Wind Field Verification for Array Weather Radar at Changsha Airport
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摘要: 利用2019年4—9月高时空分辨率的长沙机场阵列天气雷达资料开展三维变分(three-dimensional variational data assimilation,3DVAR)风场反演研究。为验证该算法的反演效果,选取外场试验中10次降水过程,在阵列天气雷达的三维精细探测区内,采用阵列天气雷达合成风场和1部L波段边界层风廓线雷达产品作为参考值对阵列反演风场进行验证评估。结果表明:在稳定性降水条件下,阵列反演风场与阵列合成风场、风廓线雷达产品的结果较为一致;在对流性降水条件下,由于不均匀性会造成风廓线测风精度下降,风廓线雷达产品与阵列反演风场和阵列合成风场差异较大。阵列反演风场与阵列合成风场在稳定性、对流性降水条件下水平风速相对偏差分别低于19%,29%,水平风向差分别低于14.92°,26.35°,稳定性降水条件下阵列反演风场更优,误差在可接受范围内。两种算法得到的风场结构符合各类天气系统的基本特征,水平风场空间分布和风速、风向非常接近。Abstract: Synthesizing or retrieving the radial velocity of weather radar can obtain a three-dimensional wind field, which is an important research direction in radar meteorology. A fine three-dimensional wind field helps to study the structure and motion characteristics of small-scale and meso-scale weather systems. Array weather radar (AWR) consists of three-phased array transmit-receive subarrays (referred as transceiver subarrays), which is used for synchronous detection. AWR data are of high temporal and spatial resolution, thus ensuring the correctness of wind field synthesis and retrieval.According to domestic and aboard research, three-dimensional variational data assimilation (3DVAR) wind field retrieval algorithm is quite mature. Using AWR data of 10 rainfall cases at Changsha airport from April to September in 2019, the wind field is retrieved and evaluated. In the three-dimensional fine detection area of the AWR, detection data of a L-band boundary layer wind profile radar and the AWR synthetic wind field are used as reference value to evaluate the retrieved wind field.Results show that the retrieved wind field, the synthetic wind field, and wind profile radar product are more consistent and reasonable in stable precipitation process. In addition, the result error is larger in convective precipitation. The unevenness of the environmental wind field in convective precipitation can reduce the accuracy of wind measurement, and therefore it is not enough to explain the rationality of the AWR retrieved wind field. The wind profile radar is quite different from the AWR retrieved and the synthetic wind field. For different precipitation types, the wind field structure retrieved by AWR and the wind field obtained by AWR synthetic wind field are consistent with the basic characteristics of various weather systems. The spatial distribution and size direction of the horizontal wind field of two algorithms are very close. Error results show that the relative deviation of horizontal wind speed in the stable and convective precipitation is less than 19% and 29%, and the difference of horizontal wind direction is lower than 14.92° and 26.35°, respectively. The error is within the acceptable range. Compared with the AWR synthetic wind field, the retrieved wind field result during stable precipitation process is better than that during convective precipitation process.
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图 1 3个收发子阵阵列天气雷达布局及探测区示意图
(红色矩形为3DVAR风场反演范围,红色圆点为机场风廓线雷达站点)
Fig. 1 Deployment diagram and spatial detection schematic diagram of the AWR consisting of three transmit-receive subarrays
(the red rectangle denotes the 3DVAR wind field retrieval area, the red dot denotes the wind profile radar station at the airport)
图 5 2019年8月25日17:05:12阵列合成风场和阵列反演风场不同高度的水平风矢(填色为反射率因子)
(a)阵列合成风场, 3 km高度,(b)阵列反演风场, 3 km高度,(c)阵列合成风场, 5 km高度, (d)阵列反演风场, 5 km高度
Fig. 5 Horizontal wind for the AWR synthetic wind field and the AWR retrieved wind field at 170512 BT 25 Aug 2019 (the shaded is the reflectivity factor)
(a)the AWR synthetic wind field, 3 km height, (b)the AWR retrieved wind field, 3 km height, (c)the AWR synthetic wind field, 5 km height, (d)the AWR retrieved wind field, 5 km height
图 7 2019年8月21日14:52:00阵列合成风场和阵列反演风场不同高度的水平风矢(填色为反射率因子)
(a)阵列合成风场, 3 km高度,(b)阵列反演风场, 3 km高度,(c)阵列合成风场, 5 km高度, (d)阵列反演风场, 5 km高度
Fig. 7 Horizontal wind for the AWR synthetic wind field and the AWR retrieved wind field at 145200 BT 21 Aug 2019 (the shaded is reflectivity factor)
(a)the AWR synthetic wind field, 3 km height, (b)the AWR retrieved wind field, 3 km height, (c)the AWR synthetic wind field, 5 km height, (d)the AWR retrieved wind field, 5 km height
表 1 降水个例及描述
Table 1 Precipitation cases and description
降水个例 时间 降水类型 与风廓线雷达产品对比时段 对比分析时刻 1 2019-04-26T17:50—20:00 对流性降水 18:00—20:00 19:00:00 2 2019-04-29T13:00—14:30 稳定性降水 13:15—14:15 13:35:12 3 2019-05-12T15:30—18:00 稳定性降水 15:30—17:30 16:30:00 4 2019-06-01T13:00—14:30 稳定性降水 13:00—13:50 13:20:00 5 2019-07-12T12:20—14:30 稳定性降水 12:25—13:05 12:47:12 6 2019-07-19T14:00—15:50 对流性降水 14:30—15:20 14:50:00 7 2019-08-18T18:00—19:30 对流性降水 18:35—19:05 18:55:12 8 2019-08-21T14:00—16:00 对流性降水 降水回波未经过风廓线 14:52:00 9 2019-08-25T16:50—18:20 稳定性降水 16:55—17:15 17:05:12 10 2019-09-10T17:50—19:00 对流性降水 18:00—18:30 18:15:12 表 2 不同子阵连线上中点位置的径向速度一致性分析
Table 2 Radial velocity consistency analysis of the midpoint position on the connecting line of different subarrays
高度/km A点径向速度/(m·s-1) B点径向速度/(m·s-1) C点径向速度/(m·s-1) 子阵1 子阵2 子阵1 子阵3 子阵2 子阵3 1.0 2.43 -2.53 7.62 -8.09 5.64 -6.75 1.5 -11.86 9.84 6.58 -5.18 7.19 -7.42 2.0 -19.41 17.30 -14.17 12.03 6.70 -8.09 2.5 -19.13 17.66 -18.94 16.59 3.02 -4.69 3.0 -20.48 18.60 -21.43 18.92 4.60 -5.24 3.5 -19.46 17.13 -20.98 18.95 2.46 -3.06 4.0 -17.84 16.89 -19.78 17.38 1.97 -1.63 4.5 -17.25 16.34 -16.56 15.67 2.94 -3.85 5.0 -15.94 14.04 -15.50 15.84 2.99 -3.55 表 3 个例分析时段内阵列反演风场与风廓线雷达产品水平风速和水平风向的平均绝对偏差、均方根误差和相对均方根误差
Table 3 Mean absolute deviation, root mean square error and relative root mean square error of horizontal wind speed and direction of the AWR retrieved wind field and wind profile radar products in the case analysis period
降水个例 分析时段 水平风速 水平风向 平均绝对偏差/ (m·s-1) 均方根误差/ (m·s-1) 相对均方根误差/% 平均绝对偏差/(°) 均方根误差/(°) 2 2019-04-29T13:15—14:15 2.85 3.27 24 7.15 10.06 3 2019-05-12T15:30—17:30 3.74 3.21 20 10.81 15.87 4 2019-06-01T13:00—13:50 3.96 3.48 29 9.19 15.55 5 2019-07-12T12:25—13:05 2.10 2.91 19 5.72 7.56 9 2019-08-25T16:55—17:15 1.28 3.92 31 9.42 17.49 表 4 个例分析时段内阵列反演风场与风廓线雷达产品水平风速和水平风向的平均绝对偏差、均方根误差和相对均方根误差
Table 4 Mean absolute deviation, root mean square error and relative root mean square error of horizontal wind speed and direction of the AWR retrieved wind and wind profile radar products in the case analysis period
降水个例 分析时段 水平风速 水平风向 平均绝对偏差/ (m·s-1) 均方根误差/ (m·s-1) 相对均方根误差/% 平均绝对偏差/(°) 均方根误差/(°) 1 2019-04-26T18:00—20:00 2.07 3.47 44 47.60 41.46 6 2019-07-19T14:30—15:20 1.96 2.66 56 42.82 33.88 7 2019-08-18T18:35—19:05 2.94 5.57 55 55.89 58.24 10 2019-09-10T18:00—18:30 1.52 4.54 73 39.79 52.89 -
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