Severe convection radar product system

Abstract

The embodiment of the invention provides a severe convection radar product system. The system comprises a radar basic product processing module, a severe convection radar identification extrapolation module, a radar two-dimensional and three-dimensional visualization module and a radar product inspection and analysis module. The radar basic product processing module is used for calculating a radar basic quantity product and a physical quantity product on the basis of the multiband weather radar and the radar data after quality control; the severe convection radar identification extrapolation module is used for integrating a plurality of severe convection identification algorithms and a deep learning extrapolation algorithm; the radar two-dimensional and three-dimensional visualization module is used for issuing two-dimensional and three-dimensional data services based on a B / S architecture and a system micro-service framework in combination with a geographic space data standard; and the radar product inspection and analysis module is used for quantitatively estimating rainfall, identifying severe convection and extrapolating lattice point inspection and site inspection of products. In the embodiment of the invention, the system can be compatible with a new-generation weather radar with service operation, and novel radars such as X-band radars and phased array radars.

Description

Technical Field

[0001] The present disclosure relates to the technical field of meteorological services, and in particular to a severe convection radar product system. Background Art

[0002] At present, the radar analysis software used in business mainly uses the ROSE software with C / S architecture deployed at the station end. This software is mainly used to analyze and display single-station data and products. In the process of product generation, it needs to be jointly operated by RPG and PUP software to be displayed. The installation and operation are complicated, and it is difficult to meet the regional and even national radar product service needs. At the same time, with the development and deployment of X-band radar, phased array radar and other technologies, it is urgent to build a strong convection radar product system with B / S architecture focusing on multi-system multi-band radar, which is compatible with the new generation of weather radars in business operation, as well as new radars such as X-band and phased array, to realize the integrated function of refined monitoring of national, provincial, municipal and county radars. National, provincial, municipal and county users only need to log in to the webpage to view, and do not need to install and maintain each station, saving a lot of manpower and material resources. Summary of the invention

[0003] An embodiment of the present disclosure provides a severe convection radar product system, the system comprising:

[0004] Radar basic product processing module, severe convection radar identification and extrapolation module, radar 2D and 3D visualization module, radar product inspection and analysis module;

[0005] The radar basic product processing module is used to calculate radar basic quantity products and physical quantity products based on multi-band weather radar and quality-controlled radar data;

[0006] The severe convection radar identification and extrapolation module is used to integrate multiple severe convection identification algorithms and deep learning extrapolation algorithms;

[0007] The radar 2D and 3D visualization module is used to publish 2D and 3D data services based on B / S architecture, system microservice framework, and geographic spatial data standards;

[0008] The radar product verification and analysis module is used for grid verification and site verification of quantitative precipitation estimation, severe convection identification products and extrapolation products.

[0009] In some implementable embodiments of the first aspect, the radar basic product processing module is also used to perform real-time three-dimensional stitching processing on radar data of multiple weather radars to generate refined three-dimensional radar networking puzzles and large and small radar fusion networking products.

[0010] In some implementations of the first aspect, the radar basic quantity products include reflectivity factor, radial velocity, velocity spectrum width, differential reflectivity, correlation coefficient, differential phase shift rate, and differential phase shift;

[0011] Physical quantity products include combined reflectivity, vertically integrated liquid water content, mixed reflectivity factor, echo top height, 1-hour accumulated precipitation, and 3-hour accumulated precipitation.

[0012] In some implementable embodiments of the first aspect, the strong convection radar identification and extrapolation module is specifically used to establish an algorithm integration and processing pipeline, and based on the algorithm integration and processing pipeline standard, establish a strong convection identification algorithm and a deep learning extrapolation algorithm integration standard framework, and integrate multiple strong convection identification algorithms and deep learning extrapolation algorithms.

[0013] In some implementations of the first aspect, the radar 2D and 3D visualization module uses Cesium as a 3D visualization framework.

[0014] In some implementation methods of the first aspect, the radar 2D and 3D visualization module is extended based on Cesium using WebGL technology, and the radar 3D visualization algorithm is encapsulated through WebGL, and then the encapsulated algorithm is integrated into Cesium.

[0015] In some implementations of the first aspect, the radar two or three-dimensional visualization module is also used to display multiple types of data, including raster data, polar coordinate data, and special format data.

[0016] In some possible implementations of the first aspect, the radar two- and three-dimensional visualization module is also used to display products in various forms, including pop-up products, text products, two-dimensional products, three-dimensional products, map overlay products, and PPI products.

[0017] In some implementable embodiments of the first aspect, the radar product inspection and analysis module is also used to inspect and compare multiple types of algorithms for the same type of products; and to perform foreground parameter adjustment and configuration of algorithm input parameters.

[0018] In some implementable methods of the first aspect, data synchronization and application requests are performed between the radar basic product processing module, the severe convection radar identification and extrapolation module, the radar two and three-dimensional visualization module, and the radar product inspection and analysis module through a message mechanism.

[0019] In the embodiments of the present disclosure, the system can be compatible with the new generation of weather radars in business operation, as well as new radars such as X-band and phased array, to achieve the integrated function of refined monitoring of national, provincial, municipal and county radars. National, provincial, municipal and county users only need to log in to the webpage to view it, and there is no need for installation and maintenance of each station, saving a lot of manpower and material resources.

[0020] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0022] Figure 1 A structural diagram of a severe convection radar product system provided by an embodiment of the present disclosure is shown;

[0023] Figure 2 A schematic diagram of a cross-sectional analysis provided by an embodiment of the present disclosure is shown;

[0024] Figure 3 A three-dimensional analysis schematic diagram provided by an embodiment of the present disclosure is shown;

[0025] Figure 4 A schematic diagram of inspection and evaluation provided by an embodiment of the present disclosure is shown;

[0026] Figure 5 A schematic diagram showing the working principle of a message mechanism provided by an embodiment of the present disclosure is shown;

[0027] Figure 6 A schematic diagram of a parallel computing framework provided by an embodiment of the present disclosure is shown;

[0028] Figure 7 A schematic diagram of constructing a spatial standard vector database provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0030] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0031] In response to the problems arising in the background technology, the embodiments of the present disclosure provide a strong convection radar product system that is compatible with the new generation of weather radars in business operation, as well as new radars such as X-band and phased array, to achieve the integrated function of refined monitoring of national, provincial, municipal and county radars. National, provincial, municipal and county users only need to log in to the webpage to view the information, and there is no need for installation and maintenance of each station, saving a lot of manpower and material resources.

[0032] A strong convection radar product system provided by an embodiment of the present disclosure is described in detail below with reference to the accompanying drawings through specific embodiments.

[0033] Figure 1 FIG. 4 shows a structural diagram of a severe convection radar product system provided by an embodiment of the present disclosure, such as Figure 1 As shown, the severe convection radar product system (also known as the Leisheng-P system) can include: radar basic product processing module, severe convection radar identification and extrapolation module, radar two- and three-dimensional visualization module, and radar product inspection and analysis module.

[0034] The radar basic product processing module is used to calculate radar basic quantity products and physical quantity products based on multi-band weather radars and radar data after quality control; it performs real-time three-dimensional splicing processing on radar data of multiple weather radars to generate refined three-dimensional radar network puzzles and large and small radar fusion network products. This module can process national S / C / X band and phased array radars, and supports adding new radars at any time to realize radar product display in different ranges such as single station, region, and country.

[0035] Among them, the above-mentioned radar basic quantity products may include reflectivity factor, radial velocity, velocity spectrum width, differential reflectivity, correlation coefficient, differential phase shift rate, differential phase displacement, etc.; physical quantity products may include combined reflectivity, vertically integrated liquid water content, mixed reflectivity factor, echo top height, 1h accumulated precipitation, 3h accumulated precipitation, etc.

[0036] The strong convection radar identification and extrapolation module is used to integrate a variety of strong convection identification algorithms and deep learning extrapolation algorithms. Specifically, an algorithm integration and processing pipeline is established. Based on the algorithm integration and processing pipeline standard, a strong convection identification algorithm and a deep learning extrapolation algorithm integration standard framework is established to integrate a variety of strong convection identification algorithms and deep learning extrapolation algorithms. Furthermore, other commonly used weather process analysis products can also be integrated. Since this module integrates a variety of strong convection identification algorithms, deep learning extrapolation algorithms and other commonly used weather process analysis products with different technical routes, it can provide comparative verification of different algorithms of the same product, realize the rapid integration of third-party algorithms based on standardized interfaces, and the efficient operation and low-latency identification capabilities of various products.

[0037] Among them, the above-mentioned severe convection identification algorithms may include identification algorithms such as hail, tornadoes, thunderstorms, and short-term heavy rainfall; deep learning extrapolation algorithms may include extrapolation algorithms such as echo and short-term heavy rainfall; other commonly used weather process analysis products may include mesocyclones, boundary layer convergence lines, storms and severe convection tracking, wind fields, rain and snow phase identification and other products.

[0038] The radar 2D and 3D visualization module is used to release 2D and 3D data services based on the B / S architecture and the system microservice framework, in combination with the geographic spatial data standards. The front-end display here uses Cesium as the basic framework for 3D visualization. On the basis of Cesium, it uses WebGL technology for expansion, encapsulates the radar 3D visualization algorithm through WebGL, and then integrates the encapsulated algorithm into Cesium to form a visualization display effect with the characteristics of meteorological spatial data, thereby realizing the multi-layer display, profile analysis, and 3D volume drawing of high-resolution single-station and networked radar echoes, and realizing the production of radar analysis products for typical disaster weather processes. Various interactive functions can be used to support huge amounts of data and real-time process rendering effects. This module is also used to display various types of data, including raster data, polar coordinate data, and special format data; display various forms of products, including pop-up products, text products, 2D products, 3D products, map overlay products, and PPI products; support a variety of interactive functions, including point marking, ruler distance measurement, and profile analysis (such as Figure 2 ), three-dimensional analysis (such as Figure 3 As shown), screenshot making, dynamic graph making, historical case back calculation, historical case query and other functions.

[0039] The radar product inspection and analysis module is used for quantitative estimation of precipitation, grid inspection and site inspection of severe convection identification products and extrapolation products. Here, real-time inspection and evaluation are carried out for the accuracy, deviation and structure of echo extrapolation, precipitation extrapolation, hail, thunderstorm gale, tornado and short-term heavy precipitation identification (such as Figure 4 ), and provides split-screen comparison of different algorithms, spatiotemporal consistency test, and quantitative analysis and evaluation. This module is also used to test and compare multiple algorithms of the same type of product; to configure the algorithm input parameters for the front desk; and to provide the best decision-making service for disastrous weather phenomena through testing, comparison, and integration between algorithms.

[0040] It should be noted that the radar basic product processing module, severe convection radar identification and extrapolation module, radar two and three-dimensional visualization module, and radar product inspection and analysis module here use the message mechanism to synchronize data and make application requests.

[0041] To facilitate further understanding, the technical implementation of the above modules is described below in conjunction with specific embodiments:

[0042] 1) Server: Use Linux operating system CentOS7.2 or above, or domestic operating system Kylin V10.

[0043] 2) Database: Distributed NAS is used as the file storage system, PostgreSQL is used as the relational data management system for meteorological data, and Redis is used as the cache database to provide direct data support for each module.

[0044] 3) Use a message mechanism (such as Figure 5 (as shown) coordinate data synchronization and application requests between modules: a) Algorithms are parallel between single stations, and the upper limit of the process pool is set in a single server. The number of parallel processes is adjusted dynamically in real time according to the number of the latest base data stations; b) Single-station products are serial, and some time-consuming algorithms are multi-threaded to make full use of CPU computing resources. Reasonable design and integration of algorithm operation logic framework, reduce repeated calculations and save computing resources; c) After the single-station product is produced, the product generation status information is sent to the database in real time and the product information is stored in the warehouse to reduce the delay of the page radar product; d) The grid tiles of the national networking products are processed in parallel using multi-threading technology to reduce network transmission pressure and improve system fluency.

[0045] 4) Establish a parallel computing framework for high-precision, large-scale radar data (such as Figure 6 As shown in the figure, the framework calling method is the inheritance class method, that is, the class that needs to implement the algorithm framework calls the integrated framework class as the base class and implements its interface function to realize the integration of the framework to the algorithm; the framework includes the algorithm tool interface for converting polar coordinates to longitude and latitude coordinates, the radar networking algorithm tool interface, the standard deviation test algorithm tool interface, the base data file parsing module tool interface for various radar models, the netCDF file writing and compression tool interface, etc.

[0046] 5) Use space standard vector tile technology to build a set of radar standard service data solutions (such as Figure 7 As shown). First, based on the multi-band radar basic products, secondary inversion and identification products and early warning products, a full-process technical system of data collection, vector processing, vector database management and service release is realized. Develop an industry service support platform based on spatial standard vector tile data: Based on the spatial standard vector database, an industry service support platform for radar data products is established to realize the query, display and two-dimensional fast rendering of points, lines, surfaces and volumes. The data types applicable to the spatial standard vector tile processing technology include raster data and structured data.

[0047] 6) Development language: mainly programming languages ​​such as JAVA, JavaScript and PYTHON.

[0048] 7) System architecture: B / S architecture model is adopted.

[0049] 8) Use Cesium as the basic framework for 3D visualization, call the system GPU, and use web workers and other means to achieve high efficiency and no lag in rendering. Based on Cesium, use Webgl technology for expansion, encapsulate the radar 3D visualization algorithm through Webgl, and then integrate the encapsulated algorithm into the Cesium framework to form a visualization display effect with the characteristics of meteorological spatial data.

[0050] In summary, according to the embodiments of the present invention, at least the following technical effects are achieved:

[0051] (1) Efficient algorithm engineering process.

[0052] Efficient algorithm processing pipeline: A parallel scheduling system based on an algorithm integration framework improves operational efficiency and stability, and quickly integrates product processing pipelines.

[0053] Standardized processing lines improve algorithm reuse efficiency: Code engineering is rewritten based on the algorithm integration framework, and the algorithm scheduling interface is specified, reducing the reuse cycle from several days to 1-2 hours.

[0054] Realize the integration of different algorithms: For the same type of products, integrate different algorithm methods to output the optimal results and complement each other in time and space.

[0055] Verify different algorithms: Provide different algorithm testing and evaluation, and visual comparative analysis tools; provide comparison of different algorithms for the same product, and provide mutual verification and optimal decision-making basis for different weather processes.

[0056] (2) Flexible, lightweight, and diverse fast rendering and display capabilities.

[0057] The independently developed radar 3D visualization engine is based on the WebGL underlying language. Combining the characteristics and application scenarios of meteorological data, we develop a visualization algorithm based on meteorological radar data to achieve efficient and accurate visualization effects on the B / S side.

[0058] Based on the ultimate data compression solution and on-demand loading solution, data preloading is minimized, data loading time is reduced, and data rendering response speed is improved. Based on GPU+CPU acceleration tools, ultra-large matrix fast loading and rendering are achieved, and the front-end rendering efficiency is greatly improved, which can meet the Web end's rendering needs for large amounts of spatial data.

[0059] By utilizing the open source, diverse, efficient, and scalable features of the Cesium framework, a variety of human-computer interaction functions can be realized, including frame selection, point selection, threshold selection, etc.; combined with the GIS engine, the three-dimensional space vector data extraction, drawing, analysis, and query capabilities can be realized, and different altitudes, different times, and different elements can be switched freely to realize the real three-dimensional space-time of weather evolution, assisting forecasters to quickly identify and judge severe convective weather processes.

[0060] It should be understood that the above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A severe convection radar product system, characterized in that: The system comprises: Radar basic product processing module, severe convection radar identification and extrapolation module, radar 2D and 3D visualization module, radar product inspection and analysis module; The radar basic product processing module is used to calculate radar basic quantity products and physical quantity products based on multi-band weather radar and quality-controlled radar data; The severe convection radar identification and extrapolation module is used to integrate a variety of severe convection identification algorithms and deep learning extrapolation algorithms; The radar 2D and 3D visualization module is used to publish 2D and 3D data services based on B / S architecture, system microservice framework, and geographic spatial data standards; The radar product verification and analysis module is used for quantitative estimation of precipitation, grid verification and site verification of severe convection identification products and extrapolation products.

2. The system according to claim 1, characterized in that The radar basic product processing module is also used to perform real-time three-dimensional splicing processing on radar data of multiple weather radars to generate refined three-dimensional radar networking puzzles and large and small radar fusion networking products.

3. The system according to claim 1, characterized in that The radar basic quantity products include reflectivity factor, radial velocity, velocity spectrum width, differential reflectivity, correlation coefficient, differential phase shift rate, and differential phase shift; The physical quantity products include combined reflectivity, vertically integrated liquid water content, mixed reflectivity factor, echo top height, 1-hour accumulated precipitation, and 3-hour accumulated precipitation.

4. The system according to claim 1, characterized in that The strong convection radar identification and extrapolation module is specifically used to establish an algorithm integration and processing pipeline. Based on the algorithm integration and processing pipeline standard, a strong convection identification algorithm and a deep learning extrapolation algorithm integration standard framework are established to integrate a variety of strong convection identification algorithms and deep learning extrapolation algorithms.

5. The system according to claim 1, characterized in that The radar 2D and 3D visualization module uses Cesium as the 3D visualization framework.

6. The system according to claim 5, characterized in that The radar 2D and 3D visualization module is extended based on Cesium using WebGL technology, encapsulates the radar 3D visualization algorithm through WebGL, and then integrates the encapsulated algorithm into Cesium.

7. The system according to claim 1, characterized in that The radar 2D and 3D visualization module is also used to display various types of data, including grid data, polar coordinate data, and special format data.

8. The system according to claim 1, characterized in that The radar two-dimensional and three-dimensional visualization module is also used to display products in various forms, including pop-up products, text products, two-dimensional products, three-dimensional products, map overlay products, and PPI products.

9. The system according to claim 1, characterized in that The radar product inspection and analysis module is also used to inspect and compare multiple algorithms for the same type of product; and to perform front-end parameter adjustment and configuration on the algorithm input parameters.

10. The system according to claim 1, characterized in that The radar basic product processing module, the severe convection radar identification and extrapolation module, the radar two or three-dimensional visualization module, and the radar product inspection and analysis module perform data synchronization and application requests through a message mechanism.

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