| S波段多普勒天气雷达非降水气象回波识别 |
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| 引用本文： | 李丰,刘黎平,王红艳,江源.S波段多普勒天气雷达非降水气象回波识别[J].应用气象学报,2012,23(2):147-158. |
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| 作者姓名： | 李丰 刘黎平 王红艳 江源 |
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| 作者单位： | 1.兰州大学大气科学学院，兰州 730000 |
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| 基金项目： | 中国气象科学研究院基本科研业务费项目(2011Y004)，中国气象局新一代天气雷达建设业务软件系统开发项目（二期）“雷达资料质量控制算法研制”，公益性行业（气象）科研专项（GYHY200806003） |
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| 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。
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| 关 键 词： | 非降水气象回波 模糊逻辑 回波识别 动态阈值 |
| 收稿时间： | 2011-07-27 |
| 修稿时间： | 2/2/2012 12:00:00 AM |
Identification of Non precipitation Meteorological Echoes with Doppler Weather Radar |
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| Li Feng,Liu Liping,Wang Hongyan and Jiang Yuan.Identification of Non precipitation Meteorological Echoes with Doppler Weather Radar[J].Quarterly Journal of Applied Meteorology,2012,23(2):147-158. |
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| Authors: | Li Feng Liu Liping Wang Hongyan and Jiang Yuan |
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| Institution: | 1.College of Atmospheric Sciences, Lanzhou University, Lanzhou 7300002.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081 |
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| 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 km~2,the echoes in the piece are also recognized with a threshold of 0.5.If the area of an echo piece reaches 2000 km~2,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. |
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| Keywords: | non-precipitation meteorological echo fuzzy logical echo identification dynamic threshold |
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