A method and device for collaborative observation of strong convective cells using X-band radar networking

Abstract

This application discloses a method and apparatus for collaborative observation of strong convective cells using X-band radar networks. The method includes: upon detecting a strong convective region, performing strong convective cell segmentation based on the region; if multiple strong convective cells are obtained, determining the hazard level of each cell based on its corresponding target characteristic; and assigning corresponding scanning tasks to each X-band radar in the radar network based on the hazard levels of each cell, the distance between the centroid of each cell and the X-band radars in the radar network framework, and the vertical broadening of each cell. These scanning tasks include volume scans and vertical scans. This method improves the observation effect of strong convective cells.

Description

Technical Field

[0001] This application relates to the field of meteorological observation technology, specifically to a method and device for X-band radar network collaborative observation of strong convective cells. Background Technology

[0002] Weather radar plays an irreplaceable role in severe weather monitoring and early warning, and is crucial in reducing loss of life and property caused by meteorological disasters. Currently, the weather radars in operational use are mainly S-band and C-band. However, factors such as the curvature of the Earth, terrain obstruction, and long radar scanning cycles create low-altitude blind spots during radar scanning, and there is also the problem of missing observations of rapidly developing small and medium-sized severe convective cells. To overcome these shortcomings, a scheme using multiple small X-band weather radars to build a radar network framework has emerged. This radar network framework can achieve refined detection of weather targets.

[0003] However, the current radar network framework generally has the following problems: when there are multiple strong convective cells in the observation area, there is no clear mechanism to allocate scanning tasks for the strong convective cells. Summary of the Invention

[0004] This application provides a method and apparatus for collaborative observation of strong convective cells using X-band radar networks. When multiple strong convective cells exist in the observation area, a corresponding scanning task can be assigned to each X-band radar in the radar network framework based on a specific mechanism.

[0005] In view of this, the first aspect of this application provides a method for X-band radar network-based collaborative observation of strong convective cells, the method comprising:

[0006] If a strong convection region is detected, a strong convection cell segmentation operation is performed based on the strong convection region to obtain at least one strong convection cell.

[0007] If multiple strong convective cells are obtained through the strong convective cell segmentation operation, then for each strong convective cell, the hazard level corresponding to the strong convective cell is determined according to the target feature quantity corresponding to the strong convective cell.

[0008] Based on the hazard level of each of the multiple strong convective cells, the distance between the centroid of each of the multiple strong convective cells and the X-band radar in the radar network framework, and the vertical span of each of the multiple strong convective cells, a corresponding scanning task is assigned to each of the X-band radars in the radar network framework; the scanning task includes volume scan task and vertical scan task, which are used to indicate the volume scan mode and the strong convective cells to be scanned vertically when a strong convective cell appears within the scanning range of the X-band radar.

[0009] A second aspect of this application provides an X-band radar network collaborative observation device for strong convective single-unit observation, the device comprising:

[0010] The single-unit segmentation module is used to perform a strong convection single-unit segmentation operation based on the strong convection region when a strong convection region is detected, so as to obtain at least one strong convection single unit.

[0011] The hazard determination module is used to determine the hazard level of each strong convective cell based on the target feature quantity corresponding to the strong convective cell if multiple strong convective cells are obtained through the strong convective cell segmentation operation.

[0012] The task allocation module is used to assign corresponding scanning tasks to each of the X-band radars in the radar network framework based on the hazard level of each of the multiple strong convective cells, the distance between the centroid of each of the multiple strong convective cells and the X-band radars in the radar network framework, and the vertical span of each of the multiple strong convective cells. The scanning tasks include volume scan tasks and vertical scan tasks, which are used to indicate the volume scan mode and the strong convective cells to be scanned vertically when strong convective cells appear within the scanning range of the X-band radar.

[0013] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0014] This application provides a method for collaborative observation of strong convective cells using X-band radar networks. In this method, when multiple strong convective cells are obtained based on the segmentation of a strong convective region, the hazard level of each cell can be determined. Then, referring to the hazard level of each cell, the distance between the X-band radar and the centroid of each cell in the radar network framework, and the vertical coverage of each cell, a corresponding scanning task is assigned to each X-band radar in the radar network framework. The assigned scanning tasks include volume scan tasks and vertical scan tasks. The volume scan task indicates the volume scan mode of the X-band radar, and the vertical scan task indicates the vertical scan target of the X-band radar, i.e., the strong convective cells that the X-band radar needs to vertically scan. By assigning corresponding scan targets to each X-band radar in the radar network framework based on the above mechanism, priority can be given to scanning strong convective cells with higher hazard levels, and it can be ensured that each X-band radar can scan the observed strong convective cells effectively, thereby improving the observation effect of strong convective cells. Attached Figure Description

[0015] Figure 1 A flowchart illustrating a method for collaborative observation of strong convective cells using X-band radar networking, provided in an embodiment of this application;

[0016] Figure 2 This is a schematic diagram of strong convection cell segmentation provided in an embodiment of this application;

[0017] Figure 3 A flowchart illustrating another X-band radar network-based collaborative observation method for strong convective cells provided in this application embodiment;

[0018] Figure 4 This is a schematic diagram of the structure of an X-band radar network-based collaborative observation device for strong convection in an embodiment of this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0020] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] To facilitate understanding of the X-band radar network-based collaborative observation method for strong convective cells provided in this application embodiment, the following explanations of the terms and concepts involved in this application embodiment are provided.

[0022] A severe convective cell, also known as a storm cell, refers to a single thunderstorm cloud. It typically has a small horizontal scale, a short lifespan, and can cause localized hail, occasionally resulting in severe damage. On radar images, a severe convective cell usually consists of a strong core and a weak boundary. Meteorologically, a severe convective cell is interpreted as a convective precipitation process, which is a thunderstorm process prone to causing disasters.

[0023] RHI (Radar Height Indicator) scan: This is a vertical scanning mode of weather radar that uses a fixed azimuth angle to scan an area within a certain elevation angle range. For example, a 0 to 40° RHI scan means that the weather radar turns its antenna to a fixed azimuth and then performs a vertical scan between 40° and 0° elevation.

[0024] Network fusion: Based on reflectivity data from various radar types (mainly commonly used S-band and C-band weather radars), radar reflectivity factor mosaics at the same altitude are generated. The advantage is that it can obtain reflectivity network mosaics at the same altitude with a larger detection range. In this embodiment, it can provide more accurate and refined reference information for the identification and segmentation of strong convective cells.

[0025] VCP11, VCP21, or VCP31 modes refer to the scanning modes of weather radar. For example, there are three volume scan modes for Synthetic Aperture (SA) radar: VCP11 (completes a PPI scan of 14 elevation angles in 5 minutes, denoted as 14 / 5), VCP21 (completes a PPI scan of 9 elevation angles in 6 minutes, denoted as 9 / 6), and VCP31 (completes a PPI scan of 5 elevation angles in 10 minutes, denoted as 5 / 10).

[0026] The following describes the method for collaborative observation of strong convective cells using X-band radar networking provided in this application through method embodiments.

[0027] See Figure 1 , Figure 1 This is a flowchart illustrating the X-band radar network-based collaborative observation method for strong convective cells provided in this application embodiment. Figure 1 As shown, the method includes the following steps:

[0028] Step 101: If a strong convection region is detected, perform a strong convection cell segmentation operation based on the strong convection region to obtain at least one strong convection cell.

[0029] In this embodiment of the application, a volume scan task can be performed first using a conventional operational radar (such as at least one of S-band radar and C-band radar), and then, based on the scan data obtained through the volume scan task, it can be detected whether a strong convection region has appeared.

[0030] In related technologies, adaptive scanning control is typically performed solely based on reflectivity data generated by X-band radar. However, this method of self-coordinated control by the X-band radar can lead to blind spots. When the X-band radar scans a strong convection area (usually a sector scan), it may miss other suddenly formed localized strong convection areas. In this embodiment, conventional operational radar is first used to comprehensively detect the strong convection area, and then the X-band radar is used to scan the strong convective cells within the strong convection area. This effectively avoids the missed scans caused by the self-control of the X-band radar in the aforementioned related technologies, achieving comprehensive detection of the strong convection area.

[0031] Upon detection of a region of strong convection, a segmentation operation is performed based on this region to identify the individual strong convective cells within it. It should be understood that this segmentation operation can identify at least one strong convective cell.

[0032] In one possible implementation, a convective cell identification and segmentation method based on "dual reflectivity contour lines" can be used to segment strong convective cells within a strong convection region, yielding at least one strong convective cell. Specifically, the core of a strong convective cell within the strong convection region can be detected based on a core reflectivity detection threshold, where the area of ​​the core is greater than a preset core area threshold. For each core, an expansion search is performed according to a preset reflectivity step size until the expansion search reaches the cell boundary corresponding to the boundary reflectivity detection threshold, or until it intersects with the boundaries of other strong convective cells, thus obtaining the strong convective cell corresponding to that core. Here, the boundary reflectivity detection threshold is less than the aforementioned core reflectivity detection threshold.

[0033] It should be noted that the aforementioned core reflectivity detection threshold is a reflectivity threshold used to segment the core of a strong convective cell, which can be, for example, 45 dBZ; the aforementioned boundary reflectivity detection threshold is a reflectivity threshold used to distinguish the boundary regions of each strong convective cell, which can be, for example, 35 dBZ. It should be understood that the aforementioned core reflectivity detection threshold and boundary reflectivity detection threshold can be appropriately adjusted according to weather conditions. For example, lower core reflectivity detection thresholds and boundary reflectivity detection thresholds can be set for moderate-intensity convective environments, and higher core reflectivity detection thresholds and boundary reflectivity detection thresholds can be set for high-intensity convective environments. This application does not specifically limit the core reflectivity detection thresholds and boundary reflectivity detection thresholds in this embodiment.

[0034] When performing strong convective cell segmentation, a core reflectivity detection threshold can be used to search for strong convective cell cores within the strong convection region. For example, with a core reflectivity detection threshold of 45 dBZ, strong convective cell cores can be searched based on 45 dBZ isopleths within the strong convection region. For the found strong convective cell cores, their areas can be calculated, retaining the larger ones and discarding the smaller ones. For example, a preset core area threshold (e.g., 5 km²) can be set. 2 The system retains strong convective cell cores with an area greater than the preset core area threshold and discards strong convective cell cores with an area less than the preset core area threshold.

[0035] For each retained strong convective cell core, an expansion search can be performed based on that core at a preset reflectivity step size (e.g., 0.5 dBZ) until the cell boundary corresponding to the boundary reflectivity detection threshold (e.g., 35 dBZ) is found, or until the boundary of the strong convective cell to which another strong convective cell core belongs is found. Through this expansion search, the cell boundary corresponding to that strong convective cell core will be obtained accordingly. In this way, for each strong convective cell core, the corresponding strong convective cell can be determined using it and its corresponding cell boundary. Figure 2 This is a schematic diagram of strong convection cell segmentation provided in an embodiment of this application, as shown below. Figure 2 As shown, the individual strong convection cells have been circled.

[0036] In related technologies, a single reflectance threshold is typically used to identify strong convective cells. However, this method easily identifies many false strong convective cells, resulting in poor accuracy in identifying strong convective cells. In contrast, this application employs a dual reflectance contour line method to segment strong convective cells. A higher reflectance detection threshold is used to identify the core of the strong convective cell, while a lower reflectance detection threshold is used to identify the boundary. This approach achieves accurate identification of strong convective cells and improves the overall accuracy of their identification.

[0037] Step 102: If multiple strong convective cells are obtained through the strong convective cell segmentation operation, then for each strong convective cell, the hazard level corresponding to the strong convective cell is determined according to the target feature quantity corresponding to the strong convective cell.

[0038] In this embodiment of the application, if multiple strong convective cells are obtained by segmenting through the strong convective cell segmentation operation in step 101, then for each strong convective cell, the hazard level of the strong convective cell needs to be determined according to the target feature vector of the strong convective cell. This facilitates the subsequent allocation of X-band radar to the strong convective cells with higher hazard levels for vertical scanning, thereby obtaining the refined vertical structure of the strong convective cells with higher hazard levels.

[0039] In one possible implementation, the hazard level of a strong convective cell can be determined by: determining the maximum value of the highest reflectivity corresponding to each strong convective cell as a reflectivity reference value; determining the maximum value of the height corresponding to each strong convective cell as a height reference value; determining the maximum value of the liquid water content corresponding to each strong convective cell as a liquid water content reference value; and for each strong convective cell, determining the hazard level of that strong convective cell based on the ratio of its highest reflectivity to the reflectivity reference value, the ratio of its height to the height reference value, and the ratio of its liquid water content to the liquid water content reference value.

[0040] In this embodiment, the hazard level of a strong convective cell can be determined based on the following three target characteristics: the highest reflectivity of the strong convective cell, the height of the strong convective cell (the difference between the top height and bottom height of the strong convective cell), and the liquid water content. Specifically, the hazard level corresponding to the strong convective cell can be determined by the following formula (1):

[0041]

[0042] Among them, P k Z represents the hazard level corresponding to the k-th strong convective cell. k Let max(Z) be the highest reflectivity of the k-th strong convection cell. m ) represents the maximum value among the highest reflectances of each of the m strong convective cells, i.e., the reflectance reference value, where m is the total number of strong convective cells segmented. H k Let H be the height of the k-th strong convective cell, max(H) m The value represents the maximum height among the m strongly convective cells, i.e., the height reference value. VIL k Let max(VIL) be the liquid water content of the k-th strongly convective monomer. m ) represents the maximum liquid water content among the m strongly convective cells, i.e., the reference value of liquid water content. w1, w2, and w3 are the weighting coefficients of the three characteristic quantities, and the sum of w1, w2, and w3 is 1.

[0043] Thus, by using the above method, the hazard level of a strong convective cell can be calculated based on its highest reflectivity, height, and liquid water content, ensuring that the calculated hazard level accurately measures the degree of harm caused by the strong convective cell.

[0044] It should be understood that if only one strong convective cell is obtained through the strong convective cell segmentation operation in step 101, the X-band radar in the radar network framework can be used directly to scan the strong convective cell in the vertical direction without performing the above-mentioned hazard calculation operation.

[0045] Step 103: Based on the hazard level of each of the multiple strong convective cells, the distance between the centroid of each of the multiple strong convective cells and the X-band radar in the radar network framework, and the vertical span of each of the multiple strong convective cells, assign corresponding scanning tasks to each of the X-band radars in the radar network framework; the scanning tasks include volume scan tasks and vertical scan tasks, which are used to indicate the volume scan mode and the strong convective cells to be scanned vertically when strong convective cells appear within the scanning range of the X-band radar.

[0046] After determining the hazard level of each of the multiple strong convective cells, a corresponding scanning task can be assigned to each X-band radar in the radar network framework based on the hazard level of each strong convective cell, the distance between the centroid of each strong convective cell and the X-band radar in the radar network framework, and the broadening of each strong convective cell. The scanning task includes volume scan task and vertical scan task. The volume scan task is used to indicate the volume scan mode of the X-band radar, that is, the scanning mode used when the X-band radar scans strong convective cells horizontally. The vertical scan task can indicate the strong convective cells that the X-band radar scans vertically. Here, vertical scan is RHI scan.

[0047] In one possible implementation, each X-band radar in the radar network framework can be assigned a corresponding scanning task in the following manner: Multiple strong convective cells are sorted according to their hazard level from highest to lowest; radar allocation is performed on each strong convective cell in a sequential order, assigning an X-band radar to vertically scan that cell, until all X-band radars in the radar network framework are assigned a vertical scanning target. The radar allocation process includes: among the X-band radars in the radar network framework that have not yet been assigned a vertical scanning task, identifying the X-band radar whose scanning range can cover the vertical expansion of the strong convective cell, as a candidate X-band radar for that cell; and among the candidate X-band radars for that cell, identifying the X-band radar with the closest centroid distance to the strong convective cell, as the X-band radar used to perform the vertical scanning task on that cell.

[0048] After calculating the hazard level of each severe convective cell, the severe convective cells are ranked in descending order of their respective hazard levels. It should be understood that the severe convective cells ranked higher are more hazard-prone and require more attention.

[0049] Specifically, when assigning X-band radars to strong convective cells, priority can be given to assigning X-band radars to the cells with the highest hazard level. Specifically, among the various X-band radars included in the radar network framework, the X-band radar whose scanning range can cover the vertical broadening (e.g., 30 dBZ) of the strongest convective cell with the highest hazard level can be identified as a candidate X-band radar for that cell. Then, among these candidate X-band radars, the X-band radar closest to the centroid of the cell is selected as the X-band radar used for vertical scanning of the strongest convective cell with the highest hazard level. In other words, the vertical scanning task assigned to this X-band radar instructs it to vertically scan the strongest convective cell with the highest hazard level.

[0050] Then, an X-band radar can be assigned to the second most hazardous convective cell. Specifically, among the X-band radars that have not yet been assigned vertical scanning tasks in the radar network framework (i.e., other X-band radars besides the X-band radar used for vertical scanning of the most hazardous convective cell), an X-band radar with a scanning range that can cover the vertical broadening (e.g., 30 dBZ) of the second most hazardous convective cell can be identified as a candidate X-band radar corresponding to that cell. Then, among these candidate X-band radars, the X-band radar closest to the centroid of the cell is selected as the X-band radar used for vertical scanning of the second most hazardous convective cell. That is, the vertical scanning task assigned to this X-band radar instructs it to vertically scan the second most hazardous convective cell.

[0051] This process continues until vertical scanning tasks are assigned to each X-band radar in the radar network framework.

[0052] It should be understood that if the number of strong convective cells obtained from the segmentation is less than the number of X-band radars in the radar network framework, then there will be situations where X-band radars are not assigned vertical scanning targets. In this case, for each X-band radar that is not assigned a vertical scanning target, among each strong convective cell, the strong convective cells that the X-band radar can cover in its vertical expansion can be identified as candidate strong convective cells corresponding to the X-band radar. Then, among these candidate strong convective cells, the strong convective cell whose centroid is closest to the X-band radar is selected as the vertical scanning target of the X-band radar.

[0053] It should be understood that if the number of strong convective cells obtained by segmentation is greater than the number of X-band radars in the radar network framework, then for strong convective cells with lower hazard levels, it is possible to abandon the allocation of corresponding X-band radars to them, that is, abandon the vertical scanning of strong convective cells with lower hazard levels.

[0054] It should be noted that the effective observation range of X-band radar is 150km, and the range for quantitative measurement is 75km. Therefore, in the radar network framework, the distance between every two X-band radars should be maintained within the range of 30km to 50km. Each X-band radar can be distributed in a triangular or diamond shape to form cross coverage.

[0055] X-band radars participating in collaborative observation typically employ two scanning modes: normal mode and collaborative adaptive mode (also known as smart mode). In normal mode, each X-band radar operates independently, and each radar can choose between VCP11, VCP21, or VCP31 depending on whether the weather is clear or rainy. In collaborative adaptive mode, the system can be automatically triggered based on weather conditions. The switching conditions for this collaborative adaptive task are as follows: based on the network fusion of conventional operational radars (S-band or C-band radars), through the identification of strong convective cells within the detection range, the calculation of the hazard level of strong convective cells, and the allocation of vertical scanning tasks, the X-band radar can ultimately perform scanning tasks based on the collaborative scanning mode, which consists of a rapid volume scan mode and a RHI scan mode.

[0056] The rapid volume scan mode is used to perform volume scan tasks corresponding to X-band radar, acquiring information on strong convective cells in the horizontal direction within strong convective regions. When X-band radar performs scanning tasks for small-to-medium scale severe weather (such as tornadoes, hail, and short-duration heavy precipitation), it needs to achieve faster radar response and scanning speed, comprehensively considering the impact of factors such as the rate of change of meteorological targets and radar scanning speed. The strategic focus of the rapid volume scan task is to acquire high spatiotemporal resolution meteorological information on severe convective weather below the atmospheric boundary layer. Therefore, in the design of the rapid volume scan mode, based on the conventional VCP21 mode, some scanning elevation angles are reduced, and the scanning speed of each elevation angle is increased to improve the temporal resolution of the rapid volume scan. The scanning elevation angles of the rapid scan mode are selected from the five lower elevation angles in the VCP21 mode to ensure that the rapid volume scan can be completed within 2 minutes, improving the temporal resolution of the scanning mode. Based on this, it can effectively improve the low low-altitude coverage of operational radars (S-band and C-band radars), thereby improving the accuracy and precision of weather radar network fusion application products. Table 1 shows the radar parameter settings in the rapid volume scan mode.

[0057] Table 1

[0058]

[0059] The RHI scan mode is used to perform the vertical scan task corresponding to the X-band radar to obtain information about the strong convective cell in the vertical direction indicated by the vertical scan task. Specifically, the RHI scan mode uses each X-band radar to perform a single RHI scan. Based on the allocation of the vertical scan task, multiple X-band radars perform a single RHI vertical scan on the strongest center of their respective strong convective cell (i.e., the strong convective cell indicated by the vertical scan task), obtaining the refined vertical structure of the strong convective cell target. The elevation scan angle range of the RHI scan task is, for example, 0.3° to 40°, and the single scan time is approximately 30 seconds. The radar scan task configuration parameters for RHI scanning are shown in Table 2 below.

[0060] Table 2

[0061]

[0062] In the X-band radar network collaborative observation method for strong convective cells provided in this application embodiment, when multiple strong convective cells are obtained based on the segmentation of strong convective regions, the hazard level of each strong convective cell can be determined. Then, referring to the hazard level of each strong convective cell, the distance between the X-band radar and the centroid of each strong convective cell in the radar network framework, and the vertical coverage of each strong convective cell, a corresponding scanning task is assigned to each X-band radar in the radar network framework. The assigned scanning tasks include volume scan tasks and vertical scan tasks. The volume scan task indicates the volume scan mode of the X-band radar, and the vertical scan task indicates the vertical scan target of the X-band radar, i.e., the strong convective cells that the X-band radar needs to vertically scan. Based on the above mechanism, assigning corresponding scan targets to each X-band radar in the radar network framework can prioritize scanning of strong convective cells with higher hazard levels and ensure that each X-band radar can scan the strong convective cells it observes effectively, thereby improving the observation effect of strong convective cells.

[0063] Figure 3 This is a flowchart illustrating the X-band radar network-based collaborative observation method for strong convective cells provided in this application embodiment. Figure 3As shown in this embodiment, a conventional operational radar (S-band radar or C-band radar) can first be used in a conventional volume scan mode to detect whether a strong convective region exists. If a strong convective region is detected, the strong convective cells within it are segmented and identified to obtain at least one strong convective cell. If multiple strong convective cells are obtained, the hazard level corresponding to each strong convective cell can be determined based on various feature quantities. Based on the hazard level of each strong convective cell, they are sorted, and a corresponding X-band radar is assigned to each strong convective cell for vertical scanning according to the sorting result, thus determining the vertical scanning object of each X-band radar in the radar network framework. When each X-band radar in the radar network framework is working, a cooperative scanning mode is used, i.e., a rapid volume scan is performed first, followed by a RHI scan, thereby obtaining the horizontal and vertical information of the strong convective cells. Furthermore, it detects whether the intensity of the weather process has weakened or exceeded the detection range. If so, it uses conventional operational radar in conventional volume scan mode to detect whether a strong convective area has appeared. If not, it re-segments and identifies the strong convective cells.

[0064] This application also provides an X-band radar network-based collaborative observation device for strong convection individual units; see [link to relevant documentation]. Figure 4 , Figure 4 This is a schematic diagram of the structure of an X-band radar network-based collaborative observation device for strong convection in an embodiment of this application. Figure 4 As shown, the X-band radar network collaborative observation device for strong convection includes:

[0065] The single-unit segmentation module 401 is used to perform a strong convection single-unit segmentation operation based on the strong convection region when a strong convection region is detected, so as to obtain at least one strong convection single unit.

[0066] The hazard determination module 402 is used to determine the hazard level of each strong convective cell based on the target feature quantity corresponding to the strong convective cell if multiple strong convective cells are obtained through the strong convective cell segmentation operation.

[0067] The task allocation module 403 is used to allocate corresponding scanning tasks to each of the X-band radars in the radar network framework based on the hazard level of each of the multiple strong convective cells, the distance between the centroid of each of the multiple strong convective cells and the X-band radars in the radar network framework, and the vertical span of each of the multiple strong convective cells; the scanning tasks include volume scan tasks and vertical scan tasks, which are used to indicate the volume scan mode and the strong convective cells to be scanned vertically when strong convective cells appear in the scanning range of the X-band radar.

[0068] Optionally, the device further includes:

[0069] The region detection module is used to perform a volume scan task using at least one of S-band radar and C-band radar; and to detect whether the strong convection region appears based on the scan data obtained by the volume scan task.

[0070] Optionally, the single-unit segmentation module 401 is specifically used for:

[0071] Based on the core reflectivity detection threshold, the core of the strong convection cell in the strong convection region is detected; the area of ​​the core of the strong convection cell is greater than a preset core area threshold.

[0072] For each of the strong convection cell cores, an expansion search is performed based on the strong convection cell core according to a preset reflectivity step size, until the expansion search reaches the cell boundary corresponding to the boundary reflectivity detection threshold, or until the expansion search intersects with the boundary of other strong convection cells, thus obtaining the strong convection cell corresponding to the strong convection cell core; the boundary reflectivity detection threshold is less than the core reflectivity detection threshold.

[0073] Optionally, the hazard determination module 402 is specifically used for:

[0074] The maximum value among the highest reflectances corresponding to each of the plurality of strong convection cells is determined as a reflectance reference value; the maximum value among the heights corresponding to each of the plurality of strong convection cells is determined as a height reference value; the maximum value among the liquid water content corresponding to each of the plurality of strong convection cells is determined as a liquid water content reference value.

[0075] For each of the aforementioned strong convection cells, the hazard level of the strong convection cell is determined based on the ratio of the highest reflectivity of the strong convection cell to the reflectivity reference value, the ratio of the height of the strong convection cell to the height reference value, and the ratio of the liquid water content of the strong convection cell to the liquid water content reference value.

[0076] Optionally, the task allocation module 403 is specifically used for:

[0077] The multiple strong convective cells are sorted in descending order of their corresponding hazard levels;

[0078] In a sequential order, radar allocation is performed on each of the strong convective cells to assign an X-band radar to vertically scan the strong convective cell, until each X-band radar in the radar network framework is assigned a vertical scanning target.

[0079] The radar allocation process includes: among the X-band radars in the radar network framework that have not yet been assigned vertical scanning tasks, determining the X-band radar whose corresponding scanning range can cover the vertical expansion of the strong convective cell, and using it as a candidate X-band radar corresponding to the strong convective cell; among the candidate X-band radars corresponding to the strong convective cell, determining the X-band radar that is closest to the centroid of the strong convective cell, and using it as the X-band radar for performing vertical scanning tasks on the strong convective cell.

[0080] Optionally, the scanning mode of the X-band radar in the radar networking framework is a cooperative scanning mode, which consists of a fast volume scan mode and a RHI scan mode.

[0081] The rapid volume scan mode is used to perform the volume scan task corresponding to the X-band radar to obtain information about the strong convective cell in the strong convection region in the horizontal direction; the RHI scan mode is used to perform the vertical scan task corresponding to the X-band radar to obtain information about the strong convective cell in the vertical direction indicated by the vertical scan task.

[0082] Optionally, the fast volume scan mode reduces some of the scanning elevation angles compared to the conventional VCP21 mode.

[0083] In the X-band radar network collaborative observation device for strong convective cells provided in this application embodiment, when multiple strong convective cells are obtained based on the segmentation of the strong convective region, the hazard level of each strong convective cell can be determined. Then, referring to the hazard level of each strong convective cell, the distance between the X-band radar and the centroid of each strong convective cell in the radar network framework, and the vertical coverage of each strong convective cell, a corresponding scanning task is assigned to each X-band radar in the radar network framework. The assigned scanning tasks include volume scan tasks and vertical scan tasks. The volume scan task indicates the volume scan mode of the X-band radar, and the vertical scan task indicates the vertical scan target of the X-band radar, i.e., the strong convective cells that the X-band radar needs to vertically scan. By assigning corresponding scan targets to each X-band radar in the radar network framework based on the above mechanism, priority can be given to scanning strong convective cells with higher hazard levels, and it can be ensured that each X-band radar can scan the strong convective cells it observes effectively, thereby improving the observation effect of strong convective cells.

[0084] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0086] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0087] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0088] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing computer programs.

[0089] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0090] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for collaborative observation of strong convective cells using X-band radar networking, characterized in that, The method includes: If a strong convection region is detected, a strong convection cell segmentation operation is performed based on the strong convection region to obtain at least one strong convection cell. If multiple strong convective cells are obtained through the strong convective cell segmentation operation, then for each strong convective cell, the hazard level corresponding to the strong convective cell is determined according to the target feature quantity corresponding to the strong convective cell. The multiple strong convective cells are sorted in descending order of their corresponding hazard levels; In a sequential order, radar allocation is performed on each of the strong convective cells to assign an X-band radar to vertically scan the strong convective cell, until each X-band radar in the radar network framework is assigned a vertical scanning target. The radar allocation process includes: among the X-band radars in the radar network framework that have not yet been assigned vertical scanning tasks, determining the X-band radar whose corresponding scanning range can cover the vertical expansion of the strong convective cell, as a candidate X-band radar corresponding to the strong convective cell; among the candidate X-band radars corresponding to the strong convective cell, determining the X-band radar with the closest centroid distance to the strong convective cell, as the X-band radar for performing vertical scanning tasks on the strong convective cell; the scanning task includes volume scan task and vertical scan task, used to indicate the volume scan mode and the strong convective cell to be vertically scanned when the X-band radar appears within the scanning range.

2. The method according to claim 1, characterized in that, The strong convection region is detected in the following way: Volume scanning is performed using at least one of S-band and C-band radars; Based on the scan data obtained through the volumetric scanning task, it is detected whether the strong convection region appears.

3. The method according to claim 1 or 2, characterized in that, The step of performing strong convection cell segmentation based on the strong convection region to obtain at least one strong convection cell includes: Based on the core reflectivity detection threshold, the core of the strong convection cell in the strong convection region is detected; the area of ​​the core of the strong convection cell is greater than a preset core area threshold. For each of the strong convection cell cores, an expansion search is performed based on the strong convection cell core according to a preset reflectivity step size, until the expansion search reaches the cell boundary corresponding to the boundary reflectivity detection threshold, or until the expansion search intersects with the boundary of other strong convection cells, thus obtaining the strong convection cell corresponding to the strong convection cell core; the boundary reflectivity detection threshold is less than the core reflectivity detection threshold.

4. The method according to claim 1, characterized in that, For each of the strong convective cells, determining the hazard level of the strong convective cell based on the target characteristic quantity corresponding to the strong convective cell includes: The maximum value among the highest reflectances corresponding to each of the plurality of strong convection cells is determined as a reflectance reference value; the maximum value among the heights corresponding to each of the plurality of strong convection cells is determined as a height reference value; the maximum value among the liquid water content corresponding to each of the plurality of strong convection cells is determined as a liquid water content reference value. For each of the aforementioned strong convection cells, the hazard level of the strong convection cell is determined based on the ratio of the highest reflectivity of the strong convection cell to the reflectivity reference value, the ratio of the height of the strong convection cell to the height reference value, and the ratio of the liquid water content of the strong convection cell to the liquid water content reference value.

5. The method according to claim 1, characterized in that, The X-band radar in the radar network framework uses a cooperative scanning mode, which consists of a rapid volume scan mode and a RHI scan mode. The rapid volume scan mode is used to perform the volume scan task corresponding to the X-band radar to obtain information about the strong convective cell in the strong convection region in the horizontal direction; the RHI scan mode is used to perform the vertical scan task corresponding to the X-band radar to obtain information about the strong convective cell in the vertical direction indicated by the vertical scan task.

6. The method according to claim 5, characterized in that, The rapid volume scan mode reduces some of the scanning elevation angles compared to the conventional VCP21 mode.

7. A networked collaborative observation device for strong convection in X-band radar, characterized in that, The device includes: The single-unit segmentation module is used to perform a strong convection single-unit segmentation operation based on the strong convection region when a strong convection region is detected, so as to obtain at least one strong convection single unit. The hazard determination module is used to determine the hazard level of each strong convective cell based on the target feature quantity corresponding to the strong convective cell if multiple strong convective cells are obtained through the strong convective cell segmentation operation. The task allocation module is used to sort the multiple strong convective cells in descending order of their corresponding hazard levels. In a sequential order, radar allocation is performed on each of the strong convective cells to assign an X-band radar to vertically scan the strong convective cell, until each X-band radar in the radar network framework is assigned a vertical scanning target. The radar allocation process includes: among the X-band radars in the radar network framework that have not yet been assigned vertical scanning tasks, determining the X-band radar whose corresponding scanning range can cover the vertical expansion of the strong convective cell, as a candidate X-band radar corresponding to the strong convective cell; among the candidate X-band radars corresponding to the strong convective cell, determining the X-band radar with the closest centroid distance to the strong convective cell, as the X-band radar for performing vertical scanning tasks on the strong convective cell; the scanning task includes volume scan task and vertical scan task, used to indicate the volume scan mode and the strong convective cell to be vertically scanned when the X-band radar appears within the scanning range.

8. The apparatus according to claim 7, characterized in that, The device further includes: The region detection module is used to perform a volume scan task using at least one of S-band radar and C-band radar; and to detect whether the strong convection region appears based on the scan data obtained by the volume scan task.

9. The apparatus according to claim 7, characterized in that, The single-unit segmentation module is specifically used for: Based on the core reflectivity detection threshold, the core of the strong convection cell in the strong convection region is detected; the area of ​​the core of the strong convection cell is greater than a preset core area threshold. For each of the strong convection cell cores, an expansion search is performed based on the strong convection cell core according to a preset reflectivity step size, until the expansion search reaches the cell boundary corresponding to the boundary reflectivity detection threshold, or until the expansion search intersects with the boundary of other strong convection cells, thus obtaining the strong convection cell corresponding to the strong convection cell core; the boundary reflectivity detection threshold is less than the core reflectivity detection threshold.

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