Severe convection refined collaborative observation method based on adjacent weather radar networking

Abstract

The invention discloses a severe convection refined collaborative observation method based on adjacent weather radar networking, and the method comprises the steps: selecting a weather radar station for networking as a networking member, and transmitting PPI data scanned by each group of networking members in real time to a data buffer for accumulation in a stream transmission communication mode; transmitting the data accumulated in the data buffer area to an algorithm server, processing and packaging the real-time PPI data on the algorithm server, and constructing a networking cooperation minute observation data set; observing the data set by networking collaboration minutes, developing networking products released by the service, and storing the generated products into a product data pool; and the downstream customer extracts the required product from the product data pool. According to the method, the application potential of multi-weather radar joint observation data in short-term and temporary services is fully excavated, the scanning updating frequency of a severe convection system is improved to 1 minute, and the observation grid horizontal density is encrypted to a sub-kilometer level.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric data analysis and meteorological early warning, and in particular to a method for fine-grained collaborative observation of severe convection based on networking of adjacent weather radars. Background Art

[0002] The short-term weather service in the weather forecast and warning service is also called the short-term near-term weather service. It usually refers to the weather forecast service for the next 0-2 hours. The core of the short-term service is the monitoring and warning of severe convective weather. Severe convective weather is a general term for four types of weather processes: hail, tornado, thunderstorm gale and short-term heavy precipitation. This type of weather has the characteristics of local suddenness, great destructive power and short life history. The main forecasting means to deal with such processes include real-time monitoring products including meteorological observation equipment such as the new generation of weather radars, rapid cycle update assimilation model products based on limited areas, and extrapolation products based on historical data. Since the rapid cycle update assimilation model and extrapolation products have certain R&D costs, real-time observations based on the new generation of weather radars are still the main means for most grassroots meteorological business departments in China to carry out short-term work, especially the real-time observations of local single weather radar stations deployed in various regions.

[0003] Taking the S-band new generation Doppler weather radar widely deployed in southeastern my country as an example, the radar transmitting antenna performs a 360° circular scan at the location of the radar station. Figure 1 As shown in (a), the scanning process is achieved by transmitting pulse signals in different azimuth directions during the rotation of the radar antenna and receiving the returned signals in real time, and then converting the atmospheric information in the signals into digital signals through the signal processing module. The scanning result of one azimuth corresponds to a radial data. A radial data consists of multiple distance library data, which corresponds to the signal data continuously returned by this beam at a fixed distance resolution. The radial data set obtained by scanning a fixed elevation angle is defined as a PPI (Plan Position Indicator) data. After each PPI scan, the radar servo system will change the pitch of the transmitting antenna by adjusting the mechanical mechanism of the radar transmitting antenna base, as shown in Figure 1. Figure 1 As shown in (b), the elevation angle of signal transmission and reception is changed to obtain a new layer of PPI observation data. By designing the scanning speed and pitch adjustment frequency of the radar transmitting antenna and adapting different pulse repetition frequencies, transmission power and other parameters, different levels of space observation data can be obtained according to business needs. There are three main scanning methods used in daily business, namely:

[0004] Scanning method #1: 5 minutes to complete 14 scans at different elevation angles;

[0005] Scanning method #2: 6 minutes to complete scanning at 9 different elevation angles;

[0006] Scanning method #3: 10 minutes to complete scanning at 5 different elevation angles;

[0007] Based on the above three scanning methods, a total of 4 commonly used scanning modes are configured, namely rain measurement mode (VCP21 or VCP11) and clear sky mode (VCP31 or VCP32), as follows:

[0008] VCP11: It stipulates that 14 specific elevation angles must be scanned within 5 minutes. The scanning elevation angles are 0.5, 1.45, 2.4, 3.35, 4.3, 5.25, 6.2, 7.5, 8.7, 10.0, 12.0, 14.0, 16.7, and 19.5 respectively.

[0009] VCP21: It stipulates that nine specific elevation angles must be scanned within six minutes. The scanning elevation angles are 0.5°, 1.45°, 2.4°, 3.4°, 4.3°, 6°, 9.9°, 14.6°, and 19.5°.

[0010] VCP31: It stipulates that five specific elevation angles must be scanned within 10 minutes, and the scanning angles are 0.5°, 1.5°, 2.5°, 3.5°, and 4.5° respectively;

[0011] VCP32: It is stipulated that five specific elevation scans shall be completed within 10 minutes. The elevation angle is the same as that of VCP31, but VCP31 uses a long pulse beam, while VCP32 uses a short pulse beam.

[0012] Severe convective weather is an important observation object of weather radar and the core of short-term services in various regions. It is generated by severe convective systems. The horizontal scale of the system is generally 2-2000km, and the life history is generally no more than 12 hours. Taking the more common β-scale convective system in my country as an example, the horizontal scale is 20-200km, and the life history is generally 1 to 2 hours. The observation of traditional single weather radar often has the following problems when dealing with such systems:

[0013] (1) In the VCP21 scanning mode, the data update frequency is 6 minutes, which cannot timely track the sub-minute fast-changing convection system. At the same time, the transmitting antenna is a mechanical scanning mechanism, and each layer of PPI needs to wait for the previous layer of PPI scanning to complete before starting to acquire data. The PPI data set after 6 minutes will have different degrees of deformation in the characterization of the convection system, affecting the interpretation effect of various algorithms in the computing server;

[0014] (2) The scanning pitch angle of a single radar is limited. There is a "cone of silence" above the radar station where observation is not possible, which affects the monitoring effect of some severe convective systems passing through the radar station.

[0015] (3) A single radar may occasionally fail due to aging of the mechanical structure, disconnection of the signal line, etc., which may affect the weather observation at that time;

[0016] (4) The low-level observation capability of a single radar is limited. Due to the curvature of the earth's surface, under standard atmospheric refraction conditions, the scanning beam of a single radar will bend upward as the scanning distance increases, making it impossible to observe the low-level space beyond a certain distance. Taking the S-band weather radar VCP21 mode as an example, except for the lowest 0.5° layer with a PPI elevation angle of about 2 km at the 230km boundary, the 1.5° layer and above correspond to an altitude of more than 6 km, that is, there is little available observation data in the middle and lower troposphere (below 3km).

[0017] It can be seen that the short-term forecast and warning services currently carried out through the above methods still have many shortcomings and cannot fully meet the social service needs. Summary of the invention

[0018] The present invention aims to solve one of the technical problems existing in the related art at least to a certain extent.

[0019] To this end, the present invention fully explores the application potential of multi-weather radar joint observation data in short-term and impending services, and proposes a refined collaborative observation method for severe convection based on adjacent weather radar networking, which increases the scanning and update frequency of severe convection systems to 1 minute and encrypts the horizontal density of observation grids to sub-kilometer level, thereby improving the accuracy of real-time, short-term and impending forecast and warning services.

[0020] In order to achieve the above-mentioned purpose, the present invention provides, on the one hand, a method for fine-tuning collaborative observation of severe convection based on a network of adjacent weather radars, comprising:

[0021] The weather radar sites used for networking are selected as networking members, and the PPI data scanned by each networking member in real time is transmitted to the data buffer for accumulation in a streaming communication mode;

[0022] The data accumulated in the data buffer is transmitted to the algorithm server, and the real-time PPI data on the algorithm server is processed and packaged to build a network-based collaborative minute observation data set;

[0023] Develop networking products for business interpretation by networking and coordinating minute observation data sets, and store the generated products in the product data pool;

[0024] Downstream customers extract the required products from the product data pool.

[0025] A further preferred technical solution of the present invention is that the weather radar site for networking is selected as a networking member, and the specific method is:

[0026] Set boundary grids according to the area to be warned;

[0027] Taking the weather radar station near the center of the area as the center, at least three neighboring weather radar stations with different locations are selected to build a network together. The weather radar stations participating in the network serve as network members.

[0028] Preferably, the PPI data collected by each network member in the scanning mode is transmitted to the data buffer, and the specific method is:

[0029] Each network member independently completes real-time atmospheric scanning and collects atmospheric information;

[0030] After each networking member obtains a layer of PPI data, it transmits the PPI data to the data buffer in real-time streaming mode.

[0031] Preferably, the data buffer is used to receive real-time PPI data transmitted by each networking member and accumulate the data;

[0032] The data buffer is also provided with a memory clearing switch, which is controlled by a timing controller. When the set conditions are met, all or part of the data accumulated in the data buffer are cleared.

[0033] Preferably, the transmitting the data accumulated in the data buffer to the algorithm server comprises:

[0034] Arrange a three-minute data pool between the data buffer and the algorithm server;

[0035] The data buffer transmits multi-layer PPI data minute by minute to the three-minute data pool under the control of the timing controller;

[0036] The three-minute data pool stores the incoming data for a short period of time for the algorithm server to call.

[0037] Preferably, the real-time PPI data on the algorithm server is processed and packaged to construct a network collaborative minute observation data set; including:

[0038] Extract 1 minute of PPI data from the three-minute data pool and check the data integrity. If the integrity requirement is not met, extract the PPI data from the three-minute data pool again.

[0039] Standardize the extracted PPI data;

[0040] Perform quality control on the extracted PPI data and filter out interference from non-meteorological echoes;

[0041] According to the data requirements of the processing algorithms of different networking products, the PPI data is classified, reorganized and packaged to construct data sets for different networking products.

[0042] Preferably, the 1-minute PPI data in the 3-minute data pool is extracted, and the data integrity is checked. If the integrity requirement is not met, the PPI data in the 3-minute data pool is extracted again. The specific method is:

[0043] Construct a minute data extraction module to extract 1-minute PPI data from the 3-minute data pool. The extraction method is to extract all PPI data within 1 minute before the current moment according to the time when each PPI is input, and package them into a data set;

[0044] The data integrity of the data set is verified by the data verifier. The judgment standard of the integrity verification is: whether the data set contains one of the PPIs corresponding to the two elevation angles of the lowest layer. If it does, the data set meets the data integrity requirements, and the earliest 1-minute PPI data in the three-minute data pool is cleared; if it does not, the data set is supplemented;

[0045] Construct data supplementation, extract the PPI data with the lowest elevation angle from the three-minute data pool according to the PPI elevation angle mark. If there are multiple PPI data with the same elevation angle, select the PPI data closest to the current moment and put it into the data set;

[0046] The data set is checked for data integrity again. The judgment criteria for the integrity check are: whether the data set contains one of the PPIs corresponding to the two elevation angles of the lowest layer. If so, the data set meets the data integrity requirements, and the earliest 1-minute PPI data in the three-minute data pool is cleared; if not, an early warning message is issued, and the originally extracted 1-minute PPI data is passed to the next step, and the earliest 1-minute PPI data in the three-minute data pool is cleared.

[0047] Preferably, the extracted PPI data is quality controlled to filter out interference from non-meteorological echoes; the specific method is:

[0048] The wave velocity blocking situation is determined by the digital elevation model data at each elevation layer. For the blocked part, the radar reflectivity factor profile is used to correct the echo azimuth, and then the non-meteorological echo in the echo is filtered out by the correlation coefficient CC in the dual-polarization parameters.

[0049] On the other hand, the present invention provides a non-transitory computer-readable storage medium having computer instructions stored thereon, which enable a computer to execute the above-mentioned method for refined collaborative observation of severe convection based on networking of adjacent weather radars.

[0050] On the other hand, the present invention provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus, and the processor calls the logic instructions in the memory to execute the above-mentioned method for refined collaborative observation of severe convection based on networking of adjacent weather radars.

[0051] Beneficial effects: The present invention designs a refined observation method with an update frequency of 1 minute and a grid resolution of sub-kilometer level for limited observation areas; based on the method of the present invention, refined regional networking collaborative observation for limited areas can provide favorable support for grassroots meteorological stations to carry out real-time severe convective forecast and warning services, and thanks to the real-time streaming communication mode and the memory control mechanism of the algorithm, grassroots stations with relatively limited hardware resources can also be deployed. In addition, as meteorological departments in various places begin to deploy X-band phased array radars, the data update frequency is gradually matched to the 1 minute of phased array radars. Through the present invention, the S-band and C-band data products can be fused and verified with the X-band products or other algorithms can be developed to produce more minute-level business products suitable for the local area. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a routine operational volume scanning model for the new generation of weather radars;

[0053] Figure 2 A schematic diagram of the scanning situation of each network member for regional networking collaborative observation;

[0054] Figure 3 It is a flow chart of the method for fine-grained collaborative observation of severe convection based on the networking of adjacent weather radars of the present invention;

[0055] Figure 4 This is a schematic diagram of the screening conditions for network collaboration members. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0057] Combine the following Figure 1-Figure 4 The present invention describes a method for refined collaborative observation of severe convection based on a network of adjacent weather radars.

[0058] Embodiment: This embodiment provides a method for refined collaborative observation of severe convection based on networking of adjacent weather radars.

[0059] In this embodiment, the specific implementation steps of the present invention are first described, and the implementation steps are explained using a specific networking process.

[0060] The implementation of the method of this embodiment mainly relies on weather radar observation resources and data stream transmission communication technology.

[0061] As for weather radar observation resources, there are currently 243 new generation weather radar observation stations in operation, with most of them distributed in the central and eastern coastal areas, southern coastal areas and Beijing-Tianjin-Hebei region. Taking the eastern coastal city of Ningbo as an example, the limited observation area in Ningbo is designed to include Ningbo Station Z9574, Jiaxing Station Z9575, Zhoushan Station Z9580 and Shaoxing Station Z9573. Within the 230km best observation range of Ningbo Station, there are also Shanghai Station Z9002, Jinhua Station Z9579, Hangzhou Station Z9571, Huzhou Station Z9572, Taizhou Station Z9576, and Quzhou Station Z9570, Lishui Station Z9578 and Wenzhou Station Z9577 are also nearby. The ground-to-air meteorological observation resources are rich, and it is relatively easy to complete a detailed scan of the area based on multi-station collaboration.

[0062] Another data stream transmission communication technology refers to the general term for technologies that transmit streaming data (such as video, audio, etc.) through the network. It is widely used in the field of real-time communication and can be divided into sequential streaming transmission and real-time streaming transmission.

[0063] Sequential streaming transmission means sending the complete file to the target server in a sequential stream through the HTTP server. This method is the mainstream radar data transmission mode at this stage. The new generation of weather radar will generate corresponding standard format files based on the scanning results hour by hour. For example, in VCP21 mode, a PPI data set binary file containing 9 elevation angles is stored every 6 minutes, and then sent to the server through the HTTP server;

[0064] Real-time streaming refers to the application of real-time protocols such as RTSP, which directly transmits data information to the target server in the form of binary encoding, etc., without waiting for the complete file to be generated. For example, in VCP21 mode, a complete PPI data is transmitted to the target server in about 30 seconds, without waiting for the complete 6-minute file to be generated;

[0065] At present, new generation weather radars in various places are gradually beginning to use these two data transmission methods simultaneously to adapt to the observation needs of different scenarios; this embodiment uses real-time streaming transmission to construct minute-by-minute observation data sets.

[0066] The method of this embodiment is constructed on the principle of shortening the observation time interval as much as possible while ensuring a sufficient amount of effective data, so as to track the development of fast-changing convective systems in real time and serve the short-term business analysis.

[0067] The standard for a sufficient amount of effective data is to be able to meet the construction requirements of most strong convection algorithms. The current minimum standard is that at least two layers of PPI enter the algorithm. Taking the storm cell identification and tracking (SCIT) algorithm as an example, the single-layer PPI is first identified as a one-dimensional storm segment according to a certain threshold standard, and then combined into a two-dimensional storm segment according to the centroid of the one-dimensional storm segment and other information. After completing the above operations for multiple layers of PPI, the centroids of the two-dimensional storm segments are vertically associated to obtain the final storm cell identification result, requiring each cell to contain at least two layers of PPI information; another example is the weather radar radial velocity product defuzzification algorithm. For each layer of PPI, the adjacent radials are bidirectionally blurred by the defuzzification formula according to the velocity value of each distance library on the initial reference radial. The selection of the initial reference radial needs to refer to the orientation of the initial reference radial in the previous layer of PPI, that is, it should also contain at least two layers of PPI; in addition, the vertical cumulative liquid water content algorithm, hail identification algorithm, strong hail index algorithm, downburst identification algorithm, etc. should also contain at least two layers of PPI to complete vertical integration and high-low layer association operations.

[0068] Taking the provision of two layers of PPI at each station as the standard, the single-layer PPI is based on real-time data stream transmission communication technology. The time for transmission from the weather radar observation end to the business end is approximately 30 seconds. Therefore, it is feasible to build a collaborative network with the reception of two layers of PPI as the shortest time step, that is, it is appropriate to set the observation data update frequency to 1 minute.

[0069] The conceptual model of the method in this embodiment is as follows Figure 2 As shown, for the selected limited area, a boundary grid is set and the weather radar site members for building the network (hereinafter referred to as networking members) are determined. The PPI data captured by each site is streamed in real time through the communication modules of their respective servo systems. The streaming data of each member is merged into the data buffer of the acquisition server, where the real-time PPI data is accumulated and then enters the algorithm server. Through multiple specific functional modules in the algorithm server, refined networking collaborative algorithm products are produced, and then the relevant products are transferred to the data pool for extraction and use by downstream customers.

[0070] The overall process of the method of this embodiment is roughly divided into four algorithm sections according to the data flow direction. The real-time weather observation section is composed of the networking member sites and their respective real-time streaming communication modules; the raw data processing section is used to process and package the received real-time PPI data to build a suitable networking collaborative minute observation data set; the networking product processing section receives the upstream standardized raw data, develops networking products for business release, and stores the generated products in the product data pool; the user section is the distribution section for processed products, and downstream customers can freely extract the required products from the data pool. The first three sections together constitute a real-time business flow, occupying the virtual memory space of the system. With the real-time transmission of observation data, the memory is repeatedly updated, and the algorithm products are processed. The relevant products are stored in the data pool in the form of binary complete files, and the corresponding hard disk storage space is used so that different users can read the required processed products from it. The details of each module are introduced as follows. If there is no special explanation for the site scanning mode, the default is the VCP21 scanning mode.

[0071] The method of this embodiment, the specific process is as follows Figure 3 As shown, including:

[0072] (1) Network member selection

[0073] The method starts with real-time observation of networking members. At present, the weather radars in operation in my country are mainly S-band and C-band Doppler pulse weather radars. In the past two years, X-band phased array weather radars have also been gradually deployed. The method of this embodiment is mainly applicable to S-band and C-band Doppler weather radars in volume scanning mode, and the selection of networking collaborative members should be within this scope.

[0074] At the same time, in order to ensure the success rate of the construction of various networking collaborative algorithms, it is necessary to ensure that the data stream transmitted to the algorithm every minute should contain at least one of the PPIs of the two lowest elevation scans of any network member, that is, the number of network members should be at least 3 (that is, there is a PPI of the lowest two layers every minute in the 6-minute scanning system). There should be at least one weather radar network member near the center of the regional network. Therefore, in this embodiment, Figure 4 As shown in the figure, a 4-station model is used as an example for the development of regional networking collaborative observation algorithms in various regions. With the selected weather radar networking member as the center, within the optimal observation radius of 230km, there should be at least 3 or more network member sites scattered around, and the straight-line distance between network members should be more than 60km to eliminate or reduce the negative impact of the "cone of silence" of a single site and the observation blind spots caused by the curvature of the earth.

[0075] Taking the Ningbo regional networking collaborative observation network as an example, Ningbo Station is selected as the central site of the network. The surrounding 230km circle includes 8 stations such as Shanghai Station, Jiaxing Station and Zhoushan Station. Since Shanghai Station and Jiaxing Station are close to each other, one of them can be selected as a networking member and the other as a standby member. The collaborative observation of Ningbo Station, Jiaxing Station, Shaoxing Station and Zhoushan Station can meet the minimum requirements for network construction. Adding Taizhou Station, Hangzhou Station and Shanghai Station can provide more abundant minute-level original observation data, but it will also increase communication and computing costs, which can be flexibly adjusted according to the resource conditions in the selected area.

[0076] (2) Streaming communication

[0077] After selecting the networking members, the real-time observation data of each member will be uploaded to the superior business department through the communication module of their respective servo systems. There are two main forms of uploading data. The mainstream business transmission method is sequential stream transmission, that is, a complete weather radar volume scan file is packaged into a binary file with a fixed format, and such files are uploaded to the server hour by hour; the second method is real-time stream transmission communication, that is, without waiting for the complete volume scan file to be generated, the observation data at that time is uploaded to the business end in real time in the form of a binary data stream, and the data organization and interpretation are completed at the business end. This embodiment uses this technology to complete data reception. In recent years, with the update and iteration of communication technology in various places, meteorological business departments at all levels have begun to widely deploy such lines, laying a good foundation for the wide application of this embodiment.

[0078] Taking Ningbo as an example, it takes about 30 seconds for a network member to complete a PPI observation. That is, through the real-time streaming mode, the local server can receive a layer of PPI data from Ningbo, Jiaxing, Shaoxing and Zhoushan every 30 seconds. However, since the scanning start time of each station is not uniformly controlled, the actual situation is that within the time range of 0-30 seconds, Ningbo, Jiaxing, Shaoxing and Zhoushan will send a PPI data to the local server respectively, and there is uncertainty in the sending order and the time point of arrival. Ideally, referring to the 6-minute scanning system of the VCP21 mode, the local server should obtain at least one PPI in the 6 time periods of 30-60, 60-90, 150-180, 180-210, 270-300 and 300-330 seconds to ensure the success rate of the downstream algorithm. In actual business, it is difficult to meet the above requirements by building a network with 3 stations. It can be corrected by appropriately increasing the number of network members and adopting a variety of PPI supplementary algorithms.

[0079] (3) Set up the data buffer

[0080] The data buffer is responsible for receiving real-time PPI data sent from the observation segment and creating a streaming data accumulation area on the local server. At the same time, as the starting point of the original data processing segment, it is responsible for delivering real-time data to the downstream. A memory clearing switch is also designed in the data buffer module, which is controlled by the downstream timing controller to ensure the normal use of the server memory.

[0081] The function of the data buffer is similar to that of the local database, both of which are used for resource storage. However, the local database is often used to store complete binary files or other types of complete files, which has certain requirements for local hard disk storage space. The data buffer is only used to store the binary stream data required for the current period, and has no requirements for local hard disk storage resources, which can effectively reduce the hardware cost of the computing server.

[0082] Taking Ningbo as an example, the local server in Ningbo needs to receive real-time streaming data from Ningbo Station, Jiaxing Station, Shaoxing Station and Zhoushan Station. For some encrypted service processes, it is necessary to receive additional relevant data from Taizhou Station, Hangzhou Station and Shanghai Station, etc., which requires about 4 to 6G of memory space per minute. Through data transmission and self-cleaning mechanisms, it can ensure that the downward transmission of data is completed within 8G of memory space, which can effectively alleviate the pressure on server hardware. In daily business, in addition to real-time streaming transmission lines, Ningbo also saves observation data from surrounding stations through sequential streaming transmission, which does not occupy the server's virtual memory and is only stored in the server's hard disk space. The amount of data received by each station every six minutes is about 0.4G in total, and the daily storage capacity is 100G. Business units with better hardware resources can also extract historical data based on this strategy for individual case playback.

[0083] (4) Setting the timing controller

[0084] The timing controller is the data scheduling and memory control end of the data buffer. It is used to control the frequency of data transmission and the amount of data accumulation in the server memory space. It can allocate algorithms according to the local server hardware resource conditions to ensure the normal operation of the algorithm. The specific logic is that the data buffer continuously receives real-time PPI data sent from the observation segment, accumulates it in the memory, and is cyclically checked by the timing controller. The trigger time interval defaults to 1 minute, which can be flexibly adjusted according to service requirements and downstream product algorithms. When the time interval is reached, the buffer is extracted, and all complete PPI data areas currently stored are scheduled to be transmitted to the 3-minute data pool, and the complete PPI data in the buffer is cleared, the PPI data that is still being transmitted is retained, and the reception of new data continues. If the time interval is not reached, the buffer is allowed to continue to complete data reception.

[0085] Taking Ningbo as an example, the timing controller will transmit all complete PPI data within 1 minute before the current moment to Ningbo Station, Jiaxing Station, Shaoxing Station and Zhoushan Station. Each station can transmit about two layers of PPI downward, that is, the timing controller will release about 8 layers of PPI data to the downstream data pool each time. If encrypted, it will schedule the PPI data of other surrounding network members, that is, more than 10 complete PPI data will be transmitted every minute. At the same time, the timing controller will trigger the memory clear switch of the buffer to clear data other than incomplete PPI to ensure effective use of local server memory space.

[0086] (5) Set up a three-minute data pool

[0087] The three-minute data pool is used to receive the multi-layer PPI data transmitted by the data buffer through the timing controller, and to store the 3-minute data for a short time for the downstream algorithm to complete self-checking and supplementary correction. In order to ensure that the real-time observation data can fully restore the convection system at that time, and meet the construction requirements of various downstream algorithms for low-level PPI, the data pool is designed with a step size of 3 minutes for downstream secondary scheduling. The data in this part also requires a certain amount of virtual memory space. The memory space required for the 3-minute PPI data of a single networking member is about 1 to 1.5G.

[0088] Taking Ningbo as an example, the 3-minute data pool can store about 5 to 6 layers of complete PPI data of any networking member. Ningbo Station, Jiaxing Station, Shaoxing Station and Zhoushan Station can store more than 20 layers of PPI data at a time, and more than 40 layers in the case of encryption. The maximum virtual memory space occupied is about 20G, which can effectively improve the tolerance of downstream algorithms for extraction and selection. At the same time, in conjunction with the data supplement module, it can provide effective guarantee for the success rate of various downstream algorithms.

[0089] (6) Minute data extraction and data verification 1

[0090] Appropriate minute PPI data are dispatched from the 3-minute data pool to control the data source of the downstream algorithm. The design of network collaboration is based on minute intervals, in order to restore the complete morphological and structural characteristics of the convective system within minutes as much as possible under the condition of limited observation resources. The extraction method is to extract all PPI data within 1 minute before this moment according to the time of each PPI transmission, package them into a new data set, and then pass them downstream; the data set transmitted to the downstream will first be verified for data integrity by the data verifier 1. The judgment standard of the integrity verification is whether the data set contains at least one PPI data with the lowest elevation angle. In the VCP21 mode, the elevation angles of the lowest two layers are 0.5° and 1.5° respectively. If this data is included, it will be passed to the data structure reorganization module and the earliest 1-minute PPI data in the 3-minute data pool will be cleared to control the memory space. If this data is not included, it will enter the data supplement module.

[0091] Taking Ningbo as an example, the real-time PPI data transmitted from Ningbo Station, Jiaxing Station, Shaoxing Station and Zhoushan Station will have a time mark. According to the time mark, all PPI data within 1 minute before the current moment will be extracted and packaged. The packaged minute data of the 4 stations occupies about 2G of memory space, and occupies about 4G of memory space in the encrypted case. The packaged data will enter the data verifier 1 to determine whether it contains the lowest two layers of PPI data. If so, it will trigger the cleanup procedure of the 3-minute data pool and enter the next module. Otherwise, it will enter the data supplement module to complete the necessary PPI data supplement to meet the algorithm requirements of the downstream product processing module.

[0092] (7) Data supplement module and data checker 2

[0093] To process the data sets that do not meet the standards, extract the PPI observation data with the lowest elevation angle from the 3-minute data pool module according to the PPI elevation mark. If there are multiple PPIs with the same elevation angle, select the PPI closest to the current moment and put it into the data set, and then enter the data verifier 2 for judgment. If one of the PPIs corresponding to the two lowest elevation angles is included, the data structure is also reorganized and the earliest 1-minute PPI data in the 3-minute data pool is cleared. Otherwise, a system prompt is sent to the server first, indicating that the product at this moment fails to include the low-level elevation angle, which may cause the downstream algorithm to be abnormal. Then the data set transmitted by the data verifier 1 is directly passed to the data structure reorganization and the 3-minute data pool is also cleared.

[0094] Data supplementation is an important component of this embodiment, which is used to ensure the smooth production of downstream networking collaborative products. The purpose of data supplementation is to ensure that in each minute time period, PPI data of each main height level in the observation area are obtained as much as possible. For most strong convection-related algorithms, low-level PPI is the starting point for developing algorithm operations. Ensuring sufficient low-level PPI data is the basic condition for the smooth operation of downstream related algorithms. There are many ways to fill the lowest two layers of PPI. This module allows the system to have certain errors in the morphological structure in order to reflect the actual situation of the observed object as objectively as possible, and does not use strategies such as spatiotemporal difference. However, the best solution is still to schedule the data supplement part as much as possible, by increasing the members of the weather radar network and making unified control of each station, etc., to ensure that the minute-level data obtained can contain at least one layer of low-level PPI data.

[0095] Taking Ningbo as an example, the local weather radar observation resources are relatively rich. It is feasible to reduce the scheduling frequency of this module by increasing the number of networking members. However, due to the lack of unified control authority over surrounding radar sites, it is still common that at least one low-level PPI cannot be obtained within a single time period. The data supplement module is still needed to provide certain data support.

[0096] (8) Data structure reorganization

[0097] Receive the minute-level data set processed upstream and standardize it to provide a fixed-structure data interface for the downstream algorithm to facilitate the development and iteration of the downstream algorithm. The data in the data set is binary stream data transmitted through the real-time streaming transmission line, which is incompatible with the interface of the commonly used algorithm. Here, the STL data container in VS2022 is used to encapsulate all PPI data in a two-dimensional structure, and the data is also stored in the virtual memory. The standardized PPI structure designed in this embodiment is shown in Table 1. The screened data set is reorganized according to the data structure of Table 1 to obtain a minute-level form corresponding to this moment. The form can accommodate PPI observation data of multiple stations. Each PPI data is decomposed into multiple radial data for storage, and the front of the radial data is marked with attributes, such as the site number, scanning elevation angle, scanning azimuth angle and maximum unambiguous speed in Table 1, so that the downstream related algorithm can extract data, and reserve some algorithm mark bits for the downstream algorithm to operate and mark the original data.

[0098] Table 1 Standardized data structure of refined regional networking collaborative observation algorithm

[0099]

[0100] (9) Data quality control

[0101] The echo information such as noise and interference of the original data is processed and then transmitted to the next step so that the downstream algorithm can obtain relatively reliable weather observation data. Data quality control of weather radar is a very complex systematic project. There are many reasons for the poor quality of weather radar data, including beam blocking interference such as tall buildings and trees near the radar, interference from other ground clutter, interference from abnormal electromagnetic signals, atmospheric super-refraction interference, and interference from non-meteorological echoes such as insects, birds, and flying objects.

[0102] This step only uses the quality control strategy commonly used in the business to perform preliminary quality control on the reflectivity data, that is, through the digital elevation model data, the wave velocity blocking situation is determined at each elevation layer. For the blocked part, the radar reflectivity factor profile is used to correct the echo azimuth, and then the correlation coefficient CC (0.7 threshold method) in the dual polarization parameters is used to filter out non-meteorological echoes in the echo.

[0103] Taking Ningbo as an example, Ningbo Station is located on the top of Dapeng Mountain in the northeast of Ningbo City, with an altitude of 454m. Common interference sources in the surrounding area include electromagnetic signal interference emitted by various signal towers, obstruction by surrounding tall trees, and terrain obstruction caused by the Siming Mountain Range on the east side. During the transition period from spring and autumn to winter and summer every year, there is often a large difference in air temperature and humidity near the ground layer and above the ground layer, which causes the pulse beam emitted by the radar weather to be super-refracted, and many non-meteorological noise signals often appear, affecting the data quality. This module can only complete the basic data quality control of radar data, and it is difficult to fundamentally guarantee the reliability of data quality. It can only be updated through later technical iterations.

[0104] (10) Data classification

[0105] Data classification is a pre-processing step for the product processing algorithm. The raw data is classified, reorganized and packaged, and then distributed to the corresponding processing algorithm module for the downstream module to smoothly carry out product processing. The raw data of the S-band and C-band Doppler weather radars include reflectivity factor dBz, radial velocity V and velocity spectrum width W. After the dual-polarization upgrade, the station can additionally obtain four dual-polarization parameters, including correlation coefficient CC, differential reflectivity factor ZDR, differential propagation phase FhiDP and differential phase shift rate KDP. The data in the data classifier will be continuously updated and covered as the upstream data is transmitted downward, and the update frequency is consistent with the setting of the timing controller.

[0106] Taking the SCIT convective cell identification algorithm as an example, the only data needed is the reflectivity factor dBz. The data classifier extracts the reflectivity factor through the data category marker according to the standard data structure table. The extraction result is consistent with the standard data structure table. The SCIT algorithm receives this data and separates the PPI data through information such as the elevation layer marker, completes the data screening and association on the PPI, and can separate the detection information of the convective cell; the mesocyclone identification algorithm only needs to use the radial velocity V data, and the data extraction and transmission are also completed by the data classifier. The algorithm separates the PPI data of the radial velocity through the relevant marker, identifies the velocity pair information of the positive and negative radial velocities on the PPI, and then performs vertical correlation to obtain the relevant algorithm products; the phase state identification product requires relatively more basic data, including reflectivity factors, correlation coefficients and differential reflectivity factors, etc. The data classifier packages and transmits the above data separately for the algorithm to process.

[0107] (11) Product Processing

[0108] Product processing is a general term for a series of short-term product processing algorithms that are suitable for networked collaborative observation minute data sets. According to the standardized minute-level data set provided by this embodiment, each product processing algorithm extracts the schedule from the data classifier to obtain the required original minute data set.

[0109] Taking Ningbo as an example, the minute-level network collaborative observation product algorithms currently developed locally in Ningbo include: refined combined reflectivity puzzle products, refined hail identification and warning products, refined downburst identification products, and refined atmospheric vertical accumulated liquid water content products. Taking the refined combined reflectivity puzzle product as an example, the reflectivity factor PPI observation data set of each network member of this minute is extracted from the data classifier. First, each PPI data is expanded by radial linear interpolation to achieve an azimuth resolution of 0.1 degrees and a distance library resolution of 125m. Then, the coordinate conversion formula from the spherical coordinate system of a single point on the earth's spherical surface to the Cartesian coordinate system is used to complete the interpolation processing of the radial distance library data on the PPI to the grid points of the Ningbo network collaborative observation area. The interpolation strategy adopts the best neighbor interpolation algorithm. The maximum value of multiple observation data on the same horizontal grid point is selected as the observation result of this grid point, and the refined combined reflectivity puzzle product of this time can be obtained. Then, the product data is stored in binary form and put into the local hard disk storage space (product data pool), which completes the product processing of this time.

[0110] (12) Product data pool and downstream customers

[0111] The product data pool refers to the hard disk space used to store various algorithm products produced upstream. If the algorithm processing products need to be stored for a long period of time, there are certain requirements for local hard disk resources. The downstream customer package refers to the user unit or software platform that extracts the required data from the data pool to provide services, generally including the business terminal of the grassroots meteorological station, the mobile user's mobile phone terminal, the grassroots meteorological grid information terminal, the specific user port, the emergency management department information terminal or the information terminal of the superior competent department, etc.

[0112] Taking Ningbo as an example, in addition to providing meteorological information to local radio stations, television stations and other units, it is also necessary to provide timely weather data to local specific users such as large chemical zones such as Zhenhai Refining and Chemical, port areas such as Zhoushan Port and Ningbo Port that carry large cargo ships, and organizers of some special outdoor activities. When high-impact weather occurs, such as typhoons, hail or snowfall, real-time meteorological data must be sent to emergency management departments and other higher-level authorities. Different data management permissions can be assigned to these downstream customers to extract the required data from the product data pool.

[0113] The method of this embodiment is based on the actual development of short-term weather services at the grassroots level in various regions, and constructs a set of refined regional networking collaborative observation methods based on the new generation of weather radars. Its advantages are:

[0114] 1. Increase the data update frequency to 1 minute. Traditional short-term services are mostly based on single-station radar observations, using sequential stream transmission, and the product update interval is 6 minutes; multi-station-based puzzle product development is also mostly based on complete data obtained from sequential stream transmission, and then puzzle processing is performed, and the required time interval is generally more than 6 minutes. This algorithm is based on real-time stream transmission, and the algorithm product is locally deployed, increasing the update frequency of observation products to 1 minute;

[0115] 2. Small virtual memory usage and low requirements for the computing environment. In the loop, the space usage of virtual memory is strictly limited through timing control and data checker, and the requirements for the memory hardware part of the computing service are relatively low. The Ningbo regional networking collaborative observation network uses 7 S-band dual-polarization radars, and the server memory space occupied by the entire algorithm process does not exceed 64G;

[0116] 3. It is relatively easy to update and iterate product algorithms. Through the data structure reorganization module, the original data of each network member is standardized, providing a standardized input interface for downstream algorithms, which is convenient for the development and iteration of downstream algorithms. Currently, the network collaboration algorithm modules completed for this interface include downburst identification and warning algorithm, hail identification algorithm, strong hail index construction algorithm, convective monomer identification algorithm, etc., and can continuously update and iterate themselves in business practice;

[0117] 4. The computing power and space occupation of the product algorithm are controllable. Based on the data structure reorganization module, the formats of weather radar data of different types or detection modes are optimized and unified, which further optimizes the memory throughput requirements and indirectly controls the number of CPU operations. For stations with limited computing power resources, the computing power requirements can be reduced by adjusting the data resolution, while for stations with better computing power resources, the data resolution can be appropriately increased to obtain richer grid information;

[0118] 5. Reduce the impact of missing meteorological data caused by single radar failure. Thanks to the advantage of the number of network members, weather observations based on a single station occasionally fail to obtain real-time observation information due to radar failure. This algorithm can effectively alleviate such problems by networking the surrounding radar sites.

[0119] 6. Low deployment cost. Based on the real-time data stream transmission communication line, the downstream algorithm server only needs to meet the virtual memory space of more than 64G and the CPU with i7-level computing power. The estimated server firmware cost is less than 5,000 yuan, which can be deployed in most grassroots stations in my country. It is also easy to install and deploy on private PCs for grassroots business personnel to develop and iterate.

[0120] Embodiment 2: This embodiment provides a non-transitory computer-readable storage medium on which computer instructions are stored. The computer instructions enable a computer to execute a method for fine-tuning collaborative observation of severe convection based on a network of adjacent weather radars. The method comprises the following steps:

[0121] The weather radar sites used for networking are selected as networking members, and the PPI data scanned by each networking member in real time is transmitted to the data buffer for accumulation in a streaming communication mode;

[0122] The data accumulated in the data buffer is transmitted to the algorithm server, and the real-time PPI data on the algorithm server is processed and packaged to build a network-based collaborative minute observation data set;

[0123] Develop networking products for business interpretation by networking and coordinating minute observation data sets, and store the generated products in the product data pool;

[0124] Downstream customers extract the required products from the product data pool.

[0125] Embodiment 3: This embodiment provides an electronic device, which may include: a processor, a communications interface, a memory, and a communication bus, wherein the processor, the communications interface, and the memory communicate with each other through the communication bus. The processor may call the logic instructions in the memory to execute a method for fine-tuning collaborative observation of severe convection based on a network of adjacent weather radars, the method comprising the following steps:

[0126] The weather radar sites used for networking are selected as networking members, and the PPI data scanned by each networking member in real time is transmitted to the data buffer for accumulation in a streaming communication mode;

[0127] The data accumulated in the data buffer is transmitted to the algorithm server, and the real-time PPI data on the algorithm server is processed and packaged to build a network-based collaborative minute observation data set;

[0128] Develop networking products for business interpretation by networking and coordinating minute observation data sets, and store the generated products in the product data pool;

[0129] Downstream customers extract the required products from the product data pool.

[0130] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0131] Embodiment 4: This embodiment provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a method for fine-tuning collaborative observation of severe convection based on a network of adjacent weather radars. The method includes the following steps:

[0132] The weather radar sites used for networking are selected as networking members, and the PPI data scanned by each networking member in real time is transmitted to the data buffer for accumulation in a streaming communication mode;

[0133] The data accumulated in the data buffer is transmitted to the algorithm server, and the real-time PPI data on the algorithm server is processed and packaged to build a network-based collaborative minute observation data set;

[0134] Develop networking products for business interpretation by networking and coordinating minute observation data sets, and store the generated products in the product data pool;

[0135] Downstream customers extract the required products from the product data pool.

[0136] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0137] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for fine-grained collaborative observation of severe convection based on adjacent weather radar networking, characterized in that: include: The weather radar sites used for networking are selected as networking members, and the PPI data scanned by each networking member in real time is transmitted to the data buffer for accumulation in a streaming communication mode; The PPI data scanned by each network member in real time is transmitted to the data buffer in a streaming communication mode. The data accumulated in the data buffer is transmitted to the algorithm server, and the real-time PPI data on the algorithm server is processed and packaged to build a network-based collaborative minute observation data set; Develop networking products for business interpretation by networking and coordinating minute observation data sets, and store the generated products in the product data pool; Downstream customers extract the required products from the product data pool.

2. The method for fine-grained collaborative observation of severe convection based on adjacent weather radar networking according to claim 1 is characterized in that: The method for selecting the weather radar site for networking as a networking member is as follows: Set boundary grids according to the area to be warned; Taking the weather radar station near the center of the area as the center, at least three neighboring weather radar stations with different locations are selected to build a network together. The weather radar stations participating in the network serve as network members.

3. The method for fine-tuning collaborative observation of severe convection based on adjacent weather radar networking according to claim 1 is characterized in that: The specific method of transmitting the PPI data collected by the scanning mode of each network member to the data buffer is as follows: Each network member independently completes real-time atmospheric scanning and collects atmospheric information; After each networking member obtains a layer of PPI data, it transmits the PPI data to the data buffer in real-time streaming mode.

4. The method for fine-grained collaborative observation of severe convection based on adjacent weather radar networking according to claim 1 is characterized in that: The data buffer is used to receive the real-time PPI data transmitted by each networking member and accumulate the data; The data buffer is also provided with a memory clearing switch, which is controlled by a timing controller. When the set conditions are met, all or part of the data accumulated in the data buffer are cleared.

5. The method for fine-grained collaborative observation of severe convection based on adjacent weather radar networking according to claim 1 is characterized in that: The transmitting of the data accumulated in the data buffer to the algorithm server comprises: Arrange a three-minute data pool between the data buffer and the algorithm server; The data buffer transmits multi-layer PPI data minute by minute to the three-minute data pool under the control of the timing controller; The three-minute data pool stores the incoming data for a short period of time for the algorithm server to call.

6. The method for fine-grained collaborative observation of severe convection based on adjacent weather radar networking according to claim 5 is characterized in that: The real-time PPI data on the algorithm server is processed and packaged to construct a network collaborative minute observation data set; including: Extract 1 minute of PPI data from the three-minute data pool and check the data integrity. If the integrity requirement is not met, extract the PPI data from the three-minute data pool again. Standardize the extracted PPI data; Perform quality control on the extracted PPI data and filter out interference from non-meteorological echoes; According to the data requirements of the processing algorithms of different networking products, the PPI data is classified, reorganized and packaged to construct data sets for different networking products.

7. The method for fine-grained collaborative observation of severe convection based on adjacent weather radar networking according to claim 6 is characterized in that: The method of extracting 1 minute of PPI data from a three-minute data pool and checking the data integrity is as follows: if the data integrity requirement is not met, the PPI data from the three-minute data pool is extracted again; the specific method is as follows: Construct a minute data extraction module to extract 1-minute PPI data from the 3-minute data pool. The extraction method is to extract all PPI data within 1 minute before the current moment according to the time when each PPI is input, and package them into a data set; The data integrity of the data set is verified by the data verifier. The judgment standard of the integrity verification is: whether the data set contains one of the PPIs corresponding to the two elevation angles of the lowest layer. If it does, the data set meets the data integrity requirements, and the earliest 1-minute PPI data in the three-minute data pool is cleared; if it does not, the data set is supplemented; Construct data supplementation, extract the PPI data with the lowest elevation angle from the three-minute data pool according to the PPI elevation angle mark. If there are multiple PPI data with the same elevation angle, select the PPI data closest to the current moment and put it into the data set; The data set is checked for data integrity again. The judgment criteria for the integrity check are: whether the data set contains one of the PPIs corresponding to the two elevation angles of the lowest layer. If so, the data set meets the data integrity requirements, and the earliest 1-minute PPI data in the three-minute data pool is cleared; if not, an early warning message is issued, and the originally extracted 1-minute PPI data is passed to the next step, and the earliest 1-minute PPI data in the three-minute data pool is cleared.

8. The method for fine-grained collaborative observation of severe convection based on adjacent weather radar networking according to claim 6 is characterized in that: The extracted PPI data is quality controlled to filter out interference from non-meteorological echoes; the specific method is: The wave velocity blocking situation is determined by the digital elevation model data at each elevation layer. For the blocked part, the radar reflectivity factor profile is used to correct the echo azimuth, and then the non-meteorological echo in the echo is filtered out by the correlation coefficient CC in the dual-polarization parameters.

9. A non-transitory computer-readable storage medium having computer instructions stored thereon, the computer instructions enabling a computer to execute the method for refined collaborative observation of severe convection based on networking of adjacent weather radars as described in any one of claims 1-8.

10. An electronic device comprising: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus, and the processor calls the logic instructions in the memory to execute the method for refined collaborative observation of severe convection based on the networking of adjacent weather radars as described in any one of claims 1-8.

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