Inversion of Precipitation Parameters and Precipitation Type Classification Based on Micro Rain Radar
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摘要: 本文基于微雨雷达原始的后向散射信号，采用一种新的功率谱处理算法（RaProM算法），在功率谱计算、噪声去除、退模糊等处理的基础上计算了雷达基本参量, 并反演了液态降水参数，例如雷达反射率因子、雨强等, 并对粒子相态进行识别。RaProM算法综合考虑粒子下落速度、等效雷达反射率因子、不同相态粒子的尺度特征以及是零度层亮带位置等信息，可识别的粒子相态包括雪、毛毛雨、雨、冰雹以及混合相态。选取了三个山东地区较为典型的个例对RaProM反演算法进行验证，即2021年7月2日典型层状云降水个例、2019年12月25日雨雪转换个例以及2018年3月4日零度层高度逐渐降低的降水个例。结果显示：粒子识别方法应用于典型层状云降水，垂直方向上不同相态粒子的分层较为明显，过冷层里的固态降水雪花、零度层附近冰水转换区的混合相态降水以及零度层以下的液态降水符合现有认识，验证了反演算法以及粒子识别算法的有效性。将结果进一步在雨雪转换降水相态识别中和零度层高度的检测，该反演算法均能得到较好应用，与同址同步观测的微波辐射计、云雷达、二维视频雨滴谱仪等观测结论一致。另外，与微雨雷达标准反演算法对比，RaProM算法的优势是没有粒子相态的原始假设，且考虑降水粒子向上的速度，反演结果与微波辐射计、云雷达在垂直结构上有较高的一致性。与地面激光雨滴谱仪观测数据对比显示,也有效提升了微雨雷达对雨滴谱和雨强的反演能力。Abstract: Based on the original backscattering signals of the micro rain radar and a new micro rain radar processing methodology (RaProM), the equivalent radar reflectivity, the particle falling velocity, and Doppler spectrum width are calculated after power spectrum calculation, noise removal, and dealiasing. Furthermore, the precipitation types are identified. The RaProM algorithm can identify particle phases, such as snow, drizzle, rain, hail, and their mixed types, considering particle falling velocity, equivalent radar reflectivity, particle size characteristics of different precipitation types, and presence of bright bands. In addition, liquid precipitation parameters, such as radar reflectivity factor and rain intensity, are computed. Subsequently, three typical cases of stratiform cloud precipitation on July 2, 2021, transition of rain and snow on December 25, 2019, and the precipitation with the height of the zero degree layer decreasing gradually on March 4, 2018, are selected to verify and discuss the results. The method of precipitation type classification is applied to typical stratiform precipitation, the vertical structure shows snowflakes in the supercooled water area, mixed-type precipitation in the ice-liquid conversion zone near the 0°C layer, and liquid precipitation below the bright band, proving the validity of the method. The methods are then applied to precipitation type classification and bright band detection. The results show that the RaProM algorithm has the advantage of making no assumptions about precipitation type and considering particle upward velocity (such as snowflakes) over the standard inversion process of micro rain radar. The RaProM algorithm results are in good agreement with the colocated microwave radiometer and cloud radar in the vertical structure, and the deviations from the ground disdrometer in raindrop size distribution and rain intensity are reduced compared with the products of micro rain radar.
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图 1 降水粒子相态识别算法流程
Figure 1. Recognition algorithm flowchart of precipitation type classification
图 2 （a、d）2021年7月2日12:00（协调世界时，下同）、（b、e）2019年12月26日00:00和（c、f）2018年3月4日00:00探空资料给出的三个时次干球温度与湿球温度的廓线（第一行）以及干球温度与湿球温度的偏差（第二行）
Figure 2. Vertical profiles of dry-bulb and wet-bulb temperatures (top line) and biases of dry-bulb and wet-bulb temperatures (bottom line) given by three radiosonde data at (a, d) 1200 UTC on July 2, 2021, (b, e) 0000 UTC on December 26, 2019, and (c, f) 0000 UTC on March 4, 2018
图 3 2021年7月2日16:00～20:00济南反射率因子随时间和空间的演变：（a）微雨雷达原始产品；（b）RaProM算法结果。（a、b）中黑色线、蓝色线为微波辐射计探测的0°C、−10°C层高度随时间的变化。（c）2021年7月2日16:00～20:00地面雨强（单位：mm h−1）随时间的演变，其中蓝色五角形为微雨雷达小时累积雨量，黑色圆点为RaProM算法反演的小时累积雨量，紫色点为区域自动气象站的小时雨强资料
Figure 3. Time–height distributions of radar reflectivity in Jinan from 1600 UTC to 2000 UTC on July 2, 2021: (a) Products of the micro rain radar; (b) depicts the result of the RaProM algorithm. The black line and the blue lines in (a, b) are the changes in the height of 0°C and −10°C layers detected by the microwave radiometer, respectively. (c) Evolution of surface rain intensity (units: mm h−1) from 1600 UTC to 2000 UTC on July 2, 2021, with the blue solid line representing the rain intensity of the micro rain radar product, the orange solid line representing the rain intensity inversion by the RaProM algorithm, and the purple dot indicating the hourly rain intensity data of regional automatic weather stations
图 4 2021年7月2日16:00～20:00粒子下落速度随时间和空间的演变：（a）微雨雷达原始产品；（b）RaProM算法结果；（c）Ka波段云雷达探测。（a、b）中黑色线、蓝色线为微波辐射计探测的0°C层、−10°C层高度随时间的变化。（d）2021年7月2日16:00～20:00 400 m高度上粒子下落末速度随时间的演变，红色、蓝色和黄色实线分别表示微雨雷达速度原始产品、RaProM算法反演值和云雷达反演值
Figure 4. Time–height distributions of particle falling velocity from 1600 UTC to 2000 UTC on July 2, 2021:(a) Products of the micro rain radar; (b) outcomes of the RaProM algorithm; (c) products of Ka band cloud radar. In (a, b), the black and blue lines symbolize the changes in the height of the 0°C and −10°C layers detected by the microwave radiometer, respectively. (d) Particle falling velocity at 400 m height from 1600 UTC to 2000 UTC on July 2, 2021, micro rain radar velocity original product is denoted by a solid red line, RaProM algorithm calculation result is denoted by a solid blue line, and the observation of cloud radar is denoted by a solid yellow line
图 5 2021年7月2日16:00～20:00微雨雷达反演的粒子相态的时空分布，图中黑色线、蓝色线分别为微波辐射计探测的0°C、−10°C层高度随时间的变化
Figure 5. Time–height distribution of precipitation type retrieved by micro rain radar in Jinan from 1600 UTC to 2000 UTC on July 2, 2021, where the black and blue lines represent the variations in the height of the 0°C and −10°C layers detected by microwave radiometer, respectively
图 6 2019年12月25日00:00～23:59济南微雨雷达反射率因子随时间和空间的演变。（a）微雨雷达输出原始产品；（b）RaProM算法结果
Figure 6. Time–height distribution of radar reflectivity observed by micro rain radar in Jinan from 0000 UTC to 2359 UTC on December 25, 2021: (a) Products of the micro rain radar; (b) results of the RaProM algorithm
图 7 2019年12月25日00:00～23:59济南粒子下落速度随时间和空间的演变：（a）微雨雷达产品；（b）RaProM算法结果。（c）2019年12月25日00:00～23:59微雨雷达最底层（200 m）高度上的粒子下落速度随时间的演变
Figure 7. Time–height distributions of particle falling velocity detected by micro rain radar in Jinan from 0000 UTC to 2359 UTC on December 25, 2021: (a) Products of the micro rain radar; (b) results of the RaProM algorithm. (c) The particle falling velocity at the lowest level (200 m) of the micro rain radar in Jinan from 0000 UTC to 2359 UTC on December 25, 2021
图 8 2019年12月25日00:00～23:59（a）雨雪转换过程微雨雷达反演粒子相态的时空分布和（b）二维视频雨滴谱仪2DVD观测的粒子形状（红色图片为前方视角粒子形状，蓝色为同一雨滴侧方视角粒子形状）
Figure 8. (a) Time–height distributions of precipitation types retrieved by micro rain radar and (b) depicts the shape of the drop observed by 2DVD (2D Video Distrometer) in Jinan from 0000 UTC to 2359 UTC on December 25, 2021. The red picture in (b) demonstrates the shape of the raindrop from the front view, while the blue picture illustrates the shape of the raindrop from the side view of the same raindrop
图 9 2018年3月3日23:30至4日15:30降水过程雷达反射率因子随时间和空间的演变：（a）微雨雷达产品；（b）RaProM算法结果；（c）Ka波段云雷达产品。（a、b）中红色三角线、蓝色线圈为微波辐射计探测的0°C、−10°C层高度随时间的变化。（d）2018年3月3日23:30至4日15:30降水过程济南国家地面自动站（站号：54823）和区域地面自动站（站号：D6066）观测的地面温度随时间的演变
Figure 9. Time–height distributions of radar reflectivity in Jinan from 2330 UTC on March 3 to 1530 UTC on March 4, 2018: (a) Product of the micro rain radar; (b) results of the RaProM algorithm; (c) products of Ka band cloud radar. In (a, b) the red triangle dot line and the blue circle dot line represent the changes in the height of the 0°C and −10°C layers detected by microwave radiometer, respectively. (d) The surface temperature evolution with time observed by the Jinan National Ground Automatic Station (No. 54823) and regional ground automatic station (No. D6066) in Jinan from 2330 UTC on March 3 to 1530 UTC on March 4, 2018
图 10 2018年3月3日23:30至4日15:30降水过程（a）微雨雷达反演的粒子相态的时空分布（红色实线为−10°C层的高度，黑色实线为0°C层的高度）以及（b）近地层粒子下落速度（单位：m s−1）和雨强（单位：mm h−1）随时间的演变
Figure 10. (a) Time–height distributions of precipitation type retrieved by micro rain radar and (b) evolutions of surface particle falling velocity (units: m s−1) and rainfall intensity (units: mm h−1) in Jinan from 2330 UTC on March 3 to 1530 UTC on March 4, 2018, in which the black and red lines are the changes in the height of the 0°C and −10°C layers detected by microwave radiometer, respectively
图 11 2018年3月3日23:30至4日15:30降水过程（a–c）地面粒子数浓度的随时间演变：（a）地面激光雨滴谱仪观测；（b）微雨雷达近地层的原始反演结果；（c）微雨雷达应用RaProM算法的反演结果。（d）2018年3月3日23:30至4日15:30降水过程雨强随时间的演变，其中红色实线为激光雨滴谱仪反演的雨强，蓝色实线为微雨雷达反演的雨强，绿色实线为微雨雷达应用RaProM算法反演的雨强
Figure 11. (a–c) Evolutions of surface raindrop size distribution over time during precipitation from 2330 UTC March 3 to 1530 UTC March 4, 2018: (a) Ground-based disdrometer; (b) products of micro rain radar near ground level; (c) inversion findings of micro rain radar applying the RaProM algorithm. (d) Evolutions of rain intensity over time during precipitation from 2330 UTC March 3 to 1530 UTC March 4, 2018, where the red solid line is the inversion of the disdrometer, the solid blue line is the products of the micro rain radar, and the solid green line is the inversion of the micro rain radar with the RaProM algorithm
表 1 微雨雷达参数配置
Table 1. Specification of micro rain radar
参数 指标 发射频率 24.23 GHz (K-band) 波长 12.38 mm 发射功率 50 mW 收发天线 偏置抛物面; 直径: 0.6 m 波束宽度 2° 调制方式 调频连续波FM-CW
（frequency-modulated continuous wave）时间分辨率 产品10～3600 s（可调），原始功率谱10s 高度分辨率 10～1000 m（可调），本研究：200 m 距离库数 31 谱速度分辨率 0.1905 m s−1 速度范围 0～12.192 m s−1 粒子直径范围 0.109～6 mm 探测高度 本研究：最大6000 m 尺寸 0.6 m×0.6 m×0.6 m 
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