A method for observing evolution process of hail cloud in three dimensions by multi-radar networking cooperation

Abstract

The present application relates to the field of meteorological observation, and provides a multi-radar networking coordinated hail cloud evolution process stereoscopic observation method. The purpose is to solve the problem that the existing radar needs about 6 minutes to complete a scan, however the whole hail cloud develops rapidly, and the whole process of hail cloud development cannot be observed completely, the main scheme includes determining the early cloud body of hail cloud, continuously tracking and scanning the hail cloud body to obtain RHI observation data; the vertically pointing radar continuously scans the hail cloud body in the time domain from the early stage of hail cloud body development, and obtains vertical observation data; the RHI observation data and the vertical observation data at the same time are associated in the time domain, and stereoscopic observation data is obtained; the stereoscopic observation data is quality controlled, and a real data set of hail cloud development and change is obtained; then human marking is performed, and the evolution characteristics of various macro and micro physical parameters of hail cloud in the whole process of hail initial stage, jump-increase stage, incubation stage, hail fall stage and decline stage are obtained.

Description

Technical Field

[0001] This invention relates to the field of meteorological observation and provides a three-dimensional observation method for the evolution of hail clouds using a multi-radar network. Background Technology

[0002] Hail Introduction: China is one of the countries most severely affected by hail disasters, causing enormous losses to agriculture, power, transportation, construction, people's property, and lives every year. Hail disasters are meteorological disasters triggered by strong convective storms, often occurring during the transitional season when convection is vigorous, generally between May and September. Hail is characterized by rapid development, short duration, and strong destructive power, and its occurrence is often accompanied by severe weather processes such as strong winds and heavy precipitation. Specifically, hail disasters are highly localized, generally affecting an area within a few kilometers; the development process of hail is very short, with hail clouds typically dissipating after a few minutes of hailfall, and hail events lasting more than half an hour are rare; the formation and occurrence of hail are heavily influenced by topography. Simply put, given other triggering mechanisms, areas with more complex terrain are more prone to hail events. Based on the above impacts of hail disasters on various aspects of people's lives and the prominent characteristics of hail cloud development and changes, hail cloud identification and hail forecasting methods are constantly being proposed, while severe weather monitoring and early warning equipment is continuously being upgraded. Typical characteristics of hail clouds: rapid development and rapid ascent. Their life cycle is divided into several stages, including initiation, amplification, incubation, hailfall, and decay, which generally last 30-90 minutes. The initiation stage lasts for more than 10 minutes, the amplification stage lasts for 2-8 minutes, the cloud top rises rapidly, reaching several kilometers in just a few minutes, and the reflectivity increases rapidly. The incubation stage lasts for 3-9 minutes, the hailfall stage lasts for several minutes to tens of minutes, and the decay stage lasts for a few minutes.

[0003] Because hail clouds develop and change rapidly, existing radar volume scan modes, such as the VCP21 weather radar, are designed for shallow convective weather. A complete volume scan consists of nine independent PPI scans and takes approximately six minutes to complete. Therefore, existing radars are far from sufficient to detect and identify the rapid changes in developing severe convective hail weather processes, resulting in significant deficiencies in hail suppression command and effectiveness assessment. To address scientific hail suppression techniques and effectiveness assessment, refined observation of hail cloud evolution is needed to understand its development and change mechanisms. Therefore, this invention proposes a collaborative three-dimensional observation technology based on multiple radars and satellites, enabling refined observation of hail cloud evolution.

[0004] Current methods for identifying hail clouds primarily rely on radar reflectivity as a standard. They cannot distinguish the evolution process of hail clouds or determine their development and changes. They simply identify hail clouds as hail when their reflectivity exceeds a certain threshold, and then direct hail suppression operations. However, the operational methods vary from place to place. Some areas use quantitative seeding, while others use excessive seeding. The seeding location and timing are arbitrary, lacking unified standards and scientific guidance, resulting in poor operational efficiency.

[0005] Existing methods for observing hail clouds include fixed VCP21 conventional operational scanning, which identifies hail clouds by their reflectivity, or dual-polarization radar, which also uses operational scanning and identifies hail clouds by comprehensively analyzing various polarization parameters.

[0006] Existing technical solutions typically complete a volume scan in 6 minutes. This is too long and scanning at a few fixed angles fails to observe the rapid changes in hail clouds, making it impossible to clearly understand their development and transformation characteristics. Within 6 minutes, hail clouds can undergo significant changes and rise rapidly (at speeds of tens of meters per second). This process, especially vertical changes in the cloud structure, is simply impossible to observe using current systems.

[0007] The invention patent application number 201910771501.2, namely the invention patent for "Online Monitoring Data Early Warning Method of Polarization Radar ZDR Columns for Hailfall in Convective Cells," is based on online monitoring data of the number of columns in a polarization radar ZDR column and the maximum value of the ZDR within the column. It simplifies the analysis and calculation process while ensuring accurate early warning, thus achieving timely early warning before hailfall. This invention differs from the method and mechanism of this invention, which focuses on observing the development process of hail clouds. Summary of the Invention

[0008] The purpose of this invention is to solve the problem that existing radars take about 6 minutes to complete a scan, while the entire hail cloud develops rapidly, making it impossible to observe the entire development process of the hail cloud. In order to obtain the entire process of the rapid development and change of hail clouds in real time, the existing observation method is improved by using three-dimensional collaborative observation with different scanning modes of multiple ground radars. The operational radar performs routine operational scans and provides weather background warnings, while the vertical pointing radar observes the complete development process of the vertical pointing hail cloud, capturing the vertical structural change characteristics of the hail cloud in real time, and obtaining the rapid change characteristics of the hail cloud's appearance structure, vertical evolution, microphysics, etc.

[0009] To achieve the above objectives, the present invention employs the following technical means: A three-dimensional observation device for hail cloud evolution using a multi-radar network, characterized in that it comprises: Operational radar: Scans and detects convective clouds, and obtains early cloud structures of hail clouds by comprehensively judging the echo information; Vertical pointing radar: Set up in areas where hail is frequent, it observes the vertical changes of hail clouds directly above the radar to obtain continuous vertical evolution data of hail clouds in the time domain. Dynamic sector-shaped RHI volume scanning radar: The scanning angle of the RHI volume scanning radar is set according to the width of the early hail cloud obtained by the operational radar, and the volume scanning is performed in real time to obtain RHI observation data with a time observation period. The size of the time observation period is related to the size of the scanning angle. In the above technical solution, the vertical evolution data includes: precipitation particle characteristics, which can be obtained by inversion of the precipitation particle spectrum, Doppler velocity, vertical velocity of precipitation particles moving in the atmosphere, vertical velocity including atmospheric vertical motion velocity, particle falling velocity, velocity spectrum width, which can reflect atmospheric disturbance characteristics, and the variation characteristics of the above elements at different altitudes over time.

[0010] In the above technical solution, the fan-shaped RHI volume scanning radar can detect the three-dimensional vertical structure of the atmosphere at multiple azimuth angles in a certain fan-shaped area, including three-dimensional echo characteristics such as echo reflectivity, radial velocity, and spectral width, and can obtain information on the macroscopic structure of clouds and the distribution of precipitation particles inside clouds. The above technical solution includes the following steps: Step 1: The operational radar uses a determined elevation angle sequence to perform volumetric scanning detection of convective clouds. By comprehensively judging the echo information, it identifies early-stage hail clouds with development potential. Step 2: Based on the width of the early hail cloud body, set up a radar dynamic sector RHI scanning strategy to scan the early hail cloud body identified in Step 1. Start continuous tracking and scanning from the early stage of hail cloud body development to obtain RHI observation data of the entire process of rapid change and development of the hail cloud. Vertical pointing radar continuously scans hail clouds in the time domain from the early stages of their development, obtaining vertical observation data of the entire process of rapid and continuous changes in the hail cloud at each stage of its development. In the time domain, RHI observation data and vertical observation data at the same time are correlated to obtain stereo observation data; Step 3: Perform quality control on the three-dimensional observation data to obtain a real dataset of hail cloud development and changes; Step 4: Based on the real dataset obtained in Step 3, obtain the evolution characteristics of various macro- and micro-physical parameters of hail clouds throughout the entire process of hail initiation, growth, incubation, hailfall, and decay.

[0011] The above technical solution also includes step 5, which uses the evolution characteristics of various macro and micro physical parameters of hail clouds obtained in step 4 for machine learning and for hail cloud early identification, hail cloud tracking and hail cloud forecasting.

[0012] In the above technical solution, step 2, continuous tracking scanning, refers to performing a reciprocating cyclical scan based on the width range of the early hail cloud body.

[0013] In the above technical solution, step 3 specifically includes: Step 3.1 Perform ground clutter removal on each radar observation data; Step 3.2 Perform attenuation correction on the radar observation data; Step 3.3: Conduct a comprehensive study of the attenuation-corrected data from various radars to obtain a real dataset of hail cloud development and changes.

[0014] In the above technical solution, step 4 specifically includes: obtaining the horizontal and vertical scales and macroscopic intensity characteristics of the flow cell hail cloud in the real dataset of hail cloud development and change, and using multiple polarization quantities such as radar reflectivity, differential reflectivity, differential phase shift, and correlation coefficient to obtain the drop spectrum change characteristics and hail spectrum of the particle falling process, and obtaining the evolution characteristics of multiple macroscopic and microscopic physical parameters of hail cloud throughout the entire process of hail occurrence, including the initiation period, leap period, incubation period, hail falling period, and decay period, through artificial annotation.

[0015] The evolution characteristics of the macro- and micro-physical parameters include the following: changes in the configuration of small- and medium-scale circulation fields, unstable energy, vertical airflow inside and outside the hail cloud, and the growth mode of hail embryos.

[0016] This invention also provides an automatic labeling method for the development process of RHI observation data. Vertical observation data from a real dataset is used for machine learning to obtain a model for determining the hail initiation, catastrophic, incubation, hailfall, and decay stages. This model is then applied to new, unlabeled vertical observation data to automatically label the vertical observation data. Furthermore, based on the correlation between the vertical observation data and RHI observation data, the RHI observation data is automatically labeled, resulting in labeled RHI observation data. The labeling refers to the labeling of the hail initiation, catastrophic, incubation, hailfall, and decay stages.

[0017] Because the present invention employs the above-mentioned technical means, it has the following beneficial effects: 1. More comprehensive observation, more accurate data, and stronger real-time performance: Vertical pointing radar can record the entire development process of hail clouds in real time and has a richer set of hail cloud characteristics. Compared with dynamic fan-shaped RHI volume scanning radar, it can obtain more hail cloud characteristic information per unit time.

[0018] 2. The observation method of this invention can observe richer hail cloud features, providing basic data for hail cloud prediction or other research.

[0019] 3. Provide basic data for the command of human shadowing operations (timing, dosage, and site acquisition) and the evaluation of effects.

[0020] 4. In practical use, since it is impossible to deploy vertical radar under hail clouds at all times, the identification of hail clouds through machine learning generally relies on RHI observation data. Existing RHI observation methods have long scanning cycles, and cannot collect much observation data after a hailstorm ends. If the entire hailstorm lasts for 50 minutes, with a scanning cycle of 6 minutes, less than 10 scans of a hail cloud can be obtained. It is difficult to obtain complete hail cloud features through 10 scans. Such data will result in insufficient training samples for later machine learning models, leading to poor model training performance. However, the dynamic fan-shaped RHI volume scanning radar mode of this invention can obtain more datasets in a complete hail cloud cycle, which is more conducive to model training. Because vertical pointing radar is continuous in time, it can scan more hail cloud features. Model training using vertical observation data is undoubtedly superior to RHI observation data. Therefore, this invention uses vertical observation data for machine learning model training to label the entire hail cloud process. Then, based on the temporal correlation between vertical observation data and RHI observation data, the RHI observation data is labeled. This allows for complete model training and automatic labeling of RHI observation data with fewer observations. For example, because RHI observation data sampling is periodic, the amount of data obtained in a single hailfall process is related to its period, while vertical observation data is continuously observed, and the amount of data obtained in a single hailfall process is many times that of RHI observation data. Attached Figure Description

[0021] Figure 1 Comparison of time altitude display (THI) between X-band vertical pointing radar observations and S-band dynamic sector RHI volume scan radar observations; Figure 2 This is a schematic diagram of a dynamic sector-shaped RHI volume scan. Detailed Implementation

[0022] The embodiments of the present invention will be described in detail below. Although the present invention will be described and illustrated in conjunction with some specific embodiments, it should be noted that the present invention is not limited to these embodiments. On the contrary, any modifications or equivalent substitutions made to the present invention should be covered within the scope of the claims of the present invention.

[0023] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without these specific details.

[0024] Other prior art related to this invention is described below: The VCP21 weather radar used in my country is designed for shallow convective weather. Its complete volume scan consists of 9 independent PPI scans and takes about 6 minutes. It is insufficient to capture the rapid changes in rapidly developing severe convective hail weather processes and to observe the vertical structure characteristics within clouds.

[0025] For hail suppression operations using weather modification techniques, special attention needs to be paid to the vertical changes of hail clouds and the underlying mechanisms that cause these changes. Therefore, it is particularly important to combine observation methods with multiple vertical profile scans to track and observe the vertical structure of hail clouds and quickly perceive their development and changes.

[0026] To adapt to the rapid development and changes of hail clouds under severe convective weather conditions, it is necessary to obtain high spatiotemporal resolution cloud variation characteristics. By studying the characteristics of local cloud and precipitation occurrence, different scanning schemes are designed and the differences in the acquired observation information are analyzed. The layout of existing observation equipment and the proposed high-performance dual-polarization radar and vertical pointing radar are optimized to establish a collaborative observation scheme for hail clouds, realize real-time synchronous observation of the same cloud body, and obtain the three-dimensional variation characteristics of hail clouds. This is the first key scientific problem that needs to be solved in this project.

[0027] For hail suppression operations using weather modification techniques, special attention needs to be paid to the vertical changes of hail clouds and the underlying mechanisms that cause these changes. Therefore, it is particularly important to combine observation methods with multiple vertical profile scans to track and observe the vertical structure of hail clouds and quickly perceive their development and changes.

[0028] This project aims to select areas prone to hail on the southern slopes of the Tianshan Mountains to construct a collaborative three-dimensional observation network. It will collect relevant observational data from ground-based observation stations, weather radars, and meteorological satellites on the southern slopes of the Tianshan Mountains in recent years, focusing on the occurrence, development, and movement paths of hail clouds over the past five years. The project will analyze and determine the origin of hail clouds and the spatiotemporal distribution of hail paths, collect and understand the radar command mode and personnel operation methods for weather modification operations, simulate and study the optimal scanning area for the networked radar, select and determine the mobile dual-polarization radar (scanning mode customizable) and vertical pointing radar positions for the experiment, and deploy the vertical pointing cloud and rain radar and dual-polarization radar in locations that can effectively observe or cover the areas where hail occurs and passes. The vertical pointing radar can be deployed along hail path corridors. Through collaborative observation experiments with operational radars, the project will rapidly and in real-time capture the vertical structure characteristics of hail clouds with high spatiotemporal resolution, compensate for the limitations of operational network coverage and scanning modes, and develop a scientifically sound and reasonable radar scanning strategy for weather modification and hail suppression.

[0029] The operational radar performs fixed VCP21 routine operational scans and provides weather background warnings, enabling synchronous and coordinated scanning observations by multiple X-band radar systems. Data collection is uniformly uploaded to the control center via streaming, and after centralized processing, hail suppression operation commands and service products are distributed through the network, achieving integrated management of data acquisition, information collection and processing, and product generation. Based on continuous tracking of the development of passing convective cells, the central control system schedules and, based on a customized coordinated scanning strategy, completes multi-radar coordinated observation of multiple convective clouds with development potential. The vertical pointing radar, based on the real-time hail cloud development status obtained from the operational radar PPI scans, tracks and adjusts its scanning strategy in real time according to the cloud's distance, size, and movement azimuth. Multiple RHI scans constitute a real-time tracking volume scan of the hail cloud body, achieving "dynamic fan-shaped RHI volume scan" (…). Figure 2 This allows us to obtain the rapid changes in the appearance, structure, vertical evolution, and microphysics of hail clouds.

[0030] This invention provides a three-dimensional observation device for the evolution of hail clouds using a multi-radar network, characterized by comprising: Operational radar: Scans and detects convective clouds, and obtains early cloud structures of hail clouds by comprehensively judging the echo information; Vertical pointing radar: Set up in areas where hail is frequent, it observes the vertical changes of hail clouds directly above the radar to obtain continuous vertical evolution data of hail clouds in the time domain. Dynamic sector-shaped RHI volume scanning radar: The scanning angle of the RHI volume scanning radar is set according to the width of the early hail cloud obtained by the operational radar, and the volume scanning is performed in real time to obtain RHI observation data with a time observation period. The size of the time observation period is related to the size of the scanning angle. In the above technical solution, the vertical evolution data includes: precipitation particle characteristics, which can be obtained by inversion of the precipitation particle spectrum, Doppler velocity, vertical velocity of precipitation particles moving in the atmosphere, vertical velocity including atmospheric vertical motion velocity, particle falling velocity, velocity spectrum width, which can reflect atmospheric disturbance characteristics, and the variation characteristics of the above elements at different altitudes over time.

[0031] In the above technical solution, the fan-shaped RHI volume scanning radar can detect the three-dimensional vertical structure of the atmosphere at multiple azimuth angles in a certain fan-shaped area, including three-dimensional echo characteristics such as echo reflectivity, radial velocity, and spectral width, and can obtain information on the macroscopic structure of clouds and the distribution of precipitation particles inside clouds. The above technical solution includes the following steps: Step 1: The operational radar uses a determined elevation angle sequence to perform volumetric scanning detection of convective clouds. By comprehensively judging the echo information, it identifies early-stage hail clouds with development potential. Step 2: Based on the width of the early hail cloud body, set up a radar dynamic sector RHI scanning strategy to scan the early hail cloud body identified in Step 1. Start continuous tracking and scanning from the early stage of hail cloud body development to obtain RHI observation data of the entire process of rapid change and development of the hail cloud. Vertical pointing radar continuously scans hail clouds in the time domain from the early stages of their development, obtaining vertical observation data of the entire process of rapid and continuous changes in the hail cloud at each stage of its development. In the time domain, RHI observation data and vertical observation data at the same time are correlated to obtain stereo observation data; Step 3: Perform quality control on the three-dimensional observation data to obtain a real dataset of hail cloud development and changes; Step 4: Based on the real dataset obtained in Step 3, obtain the evolution characteristics of various macro- and micro-physical parameters of hail clouds throughout the entire process of hail initiation, growth, incubation, hailfall, and decay.

[0032] The above technical solution also includes step 5, which uses the evolution characteristics of various macro and micro physical parameters of hail clouds obtained in step 4 for machine learning and for hail cloud early identification, hail cloud tracking and hail cloud forecasting.

[0033] In the above technical solution, step 2, continuous tracking scanning, refers to performing a reciprocating cyclical scan based on the width range of the early hail cloud body.

[0034] In the above technical solution, step 3 specifically includes: Step 3.1 Perform ground clutter removal on each radar observation data; Step 3.2 Perform attenuation correction on the radar observation data; Step 3.3: Conduct a comprehensive study of the attenuation-corrected data from various radars to obtain a real dataset of hail cloud development and changes.

[0035] In the above technical solution, step 4 specifically includes: obtaining the horizontal and vertical scales and macroscopic intensity characteristics of the flow cell hail cloud in the real dataset of hail cloud development and change, and using multiple polarization quantities such as radar reflectivity, differential reflectivity, differential phase shift, and correlation coefficient to obtain the drop spectrum change characteristics and hail spectrum of the particle falling process, and obtaining the evolution characteristics of multiple macroscopic and microscopic physical parameters of hail cloud throughout the entire process of hail occurrence, including the initiation period, leap period, incubation period, hail falling period, and decay period, through artificial annotation.

[0036] The evolution characteristics of the macro- and micro-physical parameters include the following: changes in the configuration of small- and medium-scale circulation fields, unstable energy, vertical airflow inside and outside the hail cloud, and the growth mode of hail embryos.

[0037] This invention also provides an automatic labeling method for the development process of RHI observation data. Vertical observation data from a real dataset is used for machine learning to obtain a model for determining the hail initiation, catastrophic, incubation, hailfall, and decay stages. This model is then applied to new, unlabeled vertical observation data to automatically label the vertical observation data. Furthermore, based on the correlation between the vertical observation data and RHI observation data, the RHI observation data is automatically labeled, resulting in labeled RHI observation data. The labeling refers to the labeling of the hail initiation, catastrophic, incubation, hailfall, and decay stages.

[0038] Further regarding the ground clutter suppression and filtering mentioned in step 3.2: Based on previous research, the probability distributions of characteristic parameters of ground clutter and precipitation echoes can be used to select radar observations and their derived parameters (mean, median, or texture features, etc.) that can better distinguish between precipitation and ground object echoes as input parameters for the fuzzy logic recognition algorithm. Fuzzification is then performed, and a membership value T is obtained using an equal-weighted summation method. Values ​​greater than a given threshold are identified as ground object echoes and marked. Finally, the range database identified as ground object echoes is filled and corrected using precipitation echo values ​​at appropriate elevation angles from the previous layer.

[0039] Further attenuation corrections mentioned in step 3.2: Attenuation correction of reflectivity factor Zh and differential reflectivity ZDR The C-band and X-band dual-polarization weather radars operate in attenuation bands. In this project, the C-band radar data will be corrected using the KDP correction method, while the X-band radar data will be corrected using the constraint method. Multiple radars in the collaborative observation network have several overlapping scanning areas. After attenuation correction, the radar data quality will be further evaluated and analyzed, and cross-validated to ensure the comparability of the observed objects.

Claims

1. A method for observing evolution of a hail cloud in three dimensions by multi-radar network cooperation, characterized in that, Includes the following steps: Step 1: The operational radar uses a determined elevation angle sequence to perform volumetric scanning detection of convective clouds. By comprehensively judging the echo information, it identifies early-stage hail clouds with development potential. Step 2: Based on the width of the early hail cloud, set up a dynamic fan-shaped RHI scanning strategy for radar, detect the three-dimensional vertical structure of the atmosphere at multiple azimuth angles in the fan-shaped area, and specifically scan the early hail cloud identified in Step 1. Continuous tracking and scanning are carried out from the early stage of hail cloud development to obtain RHI observation data of the entire process of rapid change and development of the hail cloud. Vertical pointing radar continuously scans hail clouds in the time domain from the early stages of their development, obtaining vertical observation data of the entire process of rapid and continuous changes in the hail cloud at each stage of its development. In the time domain, RHI observation data and vertical observation data at the same time are correlated to obtain stereo observation data; Step 3: Perform quality control on the three-dimensional observation data to obtain a real dataset of hail cloud development and changes; Step 4: Based on the real dataset obtained in Step 3, perform artificial labeling to obtain the evolution characteristics of various macro- and micro-physical parameters of hail clouds throughout the entire process of hail initiation, amplification, incubation, hailfall, and decay.

2. The method according to claim 1, wherein, It also includes step 5, which uses the evolution characteristics of various macro and micro physical parameters of hail clouds obtained in step 4 for machine learning and for hail cloud early identification, hail cloud tracking and hail cloud forecasting.

3. The method for three-dimensional observation of hail cloud evolution process using multi-radar network collaboration as described in claim 1, characterized in that, Step 2 specifically includes: continuous tracking scanning refers to performing reciprocating cyclic scanning based on the width range of the early hail cloud body.

4. The method for three-dimensional observation of hail cloud evolution process using multi-radar network collaboration as described in claim 1, characterized in that, Step 3 specifically includes: Step 3.1 Perform ground clutter removal on each radar observation data; Step 3.2 Perform attenuation correction on the radar observation data; Step 3.3: Conduct a comprehensive study of the attenuation-corrected data from various radars to obtain a real dataset of hail cloud development and changes.

5. The method for three-dimensional observation of hail cloud evolution process using multi-radar network collaboration according to claim 1, characterized in that, Step 4 specifically includes: based on the horizontal and vertical scales, intensity macroscopic characteristics, radar reflectivity, differential reflectivity, differential phase shift, and correlation coefficient of the flow cell hail cloud in the real dataset of hail cloud development and changes, obtaining the droplet spectrum change characteristics and hail spectrum during the particle falling process, and obtaining the evolution characteristics of various macroscopic and microscopic physical parameters of hail clouds throughout the entire process of hail occurrence, including the initiation period, leap period, incubation period, hail falling period, and decay period, through artificial annotation.

6. The method for three-dimensional observation of hail cloud evolution process using multi-radar network collaboration according to claim 1, characterized in that, Vertical observation data from real datasets are used for machine learning to obtain a model for determining the hail initiation, catastrophic, incubation, hailfall, and decay stages. This model is then applied to new, unlabeled vertical observation data to automatically label the vertical observation data. Based on the correlation between the vertical observation data and RHI observation data, the RHI observation data is automatically labeled to obtain labeled RHI observation data. The labeling refers to the labeling of the hail initiation, catastrophic, incubation, hailfall, and decay stages.

7. A three-dimensional observation device for the evolution of hail clouds using a multi-radar network, used to achieve any one of the methods in 1-6, characterized in that, include: Operational radar: Scans and detects convective clouds, and obtains early cloud structures of hail clouds by comprehensively judging the echo information; Vertical pointing radar: Set up in areas where hail is frequent, it observes the vertical changes of hail clouds directly above the radar to obtain continuous vertical evolution data of hail clouds in the time domain. Dynamic sector-shaped RHI volume scanning radar: The scanning angle of the RHI volume scanning radar is set based on the width of the early hail cloud obtained by the operational radar. The three-dimensional vertical structure of the atmosphere at multiple azimuth angles in the sector area is detected, and real-time tracking volume scanning is performed to obtain RHI observation data with a time observation period. The size of the time observation period is related to the size of the scanning angle.

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