LIU Yang,YANG Ling,XU Zixin,et al.Analysis of Experimental Results of X-band Weather Radar Network Cooperative Observation[J].Journal of Chengdu University of Information Technology,2023,38(06):630-636.[doi:10.16836/j.cnki.jcuit.2023.06.002]
X波段天气雷达组网协同观测试验结果分析
- Title:
- Analysis of Experimental Results of X-band Weather Radar Network Cooperative Observation
- 文章编号:
- 2096-1618(2023)06-0630-07
- Keywords:
- radar netting; X-band weather radar; retrieval of three-dimensional wind fields; meso-and small scale system
- 分类号:
- TN959.4
- 文献标志码:
- A
- 摘要:
- 2016-2021年成都和北京开展了X波段天气雷达组网观测实验，旨在检验组网观测策略对中小尺度天气过程演变的观测能力和对业务S波段雷达的补充作用。介绍了雷达性能指标、观测模式与风场反演算法，并通过在成都与北京选择天气个例，对X波段雷达组网资料与S波段业务雷达资料进行对比分析。在短时强降水个例中，该组网策略观测到强对流单体初生6分钟内过程面积与强度逐渐增大的过程，能有效提升快速演变的中小尺度对流天气的探测能力，弥补S波段雷达的时间上的探测盲区。在冰雹过程中，通过组网雷达的反演风场可以反映此次天气过程中的强对流中心不同高度的风场特征是中低层高度气流辐合压迫气流向上运动，到高层向四周散开形成辐散的结构。同时，S波段雷达在同一时刻观测的径向速度场和反演风场相互印证，但通过单部S波段雷达探测会丢失部分风场随高度变化的信息。通过雷达组网和风场反演算法得到的流场结构符合天气学原理，与S波段雷达探测到的径向速度结构一致，能够更精细地展现对流天气的动力结构。在冰雹过程中的智能RHI扫描结果显示强回波中心位置的中低层辐合和高层辐散的结构，体现了智能RHI探测算法的有效性。
- Abstract:
- From 2016 to 2021, X-band weather radar network observation tests were conducted in Chengdu and Beijing, China. The purpose was to verify the observation capabilities of the network observation strategy for the evolution of meso-and small-scale weather processes, as well as its supplementary role to the S-band business radar. This article outlines the radar performance indicators, observation modes, and wind-field inversion algorithms. Through the comparison and analysis of X-band radar network data and S-band business radar data for weather examples selected in Chengdu and Beijing, the network observation strategy was found to effectively improve the detection ability of fast-evolving meso-and small-scale convective weather, and can compensate for the detection blind spots in the time domain of S-band radar. In the short-term heavy precipitation case, the network strategy observed a gradual increase in the process area and intensity of strong convective cells within the first six minutes of birth, which can effectively improve the detection ability of meso-and small-scale convective weather with rapid evolution, and can compensate for the time-domain detection blind spots of the S-band radar. During the hail process, the inversion wind field of the network radar can reflect the wind field characteristics at different heights of the strong convective center during this weather process, where the mid-and low-level height airflows converged and compressed the upward-moving airflow, and the high-level airflow spread outwards to form a divergent structure. At the same time, the radial velocity field observed by the S-band radar at the same time is mutually confirmed by the inversion wind field, but the information on the wind field variation with height will be lost through single S-band radar detection. The flow field structure obtained by the radar network and the wind field inversion algorithm conforms to the principles of meteorology and is consistent with the radial velocity structure detected by the S-band radar, which can more precisely show the dynamic structure of convective weather. The intelligent RHI scan results during the hail process show mid-and low-level convergence and high-level divergence structure of the strong echo center, reflecting the effectiveness of the intelligent RHI detection algorithm.
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备注/Memo
收稿日期:2022-11-29
基金项目:四川省自然科学基金资助项目(2022NSFSC0216)