Radar-based tornado intelligent identification method and system

Abstract

The present invention discloses a radar-based intelligent tornado identification method and system, which relates to the field of radio direction finding technology. First, multi-source historical data of a monitoring area is obtained, and each tornado-prone monitoring sub-area is analyzed to more accurately identify the tornado-prone monitoring sub-area. Then, radar signals of each tornado-prone monitoring sub-area are obtained and pre-processed and configured to improve the accuracy of radar signal analysis, reduce noise and error in radar signal measurement, and speed up radar signal transmission. Then, radar anomaly identification results of each tornado-prone monitoring sub-area are analyzed to improve the accuracy of radar signal anomaly identification, thereby improving the accuracy of tornado identification. Finally, radar configuration is adjusted for each radar signal anomaly monitoring sub-area, and tornado identification is performed. This helps improve the real-time monitoring capability of tornadoes, thereby improving the timeliness and accuracy of early warning responses.

Description

Technical Field

[0001] The present invention relates to the field of radio direction finding technology, and in particular to a radar-based tornado intelligent identification method and system. Background Art

[0002] With climate change and the frequent occurrence of extreme weather events, accurate identification of tornadoes has become an important technical means to reduce disaster losses. Tornado monitoring methods are limited by the limitations of spatial distribution and the timeliness of data collection, making it difficult to achieve efficient and accurate monitoring. With the development of radar technology, analysis of radar reflection waveforms and velocity spectra can capture the formation, movement and development process of tornadoes in real time, improving the spatial resolution and time response speed of monitoring. The tornado intelligent identification system can provide more accurate early warning information in complex meteorological environments, providing technical support for the prevention and response to tornado disasters.

[0003] The prior art, such as the invention patent announcement with announcement number: CN111398964B, discloses a radar nowcasting method based on heavy rainfall identification and driven by numerical atmospheric models, which includes the following steps: Step 1, identification of convective core grid points based on phase partitioning; Step 2, re-identification of convective core grid points using the horizontal and vertical gradients of radar reflectivity or the horizontal and radial gradients of radar reflectivity; Step 3, search for convective core grid points based on the three-dimensional region growing method until all grid points are searched; Step 4, determination of the set of all grid points as a convective zone; Step 5, continuous monitoring of convective core grid points and superposition of wind field information to determine the rainfall area; Step 6, rainfall nowcasting.

[0004] Prior art, such as the invention patent announcement with publication number CN111736156B, discloses a method and device for identifying headwind areas based on weather radar. The method includes obtaining radar-based data from a weather radar, performing quality control on the radar-based data, extracting basic reflectivity factors and velocities, converting them into grid reflectivity factors and grid velocities, calculating the radar's combined reflectivity based on the basic reflectivity factors, identifying the connected domain of convective cells based on the combined reflectivity, binarizing the velocities, and determining whether there are positive and negative velocities within the connected domain within the array based on the binarized velocity data. If so, calculating the area ratio of the regions with the two velocities, and using the region with the smaller area as the headwind area; and eliminating the headwind area identification results that are mistakenly identified as passing through the radar origin.

[0005] Combined with the above scheme, it is found that in the current field of tornado intelligent identification technology, radar data is usually only monitored and analyzed. However, the radar data collection process will be affected by external factors, which affects the accuracy and completeness of radar information collection, making it impossible to provide accurate real-time monitoring data in a timely manner. It may also lead to false alarms or missed alarms, posing a potential risk of misjudgment and affecting the response speed of the early warning. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a radar-based tornado intelligent identification method and system, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a radar-based tornado intelligent identification method, including statistically processing multi-source historical data of the monitoring area to obtain a strong convection identification model of the monitoring area, collecting multi-source real-time data and inputting it into the strong convection identification model of the monitoring area to obtain each tornado-prone monitoring sub-area.

[0008] The radar signals of each tornado-prone monitoring sub-area are obtained for pre-processing configuration, and after the pre-processing configuration, the radar signals of each tornado-prone monitoring sub-area are transmitted to the central processing platform.

[0009] The radar signal parameters of each tornado-prone monitoring sub-area in the central processing platform are retrieved, and a comprehensive analysis is performed to obtain the radar anomaly identification results of each tornado-prone monitoring sub-area.

[0010] Based on the radar anomaly identification results of each tornado-prone monitoring sub-area, the radar signal anomaly monitoring sub-area is statistically obtained, and the radar configuration of each radar signal anomaly monitoring sub-area is adjusted, and then tornado identification is performed based on the configured radar.

[0011] Furthermore, the multi-source historical data of the statistical monitoring area are processed to obtain a severe convection identification model for the monitoring area. The specific process is: the multi-source historical data of the monitoring area include the meteorological fluctuation parameters of the monitoring area and the meteorological performance parameters of the historical occurrence periods of each tornado.

[0012] The meteorological fluctuation parameters of the monitoring area include the temperature fluctuation extremes, humidity fluctuation extremes, wind speed fluctuation extremes and air pressure fluctuation extremes in the monitoring area during a preset historical period. A comprehensive analysis is performed on the meteorological fluctuation parameters of the monitoring area to obtain a comprehensive meteorological fluctuation evaluation value of the monitoring area, and a severe convective activity control value is obtained by matching the comprehensive meteorological fluctuation evaluation value of the monitoring area.

[0013] The meteorological performance parameters of each tornado historical period include the average temperature, average humidity, maximum wind speed and average air pressure of each tornado historical period. Based on the meteorological performance parameters of each tornado historical period, the severe convective activity index value of each tornado historical period is obtained.

[0014] The lowest value of the severe convective activity index during the historical period of each tornado occurrence was extracted and recorded as the first index value of severe convective activity. The second index value of severe convective activity was obtained by subtracting the strong convective activity control value from the first index value of severe convective activity.

[0015] According to the second indicator value of severe convective activity, a first interval for severe convection identification and a second interval for severe convection identification are constructed, and a severe convection identification model for the monitoring area is established based on the first interval for severe convection identification and the second interval for severe convection identification.

[0016] Furthermore, the specific process of obtaining each tornado-prone monitoring sub-area is as follows: the multi-source real-time data includes the temperature, humidity, wind speed and air pressure of each tornado monitoring sub-area.

[0017] Based on multi-source real-time data, the strong convective activity index value of each tornado monitoring sub-area is processed and input into the strong convection identification model of the monitoring area to obtain all tornado-prone monitoring sub-areas, which are recorded as tornado-prone monitoring sub-areas.

[0018] Furthermore, the radar signals of each tornado-prone monitoring sub-area are obtained and pre-processed and configured, and the specific process is: statistics are collected on the radar performance abnormality data of each tornado-prone monitoring sub-area, and the radar performance abnormality data of each tornado-prone monitoring sub-area include the radar transmission power, radar scanning direction deviation, radar signal-to-noise ratio, number of radar signal distortions and number of failures of radar electronic equipment in each tornado-prone monitoring sub-area.

[0019] Based on the radar performance anomaly data of each tornado-prone monitoring sub-area, radar performance anomaly assessment values ​​for each tornado-prone monitoring sub-area are obtained through processing. The radar performance anomaly assessment values ​​for each tornado-prone monitoring sub-area are used to comprehensively quantify the performance reliability of the radar system.

[0020] The radar performance evaluation influencing factors of each tornado-prone monitoring sub-area are obtained by matching the severe convective activity index values ​​of each tornado-prone monitoring sub-area.

[0021] Based on the radar performance anomaly assessment values ​​of each tornado-prone monitoring sub-area and combined with the radar performance assessment influencing factors of each tornado-prone monitoring sub-area, a comprehensive analysis is conducted to obtain the radar data quality benchmark values ​​of each tornado-prone monitoring sub-area. The radar data quality benchmark values ​​of each tornado-prone monitoring sub-area are used to comprehensively quantify the radar data quality.

[0022] The radar data processing parameters of each tornado-prone monitoring sub-area are obtained according to the radar data quality benchmark value matching, and the radar signal is preprocessed and configured with the corresponding radar data processing parameters.

[0023] Furthermore, the radar signal parameters of each tornado-prone monitoring sub-area in the central processing platform are retrieved, and the specific process is: according to the radar data quality benchmark value of each tornado-prone monitoring sub-area, the radar data analysis influencing factor of each tornado-prone monitoring sub-area is obtained.

[0024] The radar signal parameters of each tornado-prone monitoring sub-area include the radar echo reflectivity, the average descent rate of the radar echo, the maximum offset of the radar echo frequency, and the number of occurrences of hook echoes on the radar image of each tornado-prone monitoring sub-area. Based on the radar signal parameters of each tornado-prone monitoring sub-area, a radar operation signal anomaly assessment value of each tornado-prone monitoring sub-area is obtained through processing. The radar operation signal anomaly assessment value of each tornado-prone monitoring sub-area is used to comprehensively quantify the signal anomaly conditions of the radar during the tornado monitoring process.

[0025] According to the radar operation signal anomaly assessment value of each tornado-prone monitoring sub-area, combined with the radar data analysis influencing factors of each tornado-prone monitoring sub-area, a comprehensive analysis is conducted to obtain the radar anomaly comprehensive index value of each tornado-prone monitoring sub-area.

[0026] Furthermore, the comprehensive analysis obtains radar anomaly recognition results for each tornado-prone monitoring sub-area, and the specific process is: the radar anomaly recognition results for each tornado-prone monitoring sub-area include executing radar anomaly warning and not executing radar anomaly warning.

[0027] The radar anomaly comprehensive index value of each tornado-prone monitoring sub-area is compared with the set radar anomaly comprehensive index threshold, and the radar signal anomaly monitoring sub-area is obtained through analysis.

[0028] Furthermore, the radar configuration of each radar signal anomaly monitoring sub-area is adjusted, and the specific process is: extracting the radar anomaly comprehensive index value of each radar signal anomaly monitoring sub-area, analyzing to obtain the radar configuration parameters of each radar signal anomaly monitoring sub-area, and adjusting the configuration of the radar of each radar signal anomaly monitoring sub-area according to the radar configuration parameters of each radar signal anomaly monitoring sub-area.

[0029] Furthermore, the radar operation signal anomaly assessment value of each tornado-prone monitoring sub-area can be obtained by the following analysis method, and the specific analysis conditions are as follows:

[0030] Where, represents the radar operation signal anomaly assessment value of the i-th tornado-prone monitoring sub-area, represents the radar echo reflectivity of the i-th tornado-prone monitoring sub-area, Indicates the correction factor corresponding to the set radar echo reflectivity, represents the average descent rate of radar echoes in the i-th tornado-prone monitoring sub-area, Indicates the correction factor corresponding to the set average descent rate of radar echo. represents the maximum offset of the radar echo frequency in the i-th tornado-prone monitoring sub-area, Indicates the correction factor corresponding to the maximum offset of the set radar echo frequency. represents the number of hook echoes on the radar image of the i-th tornado-prone monitoring sub-area, It represents the correction factor corresponding to the number of hook echoes on the radar image, i represents the number of each tornado-prone monitoring sub-area, , represents the total number of tornado-prone monitoring sub-areas, and e represents a natural constant.

[0031] Furthermore, the radar-based intelligent tornado identification method also includes: collecting statistics on the real-time data of each tornado-prone monitoring sub-area for storage and management. The specific process is: based on the radar anomaly comprehensive index value of each tornado-prone monitoring sub-area, matching the monitoring data compression ratio of each tornado-prone monitoring sub-area, and storing the data of each tornado-prone monitoring sub-area in the cloud platform according to the monitoring data compression ratio of each tornado-prone monitoring sub-area.

[0032] The second aspect of the present invention provides a radar-based tornado intelligent identification system, including: a tornado-prone area identification module, which is used to statistically process multi-source historical data of the monitoring area to obtain a strong convection identification model for the monitoring area, collect multi-source real-time data and input it into the strong convection identification model of the monitoring area to obtain each tornado-prone monitoring sub-area.

[0033] The preprocessing configuration module is used to obtain the radar signals of each tornado-prone monitoring sub-area for preprocessing configuration, and after the preprocessing configuration, transmit the radar signals of each tornado-prone monitoring sub-area to the central processing platform.

[0034] The identification result analysis module is used to retrieve the radar signal parameters of each tornado-prone monitoring sub-area in the central processing platform, and comprehensively analyze to obtain the radar anomaly identification results of each tornado-prone monitoring sub-area.

[0035] The signal anomaly identification module is used to obtain statistics of each radar signal anomaly monitoring sub-area based on the radar anomaly identification results of each tornado-prone monitoring sub-area, adjust the radar configuration of each radar signal anomaly monitoring sub-area, and then identify tornadoes based on the configured radar.

[0036] The present invention has the following beneficial effects:

[0037] (1) The present invention provides a radar-based intelligent tornado identification method and system. First, the system processes and obtains each tornado-prone monitoring sub-area, which can more accurately identify the tornado-prone monitoring sub-area. Then, the radar signal is pre-processed and configured to effectively remove noise and interference in the radar signal. Then, the radar signal parameters are analyzed to improve the accuracy of tornado identification. Finally, the risk level is determined and early warning configuration processing is performed, thereby improving the refinement of the early warning.

[0038] (2) The present invention obtains radar signals from each tornado-prone monitoring sub-area and performs pre-processing configuration, which helps to timely discover equipment performance problems and then make adjustments to ensure the continuous operation of the radar system. The pre-processing configuration can identify and correct signal anomalies caused by equipment failure or performance fluctuations, reduce random noise and system errors in the signal, make the radar signal more stable, and improve the reliability of radar data.

[0039] (3) The present invention ensures the efficiency and accuracy of the identification process by retrieving the radar signal parameters of each tornado-prone monitoring sub-area in the central processing platform for comprehensive analysis, which helps to more accurately identify abnormal conditions of radar signals, thereby reducing the possibility of false alarms and missed alarms, improving the accuracy of early warnings, and enhancing the reliability of disaster early warnings.

[0040] (4) The present invention adjusts the radar configuration of each radar signal anomaly monitoring sub-area, which can significantly improve the accuracy of radar signal anomaly identification and the timeliness of early warning, thereby more accurately identifying potential tornado signals. The radar configuration adjustment for the anomaly monitoring sub-area helps to more effectively allocate monitoring and emergency resources and improve identification efficiency and accuracy.

[0041] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of the method of the present invention.

[0043] Figure 2 This is a schematic diagram of system module connections of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] See also Figure 1As shown, an embodiment of the present invention provides a technical solution: a radar-based tornado intelligent identification method, including statistically processing multi-source historical data of the monitoring area to obtain a strong convection identification model of the monitoring area, collecting multi-source real-time data and inputting it into the strong convection identification model of the monitoring area to obtain each tornado-prone monitoring sub-area.

[0046] The radar signals of each tornado-prone monitoring sub-area are obtained for pre-processing configuration, and after the pre-processing configuration, the radar signals of each tornado-prone monitoring sub-area are transmitted to the central processing platform.

[0047] The radar signal parameters of each tornado-prone monitoring sub-area in the central processing platform are retrieved, and a comprehensive analysis is performed to obtain the radar anomaly identification results of each tornado-prone monitoring sub-area.

[0048] Based on the radar anomaly identification results of each tornado-prone monitoring sub-area, the radar signal anomaly monitoring sub-area is statistically obtained, and the radar configuration of each radar signal anomaly monitoring sub-area is adjusted, and then tornado identification is performed based on the configured radar.

[0049] Specifically, the multi-source historical data of the monitoring area are statistically processed to obtain a severe convection identification model for the monitoring area. The specific process is: the multi-source historical data of the monitoring area include the meteorological fluctuation parameters of the monitoring area and the meteorological performance parameters of the historical occurrence periods of each tornado.

[0050] The meteorological fluctuation parameters of the monitoring area include the temperature fluctuation extremes, humidity fluctuation extremes, wind speed fluctuation extremes and air pressure fluctuation extremes in the monitoring area during a preset historical period. A comprehensive analysis is performed on the meteorological fluctuation parameters of the monitoring area to obtain a comprehensive meteorological fluctuation evaluation value of the monitoring area, and a severe convective activity control value is obtained by matching the comprehensive meteorological fluctuation evaluation value of the monitoring area.

[0051] It needs to be explained that the strong convective activity control value is obtained by matching the comprehensive evaluation value of the meteorological fluctuations in the monitoring area. The specific process is to match the comprehensive evaluation value of the meteorological fluctuations in the monitoring area with the strong convective activity control values ​​corresponding to each interval of the comprehensive evaluation value of meteorological fluctuations stored in the tornado intelligent identification database, and statistically calculate the strong convective activity control value corresponding to the interval in which the comprehensive evaluation value of the meteorological fluctuations in the monitoring area is located to obtain the strong convective activity control value, which is less than the first indicator value of strong convective activity.

[0052] It should be added that the comprehensive assessment value of meteorological fluctuations in the monitoring area is used to comprehensively quantify the instability and degree of change of meteorological conditions in the monitoring area during a preset historical period.

[0053] It should be noted that temperature sensors, humidity sensors, anemometers and pressure sensors can be used to measure the temperature, humidity, wind speed and air pressure of a preset historical period respectively, and a data logger can be used to continuously record the data, and the temperature fluctuation extremes, humidity fluctuation extremes, wind speed fluctuation extremes and air pressure fluctuation extremes in the monitored area can be obtained through analysis.

[0054] In this embodiment, the comprehensive assessment value of the meteorological fluctuations in the monitoring area can be obtained by the following analysis method, and the specific analysis conditions are as follows:

[0055] ;

[0056] Where, Indicates the comprehensive assessment value of meteorological fluctuations in the monitoring area, Indicates the extreme temperature fluctuation in the monitoring area, Indicates the weight factor corresponding to the set temperature fluctuation extreme value, Indicates the extreme humidity fluctuation in the monitoring area, Indicates the weight factor corresponding to the set humidity fluctuation extreme value, Indicates the extreme value of wind speed fluctuation in the monitoring area, Indicates the weight factor corresponding to the set extreme value of wind speed fluctuation, Indicates the extreme value of air pressure fluctuation in the monitoring area, It represents the weight factor corresponding to the set extreme value of air pressure fluctuation, and e represents a natural constant.

[0057] It should be noted that the comprehensive assessment value of meteorological fluctuations in the monitoring area is analyzed and matched with the control value of strong convective activity, and then a strong convection identification model for the monitoring area is constructed. This can adjust the identification interval of tornado-prone areas and thus improve the accuracy of identifying tornado-prone areas.

[0058] It should be added that, in this embodiment, the weight factors corresponding to the preset temperature fluctuation extremes, the weight factors corresponding to the humidity fluctuation extremes, the weight factors corresponding to the wind speed fluctuation extremes, and the weight factors corresponding to the air pressure fluctuation extremes are obtained from the tornado intelligent identification database.

[0059] It should be explained that the weight factors corresponding to the temperature fluctuation extremes, humidity fluctuation extremes, wind speed fluctuation extremes and air pressure fluctuation extremes are respectively used to correct the degree of influence of the temperature fluctuation extremes, humidity fluctuation extremes, wind speed fluctuation extremes and air pressure fluctuation extremes in the monitoring area on the comprehensive evaluation value of the meteorological fluctuations in the monitoring area. These corresponding relationships are pre-set mapping relationships. For example, the pre-set mapping relationship can match the input real-time meteorological fluctuation parameters of the monitoring area with the weight factors in the tornado intelligent identification database to obtain the weight factors corresponding to the meteorological fluctuation parameters of the monitoring area. The meteorological fluctuation parameters of the monitoring area correspond one-to-one to the weight factors in the tornado intelligent identification database. The temperature fluctuation extremes, humidity fluctuation extremes, wind speed fluctuation extremes and air pressure fluctuation extremes in the monitoring area are respectively matched with the weight factors in the tornado intelligent identification database to obtain the weight factors corresponding to the temperature fluctuation extremes, humidity fluctuation extremes, wind speed fluctuation extremes and air pressure fluctuation extremes in the monitoring area.

[0060] In this implementation scheme, there is a correlation between the extreme temperature fluctuations, humidity fluctuations, wind speed fluctuations and air pressure fluctuations in the monitoring area, and they do not exist independently. For example, temperature fluctuations may be accompanied by changes in humidity, and an increase in wind speed may affect the distribution of temperature and air pressure, while changes in air pressure may cause fluctuations in temperature and humidity. Comprehensive analysis can be used to obtain a comprehensive assessment value of meteorological fluctuations in the monitoring area, which can analyze more subtle and complex meteorological changes.

[0061] The meteorological performance parameters of each tornado historical period include the average temperature, average humidity, maximum wind speed and average air pressure of each tornado historical period. Based on the meteorological performance parameters of each tornado historical period, the severe convective activity index value of each tornado historical period is obtained.

[0062] It should be added that the severe convective activity index values ​​during the historical periods of tornado occurrence are used to comprehensively quantify the comprehensive characteristics of meteorological conditions during the historical tornado occurrence periods.

[0063] It should be noted that temperature sensors, humidity sensors, anemometers and pressure sensors can be used to measure the temperature, humidity, wind speed and air pressure during the historical periods of each tornado, and meteorological observation equipment can be used to record the measured data. The average temperature, average humidity, maximum wind speed and average air pressure during the historical periods of each tornado can be obtained by analysis.

[0064] In this embodiment, the severe convective activity index values ​​for each historical tornado occurrence period can be obtained by the following analysis method, and the specific analysis conditions are as follows:

[0065] ;

[0066] Where, represents the severe convective activity index value during the historical period of the kth tornado, represents the average temperature during the k-th tornado historical period, Indicates the compensation factor corresponding to the set average temperature, represents the average humidity during the k-th tornado historical period, Indicates the compensation factor corresponding to the set average humidity, represents the maximum wind speed during the kth tornado historical period, Indicates the compensation factor corresponding to the set maximum wind speed, represents the average air pressure during the historical period of the kth tornado, It represents the compensation factor corresponding to the set average air pressure, and k represents the number of the historical period of occurrence of each tornado. , represents the total number of tornadoes in historical periods, and e represents a natural constant.

[0067] It should be noted that the increase in average temperature is conducive to the enhancement of convective activity. The larger the value of the severe convective activity index, the increase in humidity means that there is more water vapor in the air, which provides more potential energy for convective activity. The larger the value of the severe convective activity index, the increase in maximum wind speed usually indicates the presence of stronger wind shear in the atmosphere. Stronger wind shear can help form rotational motion in the clouds and promote the formation of tornadoes. The value of the severe convective activity index increases. Tornadoes and severe convective activities often appear in low-pressure areas. Lower air pressure may be related to the formation of severe convection and tornadoes. The lower the average air pressure, the larger the value of the severe convective activity index.

[0068] It should be added that, in this embodiment, the compensation factors corresponding to the preset average temperature, average humidity, maximum wind speed and average air pressure are obtained from the tornado intelligent identification database.

[0069] It should be explained that the compensation factors corresponding to the average temperature, average humidity, maximum wind speed and average air pressure are respectively used to correct the degree of influence of the average temperature, average humidity, maximum wind speed and average air pressure of each tornado historical period on the severe convective activity index value. These corresponding relationships are pre-set mapping relationships. For example, the pre-set mapping relationship can match the input real-time meteorological performance parameters of each tornado historical period with the compensation factors in the tornado intelligent identification database to obtain the compensation factors corresponding to the meteorological performance parameters of each tornado historical period. The meteorological performance parameters of each tornado historical period correspond to the compensation factors in the tornado intelligent identification database one by one. The average temperature, average humidity, maximum wind speed and average air pressure of each tornado historical period are respectively matched with the compensation factors in the tornado intelligent identification database to obtain the compensation factors corresponding to the average temperature, average humidity, maximum wind speed and average air pressure of each tornado historical period.

[0070] In this implementation plan, the average temperature, average humidity, maximum wind speed and average air pressure during the historical periods of each tornado occurrence are correlated and do not exist independently. For example, an increase in average temperature may increase water evaporation, thereby affecting the average humidity. Changes in average humidity will affect cloud formation and precipitation probability, which together with the maximum wind speed may trigger the formation of a tornado. The maximum wind speed not only reflects the intensity of the wind, but also indicates the intensity of atmospheric movement. Comprehensive analysis can obtain the severe convective activity index value, which can be used to analyze the environmental conditions when the tornado occurred, providing a data basis for the establishment of an early warning system and disaster prevention.

[0071] The lowest value of the severe convective activity index during the historical period of each tornado occurrence was extracted and recorded as the first index value of severe convective activity. The second index value of severe convective activity was obtained by subtracting the strong convective activity control value from the first index value of severe convective activity.

[0072] According to the second indicator value of severe convective activity, a first interval for severe convection identification and a second interval for severe convection identification are constructed, and a severe convection identification model for the monitoring area is established based on the first interval for severe convection identification and the second interval for severe convection identification.

[0073] It should be added that the first interval for severe convection identification is , the second interval of severe convection identification is .

[0074] In this embodiment, the mathematical formula of the severe convection identification model in the monitoring area is:

[0075] ;

[0076] Where, represents the severe convective activity index value of the j-th tornado monitoring sub-area, represents the second index value of severe convective activity, j represents the number of each tornado monitoring sub-area, m, Represents the total number of tornado watch sub-areas.

[0077] It should be noted that all tornado-prone monitoring sub-areas in the first severe convection identification interval and the second severe convection identification interval are counted and recorded as tornado-prone monitoring sub-areas.

[0078] Specifically, each tornado-prone monitoring sub-area is obtained, and the specific process is: the multi-source real-time data includes the temperature, humidity, wind speed and air pressure of each tornado monitoring sub-area.

[0079] It should be added that the temperature, humidity, wind speed and air pressure of each tornado monitoring sub-area are input into the analysis method of the severe convective activity index value to obtain the severe convective activity index value of each tornado monitoring sub-area.

[0080] Based on multi-source real-time data, the strong convective activity index value of each tornado monitoring sub-area is processed and input into the strong convection identification model of the monitoring area to obtain all tornado-prone monitoring sub-areas, which are recorded as tornado-prone monitoring sub-areas.

[0081] In this implementation scheme, the strong convective activity index value of each tornado monitoring sub-area is input into the strong convection identification model of the monitoring area. If the corresponding output result of a tornado monitoring sub-area is 1, the tornado monitoring sub-area is marked as a tornado-prone monitoring sub-area, otherwise the tornado monitoring sub-area is marked as a normal monitoring sub-area. The tornado-prone monitoring sub-area is analyzed according to the strong convection identification model of the monitoring area, so as to more accurately predict and warn of strong convective weather events such as tornadoes, reduce false alarms and missed alarms, and improve the reliability of the early warning system.

[0082] It should be noted that the occurrence of tornadoes is closely related to specific meteorological conditions and climatic environment. The occurrence of tornadoes is usually the result of severe convective weather. By analyzing the meteorological performance parameters of historical tornado occurrence periods, especially parameters related to severe convective weather, it can help identify areas with a higher probability of tornado occurrence.

[0083] Specifically, radar signals of each tornado-prone monitoring sub-area are obtained for preprocessing configuration. The specific process is: statistics are collected on radar performance anomaly data of each tornado-prone monitoring sub-area, and the radar performance anomaly data of each tornado-prone monitoring sub-area include radar transmission power, radar scanning direction deviation, radar signal-to-noise ratio, number of radar signal distortions and number of failures of radar electronic equipment in each tornado-prone monitoring sub-area.

[0084] It should be noted that the radar transmission power of each tornado-prone monitoring sub-area is directly read through the built-in power meter of the radar system, the radar scanning direction deviation of each tornado-prone monitoring sub-area can be measured using the sensors and calibration equipment in the radar control system, and the radar signal-to-noise ratio of each tornado-prone monitoring sub-area refers to the ratio of the signal strength received by the radar to the noise strength, which can be measured using the signal processor of the radar receiving system. The number of radar signal distortions in each tornado-prone monitoring sub-area is recorded by the radar signal processor, and the number of failures of the radar electronic equipment in each tornado-prone monitoring sub-area can be obtained through system log records.

[0085] Based on the radar performance anomaly data of each tornado-prone monitoring sub-area, radar performance anomaly assessment values ​​for each tornado-prone monitoring sub-area are obtained through processing. The radar performance anomaly assessment values ​​for each tornado-prone monitoring sub-area are used to comprehensively quantify the performance reliability of the radar system.

[0086] In this embodiment, the radar performance anomaly assessment value of each tornado-prone monitoring sub-area can be obtained by the following analysis method, and the specific analysis conditions are as follows:

[0087] ;

[0088] Where, represents the radar performance anomaly evaluation value of the i-th tornado-prone monitoring sub-area, represents the radar transmission power of the i-th tornado-prone monitoring sub-area, represents the reference radar transmission power of the set i-th tornado-prone monitoring sub-area, represents the deviation of the radar scanning direction of the i-th tornado-prone monitoring sub-area, Indicates the weight factor corresponding to the set radar scanning direction deviation, represents the radar signal-to-noise ratio of the i-th tornado-prone monitoring sub-area, Indicates the weight factor corresponding to the set radar signal-to-noise ratio, represents the number of radar signal distortions in the i-th tornado-prone monitoring sub-area, Indicates the weight factor corresponding to the set number of radar signal distortions, represents the number of failures of radar electronic equipment in the i-th tornado-prone monitoring sub-area, It represents the weight factor corresponding to the number of failures of the radar electronic equipment, i represents the number of each tornado-prone monitoring sub-area, , Represents the total number of tornado-prone monitoring sub-areas.

[0089] It should be added that abnormal radar transmission power (too low or too high) may affect the radar's detection range and accuracy. The larger the radar performance abnormality evaluation value, the greater the radar scanning direction deviation, the number of radar signal distortions and the number of radar electronic equipment failures, indicating that the radar reliability is lower. The larger the radar performance abnormality evaluation value, the lower the radar signal-to-noise ratio, the fewer effective signals received by the radar, the more noise, and the larger the radar performance abnormality evaluation value.

[0090] It should be added that, in this embodiment, the weight factor corresponding to the preset radar scanning direction deviation, the weight factor corresponding to the radar signal-to-noise ratio, the weight factor corresponding to the number of radar signal distortions, and the weight factor corresponding to the number of failures of the radar electronic equipment are obtained from the tornado intelligent identification database.

[0091] It should be explained that the weight factors corresponding to the radar scanning direction deviation, radar signal-to-noise ratio, radar signal distortion times and radar electronic equipment failure times are respectively used to correct the degree of influence of the radar scanning direction deviation, radar signal-to-noise ratio, radar signal distortion times and radar electronic equipment failure times of each tornado-prone monitoring sub-area on the radar performance anomaly evaluation value. These corresponding relationships are pre-set mapping relationships. For example, the pre-set mapping relationship can match the input real-time radar performance anomaly data of each tornado-prone monitoring sub-area with the weight factors in the tornado intelligent identification database to obtain The weight factors corresponding to the radar performance anomaly data of each tornado-prone monitoring sub-area are matched one-to-one with the weight factors in the tornado intelligent identification database. The radar scanning direction deviation, radar signal-to-noise ratio, number of radar signal distortions, and number of failures of radar electronic equipment of each tornado-prone monitoring sub-area are matched with the weight factors in the tornado intelligent identification database respectively to obtain the weight factors corresponding to the radar scanning direction deviation, radar signal-to-noise ratio, number of radar signal distortions, and number of failures of radar electronic equipment of each tornado-prone monitoring sub-area.

[0092] In this implementation, the radar transmit power, radar scan direction deviation, radar signal-to-noise ratio, number of radar signal distortions, and number of radar electronic equipment failures in each tornado-prone monitoring sub-area are correlated and do not exist independently. For example, radar transmit power directly affects radar signal strength, which in turn affects radar detection range and clarity. However, excessive transmit power may induce excessive noise, resulting in a decrease in the signal-to-noise ratio. Therefore, the radar signal-to-noise ratio is affected by power adjustment. The deviation of the radar scan direction affects the radar's target positioning accuracy. Excessive deviation may cause errors in the radar detection direction, thereby affecting the real-time monitoring capability of tornadoes. The number of radar signal distortions and the number of electronic equipment failures are related to the stability and performance degradation of the equipment. Frequent distortion and failures usually indicate unstable radar performance and may not be able to provide accurate warning information. Comprehensive analysis of the radar performance anomaly assessment values ​​for each tornado-prone monitoring sub-area can better reflect the overall performance and actual performance of the radar in the tornado-prone monitoring area, effectively identify potential radar issues, and provide more accurate data for subsequent optimization.

[0093] The radar performance evaluation influencing factors of each tornado-prone monitoring sub-area are obtained by matching the severe convective activity index values ​​of each tornado-prone monitoring sub-area.

[0094] It should be explained that the radar performance evaluation influencing factors of each tornado-prone monitoring sub-area are obtained by matching. The specific process is to match the strong convective activity index value of each tornado-prone monitoring sub-area with the radar performance evaluation interference factors corresponding to the strong convective activity index value intervals stored in the tornado intelligent identification database, and count the radar performance evaluation interference factors corresponding to the strong convective activity index value intervals of each tornado-prone monitoring sub-area, which are recorded as the radar performance evaluation influencing factors of each tornado-prone monitoring sub-area.

[0095] It should be noted that the radar performance evaluation influencing factors of each tornado-prone monitoring sub-area are obtained based on multi-source real-time data matching, which can reflect the impact of meteorological performance data of each tornado-prone monitoring sub-area on radar data quality. If the radar data quality is evaluated without considering the impact of meteorological performance data on radar data quality, it may lead to inaccurate radar data analysis. Therefore, it is necessary to correct the radar performance abnormality evaluation value according to the radar performance evaluation influencing factors.

[0096] Based on the radar performance anomaly assessment values ​​of each tornado-prone monitoring sub-area and combined with the radar performance assessment influencing factors of each tornado-prone monitoring sub-area, a comprehensive analysis is conducted to obtain the radar data quality benchmark values ​​of each tornado-prone monitoring sub-area. The radar data quality benchmark values ​​of each tornado-prone monitoring sub-area are used to comprehensively quantify the radar data quality.

[0097] It should be added that the product of the radar performance anomaly assessment value of each tornado-prone monitoring sub-area and the radar performance assessment influencing factor of each tornado-prone monitoring sub-area is recorded as the radar data quality baseline value of each tornado-prone monitoring sub-area, which helps to reduce the impact of meteorological conditions on radar performance evaluation.

[0098] The radar data processing parameters of each tornado-prone monitoring sub-area are obtained according to the radar data quality benchmark value matching, and the radar signal is preprocessed and configured with the corresponding radar data processing parameters.

[0099] It should be explained that the radar data processing parameters of each tornado-prone monitoring sub-area are obtained by matching. The specific process is to match the radar data quality reference value of each tornado-prone monitoring sub-area with the radar data processing parameters corresponding to the radar data quality reference value intervals stored in the tornado intelligent identification database, and count the radar data processing parameters corresponding to the intervals in which the radar data quality reference values ​​of each tornado-prone monitoring sub-area are located, and record them as the radar data processing parameters of each tornado-prone monitoring sub-area.

[0100] It should be added that the radar data processing parameters for each tornado-prone monitoring sub-area include data transmission frequency and radar receiving signal gain. Preprocessing the radar signal with the corresponding radar data processing parameters helps reduce errors and noise in radar data measurement and improve the accuracy of radar data processing.

[0101] It should be added that the larger the radar data quality baseline value of each tornado-prone monitoring sub-area, the more abnormal the radar data quality in each tornado-prone monitoring sub-area. It also means that a higher data transmission frequency is required to transmit more verification or correction information to improve data quality. The improvement of the radar reception signal is to compensate for the signal loss during the transmission process and improve the clarity of the received signal. Therefore, the larger the radar data quality baseline value, the greater the matching data transmission frequency and radar reception signal gain.

[0102] Specifically, the radar signal parameters of each tornado-prone monitoring sub-area in the central processing platform are retrieved. The specific process is: according to the radar data quality benchmark value of each tornado-prone monitoring sub-area, the radar data analysis influencing factor of each tornado-prone monitoring sub-area is obtained.

[0103] It should be explained that the radar data analysis influencing factors of each tornado-prone monitoring sub-area are obtained by matching. The specific process is to match the radar data quality benchmark value of each tornado-prone monitoring sub-area with the radar data analysis interference factor corresponding to each radar data quality benchmark value interval stored in the tornado intelligent identification database, and count the radar data analysis interference factors corresponding to the interval in which the radar data quality benchmark value of each tornado-prone monitoring sub-area is located, and record them as the radar data analysis influencing factors of each tornado-prone monitoring sub-area. This helps to reduce the error of radar data measurement and thus improve the accuracy of tornado identification.

[0104] It should be noted that the radar data analysis impact factor is obtained by matching the radar data quality benchmark value of each tornado-prone monitoring sub-area, which can reflect the impact of radar data analysis anomalies in each tornado-prone monitoring sub-area on radar operation anomalies. If the radar signal is measured without considering the impact of radar data quality on the radar operation signal anomaly assessment, it may lead to inaccurate radar signal measurement, and then lead to inaccurate tornado identification. Therefore, it is necessary to correct the radar operation signal anomaly assessment value based on the radar data analysis impact factor.

[0105] The radar signal parameters of each tornado-prone monitoring sub-area include the radar echo reflectivity, the average descent rate of the radar echo, the maximum offset of the radar echo frequency, and the number of occurrences of hook echoes on the radar image of each tornado-prone monitoring sub-area. Based on the radar signal parameters of each tornado-prone monitoring sub-area, a radar operation signal anomaly assessment value of each tornado-prone monitoring sub-area is obtained through processing. The radar operation signal anomaly assessment value of each tornado-prone monitoring sub-area is used to comprehensively quantify the signal anomaly conditions of the radar during the tornado monitoring process.

[0106] It should be noted that the radar echo reflectivity of each tornado-prone monitoring sub-area refers to the signal strength of the radar wave reflected when it encounters a meteorological target (such as precipitation particles); the average descent rate of the radar echo of each tornado-prone monitoring sub-area refers to the average descent speed of the precipitation particles in the vertical direction; the maximum offset of the radar echo frequency of each tornado-prone monitoring sub-area refers to the extreme value of the radar echo frequency change caused by the movement of precipitation particles; the number of hook echoes on the radar image of each tornado-prone monitoring sub-area refers to the number of hook-shaped echoes on the radar image. The radar echo reflectivity, average descent rate of the radar echo, maximum offset of the radar echo frequency and the number of hook echoes on the radar image of each tornado-prone monitoring sub-area can be measured using a weather radar system.

[0107] Specifically, the radar operational signal anomaly assessment value for each tornado-prone monitoring sub-area can be obtained using the following analysis method. The specific analysis conditions are as follows:

[0108] ;

[0109] Where, represents the radar operation signal anomaly assessment value of the i-th tornado-prone monitoring sub-area, represents the radar echo reflectivity of the i-th tornado-prone monitoring sub-area, Indicates the correction factor corresponding to the set radar echo reflectivity, represents the average descent rate of radar echoes in the i-th tornado-prone monitoring sub-area, Indicates the correction factor corresponding to the set average descent rate of radar echo. represents the maximum offset of the radar echo frequency in the i-th tornado-prone monitoring sub-area, Indicates the correction factor corresponding to the maximum offset of the set radar echo frequency. represents the number of hook echoes on the radar image of the i-th tornado-prone monitoring sub-area, It represents the correction factor corresponding to the number of hook echoes on the radar image, i represents the number of each tornado-prone monitoring sub-area, , represents the total number of tornado-prone monitoring sub-areas, and e represents a natural constant.

[0110] It should be added that, in this embodiment, the correction factor corresponding to the preset radar echo reflectivity, the correction factor corresponding to the average descent rate of the radar echo, the correction factor corresponding to the maximum offset of the radar echo frequency, and the correction factor corresponding to the number of occurrences of hook echoes on the radar image are obtained from the tornado intelligent identification database.

[0111] It should be explained that the correction factors corresponding to the radar echo reflectivity, the average descent rate of the radar echo, the maximum offset of the radar echo frequency and the number of occurrences of hook echoes on the radar image are respectively used to correct the degree of influence of the radar echo reflectivity, the average descent rate of the radar echo, the maximum offset of the radar echo frequency and the number of occurrences of hook echoes on the radar image of each tornado-prone monitoring sub-area on the radar operation signal abnormality assessment value. These corresponding relationships are pre-set mapping relationships. For example, the pre-set mapping relationship can match the input real-time radar signal parameters of each tornado-prone monitoring sub-area with the correction factors in the tornado intelligent identification database. , obtain the correction factors corresponding to the radar signal parameters of each tornado-prone monitoring sub-area, and the radar signal parameters of each tornado-prone monitoring sub-area correspond one-to-one with the correction factors in the tornado intelligent identification database. The radar echo reflectivity, average descent rate of radar echo, maximum offset of radar echo frequency, and the number of occurrences of hook echoes on the radar image of each tornado-prone monitoring sub-area are matched with the correction factors in the tornado intelligent identification database respectively, and the correction factors corresponding to the radar echo reflectivity, average descent rate of radar echo, maximum offset of radar echo frequency, and the number of occurrences of hook echoes on the radar image of each tornado-prone monitoring sub-area are obtained.

[0112] In this implementation plan, the radar echo reflectivity, average radar echo descent rate, maximum radar echo frequency offset, and number of hook echoes on the radar image in each tornado-prone monitoring sub-area are correlated and do not exist independently. For example, the radar echo reflectivity is generally related to thunderstorm intensity and potential tornado activity. A higher radar echo reflectivity often indicates a stronger storm. The average radar echo descent rate and maximum radar echo frequency offset reflect the vertical movement of the airflow and wind speed changes, which are crucial for the formation and intensity assessment of tornadoes. The number of hook echoes on the radar image generally indicates the potential for tornadoes to occur. An increase in the number of hook echoes on the radar image is generally related to strong tornado activity. Comprehensive analysis results in the radar operating signal anomaly assessment value for each tornado-prone monitoring sub-area, which can more comprehensively and accurately assess the degree of radar signal anomaly, and thus assess the activity intensity of tornadoes, contributing to more effective early warning and prevention in tornado-prone areas.

[0113] According to the radar operation signal anomaly assessment value of each tornado-prone monitoring sub-area, combined with the radar data analysis influencing factors of each tornado-prone monitoring sub-area, a comprehensive analysis is conducted to obtain the radar anomaly comprehensive index value of each tornado-prone monitoring sub-area.

[0114] It should be added that the product of the radar operation signal anomaly assessment value of each tornado-prone monitoring sub-area and the radar data analysis influencing factor of each tornado-prone monitoring sub-area is recorded as the radar anomaly comprehensive index value of each tornado-prone monitoring sub-area, which can more accurately evaluate the degree of radar signal anomaly in each tornado-prone monitoring sub-area.

[0115] Specifically, a comprehensive analysis is performed to obtain radar anomaly recognition results for each tornado-prone monitoring sub-area. The specific process is as follows: the radar anomaly recognition results for each tornado-prone monitoring sub-area include executing radar anomaly warning and not executing radar anomaly warning.

[0116] The radar anomaly comprehensive index value of each tornado-prone monitoring sub-area is compared with the set radar anomaly comprehensive index threshold, and the radar signal anomaly monitoring sub-area is obtained through analysis.

[0117] It needs to be explained that the analysis results in each radar signal anomaly monitoring sub-area. The specific process is that if the radar anomaly comprehensive index value of a tornado-prone monitoring sub-area is lower than or equal to the set radar anomaly comprehensive index threshold, the radar anomaly identification result of the tornado-prone monitoring sub-area is marked as not executing the radar anomaly warning; otherwise, the radar anomaly identification result of the tornado-prone monitoring sub-area is marked as executing the radar anomaly warning, and the tornado-prone monitoring sub-area is recorded as the radar signal anomaly monitoring sub-area. In this way, all radar signal anomaly monitoring sub-areas are obtained and recorded as each radar signal anomaly monitoring sub-area, which can more accurately identify the areas that need warning, and help reduce false alarms and missed alarms.

[0118] Specifically, the radar configuration of each radar signal anomaly monitoring sub-area is adjusted. The specific process is: extract the radar anomaly comprehensive index value of each radar signal anomaly monitoring sub-area, analyze and obtain the radar configuration parameters of each radar signal anomaly monitoring sub-area, and adjust the radar configuration of each radar signal anomaly monitoring sub-area according to the radar configuration parameters of each radar signal anomaly monitoring sub-area.

[0119] It should be noted that the radar configuration parameters of each radar signal anomaly monitoring sub-area are obtained by analysis. The specific process is to match the radar anomaly comprehensive index value of each radar signal anomaly monitoring sub-area with the radar configuration parameters corresponding to the radar anomaly comprehensive index value interval stored in the tornado intelligent identification database, and count the radar configuration parameters corresponding to the interval of the radar anomaly comprehensive index value of each radar signal anomaly monitoring sub-area, and record them as the radar configuration parameters of each radar signal anomaly monitoring sub-area.

[0120] It should be explained that the radar configuration parameters of each radar signal anomaly monitoring sub-area include the radar beam width and the radar signal strength.

[0121] It should be added that the larger the radar anomaly comprehensive index value of each radar signal anomaly monitoring sub-area, the more abnormal the radar signal of each tornado-prone monitoring sub-area, and the greater the possibility of a tornado. At the same time, the lower the radar beam width means the higher the radar resolution, and the greater the signal strength usually means the stronger the signal reflected back to the radar. Therefore, the larger the radar anomaly comprehensive index value, the higher the resolution and stronger the signal strength required for further identification, the smaller the matched radar beam width and the greater the radar signal strength.

[0122] The radar-based intelligent tornado identification method also includes: collecting statistics on real-time data of each tornado-prone monitoring sub-area for storage and management. The specific process is: based on the radar anomaly comprehensive index value of each tornado-prone monitoring sub-area, matching the monitoring data compression ratio of each tornado-prone monitoring sub-area, and storing the data of each tornado-prone monitoring sub-area in the cloud platform according to the monitoring data compression ratio of each tornado-prone monitoring sub-area.

[0123] It should be added that based on the comprehensive radar anomaly index values ​​of each tornado-prone monitoring sub-area, the monitoring data compression ratio of each tornado-prone monitoring sub-area is matched, which helps to evaluate the importance of monitoring data according to the degree of radar anomaly, thereby improving the accuracy of important data transmission, reducing the amount of data that needs to be transmitted, and lowering network bandwidth requirements. It helps to ensure that while reducing the amount of data, key information is not lost, and the integrity and availability of data transmission are maintained.

[0124] It should be noted that, based on the radar anomaly comprehensive index value of each tornado-prone monitoring sub-area, the monitoring data compression ratio of each tornado-prone monitoring sub-area is matched and obtained. The specific process is to match the radar operation signal anomaly evaluation value of each tornado-prone monitoring sub-area with the monitoring data compression ratio corresponding to the interval of each radar operation signal anomaly evaluation value stored in the tornado intelligent identification database, and count the monitoring data compression ratio corresponding to the interval of the radar operation signal anomaly evaluation value of each tornado-prone monitoring sub-area, which is recorded as the monitoring data compression ratio of each tornado-prone monitoring sub-area. According to the monitoring data compression ratio of each tornado-prone monitoring sub-area, the data of each tornado-prone monitoring sub-area is transmitted and stored in the cloud platform.

[0125] It should be noted that the radar-based tornado intelligent identification method and system also include a tornado intelligent identification database for storing the first parameter set, the second parameter set, the third parameter set and the fourth parameter set obtained by analyzing historical data.

[0126] The first parameter set includes the severe convective activity control value corresponding to each meteorological fluctuation comprehensive assessment value interval, the weight factor corresponding to the temperature fluctuation extreme value, the weight factor corresponding to the humidity fluctuation extreme value, the weight factor corresponding to the wind speed fluctuation extreme value, the weight factor corresponding to the pressure fluctuation extreme value, the compensation factor corresponding to the average temperature, the compensation factor corresponding to the average humidity, the compensation factor corresponding to the maximum wind speed and the compensation factor corresponding to the average pressure.

[0127] The second parameter set includes the reference radar transmission power of each tornado-prone monitoring sub-area, the weight factor corresponding to the radar scanning direction deviation, the weight factor corresponding to the radar signal-to-noise ratio, the weight factor corresponding to the number of radar signal distortions, the weight factor corresponding to the number of radar electronic equipment failures, the radar performance evaluation interference factor corresponding to each severe convective activity index value interval, and the radar data processing parameters corresponding to each radar data quality benchmark value interval.

[0128] The third parameter set includes the radar data analysis interference factor corresponding to each radar data quality benchmark value interval, the correction factor corresponding to the radar echo reflectivity, the correction factor corresponding to the average descent rate of the radar echo, the correction factor corresponding to the maximum offset of the radar echo frequency, the correction factor corresponding to the number of occurrences of hook echoes on the radar image, and the threshold value of the radar anomaly comprehensive indicator.

[0129] The fourth parameter set includes the radar configuration parameters corresponding to the abnormal comprehensive index value interval of each radar and the monitoring data compression ratio corresponding to the abnormal evaluation value interval of each radar operation signal.

[0130] like Figure 2 The second aspect of the present invention provides a radar-based tornado intelligent identification system, including: a tornado-prone area identification module, which is used to statistically process multi-source historical data of the monitoring area to obtain a strong convection identification model of the monitoring area, collect multi-source real-time data and input it into the strong convection identification model of the monitoring area to obtain each tornado-prone monitoring sub-area.

[0131] The preprocessing configuration module is used to obtain the radar signals of each tornado-prone monitoring sub-area for preprocessing configuration, and after the preprocessing configuration, transmit the radar signals of each tornado-prone monitoring sub-area to the central processing platform.

[0132] The identification result analysis module is used to retrieve the radar signal parameters of each tornado-prone monitoring sub-area in the central processing platform, and comprehensively analyze to obtain the radar anomaly identification results of each tornado-prone monitoring sub-area.

[0133] The signal anomaly identification module is used to obtain statistics of each radar signal anomaly monitoring sub-area based on the radar anomaly identification results of each tornado-prone monitoring sub-area, adjust the radar configuration of each radar signal anomaly monitoring sub-area, and then identify tornadoes based on the configured radar.

[0134] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0135] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, numerous modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention.

Claims

1. A radar-based tornado intelligent identification method, characterized in that: include: The multi-source historical data of the monitoring area is processed to obtain the severe convection identification model of the monitoring area. The multi-source real-time data is collected and input into the severe convection identification model of the monitoring area to obtain the monitoring sub-areas prone to tornadoes. Acquire radar signals from each tornado-prone monitoring sub-area for pre-processing and configuration, and transmit the radar signals from each tornado-prone monitoring sub-area to a central processing platform after the pre-processing and configuration; Retrieve the radar signal parameters of each tornado-prone monitoring sub-area from the central processing platform, and conduct a comprehensive analysis to obtain radar anomaly identification results for each tornado-prone monitoring sub-area; Based on the radar anomaly identification results of each tornado-prone monitoring sub-area, the radar signal anomaly monitoring sub-area is statistically obtained, and the radar configuration of each radar signal anomaly monitoring sub-area is adjusted, and then tornado identification is performed based on the configured radar.

2. The radar-based tornado intelligent identification method according to claim 1, characterized in that: The multi-source historical data of the statistical monitoring area is processed to obtain a severe convection identification model of the monitoring area. The specific process is as follows: The multi-source historical data of the monitoring area includes meteorological fluctuation parameters of the monitoring area and meteorological performance parameters of each tornado historical occurrence period; The meteorological fluctuation parameters of the monitoring area include the temperature fluctuation extreme value, humidity fluctuation extreme value, wind speed fluctuation extreme value and air pressure fluctuation extreme value of the monitoring area in the preset historical period, and the meteorological fluctuation parameters of the monitoring area are comprehensively analyzed to obtain the meteorological fluctuation comprehensive evaluation value of the monitoring area, and the severe convective activity control value is matched according to the meteorological fluctuation comprehensive evaluation value of the monitoring area; The meteorological performance parameters of each tornado historical period include the average temperature, average humidity, maximum wind speed and average air pressure of each tornado historical period, and the severe convective activity index value of each tornado historical period is obtained based on the meteorological performance parameters of each tornado historical period; The lowest value of the severe convective activity index during the historical period of each tornado occurrence was extracted and recorded as the first severe convective activity index value. The second severe convective activity index value was obtained by subtracting the strong convective activity control value from the first severe convective activity index value. According to the second indicator value of severe convective activity, a first interval for severe convection identification and a second interval for severe convection identification are constructed, and a severe convection identification model for the monitoring area is established based on the first interval for severe convection identification and the second interval for severe convection identification.

3. The radar-based tornado intelligent identification method according to claim 1, characterized in that: The specific process of obtaining each tornado prone monitoring sub-area is as follows: The multi-source real-time data includes temperature, humidity, wind speed and air pressure of each tornado monitoring sub-area; Based on multi-source real-time data, the strong convective activity index value of each tornado monitoring sub-area is processed and input into the strong convection identification model of the monitoring area to obtain all tornado-prone monitoring sub-areas, which are recorded as tornado-prone monitoring sub-areas.

4. The radar-based tornado intelligent identification method according to claim 1, characterized in that: The radar signals of each tornado-prone monitoring sub-area are obtained and pre-processed. The specific process is as follows: Collecting radar performance anomaly data for each tornado-prone monitoring sub-area, wherein the radar performance anomaly data for each tornado-prone monitoring sub-area includes radar transmit power, radar scanning direction deviation, radar signal-to-noise ratio, number of radar signal distortions, and number of radar electronic equipment failures for each tornado-prone monitoring sub-area; Based on the radar performance anomaly data of each tornado-prone monitoring sub-area, the radar performance anomaly assessment value of each tornado-prone monitoring sub-area is processed to obtain the radar performance anomaly assessment value of each tornado-prone monitoring sub-area, wherein the radar performance anomaly assessment value of each tornado-prone monitoring sub-area is used to comprehensively quantify the performance reliability of the radar system; The radar performance evaluation influencing factors of each tornado-prone monitoring sub-region are obtained by matching the severe convective activity index values ​​of each tornado-prone monitoring sub-region; Based on the radar performance anomaly assessment value of each tornado-prone monitoring sub-region and the radar performance assessment influencing factors of each tornado-prone monitoring sub-region, a comprehensive analysis is performed to obtain a radar data quality benchmark value for each tornado-prone monitoring sub-region. The radar data quality benchmark value for each tornado-prone monitoring sub-region is used to comprehensively quantify radar data quality. The radar data processing parameters of each tornado-prone monitoring sub-area are obtained according to the radar data quality benchmark value matching, and the radar signal is preprocessed and configured with the corresponding radar data processing parameters.

5. The radar-based tornado intelligent identification method according to claim 1, characterized in that: The specific process of retrieving the radar signal parameters of each tornado-prone monitoring sub-area in the central processing platform is as follows: The radar data analysis impact factors of each tornado-prone monitoring sub-area are obtained by matching the radar data quality benchmark values ​​of each tornado-prone monitoring sub-area; The radar signal parameters of each tornado-prone monitoring sub-region include the radar echo reflectivity, the average descent rate of the radar echo, the maximum offset of the radar echo frequency, and the number of occurrences of hook echoes on the radar image of each tornado-prone monitoring sub-region. Based on the radar signal parameters of each tornado-prone monitoring sub-region, a radar operation signal anomaly assessment value of each tornado-prone monitoring sub-region is processed to obtain the radar operation signal anomaly assessment value of each tornado-prone monitoring sub-region. The radar operation signal anomaly assessment value of each tornado-prone monitoring sub-region is used to comprehensively quantify the signal anomaly condition of the radar during the tornado monitoring process. According to the radar operation signal anomaly assessment value of each tornado-prone monitoring sub-area, combined with the radar data analysis influencing factors of each tornado-prone monitoring sub-area, a comprehensive analysis is conducted to obtain the radar anomaly comprehensive index value of each tornado-prone monitoring sub-area.

6. The radar-based tornado intelligent identification method according to claim 5, characterized in that: The comprehensive analysis obtains radar anomaly identification results for each tornado-prone monitoring sub-area. The specific process is as follows: The radar anomaly recognition results of each tornado-prone monitoring sub-area include executing radar anomaly warning and not executing radar anomaly warning; The radar anomaly comprehensive index value of each tornado-prone monitoring sub-area is compared with the set radar anomaly comprehensive index threshold, and the radar signal anomaly monitoring sub-area is obtained through analysis.

7. The radar-based tornado intelligent identification method according to claim 6, characterized in that: The specific process of adjusting the radar configuration of each radar signal abnormality monitoring sub-area is as follows: The radar anomaly comprehensive index value of each radar signal anomaly monitoring sub-area is extracted, the radar configuration parameters of each radar signal anomaly monitoring sub-area are analyzed, and the radar configuration of each radar signal anomaly monitoring sub-area is adjusted according to the radar configuration parameters of each radar signal anomaly monitoring sub-area.

8. The radar-based tornado intelligent identification method according to claim 5, characterized in that: The radar operation signal anomaly assessment value of each tornado-prone monitoring sub-area can be obtained by the following analysis method, and the specific analysis conditions are as follows: ； Where, represents the radar operation signal anomaly assessment value of the i-th tornado-prone monitoring sub-area, represents the radar echo reflectivity of the i-th tornado-prone monitoring sub-area, Indicates the correction factor corresponding to the set radar echo reflectivity, represents the average descent rate of radar echoes in the i-th tornado-prone monitoring sub-area, Indicates the correction factor corresponding to the set average descent rate of radar echo. represents the maximum offset of the radar echo frequency in the i-th tornado-prone monitoring sub-area, Indicates the correction factor corresponding to the maximum offset of the set radar echo frequency. represents the number of hook echoes on the radar image of the i-th tornado-prone monitoring sub-area, It represents the correction factor corresponding to the number of hook echoes on the radar image, i represents the number of each tornado-prone monitoring sub-area, , represents the total number of tornado-prone monitoring sub-areas, and e represents a natural constant.

9. The radar-based tornado intelligent identification method according to claim 1, characterized in that: Also includes: The real-time data of each tornado-prone monitoring sub-area is collected and stored and managed. The specific process is as follows: Based on the radar anomaly comprehensive index value of each tornado-prone monitoring sub-area, the monitoring data compression ratio of each tornado-prone monitoring sub-area is matched and obtained. According to the monitoring data compression ratio of each tornado-prone monitoring sub-area, the data transmission of each tornado-prone monitoring sub-area is stored in the cloud platform.

10. The radar-based tornado intelligent identification system is characterized by: include: The tornado-prone area identification module is used to collect and process multi-source historical data of the monitoring area to obtain a severe convection identification model for the monitoring area. The multi-source real-time data is collected and input into the severe convection identification model of the monitoring area to obtain the tornado-prone monitoring sub-areas. A preprocessing configuration module is used to obtain radar signals from each tornado-prone monitoring sub-area for preprocessing configuration, and transmit the radar signals from each tornado-prone monitoring sub-area to the central processing platform after the preprocessing configuration; The identification result analysis module is used to retrieve the radar signal parameters of each tornado-prone monitoring sub-area in the central processing platform, and conduct comprehensive analysis to obtain the radar anomaly identification results of each tornado-prone monitoring sub-area; The signal anomaly identification module is used to obtain statistics of each radar signal anomaly monitoring sub-area based on the radar anomaly identification results of each tornado-prone monitoring sub-area, adjust the radar configuration of each radar signal anomaly monitoring sub-area, and then identify tornadoes based on the configured radar.

Images