Meteorological early warning analysis system based on tornado model construction analysis

Abstract

The invention relates to the technical field of meteorology, and provides a meteorological early warning analysis system based on tornado model construction analysis, which comprises a server, a meteorological data acquisition module, a tornado model evaluation module and a meteorological early warning analysis module, and is characterized in that the meteorological data acquisition module is used for acquiring meteorological data of at least two sources of a monitoring area; the tornado model evaluation module evaluates the meteorological data of at least two sources based on the meteorological data to form a meteorological evaluation result, and the meteorological early warning analysis module provides early warning analysis according to the analysis result to prompt a manager to perform real-time early warning prompt. Through cooperation between the meteorological data acquisition module and the tornado model evaluation module, meteorological parameters of a monitoring area can be acquired, and it is guaranteed that the whole system has the advantages of being high in early warning accuracy, high in real-time analysis capacity, high in meteorological analysis fusion capacity, good in refined evaluation capacity and high in intelligent degree.

Description

Technical Field

[0001] The invention relates to the technical field of meteorology, and in particular to a meteorological early warning analysis system based on tornado model construction analysis. Background Art

[0002] Severe convective weather in the atmosphere refers to tropical cyclones, tornadoes, etc. There is no consensus on the formation mechanism of the above severe convective weather. Most meteorologists believe that severe convective weather is caused by temperature difference based on thermodynamic theory, experience, estimation or speculation. The existing meteorological warning system is often unable to provide accurate early warning of tornadoes, resulting in lags and limitations in the prevention of tornado disasters. The current tornado warning system mainly relies on macro-monitoring methods such as radar and satellite data, and lacks accurate analysis and model construction of the subtle meteorological conditions (such as humidity, temperature and wind shear) required for the formation of tornadoes, resulting in insufficient accuracy and timeliness of the warning.

[0003] For example, the Chinese patent CN214201812U discloses a tornado occurrence feature detection device based on infrasound. Although it shows that infrasound is related to flow field characteristics, since vibrations close to the frequency of infrasound can be transmitted over a long distance, when a tornado weather process is about to occur, we can detect this change at a long distance, and can use a multi-point measurement and positioning method, but it ignores the fusion of multiple data, resulting in a defect of poor warning effect;

[0004] In addition, the prior art still has the following problems:

[0005] 1. Since tornadoes are highly localized meteorological phenomena with dramatic instantaneous changes, a single data source often cannot provide sufficiently detailed information, resulting in low accuracy of early warnings. In particular, it is difficult for existing technologies to make accurate predictions of the time, location and path of tornado formation.

[0006] 2. Existing systems often fail to track and warn rapidly developing tornadoes in a timely manner, delaying the golden time for disaster prevention.

[0007] 3. The existing early warning system's analysis of these complex meteorological conditions is relatively rough, and it is difficult to dynamically track the subtle changes in these factors. For example, the spatial distribution changes of humidity and temperature, as well as the local development of wind shear, are often difficult to accurately analyze, resulting in the system's inadequate recognition of early signs of tornadoes.

[0008] The present invention is made to solve the common problems in the field such as low warning accuracy, insufficient real-time performance, insufficient meteorological condition analysis, lack of refined wind direction assessment, low intelligence and low comfort of interactive warning, etc. Summary of the invention

[0009] The purpose of the present invention is to propose a meteorological early warning analysis system based on tornado model structural analysis in view of the current shortcomings.

[0010] In order to overcome the shortcomings of the prior art, the present invention adopts the following technical solutions:

[0011] A meteorological early warning analysis system based on tornado model construction analysis, the meteorological early warning analysis system comprises a server, the meteorological early warning analysis system comprises a meteorological data acquisition module, a tornado model evaluation module, and a meteorological early warning analysis module, the server is respectively connected to the meteorological data acquisition module, the tornado model evaluation module and the meteorological early warning analysis module;

[0012] The meteorological data acquisition module is used to collect meteorological data from at least two sources in the monitoring area, the tornado model evaluation module evaluates the meteorological data from at least two sources based on the meteorological data to form a meteorological evaluation result, and the meteorological early warning analysis module provides early warning analysis based on the analysis result to prompt the manager with real-time early warning prompts;

[0013] The tornado model evaluation module includes a data fusion unit, a meteorological evaluation unit and an adjustment and optimization unit. The data fusion unit performs fusion processing on meteorological data from at least two sources to generate a unified tornado model data set. The meteorological evaluation unit evaluates the current meteorology according to the tornado model data set analysis data set to form a meteorological evaluation result. The adjustment and optimization unit adjusts the meteorological evaluation result according to the change trend of the meteorological data.

[0014] The meteorological data from at least two sources include basic meteorological parameters collected by ground meteorological stations, meteorological information acquired by meteorological satellites, and environmental data of the upper atmosphere collected by floating detectors.

[0015] Optionally, the meteorological data acquisition module includes a ground meteorological station acquisition unit, a satellite remote sensing acquisition unit, a meteorological radar acquisition unit and a floating detector acquisition unit, wherein the ground meteorological station acquisition unit acquires real-time basic meteorological parameters in the monitoring area, the satellite remote sensing acquisition unit acquires meteorological information from a meteorological satellite, the meteorological radar acquisition unit uses a meteorological radar to acquire local atmospheric structure and dynamic changes, and the floating detector acquisition unit acquires environmental data of the high-altitude atmosphere;

[0016] The floating probe acquisition unit includes a sounding meteorologist and a weather balloon. The sounding meteorologist is used to collect environmental data of the upper atmosphere, and the weather balloon is used to carry the sounding meteorologist to a set height.

[0017] The environmental data includes temperature, humidity, air pressure, wind speed, and wind direction.

[0018] Optionally, the meteorological radar acquisition unit includes a radar transmitter, a radar receiver and a signal processor, the radar transmitter is used to transmit electromagnetic waves of a specific frequency to the target area, the radar receiver is responsible for receiving the electromagnetic wave reflection signal returned from the atmosphere, and the signal processor is used to process the original echo signal collected by the radar receiver and analyze the echo characteristics to generate meteorological images and data reflecting the atmospheric structure and dynamic changes.

[0019] Optionally, the data fusion unit obtains the meteorological data collected by the meteorological data collection module, and performs fusion processing according to the following steps:

[0020] S1. Data collection: obtaining basic meteorological parameters collected by the ground meteorological station collection unit, meteorological information collected by the satellite remote sensing collection unit, local atmospheric structure and dynamic changes collected by the meteorological radar collection unit, and environmental data of the high-altitude atmosphere collected by the floating detector collection unit;

[0021] S2. Perform time synchronization on the data:

[0022] Align data at different temporal and spatial scales;

[0023] S3, median filtering processing:

[0024] Perform median filtering on the time-synchronized meteorological data;

[0025] S4, data fusion processing: perform arithmetic averaging on the data that pass through the median filter to generate the final fusion result;

[0026] S5. Integrate the fused meteorological data into a unified dataset to form a tornado model dataset analysis dataset.

[0027] Optionally, the meteorological assessment unit obtains the tornado model data set after fusion processing, and calculates the warning judgment index Judge of the monitoring area according to the following formula:

[0028]

[0029] Where △ is the adjustment factor, and its value is determined by the adjustment optimization unit, Z T is the Z-Score standardized value of temperature, Z H is the Z-Score standardized value of humidity, Z P is the Z-Score standardized value of air pressure, Z W is the Z-Score normalized value of wind speed, SI is the wind shear index, and VTG is the vertical temperature gradient;

[0030] If the warning evaluation index Judge of the monitoring area exceeds the meteorological threshold Worn set by the system, the meteorological warning analysis module is triggered to analyze the meteorological warning and prompt the manager with a real-time warning prompt.

[0031] Optionally, the meteorological warning analysis module includes a warning analysis unit, a warning generation unit and a notification unit. The warning analysis unit analyzes the meteorological warning type according to the warning judgment index Judge and the changing trend of meteorological parameters, and identifies the specific meteorological warning type. The warning generation unit generates a matching warning prompt according to the warning type, and the notification unit warns the manager of the warning prompt.

[0032] Optionally, the ground meteorological station collection unit includes a collection platform and a basic meteorological collection component, wherein the collection platform is used to support the basic meteorological collection component, and the basic meteorological collection component obtains real-time basic meteorological parameters in the monitoring area;

[0033] The basic meteorological parameters include temperature, humidity, air pressure, wind speed, wind direction, rainfall and solar radiation data.

[0034] Optionally, the satellite remote sensing acquisition unit includes a command transmitter and a data buffer, the command transmitter sends a data call request to the meteorological satellite, so that the meteorological satellite responds to the data call request and transmits the meteorological information of the monitored area to the data buffer, and the data buffer is used to store the meteorological information of the monitored area.

[0035] Optionally, the notification unit includes a communication transmitter and a text message gateway, wherein the communication transmitter sends the warning information to a mobile device via a cellular network, Wi-Fi and a communication system, and the text message gateway is used to send the warning information to designated managers or mobile devices of the public via text messages.

[0036] Optionally, the basic meteorological collection components include a β temperature sensor, a β humidity sensor, a β air pressure sensor, a β wind speed sensor, a β wind direction sensor, a rain gauge, a solar radiation sensor and a data storage device, the β temperature sensor collects the air temperature of the current monitoring area, the β humidity sensor collects the air humidity of the current monitoring area, the β air pressure sensor collects the air pressure data of the current monitoring area, the β wind speed sensor collects the wind speed data of the current monitoring area, the β wind direction sensor collects the wind direction data of the monitoring area, the rain gauge collects the rainfall in the monitoring area, the solar radiation sensor measures the solar radiation intensity, and the data storage device stores the temperature data collected by the β temperature sensor, the humidity data collected by the β humidity sensor, the air pressure data collected by the β air pressure sensor, the wind speed data collected by the β wind speed sensor, the wind direction data collected by the β wind direction sensor, the rainfall data collected by the rain gauge, and the radiation intensity data of the solar radiation sensor.

[0037] The beneficial effects achieved by the present invention are:

[0038] 1. Through the cooperation between the meteorological data acquisition module and the tornado model evaluation module, the meteorological parameters of the monitoring area can be collected, ensuring that the entire system has the advantages of high early warning accuracy, high real-time analysis capability, strong meteorological analysis fusion capability, good refined evaluation capability, and high intelligence;

[0039] 2. Through the cooperation between the meteorological data collection module and the meteorological early warning analysis module, it is ensured that the meteorological data collected in real time can be directly used in the early warning analysis module, helping to timely identify extreme meteorological events, quickly generate early warning prompts, and ensure that the entire system has the advantages of high real-time early warning capability, strong refined early warning capability, and high intelligent analysis capability;

[0040] 3. Through the cooperation between the tornado model evaluation module and the meteorological warning analysis module, it can ensure that the evaluation results of the tornado model can be directly used for warning analysis, and generate accurate warning prompts by combining the warning evaluation index and the trend of meteorological parameter changes, ensuring that the entire system has the advantages of high evaluation accuracy, good interactive comfort of intelligent warning prompts, and strong comprehensive judgment ability;

[0041] 4. Through the cooperation between the meteorological data acquisition module, the tornado model evaluation module, and the meteorological warning analysis module, the meteorological warning analysis capability becomes faster and more efficient, ensuring that the entire system has the advantages of high warning accuracy, strong real-time analysis capability, strong meteorological analysis fusion capability, good refined evaluation capability and high intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0043] Figure 1 It is an overall block diagram of the present invention.

[0044] Figure 2 It is a block diagram of the data fusion unit, meteorological assessment unit, adjustment and optimization unit and meteorological early warning analysis module of the present invention.

[0045] Figure 3 It is a block diagram of the ground meteorological collection unit, satellite remote sensing collection unit, meteorological radar collection unit, floating detector collection unit, data fusion unit and meteorological evaluation unit of the present invention.

[0046] Figure 4 It is a schematic diagram of the evaluation process of the meteorological evaluation unit of the present invention.

[0047] Figure 5 It is a structural schematic diagram of the floating detector acquisition unit of the present invention.

[0048] Figure 6 for Figure 5 Schematic cross-sectional view at AA in the middle.

[0049] Figure 7 Schematic diagram of application scenario of the weather radar and monitoring area of ​​the present invention.

[0050] Figure 8 It is a structural schematic diagram of the ground weather station acquisition unit of the present invention.

[0051] Explanation of the reference numerals: 1. weather balloon; 2. traction rope; 3. traction seat; 4. traction rod; 5. traction drive mechanism; 6. monitoring area; 7. weather radar; 8. ground weather station collection unit. DETAILED DESCRIPTION

[0052] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual sizes. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0053] Embodiment 1: According to Figure 1, Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 and Figure 8 As shown, this embodiment provides a meteorological warning analysis system based on tornado model construction analysis, the meteorological warning analysis system includes a server, the meteorological warning analysis system also includes a meteorological data acquisition module, a tornado model evaluation module, and a meteorological warning analysis module, the server is respectively connected to the meteorological data acquisition module, the tornado model evaluation module and the meteorological warning analysis module, and the intermediate data and process data of the meteorological data acquisition module, the tornado model evaluation module and the meteorological warning analysis module are stored in the database of the server for query or call;

[0054] The meteorological data acquisition module is used to collect meteorological data from at least two sources in the monitoring area 6, the tornado model evaluation module evaluates the meteorological data from at least two sources based on the meteorological data to form a meteorological evaluation result, and the meteorological early warning analysis module provides early warning analysis based on the analysis result to prompt the manager with real-time early warning prompts;

[0055] The tornado model evaluation module includes a data fusion unit, a meteorological evaluation unit and an adjustment and optimization unit. The data fusion unit performs fusion processing on meteorological data from at least two sources to generate a unified tornado model data set. The meteorological evaluation unit evaluates the current meteorology according to the tornado model data set analysis data set to form a meteorological evaluation result. The adjustment and optimization unit adjusts the meteorological evaluation result according to the change trend of the meteorological data.

[0056] The meteorological data from at least two sources include basic meteorological parameters collected by ground meteorological stations, meteorological information acquired by meteorological satellites, and environmental data of the upper atmosphere collected by floating detectors;

[0057] The meteorological warning analysis system also includes a central processor, which is respectively connected to the meteorological data acquisition module, the tornado model evaluation module, and the meteorological warning analysis module, and centrally controls the meteorological data acquisition module, the tornado model evaluation module, and the meteorological warning analysis module based on the central processor, and stores the control data in the database of the server, so as to ensure that the whole system is more accurate and reliable in meteorological warning analysis;

[0058] In this embodiment, the so-called tornado model data set can be used to analyze the tornado model, thereby determining and evaluating the state of the tornado, and can also be used to issue early warnings for weather conditions other than tornadoes;

[0059] Optionally, the meteorological data acquisition module includes a ground meteorological station acquisition unit 8, a satellite remote sensing acquisition unit, a meteorological radar 7 acquisition unit and a floating detector acquisition unit, wherein the ground meteorological station acquisition unit 8 acquires real-time basic meteorological parameters in the monitoring area 6, the satellite remote sensing acquisition unit acquires meteorological information from a meteorological satellite, the meteorological radar 7 acquisition unit uses the meteorological radar 7 to acquire local atmospheric structure and dynamic changes, and the floating detector acquisition unit acquires environmental data of the high-altitude atmosphere;

[0060] The floating probe acquisition unit includes a sounding meteorologist and a weather balloon 1. The sounding meteorologist is used to collect environmental data of the upper atmosphere, and the weather balloon 1 is used to carry the sounding meteorologist to a set height.

[0061] Wherein, the environmental data includes temperature, humidity, air pressure, wind speed, and wind direction;

[0062] In this embodiment, the sounding meteorological instrument includes but is not limited to the following: an alpha temperature sensor, an alpha humidity sensor, an alpha air pressure sensor, an alpha wind speed sensor and an alpha wind direction sensor, wherein the alpha temperature sensor is used to collect ambient temperature data of the upper atmosphere, the alpha humidity sensor is used to collect ambient humidity data of the upper atmosphere, the alpha air pressure sensor is used to collect air pressure data of the upper atmosphere, the alpha wind speed sensor is used to collect ambient wind speed data of the upper atmosphere, and the alpha wind direction sensor is used to collect wind direction data of the upper atmosphere;

[0063] The sounding meteorological instrument is arranged on the weather balloon 1, and follows the weather balloon 1 to collect environmental data at different altitudes in the sky;

[0064] The floating probe acquisition unit also includes a data recovery station, a traction component and a communication component.

[0065] The traction component tractions the weather balloon 1 to release the weather balloon 1 at different altitudes, and the communication component transmits the environmental data collected by the sounding meteorological instrument back to the ground data recycling station, and the data recycling station is used to store the environmental data transmitted by the communication component;

[0066] The traction member comprises a traction seat 3, a traction rope 2, a traction drive mechanism 5 and a traction rod 4. The traction seat 3 is provided with a rolling cavity. One end of the traction rod 4 is drivingly connected to the traction drive mechanism 5 to form a traction portion. The traction portion is arranged in the side wall of the rolling cavity so that the other end of the traction rod 4 is hingedly arranged on one side wall of the rolling cavity. One end of the traction rope 2 is connected to the rod body of the traction rod 4, and the other end of the traction rope 2 is connected to the weather balloon 1.

[0067] The communication component includes a communicator and a transmission line, wherein the communicator is used to collect environmental data collected by the sounding meteorological instrument, and the transmission line transmits the environmental data collected by the communicator and transmits the environmental data to a data recycling station on the ground;

[0068] In this embodiment, the transmission operation through the transmission line effectively improves the transmission efficiency and reliability of the environmental data, ensuring that the environmental data can be accurately transmitted;

[0069] Optionally, the meteorological radar 7 acquisition unit includes a radar transmitter, a radar receiver and a signal processor, wherein the radar transmitter is used to transmit electromagnetic waves of a specific frequency to the target area, the radar receiver is responsible for receiving the electromagnetic wave reflection signal returned from the atmosphere, and the signal processor is used to process the original echo signal acquired by the radar receiver and analyze the echo characteristics to generate meteorological images and data reflecting the structure and dynamic changes of the atmosphere;

[0070] In this embodiment, the following steps are performed:

[0071] S11. Radar transmitter emits electromagnetic waves: Radar transmitter is responsible for emitting electromagnetic waves of specific frequencies to the target area to detect meteorological phenomena in the atmosphere;

[0072] Among them, the emission of electromagnetic waves: the radar transmitter emits high-frequency electromagnetic waves to the target area in the atmosphere through the antenna; weather radar7 often uses the microwave band (usually between 1GHz and 10GHz) because electromagnetic waves of this wavelength can penetrate clouds and interact with particles in the atmosphere;

[0073] The weather radar 7 transmits pulsed electromagnetic waves, that is, intermittently transmits short-duration high-frequency electromagnetic wave pulses; there is a short time interval between the transmission and reception of each pulse so that the radar can receive the echo signal;

[0074] At the same time, by controlling the direction of the antenna, the radar can rotate the transmission beam to scan different areas and altitude layers, thereby covering a larger spatial range;

[0075] S12. Interaction between electromagnetic waves and the atmosphere:

[0076] The emitted electromagnetic waves pass through the atmosphere and interact with various meteorological phenomena in the atmosphere (such as raindrops, ice crystals, wind speed, aerosols, etc.), causing part of the electromagnetic waves to be scattered or reflected back to the radar;

[0077] When electromagnetic waves encounter targets in the atmosphere (such as water droplets, snowflakes, ice particles or aerosols), part of the electromagnetic waves will be reflected or scattered back to the radar receiver by these objects; this phenomenon is called echo;

[0078] The intensity of the echo is related to factors such as the size, shape, and density of the meteorological target; for example, the larger the raindrops, the stronger the reflected electromagnetic waves and the more obvious the echo signal;

[0079] S13. Radar receiver receives echo signal: Function: The radar receiver is responsible for receiving the electromagnetic wave echo signal reflected from the atmosphere;

[0080] Receiving reflected echo: The radar receiver's antenna receives the returned electromagnetic wave echo signal; the receiver usually shares the same antenna with the transmitter so that transmission and reception operations can be performed simultaneously;

[0081] The receiver not only records the intensity of the echo, but also the time interval between the emission of the electromagnetic wave and the return of the echo; this is the key to ranging. By measuring this time interval, the radar can calculate the distance to the target (such as clouds and precipitation);

[0082] Radar receivers can also detect Doppler shift, which is a change in the frequency of echoes caused by the movement of a target object (such as clouds, precipitation, or wind); this frequency change is used to infer the speed and direction of air currents.

[0083] S14, the signal processor processes the original echo signal:

[0084] The signal processor processes and analyzes the raw echo signals captured by the receiver to generate meteorological images and data that can reflect the structure and dynamic changes of the atmosphere;

[0085] Among them, the original echo signal received by the radar may contain noise (such as interference signals from the environment). The signal processor first filters the echo signal to remove irrelevant noise and enhance signal quality;

[0086] The signal processor analyzes the intensity of the echo signal. The intensity value is related to the size and number of water droplets and ice particles in the atmosphere, reflecting information such as precipitation intensity or cloud thickness in the target area.

[0087] By calculating the time difference between the transmitted and received electromagnetic waves, the signal processor can determine the distance of the reflecting target and determine the location of the target based on the radar's scanning angle;

[0088] The signal processor uses the frequency changes of the echo to detect the moving speed of the target object (such as precipitation clouds or wind); this technology is called Doppler radar and is used to measure wind speed and direction and analyze the dynamic changes of the wind field;

[0089] Through the radar's rotational scanning, the signal processor constructs atmospheric structure information covering different altitude layers; different echo intensities and frequency shift information are integrated together to generate a three-dimensional meteorological image that reflects the spatial distribution of clouds, wind fields, precipitation, etc.

[0090] S15. Generate meteorological images and data:

[0091] The signal processor converts the processed echo signal into meteorological images and data that are easy to analyze, and provides them to the meteorological warning analysis module for use;

[0092] The signal processor integrates the echo intensity, ranging data and Doppler shift information to generate images reflecting meteorological phenomena, such as: Reflectivity map: It shows the intensity and distribution of precipitation in the atmosphere and is usually used to monitor thunderstorms, heavy rains, hail, etc.;

[0093] Speed ​​map: Displays the distribution of wind speed and direction, mainly used to detect changes in the wind field, especially the identification of strong wind weather such as tornadoes;

[0094] Vertical wind shear chart: shows the wind speed changes at different altitudes, helping to analyze convective activity and atmospheric instability;

[0095] In addition to images, the signal processor also generates data files containing meteorological parameters in numerical form (such as wind speed, wind direction, precipitation intensity, airflow height, etc.) for subsequent analysis and early warning;

[0096] Real-time data update: Radar usually collects and processes data in real time, and can continuously generate the latest meteorological images and data to ensure the timeliness of meteorological warnings;

[0097] In this embodiment, the echo intensity analysis includes: the echo intensity is closely related to the size, quantity and distribution of the target object (such as raindrops or ice crystals); a strong echo usually indicates that there are larger or denser targets (such as heavy rain, hail), and the calculation formula of the radar reflectivity (Z) is:

[0098]

[0099] Where P r is the received echo power, obtained directly by the power received by the radar receiver, P t is the transmitted power, obtained directly from the radar’s transmit power;

[0100] The precipitation intensity is estimated by analyzing the radar reflectivity; the higher the reflectivity, the stronger the precipitation. The commonly used precipitation rate R formula is:

[0101] R=a×Z b ;

[0102] Where Z is the radar reflectivity, a and b are empirical coefficients, which depend on the precipitation type;

[0103] In this embodiment, different types of precipitation (such as thunderstorms, drizzles, snow or hail) have different a and b values ​​based on different precipitation types; these empirical coefficients are obtained through a large number of actual observations and experimental measurements; the researchers find the a and b values ​​that best match the observation data by comparing the ground precipitation observation data with the radar echo under different meteorological conditions;

[0104] The empirical coefficients for common precipitation types are as follows:

[0105] Thunderstorm: a=200, b=1.6;

[0106] Continuous rainfall (such as drizzle): a = 300, b = 1.4;

[0107] Hail: a=75, b=1.75;

[0108] Snow: a=320, b=1.2;

[0109] Heavy rain: The precipitation intensity is high and the raindrop size is large. The empirical coefficients of a=250 and b=1.5 are usually used to reflect the heavy precipitation phenomenon;

[0110] Typhoon: Storms are accompanied by heavy rainfall and strong winds. Use a=150 and b=1.6 to reflect complex meteorological phenomena.

[0111] Tornado: The main feature is violent airflow rotation, and a=200 and b=1.7 are used to assist in identification;

[0112] Hail: Hail has a very high reflectivity, but the precipitation is small, and the empirical coefficients are a=75 and b=1.75;

[0113] Snow: Snow has a low reflectivity, so the empirical coefficients of a=320 and b=1.2 are used to estimate snowfall;

[0114] Freezing rain: The raindrop density is high, but the intensity is not strong. The coefficients a=220 and b=1.5 are used to estimate the precipitation intensity.

[0115] The above empirical coefficients are derived from a large amount of meteorological research data and long-term experimental analysis in different regions around the world. They are applicable to different radar frequency bands (such as C-band, S-band, etc.) and reflect the relationship between the intensity of a specific type of precipitation and radar reflectivity.

[0116] In short, it is necessary to select an appropriate empirical coefficient value according to different monitoring scenes or regions and seasons. This is a technical means well known to those skilled in the art. Those skilled in the art can refer to relevant technical manuals to learn about the technology, so it will not be described in detail in this embodiment.

[0117] A reflectivity map is generated based on radar reflectivity. Here, the reflectivity map is a two-dimensional or three-dimensional image that shows the precipitation intensity and distribution in different regions. The darker the color, the stronger the precipitation.

[0118] The frequency change of the echo signal (Doppler effect) reflects the velocity of the target object, especially the wind speed. The greater the echo frequency shift, the faster the moving speed of the target object. By analyzing the Doppler frequency shift, the meteorological radar calculates the radial velocity of the target object (i.e., the velocity of the target object towards or away from the radar).

[0119] The wind speed calculation formula is:

[0120]

[0121] In the formula, V r is the wind speed, that is, the radial velocity of the wind, f d is the Doppler frequency shift, and λ is the wavelength of the radar wave of the meteorological radar; its value is directly determined by the transmission parameters of the radar transmitter.

[0122] For the Doppler frequency shift f d it is determined according to the following steps:

[0123] 1) Transmit electromagnetic waves of a fixed frequency: The radar transmitter emits electromagnetic waves of a fixed frequency (referred to as the transmission frequency f0).

[0124] 2) Receive the echo: The radar receiver receives the reflected wave from the target and records the frequency f r ;

[0125] 3) Calculate the frequency difference: By measuring the difference between the transmission frequency f0 and the received frequency f r the Doppler frequency shift f d is determined:

[0126] f d = f r - f0;

[0127] If the target is moving towards the radar, f r > f0, and the frequency shift is positive;

[0128] If the target is moving away from the radar, f r < f0, and the frequency shift is negative;

[0129] Through this measurement process, the radar determines the radial movement speed of the target object relative to the radar.

[0130] In short, through the Doppler frequency shift detection of the radar, a wind field map showing the wind speed and direction is generated, which shows the speed distribution and direction change of the wind at different heights; this is particularly important for monitoring extreme weather such as tornadoes.

[0131] Optionally, the ground meteorological station collection unit 8 includes a collection platform and a basic meteorological collection component, wherein the collection platform is used to support the basic meteorological collection component, and the basic meteorological collection component obtains real-time basic meteorological parameters in the monitoring area 6;

[0132] The basic meteorological parameters include temperature, humidity, air pressure, wind speed, wind direction, rainfall and solar radiation data;

[0133] In this embodiment, the collection station can be set at at least two locations on the ground of the monitoring area 6 to obtain the basic meteorological parameters of the monitoring area 6;

[0134] Optionally, the basic meteorological collection component includes a β temperature sensor, a β humidity sensor, a β air pressure sensor, a β wind speed sensor, a β wind direction sensor, a rain gauge, a solar radiation sensor and a data storage device, wherein the β temperature sensor collects the air temperature of the current monitoring area 6, the β humidity sensor collects the air humidity of the current monitoring area 6, the β air pressure sensor collects the air pressure data of the current monitoring area 6, the β wind speed sensor collects the wind speed data of the current monitoring area 6, the β wind direction sensor collects the wind direction data of the monitoring area 6, the rain gauge collects the rainfall in the monitoring area 6, the solar radiation sensor measures the solar radiation intensity, and the data storage device stores the temperature data collected by the β temperature sensor, the humidity data collected by the β humidity sensor, the air pressure data collected by the β air pressure sensor, the wind speed data collected by the β wind speed sensor, the wind direction data collected by the β wind direction sensor, the rainfall data collected by the rain gauge, and the radiation intensity data of the solar radiation sensor;

[0135] Optionally, the satellite remote sensing acquisition unit includes a command transmitter and a data buffer, the command transmitter sends a data call request to the meteorological satellite, so that the meteorological satellite responds to the data call request and transmits the meteorological information of the monitoring area 6 to the data buffer, and the data buffer is used to store the meteorological information of the monitoring area 6;

[0136] The meteorological information includes but is not limited to the following: satellite cloud images, temperature images (obtained by measuring the temperature distribution of the surface and different layers of the atmosphere by infrared sensors or microwave sensors installed on meteorological satellites), water vapor distribution and humidity information (obtained by satellite monitoring through microwave sensors or infrared spectrometers);

[0137] Optionally, the data fusion unit obtains the meteorological data collected by the meteorological data collection module, and performs fusion processing according to the following steps:

[0138] S1, data collection: obtaining the basic meteorological parameters collected by the ground meteorological station collection unit 8, the meteorological information collected by the satellite remote sensing collection unit, the local atmospheric structure and dynamic changes collected by the meteorological radar 7 collection unit, and the environmental data of the high-altitude atmosphere collected by the floating detector collection unit;

[0139] Acquire meteorological data from multiple data sources, covering meteorological parameters at different altitudes, time periods and spatial ranges; mainly including:

[0140] Ground weather station: collect basic meteorological parameters (temperature, humidity, air pressure, wind speed, wind direction, rainfall, solar radiation);

[0141] Satellite remote sensing: provides information such as cloud maps, surface temperature, humidity, and water vapor content, usually covering a large area;

[0142] Meteorological radar 7: obtain local precipitation intensity, wind field distribution and atmospheric structure information;

[0143] Floating detector: collects environmental data of the upper atmosphere (such as upper air temperature, humidity, air pressure, wind speed, and wind direction);

[0144] Specifically: Ground weather station: data is collected every minute, including temperature, humidity, air pressure, wind speed, etc.;

[0145] Satellite remote sensing: data is updated every 30 minutes or 1 hour, including temperature distribution map, humidity map, cloud map, water vapor content, etc.;

[0146] Weather radar 7: Updated every 5-10 minutes to obtain local wind speed, precipitation and air flow information;

[0147] Floating detector: collects high-altitude atmospheric environment data every 10 minutes;

[0148] S2. Perform time synchronization on the data:

[0149] Align data at different temporal and spatial scales;

[0150] In this embodiment, data from different sources are usually collected at different frequencies, so it is necessary to align data collected at different times to the same time point; interpolation and resampling methods are used to achieve temporal consistency;

[0151] Time synchronization steps:

[0152] Timestamp extraction: Get timestamps from various data sources and standardize them to UTC;

[0153] Select a reference time point: Select a time base (such as the time point of ground weather station or radar data) as the time base, and align other data to this time point;

[0154] Linear interpolation: For data with small time differences, linear interpolation is used for time alignment; for example, assuming the time at the ground weather station is 12:00 and the satellite data is 12:03, the interpolation formula calculates the satellite data at 12:00;

[0155] In this embodiment, the linear interpolation formula used is:

[0156]

[0157] Where t1 and t2 are the times of two known data points, X(t1) and X(t2) are the data values ​​(such as temperature, humidity, etc.) at time points t1 and t2, t is the time point to be synchronized, and X(t) is the interpolation result at that time point;

[0158] Resampling: For high-frequency or low-frequency data, resampling techniques can be used to reduce the data frequency or fill in missing time points;

[0159] Specifically: For time period T k The high-frequency data in can be aggregated into a low-frequency data point; the formula is:

[0160]

[0161] Where, X avg (T k ) is the time period T k The average value after internal resampling; X(t i ) is the high frequency data at time point t i The value on, m is in the time period T k The number of data points in the time period (i.e., how many high-frequency data are there in the time period), T k is the target time window for low-frequency data (e.g., 30-minute window);

[0162] S3, median filtering processing:

[0163] Perform median filtering on the time-synchronized meteorological data;

[0164] Median filtering steps:

[0165] Select the time window: For high-frequency data, select an appropriate sliding window (such as 5 minutes) and perform median filtering within this window;

[0166] Calculate the median: Sort all data points in the sliding window and take the median as the representative value of the time period;

[0167] Replace outliers: Replace outlier data points within the window with the median value to ensure smoothness and consistency of the data;

[0168] S4, data fusion processing: perform arithmetic averaging on the data that pass through the median filter to generate the final fusion result;

[0169] After interpolation and resampling, all meteorological data are aligned at the same time point; next, the aligned data need to be fused to generate the final fused data set;

[0170] Fusion method: Use arithmetic mean or other statistical methods to merge various data sources into one result;

[0171]

[0172] Where, X fused (t) is the fused meteorological parameter, X1(t),X2(t),…,X n (t) is the meteorological data from different data sources, and n is the number of different data sources;

[0173] S5, integrating the fused meteorological data into a unified data set to form a tornado model data set analysis data set;

[0174] The data processed by the above steps can generate a tornado model analysis data set, including key meteorological parameters, fused data values ​​at each moment (T fused (t), H fused (t), P fused (t), W fused (t)) Wind shear index (SI), vertical temperature gradient (VTG);

[0175] Optionally, the meteorological assessment unit obtains the tornado model data set after fusion processing, and calculates the warning judgment index Judge of the monitoring area according to the following formula:

[0176]

[0177] Where △ is the adjustment factor, and its value is determined by the adjustment optimization unit, Z T is the Z-Score standardized value of temperature, Z H is the Z-Score standardized value of humidity, Z P is the Z-Score standardized value of air pressure, Z W is the Z-Score normalized value of wind speed, SI is the wind shear index, and VTG is the vertical temperature gradient;

[0178] Z-Score normalized value of temperature Z T Calculate according to the following formula:

[0179]

[0180] Where, T fused (t) is the temperature value after time synchronization and data fusion. This is the fused temperature value at time t, which is the comprehensive result from multiple data sources (such as ground meteorological stations, floating detectors, satellite remote sensing, etc.). T is the average temperature under historical or climate reference conditions, σ T is the standard deviation of historical temperature data, reflecting the range of temperature fluctuations;

[0181] Among them, the historical average temperature μ W Calculate according to the following formula:

[0182]

[0183] In the formula, μ T is the historical average temperature, T i is the temperature value (original meteorological data, such as the real observed temperature value provided by ground meteorological stations, satellites, and floating detectors) at the i-th time point in history (a certain period of time in the past, such as the past 30 days, the past few months, etc.), and N is the total number of data points in the time period;

[0184] The historical standard deviation of temperature σ T Calculate according to the following formula:

[0185]

[0186] In the formula, σ T is the historical standard deviation of temperature, T i is the temperature value at the i-th time point in history, μ T is the historical average of temperature, N is the total number of data points in the time period; Z-Score standardized value of humidity H Calculate according to the following formula:

[0187]

[0188] In the formula, H fused (t) is the humidity value after fusion processing, μ H is the historical average humidity, σ H is the historical standard deviation of humidity;

[0189] Among them, the historical average humidity μ H Calculate according to the following formula:

[0190]

[0191] In the formula, μ W is the historical average humidity, H iis the humidity value (original meteorological data, such as the real observed humidity value provided by ground meteorological stations, satellites, and floating detectors) at the i-th time point in history (a certain period of time in the past, such as the past 30 days, the past few months, etc.), and N is the total number of data points in the time period;

[0192] Historical standard deviation of humidity σ H Calculate according to the following formula:

[0193]

[0194] In the formula, σ H is the historical standard deviation of wind speed, H i is the wind speed value at the i-th time point in history, μ H is the historical average of wind speed, and N is the total number of data points in the time period;

[0195] Z-Score normalized value of air pressure Z P Calculate according to the following formula:

[0196]

[0197] Where P fused (t) is the fused pressure value, μ P is the historical average of air pressure, σ P is the historical standard deviation of air pressure;

[0198] Among them, the historical average value of air pressure μ P Calculate according to the following formula:

[0199]

[0200] In the formula, μ P is the historical average of air pressure, P i is the air pressure value (original meteorological data, such as the real observed air pressure value provided by ground meteorological stations, satellites, and floating detectors) at the i-th time point in history (a certain period of time in the past (such as the past 30 days, the past few months, etc.), and N is the total number of data points in the time period;

[0201] Historical standard deviation of air pressure σ P Calculate according to the following formula:

[0202]

[0203] In the formula, σ P is the historical standard deviation of air pressure, P i is the air pressure value at the i-th time point in history, μ P is the historical average of air pressure, and N is the total number of data points in the time period;

[0204] The Z-Score normalized value of wind speed Z W Calculate according to the following formula:

[0205]

[0206] Where W fused (t) is the fused wind speed value, μ W is the historical average wind speed, σ W is the historical standard deviation of wind speed;

[0207] Among them, the historical average wind speed μ W Calculate according to the following formula:

[0208]

[0209] In the formula, μ W is the historical average wind speed, W i is the wind speed value (original meteorological data, such as the real observed wind speed value provided by ground meteorological stations, satellites, and floating detectors) at the i-th time point in history (a certain period of time in the past (such as the past 30 days, the past few months, etc.), and N is the total number of data points in the time period;

[0210] Historical standard deviation of wind speed σ W Calculate according to the following formula:

[0211]

[0212] In the formula, σ W is the historical standard deviation of wind speed, W i is the wind speed value at the i-th time point in history, μ W is the historical average of wind speed, and N is the total number of data points in the time period;

[0213] The wind shear index SI is calculated according to the following formula:

[0214]

[0215] Where W h1 is the wind speed at height h1, usually in meters per second (m / s), W h2 is the wind speed at height h2, usually in meters per second (m / s), D h1 is the wind direction at height h1, measured clockwise from the north, D h2 is the wind direction at height h2;

[0216] In this embodiment, these wind speed data are measured by ground weather stations, weather radars or floating detectors; high-altitude wind speed (W h1) is usually obtained by floating detectors or weather radars, and the surface wind speed (W h2 ) is measured directly by ground-based meteorological stations;

[0217] For the adjustment factor △, its value is determined by the adjustment optimization unit;

[0218] Specifically: the adjustment optimization unit calls the Z-Score standardized value Z of the temperature after the fusion processing T , Z-Score standardized value of humidity Z H , Z-Score standardized value of air pressure Z P , and the Z-Score standardized value of wind speed Z W , and calculated according to the following formula:

[0219]

[0220] In the formula, γ is the abnormal adjustment factor, and its value is determined according to the following formula:

[0221]

[0222] Where, dT / dt is the temperature change rate, dH / dt is the humidity change rate, dP / dt is the air pressure change rate, and dW / dt is the wind speed change rate;

[0223] The vertical temperature gradient VTG is calculated according to the following formula:

[0224]

[0225] Where, T h1 and T h2 is the temperature at different altitudes (e.g. ground and 1 km), h2-h1 is the height difference;

[0226] In this embodiment, the ground temperature Th2 is measured by a ground weather station;

[0227] Weather stations usually provide real-time temperature data with a high collection frequency (e.g., once per minute). High-altitude temperature Th1 is usually measured by floating detectors (e.g., weather balloons), satellite remote sensing, or weather radar;

[0228] If the warning evaluation index Judge of the monitored area exceeds the meteorological threshold Worn set by the system, the meteorological warning analysis module is triggered to analyze the meteorological warning and prompt the manager with a real-time warning reminder;

[0229] If the warning evaluation index Judge of the monitored area is lower than the meteorological threshold Worn set by the system, it means that the current weather is normal and there is no need to trigger the meteorological warning analysis module to analyze the meteorological warning;

[0230] The weather warning Worn set by the system is set by the system or the administrator according to the actual situation. This is a technical means well known to those skilled in the art. Those skilled in the art can refer to the relevant technical manuals to learn about the technology, so it will not be described in detail in this embodiment.

[0231] Through the cooperation between the meteorological data acquisition module and the tornado model evaluation module, the meteorological parameters of the monitoring area can be collected, ensuring that the entire system has the advantages of high early warning accuracy, high real-time analysis capability, strong meteorological analysis fusion capability, good refined evaluation capability, and high intelligence;

[0232] Optionally, the meteorological warning analysis module includes a warning analysis unit, a warning generation unit and a notification unit, wherein the warning analysis unit analyzes the meteorological warning type according to the warning judgment index Judge and the change trend of the meteorological parameters, and identifies the specific meteorological warning type, the warning generation unit generates a matching warning prompt according to the warning type, and the notification unit issues the warning prompt to the manager;

[0233] The warning analysis unit analyzes the meteorological warning type according to the following steps and identifies the specific meteorological warning type:

[0234] S21, obtaining the warning evaluation index Judge based on current meteorological parameters such as temperature, humidity, air pressure, wind speed, wind direction, wind shear index (SI), vertical temperature gradient (VTG), etc.;

[0235] Possible range of warning judgement index Judge (assumed to be 0 to 100):

[0236] A low Judge value means that the weather conditions are stable and the possibility of extreme weather is low;

[0237] A higher Judge value indicates that the weather conditions are unstable and extreme weather (such as storms, tornadoes, etc.) is more likely to occur.

[0238] S22. Analyze the changing trends of meteorological parameters:

[0239] Analyze trend indicators: In addition to the Judge index, the early warning analysis unit also needs to track the trends of multiple meteorological parameters:

[0240] Air pressure drop rate: A rapid drop in air pressure usually indicates the arrival of severe convective weather (such as a tornado or storm);

[0241] Wind speed acceleration rate: A rapid increase in wind speed, especially an increase in the difference between wind speeds aloft and on the ground (wind shear), is a precursor to a storm or tornado;

[0242] Temperature change rate: The larger the vertical temperature gradient (VTG), the greater the atmospheric instability and the greater the possibility of severe convective weather;

[0243] S23. Determine the warning type threshold

[0244] Based on historical data and meteorological experience, threshold ranges are set for different types of extreme weather. The early warning analysis unit determines whether the conditions for extreme weather are met by comparing the Judge value and the trend of meteorological parameters;

[0245] In this embodiment, the extreme weather conditions include:

[0246] 1) Tornado Warning:

[0247] Early warning judgment index Judge ≥ 80;

[0248] Rapid drop in air pressure: drop ≥10hPa per hour;

[0249] Wind shear index SI ≥ 70;

[0250] Vertical temperature gradient VTG ≥ 0.005℃ / m;

[0251] Other features: obvious differences in wind direction (e.g., large differences between ground wind direction and high-altitude wind direction);

[0252] 2) Storm Warning:

[0253] Early warning judgment index 60≤Judge<80;

[0254] Air pressure drops: 5-10 hPa per hour;

[0255] Wind speed increases sharply: wind speed increases by 10-15 m / s per hour;

[0256] Vertical temperature gradient: A moderate temperature gradient indicates strong convection;

[0257] 3) Strong wind warning:

[0258] Early warning judgment index 50≤Judge<70;

[0259] Wind speed changes rapidly: wind speed ≥ 20m / s and the increasing trend is obvious;

[0260] 4) Hail warning:

[0261] Early warning judgment index Judge ≥ 70;

[0262] Obvious temperature gradient: higher vertical temperature gradient VTG ≥ 0.006℃ / m;

[0263] Cold air moves in quickly: accompanied by increased wind speed;

[0264] 5) Lightning warning:

[0265] Early warning judgment index Judge ≥ 50;

[0266] Humidity rises sharply: The air humidity increases rapidly, such as: humidity increases by ≥10% within 30 minutes or increases by ≥15% within 1 hour;

[0267] Convection intensity:

[0268] The vertical temperature gradient VTG is between 0.005℃ / m and 0.008℃ / m (moderate convection) (or the vertical temperature gradient (VTG\geq 0.008℃ / m (strong convection));

[0269] Radar echo intensity: moderate: 35dBZ~45dBZ (or strong: ≥45dBZ);

[0270] S24. Combined with meteorological parameter trend judgment:

[0271] In the early warning analysis unit, the judgement index is the basis, and it is necessary to combine the trend changes of meteorological parameters for more accurate analysis:

[0272] Rapidly falling pressure + high Judgement value: usually indicates an increased risk of severe convection or tornadoes;

[0273] Large temperature gradient + high wind shear index: may indicate the occurrence of hail or storms;

[0274] Rapidly increasing wind speed + high humidity + medium Judgement value: may indicate stormy or thundery weather;

[0275] The warning generation unit generates a corresponding warning prompt according to the following steps:

[0276] S31. Receive warning types and related data:

[0277] The warning generation unit first receives the warning result from the warning analysis unit, including:

[0278] Warning type: such as tornado warning, lightning warning, storm warning, hail warning, etc.;

[0279] Warning level: Warning levels are usually divided into different levels, such as yellow, orange, and red warnings. The specific level depends on the severity of meteorological conditions (such as the value of the warning judgement index and the changing trend of meteorological parameters);

[0280] Time dimension: The warning generation unit also needs to know the possible time period of the meteorological event (such as about to happen, within 1 hour, within 12 hours, etc.), which helps to generate warnings of different urgency levels;

[0281] S32, matching warning template:

[0282] Based on the type and level of the received warning, the warning generation unit will search and match the corresponding warning template; the template is a standardized text or information format predefined according to the warning type and severity;

[0283] The warning template may include the following parts: Warning type: specify the warning content (such as lightning, tornado, etc.);

[0284] Warning level: usually divided into different levels, such as yellow (general warning), orange (moderate warning), and red (serious warning);

[0285] Time dimension: when the extreme weather is expected to occur (e.g., “expected within the next hour,” “imminent,” or “likely to occur within 12 hours”);

[0286] Recommended measures: remind managers or the public what emergency measures to take;

[0287] S33. Insert real-time data:

[0288] The warning generation unit extracts key parameters from the current meteorological data and inserts this real-time information into the warning template to make the warning prompt more accurate and targeted; these real-time data may include:

[0289] Current temperature, humidity, air pressure, wind speed and other specific values;

[0290] The calculated wind shear index (SI) and vertical temperature gradient (VTG) are parameters reflecting atmospheric instability;

[0291] The specific value of the warning judgment index Judge;

[0292] S34. Description of generating warning level:

[0293] According to the warning level provided by the warning analysis unit, the corresponding warning description is generated:

[0294] Yellow alert (lower level): managers are advised to pay attention, but no immediate emergency action is required;

[0295] Orange warning (medium risk): reminds managers that they may need to take preventive measures to reduce the impact of risks;

[0296] Red alert (highest level): Take immediate emergency actions, such as evacuation, closing outdoor activities, etc.;

[0297] S35. Generate recommendations and emergency measures:

[0298] According to different warning types and levels, the warning generation unit automatically generates suggestions or emergency measures; specific response measures may include:

[0299] Tornado warning: remind residents to quickly enter safe buildings or basements, close doors and windows, and avoid outdoor activities;

[0300] Lightning warning: managers are advised to suspend outdoor activities, avoid using metal objects, and airlines are warned to pay attention to flight safety;

[0301] Storm warning: Residents are reminded to avoid walking in open areas, take flood prevention measures, and the transportation department is advised to close some roads;

[0302] Hail Warning: It is recommended to park vehicles in a sheltered area, close windows, and protect property;

[0303] S36. Generate early warning prompt:

[0304] Finally, the warning generation unit integrates all the information into a complete warning prompt, which usually includes the following:

[0305] Warning type: such as "tornado warning", "storm warning", etc.;

[0306] Warning level: yellow, orange, red and other levels;

[0307] Time range: For example, "a tornado may occur in the next hour";

[0308] Current weather data: for example, "The current wind shear index is 80, the air pressure is dropping rapidly by 15hPa, and a strong tornado may occur";

[0309] Recommendations or emergency measures: For example, "Residents are advised to quickly go indoors and close doors and windows";

[0310] Optionally, the notification unit includes a communication transmitter and a text message gateway, wherein the communication transmitter sends the warning information to a mobile device via a cellular network, Wi-Fi and a communication system, and the text message gateway is used to send the warning information to a designated manager or a mobile device of the public via text messages;

[0311] In this embodiment, through the cooperation between the meteorological data collection module and the meteorological early warning analysis module, it is ensured that the meteorological data collected in real time can be directly used in the early warning analysis module, helping to timely identify extreme meteorological events, quickly generate early warning prompts, and ensure that the entire system has the advantages of high real-time early warning capability, strong refined early warning capability, and high intelligent analysis capability;

[0312] In addition, through the cooperation between the tornado model evaluation module and the meteorological warning analysis module, it can ensure that the evaluation results of the tornado model can be directly used for warning analysis, and generate accurate warning prompts by combining the warning evaluation index and the trend of meteorological parameter changes, ensuring that the entire system has the advantages of high evaluation accuracy, good interactive comfort of intelligent warning prompts, and strong comprehensive judgment ability;

[0313] Through the cooperation between the meteorological data acquisition module, the tornado model evaluation module, and the meteorological warning analysis module, the meteorological warning analysis capability becomes faster and more efficient, ensuring that the entire system has the advantages of high warning accuracy, strong real-time analysis capability, strong meteorological analysis fusion capability, good refined evaluation capability and high intelligence.

[0314] Embodiment 2: This embodiment should be understood to include all the features of any of the above embodiments, and further improve upon them. Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 and Figure 8 As shown, the early warning analysis module also includes a situation analysis unit and an early warning control unit, the situation analysis unit obtains the meteorological data processed by the fusion unit and analyzes the change state of the meteorological data according to the meteorological data to form an analysis result, and the early warning control unit controls the notification frequency of the notification unit according to the analysis result;

[0315] The situation analysis unit obtains the meteorological data processed by the fusion unit, and calculates the urgency index Urgent of the change state of the meteorological data according to the following formula:

[0316]

[0317] Where, dT / dt is the temperature change rate, dH / dt is the humidity change rate, dP / dt is the air pressure change rate, dW / dt is the wind speed change rate, t0 is the lower limit of the monitoring time interval, and t1 is the upper limit of the monitoring time interval;

[0318] If the urgency index Urgent of the meteorological data change state exceeds the trigger threshold ACT set by the system, the notification unit is triggered to adjust the notification frequency;

[0319] If the urgency index of the weather data change state exceeds the trigger threshold ACT set by the system, it means that the weather condition is relatively stable or the urgency is low, and the current notification frequency is maintained;

[0320] The trigger threshold ACT set by the system is set by the system or the administrator according to the actual situation. This is a technical means well known to those skilled in the art. Those skilled in the art can refer to relevant technical manuals to learn about the technology, so it will not be described in detail in this embodiment.

[0321] The situation analysis unit obtains the urgency index Urgent of the meteorological data change state and the trigger threshold ACT set by the system to calculate the notification frequency F notify :

[0322]

[0323] In the formula, F base It is the fixed or default reminder frequency set by the system, indicating the number of notifications issued by the system within the set time period (such as the notification frequency per minute). The larger the value, the more frequent the notifications. k is the adjustment coefficient, and its value is calculated according to the following formula:

[0324]

[0325] Where, dT / dt is the temperature change rate, dH / dt is the humidity change rate, dP / dt is the air pressure change rate, and dW / dt is the wind speed change rate;

[0326] The early warning control unit is configured to generate a notification frequency F notify The notification unit is controlled so that the notification unit is configured to receive notifications according to the notification frequency F. notify Control the frequency of notifications;

[0327] The early warning control unit includes a communication interface, a data storage device, and a control line, one end of the control line is connected to the output end of the communication interface, the control line is connected to the communication transmitter of the notification unit, and the data storage device stores the calculated notification frequency F notify , so that the central processor will notify the frequency F notify Transmitting to the control line through the control line to trigger the communication transmitter according to the notification frequency F notify Triggering

[0328] In this embodiment, through the mutual cooperation of the situation analysis unit, the early warning control unit and the notification unit, the notification delivery control is accurately achieved, ensuring that the entire system has the advantages of high interactive comfort, high intelligence, good refined management level and good reliability.

[0329] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A meteorological early warning analysis system based on tornado model construction analysis, the meteorological early warning analysis system comprising a server, characterized in that: The meteorological warning analysis system includes a meteorological data acquisition module, a tornado model evaluation module, and a meteorological warning analysis module, and the server is connected to the meteorological data acquisition module, the tornado model evaluation module, and the meteorological warning analysis module respectively; The meteorological data acquisition module is used to collect meteorological data from at least two sources in the monitoring area, the tornado model evaluation module evaluates the meteorological data from at least two sources based on the meteorological data to form a meteorological evaluation result, and the meteorological early warning analysis module provides early warning analysis based on the analysis result to prompt the manager with real-time early warning prompts; The tornado model evaluation module includes a data fusion unit, a meteorological evaluation unit and an adjustment and optimization unit. The data fusion unit performs fusion processing on meteorological data from at least two sources to generate a unified tornado model data set. The meteorological evaluation unit evaluates the current meteorology according to the tornado model data set analysis data set to form a meteorological evaluation result. The adjustment and optimization unit adjusts the meteorological evaluation result according to the change trend of the meteorological data. The meteorological data from at least two sources include basic meteorological parameters collected by ground meteorological stations, meteorological information acquired by meteorological satellites, and environmental data of the upper atmosphere collected by floating detectors.

2. The meteorological early warning analysis system based on tornado model construction analysis according to claim 1 is characterized in that: The meteorological data acquisition module includes a ground meteorological station acquisition unit, a satellite remote sensing acquisition unit, a meteorological radar acquisition unit and a floating detector acquisition unit. The ground meteorological station acquisition unit acquires real-time basic meteorological parameters in the monitoring area, the satellite remote sensing acquisition unit acquires meteorological information from meteorological satellites, the meteorological radar acquisition unit uses meteorological radar to acquire local atmospheric structure and dynamic changes, and the floating detector acquisition unit acquires environmental data of the high-altitude atmosphere; The floating probe acquisition unit includes a sounding meteorologist and a weather balloon. The sounding meteorologist is used to collect environmental data of the upper atmosphere, and the weather balloon is used to carry the sounding meteorologist to a set height. The environmental data includes temperature, humidity, air pressure, wind speed, and wind direction.

3. The meteorological early warning analysis system based on tornado model construction analysis according to claim 2 is characterized in that: The meteorological radar acquisition unit includes a radar transmitter, a radar receiver and a signal processor. The radar transmitter is used to transmit electromagnetic waves of a specific frequency to the target area. The radar receiver is responsible for receiving the electromagnetic wave reflection signal returned from the atmosphere. The signal processor is used to process the original echo signal collected by the radar receiver and analyze the echo characteristics to generate meteorological images and data reflecting the atmospheric structure and dynamic changes.

4. The meteorological early warning analysis system based on tornado model construction analysis according to claim 3 is characterized in that: The data fusion unit obtains the meteorological data collected by the meteorological data collection module and performs fusion processing according to the following steps: S1. Data collection: obtaining basic meteorological parameters collected by the ground meteorological station collection unit, meteorological information collected by the satellite remote sensing collection unit, local atmospheric structure and dynamic changes collected by the meteorological radar collection unit, and environmental data of the high-altitude atmosphere collected by the floating detector collection unit; S2. Perform time synchronization on the data: Align data at different temporal and spatial scales; S3, median filtering processing: Perform median filtering on the time-synchronized meteorological data; S4, data fusion processing: perform arithmetic averaging on the data that pass through the median filter to generate the final fusion result; S5. Integrate the fused meteorological data into a unified dataset to form a tornado model dataset analysis dataset.

5. The meteorological early warning analysis system based on tornado model construction analysis according to claim 4 is characterized in that: The meteorological assessment unit obtains the tornado model data set after fusion processing, and calculates the warning judgment index Judge of the monitoring area according to the following formula: Where △ is the adjustment factor, and its value is determined by the adjustment optimization unit, Z T is the Z-Score standardized value of temperature, Z H is the Z-Score standardized value of humidity, Z P is the Z-Score standardized value of air pressure, Z W is the Z-Score normalized value of wind speed, SI is the wind shear index, and VTG is the vertical temperature gradient; If the warning evaluation index Judge of the monitoring area exceeds the meteorological threshold Worn set by the system, the meteorological warning analysis module is triggered to analyze the meteorological warning and prompt the manager with a real-time warning prompt.

6. The meteorological early warning analysis system based on tornado model construction analysis according to claim 5 is characterized in that: The meteorological warning analysis module includes a warning analysis unit, a warning generation unit and a notification unit. The warning analysis unit analyzes the meteorological warning type according to the warning judgment index Judge and the change trend of the meteorological parameters, and identifies the specific meteorological warning type. The warning generation unit generates a matching warning prompt according to the warning type, and the notification unit warns the manager of the warning prompt.

7. The meteorological early warning analysis system based on tornado model construction analysis according to claim 6 is characterized in that: The ground meteorological station collection unit includes a collection platform and a basic meteorological collection component, wherein the collection platform is used to support the basic meteorological collection component, and the basic meteorological collection component obtains real-time basic meteorological parameters in the monitoring area; The basic meteorological parameters include temperature, humidity, air pressure, wind speed, wind direction, rainfall and solar radiation data.

8. The meteorological early warning analysis system based on tornado model construction analysis according to claim 7 is characterized in that: The satellite remote sensing acquisition unit includes a command transmitter and a data buffer. The command transmitter sends a data call request to the meteorological satellite, so that the meteorological satellite responds to the data call request and transmits the meteorological information of the monitored area to the data buffer. The data buffer is used to store the meteorological information of the monitored area.

9. The meteorological early warning analysis system based on tornado model construction analysis according to claim 8, characterized in that: The notification unit includes a communication transmitter and a text message gateway. The communication transmitter sends the warning information to the mobile device via a cellular network, Wi-Fi and a communication system. The text message gateway is used to send the warning information to the designated manager or the public's mobile device via text messages.

10. The meteorological early warning analysis system based on tornado model construction analysis according to claim 9, characterized in that: The basic meteorological collection component includes a β temperature sensor, a β humidity sensor, a β air pressure sensor, a β wind speed sensor, a β wind direction sensor, a rain gauge, a solar radiation sensor and a data storage device. The β temperature sensor collects the air temperature of the current monitoring area, the β humidity sensor collects the air humidity of the current monitoring area, the β air pressure sensor collects the air pressure data of the current monitoring area, the β wind speed sensor collects the wind speed data of the current monitoring area, the β wind direction sensor collects the wind direction data of the monitoring area, the rain gauge collects the rainfall in the monitoring area, the solar radiation sensor measures the solar radiation intensity, and the data storage device stores the temperature data collected by the β temperature sensor, the humidity data collected by the β humidity sensor, the air pressure data collected by the β air pressure sensor, the wind speed data collected by the β wind speed sensor, the wind direction data collected by the β wind direction sensor, the rainfall data collected by the rain gauge, and the radiation intensity data of the solar radiation sensor.

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