| 非线性VAD反演低层风廓线拟合阶数优化方法 |
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| 引用本文： | 马秀梅,李文兆,赵坤,唐晓文,杨洪平. 非线性VAD反演低层风廓线拟合阶数优化方法[J]. 应用气象学报, 2014, 25(3): 321-329 |
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| 作者姓名： | 马秀梅 李文兆 赵坤 唐晓文 杨洪平 |
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| 作者单位： | 1.教育部国家中尺度灾害性天气重点实验室/南京大学大气科学学院，南京 210093 |
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| 基金项目： | 资助项目：国家重点基础研究发展计划项目（2013CB430101）、公益性行业（气象）科研专项（GYHY200906004，GYHY201006007），国家自然科学基金项目（41275031），南京雷达气象与强天气开放实验室研究基金（BJG201204） |
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| 摘 要： | 结合理论和SoWMEX试验 (西南气流试验，Southwest Monsoon Experiment) 的连续多普勒天气雷达观测资料和广东省阳江雷达资料, 对非线性速度方位显示 (非线性VAD) 方法反演低层低于2 km垂直风廓线精度和能力进行定量分析。结果表明:非线性VAD基本能反演出低层风廓线在空间和时间上的演变。但当雷达径向速度数据在方位存在较大的连续性缺测、体积扫描仰角较少时，因传统非线性VAD采用的速度方位显示 (VAD) 方法拟合阶数和垂直拟合阶数过高，反演的低层风廓线会存在较大误差，造成不合理高风速区和风廓线不连续。通过实际观测资料统计分析反演参数对非线性VAD的影响，提出基于连续性数据缺测间隔和不同仰角的多少的VAD和垂直拟合阶数动态调整方法。同锋面降水和台风降水两典型个例的实际探空比对显示，调整后的非线性VAD显著改进低层风廓线反演精度，反演的风廓线结构和变化与实况相符，反演平均误差小于2 m·s-1。
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| 关 键 词： | 非线性VAD 低层垂直风廓线 多普勒天气雷达 |
| 收稿时间： | 2013-07-04 |
Optimization of Nonlinear VAD Method in the Low level Wind Retrieval |
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| Ma Xiumei,Lee Wenchau,Zhao Kun,Tang Xiaowen and Yang Hongping. Optimization of Nonlinear VAD Method in the Low level Wind Retrieval[J]. Journal of Applied Meteorological Science, 2014, 25(3): 321-329 |
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| Authors: | Ma Xiumei Lee Wenchau Zhao Kun Tang Xiaowen Yang Hongping |
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| Affiliation: | 1.Key Laboratory of Meso-scale Severe Weather, Ministry of Education, School of Atmospheric Sciences, Nanjing University, Nanjing 2100932.National Center of Atmospheric Research (NCAR), Colorado 80307, USA3.Meteorological Observation Center of CMA, Beijing 100081 |
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| Abstract: | The performance of nonlinear velocity azimuth display method in the vertical wind profile retrieval at low levels (below 2 km) is quantitatively examined by combing the theoretical analysis and cases observed by SoWMEX S-Pol radar and Yangjiang radar in Guangdong Province. Results show that the general structure and evolution of the low-level wind profile can be reasonably deduced by traditional nonlinear VAD method. The root mean square error can be used to evaluate orders of velocity azimuth display (VAD) fitting, but small error does not always mean the better performance especially with big continuous data absence, and a specific example is given. When setting the VAD fitting order to 3 instead of 2, coefficients which represent the horizontal wind u and v are closer to the wind derived from radial velocity image. However, when the fitting order comes to 4, coefficients lost their physical meaning. The wind direction differs a lot and the speed is much smaller than the value before. At the same time, the root mean square error decreases compared with the order of 3. Besides, data used in nonlinear VAD fitting come from the whole volume, which decreases quite a lot and leads to nonlinear VAD fitting error when the volume coverage pattern (VCP) only has some lower elevations (e.g., two elevations). Therefore, the retrieved wind could contain large error in certain situations, such as for a region with large continuous data absence or a volume scan with fewer elevations.After carefully evaluating the impact of the corresponding parameters on the nonlinear VAD retrievals by analyzing radar measurements, a modified nonlinear VAD method is proposed which takes account of the maximum fitting order in horizontal (VAD) and vertical adaptively according to the size of continuous data absence and the number of sweeps in a volume scan. VAD fitting is abandoned when the data absence is larger than 90°; the order is set to 3 when the data absence is between 60° and 90°; and the order is set to 4 when the data absence is smaller than 60°. The order of nonlinear VAD fitting is reduced when the VCP only has low elevations. Apply the method in two cases: One is a front case passing through Taiwan, China, the other is a typhoon case landfall in Guangdong Province, with both of them having nonlinearity in the low level wind profile. The wind profile after adjusted can significantly improve the wind retrieval, as compared with the traditional nonlinear VAD. Both wind speed and direction from modified nonlinear VAD agree with those from sounding observations, with the root mean square of the wind less than 2 m·s-1, which is obviously better than nonlinear VAD before adjusted. |
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| Keywords: | nonlinear VAD low level vertical wind profile Doppler radar |
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