因子，(d)1.3°仰角ICADA误判反射率因子, (e)0.4°仰角NPMDA误判反射率因子, (f)1.3°仰角NPMDA误判反射率因子
Fig. 7 PPI of Beijing radar at 0624 BT 24 Sep 2008(range rings at 50-km intervals)
(a) reflectivity at 0.4° elevation before echo identification, (b) reflectivity at 1.3° elevation before echo identification, (c) false detections of reflectivity at 0.4° elevation with ICADA, (d) false detections of reflectivity at 1.3° elevation with ICADA, (e) false detections of reflectivity at 0.4° elevation with NPMDA, (f) false detections of reflectivity at 1.3° elevation with NPMDA
图 8 2010年3月29日17:06北京雷达PPI识别效果分析 (相邻距离圈间隔为50 km)
(a)0.4°仰角识别前反射率因子, (b)1.3°仰角识别前反射率因子, (c)0.4°仰角ICADA误判反射率因子, (d)1.3°仰角ICADA误判反射率因子, (e)0.4°仰角NPMDA误判反射率因子, (f)1.3°仰角NPMDA误判反射率因子
Fig. 8 PPI of Beijing radar at 1706 BT 29 Mar 2010(range rings at 50-km intervals)
(a) reflectivity at 0.4° elevation before echo identification, (b) reflectivity at 1.3° elevation before echo identification, (c) false detections of reflectivity at 0.4° elevation with ICADA, (d) false detections of reflectivity at 1.3° elevation with ICADA, (e) false detections of reflectivity at 0.4° elevation with NPMDA, (f) false detections of reflectivity at 1.3° elevation with NPMDA
图 9 2010年10月24日15:48北京雷达PPI识别效果分析 (相邻距离圈间隔为50 km)
(a)0.4°仰角识别前反射率因子, (b)1.3°仰角识别前反射率因子, (c)0.4°仰角ICADA误判反射率因子, (d)1.3°仰角ICADA误判反射率因子, (e)0.4°仰角NPMDA误判反射率因子, (f)1.3°仰角NPMDA误判反射率因子
Fig. 9 PPI of Beijing radar at 1548 BT 24 Oct 2010(range rings at 50-km intervals)
(a) reflectivity at 0.4° elevation before echo identification, (b) reflectivity at 1.3° elevation before echo identification, (c) false detections of reflectivity at 0.4° elevation with ICADA, (d) false detections of reflectivity at 1.3° elevation with ICADA, (e) false detections of reflectivity at 0.4° elevation with NPMDA, (f) false detections of reflectivity at 1.3° elevation with NPMDA
表 1 识别为降水回波的条件
Table 1 Conditions of echoes identified as precipitation
条件 内容 1 回波片中回波顶高在5 km以上的面积比例≥20% 2 20 dBZ以上的回波面积比例≥35% 3 25 dBZ以上的回波面积比例≥15% 4 25 dBZ以上的回波面积≥4500 km2 
下载: 导出CSV
表 2 点识别的各参量正确率
Table 2 Identifiable accuracy of each characteristic parameter
参量 TVE MSW MDZ SDBZ DMDZ 正确率/% 65.3 73.1 89.5 69.4 72.6
下载: 导出CSV
表 3 非降水气象回波识别正确率、降水误判率 (单位：%)
Table 3 Identifiable accuracy for non-precipitation echoes and false detection rate of precipitation echoes (unit:%)
方法 总样本识别
正确率非降水气象回波
识别正确率非降水气象回波
识别误判率降水回波识别
正确率降水回波识别
误判率ICADA 78.6 65.9 34.1 91.3 8.7 NPMDA 93.3 86.7 13.3 100.0 0.0
下载: 导出CSV
-
[1] 张培昌, 杜秉玉, 戴铁丕.雷达气象学.北京:气象出版社, 2001: 327-328. [2] Wilson J W, Weckwerth T M, Vivekanandan J, et al. Boundary layer clear-air radar echoes: Origin of echoes and accuracy of derived winds. J Atmos Ocean Technol, 1994, 11: 1184-1206. doi: 10.1175/1520-0426(1994)011<1184:BLCARE>2.0.CO;2 [3] Kenichi Kusunoki. A preliminary survey of clear-air echo appearances over the Kanto Plain in Japan from July to December 1997. J Atmos Ocean Technol, 2002, 19:1063-1072. doi: 10.1175/1520-0426(2002)019<1063:APSOCA>2.0.CO;2 [4] 张旭, 周秀骥.大气折射率湍流结构系数Cn2的观测试验.应用气象学报, 1994, 5(1):19-26. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19940101&flag=1 [5] 魏鸣, 秦学, 王啸华, 等.南京地区大气边界层晴空回波研究.南京气象学院学报, 2007, 30(6):736-744. http://www.cnki.com.cn/Article/CJFDTOTAL-NJQX200706003.htm [6] Steiner M, Smith J A. Use of three-dimensional reflectivity structure for automated detection and removal of nonprecipitating echoes in radar data. J Atmos Ocean Technol, 2002, 19: 673-686. doi: 10.1175/1520-0426(2002)019<0673:UOTDRS>2.0.CO;2 [7] Kessinger C, Ellis S, Andel J V, et al. The AP Clutter Mitigation Scheme for the WSR-88D. 31st International Conference on Radar Meteorology, American Meteorological Society, 2003. [8] Kessinger C, Ellis S, Andel J V. The Radar Echo Classifier: A Fuzzy Logic Algorithm for the WSR-88D. 3rd Conference on Artificial Intelligence Applications to The Environmental Science, American Meteorological Society, 2003. [9] Kessinger C, Ellis S, Andel J V, et al. NEXRAD Data Quality Optimization—Annual Report for Fiscal Year 2002. 2003. [10] Kessinger C, Ellis S, Andel J V, et al. NEXRAD Data Quality Optimization—Annual Report for Fiscal Year 2003. 2003. [11] Gourley J J, Tabary P, Chatelet J P D. A fuzzy logic algorithm for the separation of precipitating from nonprecipitating echoes using polarimetric radar observations. J Atmos Ocean Technol, 2007, 24: 1439-1451. doi: 10.1175/JTECH2035.1 [12] 郑媛媛, 谢亦峰, 吴林林, 等多普勒雷达定量估测降水的三种方法比较试验.热带气象学报, 2004, 20(2):192-197. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX200402010.htm [13] 王楠, 刘黎平, 徐宝祥, 等.利用多普勒雷达资料识别低空风切变和辐合线方法研究.应用气象学报, 2007, 18(3):314-320. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20070353&flag=1 [14] 陈明轩, 俞小鼎, 谭晓光, 等.北京2004年"7.10"突发性对流强降水的雷达回波特征分析.应用气象学报, 2006, 17(3):333-345. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20060359&flag=1 [15] 王改利, 刘黎平, 阮征.多普勒雷达资料在暴雨临近预报中的应用.应用气象学报, 2007, 18(3):388-395. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20070363&flag=1 [16] 廖玉芳, 俞小鼎, 郭庆.一次强对流系列风暴个例的多普勒天气雷达资料分析.应用气象学报, 2003, 14(6):656-662. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20030683&flag=1 [17] 仲凌志, 刘黎平, 顾松山.层状云和对流云的雷达识别及在估测雨量中的应用.高原气象, 2007, 26(3):593-602. http://www.cnki.com.cn/Article/CJFDTotal-GYQX200703021.htm [18] 刘黎平, 吴林林, 杨引明.基于模糊逻辑的分步式超折射地物回波识别方法的建立和效果分析.气象学报, 2007, 65(2):525-260. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200702010.htm [19] 江源, 刘黎平, 庄薇.多普勒天气雷达地物回波特征及其识别方法改进.应用气象学报, 2009, 20(2):203-213. doi: 10.11898/1001-7313.20090210 [20] 俞小鼎, 姚秀萍, 熊廷南, 等.多普勒天气雷达原理与业务应用.北京:气象出版社, 2006:187-192. -
计量
- 摘要浏览量: 4631
- HTML全文浏览量: 1108
- PDF下载量: 1981
- 被引次数: 0
