Probability identification method of hail and thunderstorm gale based on X-band all-solid-state polarimetric radar RHI observation

Abstract

The present invention belongs to but is not limited to the field of weather identification technology, and discloses a method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation, extracting characteristic parameters related to hail and thunderstorm gale weather processes from weather radar RHI observation, and calculating the distribution of characteristic parameters along the horizontal distance along the RHI observation azimuth; for the two types of weather processes of hail and thunderstorm gale, respectively select parameters related to the occurrence of hail and thunderstorm gale from the calculated characteristic parameters, and use fuzzy logic to obtain the distribution of the probability of hail and thunderstorm gale with horizontal distance; take continuous data points with an occurrence probability greater than a preset threshold as an identification result, take its central coordinates as the occurrence location for identifying hail and thunderstorm gale, and take the mean of its occurrence probability as the probability of identifying hail and thunderstorm gale. The present invention improves the monitoring capability of hail and thunderstorm gale, and improves the accuracy of such weather forecast and warning.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of weather recognition technology, and in particular relates to a method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar range height indicator (RHI) observation. Background Art

[0002] Thunderstorms and hail are the main severe convective weather phenomena that cause natural disasters. Because of their rapid occurrence and development and small impact range, it is often difficult to accurately monitor and capture their occurrence and development. Therefore, the probabilistic identification of thunderstorms and hail is of great significance to the occurrence and development, forecasting, warning and multi-factor fusion analysis of such weather. The identification of hail and thunderstorms based on weather radar mainly depends on the numerical value of the observed parameters and their spatiotemporal distribution characteristics. Due to the complexity of the structure and evolution of severe convective weather accompanied by hail and thunderstorms, as well as the inherent limitations of weather radar business observation methods, the identification results of hail and thunderstorms based solely on weather radar volume scanning observations have great uncertainty. Therefore, it is necessary to express the uncertainty of the identification results in the form of probability. In recent years, the X-band all-solid-state polarization radar deployed in China has the ability to conduct continuous RHI observations, thus bringing new opportunities for the classification and identification of hail and thunderstorms.

[0003] The defects and deficiencies in the existing technologies, especially the limitations of the methods for identifying hail and thunderstorms, have led to the following technical problems in industrial applications:

[0004] 1. Insufficient vertical structure information leads to low recognition accuracy

[0005] Technical issues: Traditional methods for identifying hail and thunderstorms mainly rely on volume scanning data, but the scanning elevation angle in the volume scanning mode is relatively small and cannot accurately reflect the vertical structure information of hail and thunderstorms. However, the vertical structure of severe convective weather systems is an important feature for accurately identifying hail and thunderstorms. Therefore, the traditional volume scanning mode lacks vertical distribution details of weather systems, which limits the accuracy of weather system identification and probability estimation, resulting in an increased risk of delayed warnings or misjudgments.

[0006] Industry impact: This issue directly affects the accuracy and timeliness of weather warnings. For industries involving high risks, such as agriculture, aviation, and energy (especially wind power generation), failure to accurately forecast hail or thunderstorms will lead to serious losses. In addition, inaccurate warnings also lead to waste of resources or failure to take effective disaster prevention measures.

[0007] 2. Lack of mature identification products based on RHI observations

[0008] Technical issues: Although X-band all-solid-state polarization radar and its RHI (Range Height Indicator) observation mode have been applied in many regions, there is still a lack of objective products for hail, thunderstorm and gale identification based on RHI observations. The RHI observation mode has a high vertical resolution, but it is not widely used in the field of severe convective weather identification, resulting in the advantages of this technology not being fully utilized.

[0009] Industry impact: This has prevented the full potential of the X-band radar systems that have been put into use, affecting the overall efficiency of meteorological services. For industries that rely on high-precision weather forecasts, such as transportation, construction, agriculture, and tourism, the lack of accurate severe convective weather warnings will increase operational risks and affect the effectiveness of decision-making. At the same time, meteorological departments and service providers have not been able to fully improve the monitoring and analysis capabilities of the system, resulting in insufficient resource utilization.

[0010] 3. Data processing delays and response time lags

[0011] Technical issues: In order to meet the data update frequency, the traditional volume scanning mode sacrifices some elevation scanning coverage. Although this method can ensure a faster update cycle, it still has the problem of response time lag in the rapid identification of severe convective weather. Especially in high-risk weather conditions such as hail and thunderstorms, rapid and accurate warnings are crucial for disaster prevention.

[0012] Industry impact: In actual industrial applications, especially in densely populated areas or key infrastructure areas, delayed response time will lead to inevitable losses or disasters. If the warning of hail disasters in the agricultural field and thunderstorm and gale disasters in urban infrastructure is not timely, it will lead to a large amount of property losses and safety hazards.

[0013] 4. Inaccurate warnings and wrong decisions

[0014] Technical issues: Existing severe convective weather identification systems often fail to fully utilize existing high-precision radar data, and the accuracy of early warnings is insufficient. At the same time, existing technologies rely on simple threshold judgment methods, making it difficult to comprehensively assess the probability of different weather processes, and unable to adopt effective early warning levels and corresponding emergency measures for different regions.

[0015] Industry impact: Inaccurate warnings or unreasonable level judgments lead to wrong emergency decisions. For example, airlines, logistics companies, and public security departments may interrupt their business due to misjudgment when making decisions, or fail to take effective measures to deal with sudden severe weather.

[0016] The above problems in the existing technology have led to insufficient recognition capabilities for severe convective weather such as hail and thunderstorms, which directly affects the early warning, disaster prevention and emergency management of multiple industries. In order to improve the accuracy, real-time and reliability of the weather warning system, it is urgent to enhance the processing and application capabilities of X-band radar data based on RHI observations. This will bring significant industrial benefits to meteorology, agriculture, transportation, energy and other fields. Summary of the invention

[0017] In view of the problems existing in the prior art, the present invention provides a method and system for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation, aiming to increase the application value of RHI data in collaborative observation and improve the level of hail and thunderstorm gale probability identification.

[0018] The present invention is implemented as follows: a method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation, comprising the following steps:

[0019] Extract multiple characteristic parameters related to hail and thunderstorm weather processes from weather radar RHI observations, including radar reflectivity factor, differential reflectivity, correlation coefficient, etc.; calculate the distribution of characteristic parameters along the RHI observation azimuth with horizontal distance by combining high-performance computing platform for real-time data processing;

[0020] For hail and thunderstorm weather processes, the embedded fuzzy logic operation unit is used to automatically select the characteristic parameters highly correlated with hail and thunderstorm from the characteristic parameters calculated in step 1, and the fuzzy logic rules are optimized by combining the machine learning algorithm to calculate the distribution of the probability of hail and thunderstorm with horizontal distance.

[0021] The continuous data points with a probability greater than the preset threshold are taken as an identification result, and optimized and adjusted in combination with the dynamic adaptive threshold algorithm. The center coordinates are taken and combined with the high-precision GPS or Beidou system to accurately identify the location of hail and thunderstorms. The mean of the probability of occurrence is taken as the probability of identifying hail and thunderstorms.

[0022] The cloud computing platform integrates data from multiple radar sites, synchronizes and analyzes the identification results in real time, and uses the GIS system to visualize the spatial distribution of disastrous weather, and publishes weather warning information in real time to mobile and web terminals.

[0023] In the step 1, the height difference DOH between the center height of the strong echo and the zero-degree layer, the echo bottom height EB, the echo top height ET, the echo thickness ED and the vertical accumulated liquid water content VIL at different horizontal distances x are calculated based on the reflectivity observed by RHI.

[0024] Before calculating the height difference DOH between the center height of the strong echo and the zero-degree layer, the echo bottom height EB, the echo top height ET, the echo thickness ED and the vertical accumulated liquid water content VIL, the reflectivity factor in the polar coordinates is interpolated to the rectangular coordinates Z (x, z). The calculation methods of DOH, EB, ET, ED and VIL are:

[0025] DOH(x)=max({H(x,j)|Z(x,z j )>40})-H 0c (1)

[0026] EB(x)=min({H(x,j)|Z(x,z j )>18}) (2)

[0027] ET(x)=max({H(x,j)|Z(x,z j )>18}) (3)

[0028] ED(x)=ET-EB (4)

[0029]

[0030] Among them, H 0c represents the height of the zero-degree layer, which can be obtained from the sounding observations or numerical model prediction results that are similar in time and space; the reflectivity factor Z in formula (5) is in linear units, and the units in other formulas are dB.

[0031] In the step 1, the radial shear RS at different horizontal distances x is calculated based on the Doppler velocity observed by RHI;

[0032] First calculate the radial shear in polar coordinates:

[0033]

[0034] Then the radial shear RS in polar coordinates p Interpolation to rectangular coordinates RS c (x, z), and calculate the average of all radial convergence values ​​at each x position:

[0035]

[0036] In step 2, for the probability recognition of hail, three characteristic parameters, DOH, ED and VIL, are selected as the input of the fuzzy logic membership function to obtain the fuzzy value of hail recognition:

[0037] V hail (x) = ω DOH MF DOH +ω ED MF ED +ωVIL MF VIL (8)

[0038] For the probability identification of thunderstorms, three characteristic parameters, RS, ED and VIL, are selected as the input of the fuzzy logic membership function to obtain the fuzzy value of thunderstorm identification:

[0039] V wind (x) = ω RS MF RS +ω ED MF ED +ω VIL MF VIL (9)

[0040] MF and ω in equations (8) and (9) represent the membership function and weight value of a parameter respectively; note that the membership function and weight of the same parameter in equations (8) and (9) are different.

[0041] Step 3 specifically includes the following:

[0042] Based on the hail and thunderstorm fuzzy values ​​obtained in step 2, the value greater than a preset threshold V T All points M:

[0043] M={x i |V(x i )>V T} (10)

[0044] Assume N = {x j , x j+1 , ..., x j+K} is a subset of M and K data points are continuous in the horizontal direction, then N is a hail, thunderstorm and gale recognition result; the horizontal position and occurrence probability of the recognition result are:

[0045]

[0046] Another object of the present invention is to provide a method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observations, and a system for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observations, comprising:

[0047] The characteristic parameter extraction module extracts characteristic parameters related to hail, thunderstorm and gale weather processes from the weather radar RHI observations, and calculates the distribution of characteristic parameters along the RHI observation azimuth with horizontal distance;

[0048] The probability recognition module selects parameters related to the occurrence of hail and thunderstorm from the calculated characteristic parameters for the two types of weather processes, and uses fuzzy logic to obtain the distribution of the probability of hail and thunderstorm with horizontal distance;

[0049] The occurrence probability calculation module takes the continuous data points with an occurrence probability greater than a preset threshold as an identification result, takes its central coordinates as the occurrence location for identifying hail and thunderstorms, and takes the mean of its occurrence probability as the occurrence probability for identifying hail and thunderstorms.

[0050] Another object of the present invention is to provide a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for probabilistic identification of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation.

[0051] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observations.

[0052] Another object of the present invention is to provide an information data processing terminal, which includes the hail and thunderstorm gale probability identification system based on X-band all-solid-state polarization radar RHI observation.

[0053] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0054] First, due to the complexity of the structure and evolution of severe convection, as well as the inherent limitations of conventional weather radar observation modes, the classification and identification results of severe convection based solely on weather radar observations have great uncertainty. The present invention proposes a probabilistic identification method for severe convection classification based on X-band all-solid-state polarization weather radar RHI observations, which uses radar observation data with higher temporal and spatial resolution to carry out classification and identification of severe convective weather, improve hail, thunderstorm and gale monitoring capabilities, and improve the accuracy of such weather forecasts and warnings. The traditional probabilistic identification method for severe convection classification mainly uses the volume scan data of weather radar as the algorithm input. The traditional radar plane position display (Plan Position Indicator, hereinafter referred to as PPI) volume scan mode contains fewer scanning elevation angles and cannot accurately reproduce the vertical structure of severe convection. The present invention proposes a technical method related to the classification and identification of severe weather based on RHI vertical observation data with higher temporal and spatial resolution. Compared with the traditional PPI volume scanning mode, it can supplement the observation characteristics of the vertical space, and increase the spatial characteristic parameters of classified severe weather and improve the accuracy of classification and identification of severe convective weather by extracting the vertical characteristics of strong convection, such as the height difference between the center height of the strong echo and the zero-degree layer, the echo bottom height, the echo top height, the echo thickness and the vertical accumulated liquid water content.

[0055] Second, the expected benefits and commercial value of the technical solution of the present invention after transformation are as follows: Currently, meteorological and water conservancy departments are deploying X-band all-solid-state polarization radar networks. The patent can be applied to users who conduct RHI scanning, and the patented method is also applicable to phased array radars.

[0056] The technical solution of the present invention fills the technical gap in the industry at home and abroad: the meteorological department uses the X-band all-solid-state polarization radar to observe RHI data for business observation and analysis, mainly direct observation products, and few other objective analysis and identification products, especially identification products related to severe convection. The present invention makes up for the lack of severe convective weather probability identification products based on X-band all-solid-state polarization radar RHI observations, invents relevant probability identification methods for hail and thunderstorms, and proposes innovative hail and thunderstorm probability identification technology in the development of objective RHI data products.

[0057] The technical solution of the present invention solves the technical problem that people have been eager to solve but have never succeeded in solving: hail and thunderstorms have the characteristics of strong suddenness, small range, rapid movement, strong destructive power, etc. The monitoring, forecasting and early warning capabilities of severe convective weather have always been the pain points and difficulties of meteorological work. The probability identification method of hail and thunderstorms based on X-band all-solid-state polarization radar RHI observation can improve the classification, identification, monitoring, forecasting and early warning capabilities of severe convective weather to a certain extent based on the numerical values ​​of the observed parameters and their temporal and spatial distribution characteristics, especially in areas where ground observation data are lacking, and can make up for the lack of ground monitoring.

[0058] Third, the probability identification method of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observations of the present invention solves multiple key problems in the prior art in industrial applications and has achieved significant technical progress.

[0059] 1. Existing technical problems solved:

[0060] Limitations of traditional radar: When observing severe convective weather (such as hail and thunderstorms), existing radar technology can only identify it through a single parameter such as reflectivity. It lacks comprehensive analysis of multiple parameters and is difficult to accurately predict the probability of hail and thunderstorms. Traditional methods are insufficient in spatial resolution and recognition accuracy, resulting in insufficiently timely and accurate warnings.

[0061] Lack of intelligent identification means: In existing technologies, the identification of severe convective weather phenomena mostly relies on manual experience or simple threshold judgment, and cannot use complex weather characteristic parameters for intelligent identification. This method is prone to misjudgment and missed judgment, resulting in unsatisfactory early warning effects for hail and thunderstorms.

[0062] Unable to accurately locate the location of weather phenomena: Many existing systems can only predict the overall trend of weather phenomena, and are unable to accurately locate the location of disastrous weather such as hail and thunderstorms. This makes it difficult to accurately respond to disaster prevention in specific areas.

[0063] 2. Technological advancement:

[0064] Multi-parameter fusion analysis: This method uses X-band all-solid-state polarization radar to extract multiple characteristic parameters related to hail and thunderstorms (such as radar reflectivity, differential reflectivity, correlation coefficient, etc.), and combines fuzzy logic algorithms to more comprehensively analyze the characteristics of these weather processes. Compared with traditional single parameter judgment, this method integrates multiple physical quantities and significantly improves the accuracy of identifying hail and thunderstorms.

[0065] Intelligent probability prediction: Through the fuzzy logic method, the present invention can calculate the probability of hail and thunderstorm gale according to the changes in characteristic parameters in radar observation, and automatically identify the nature of disastrous weather through threshold judgment. This intelligent identification method greatly improves the accuracy and real-time nature of the prediction, allowing early warnings to be issued earlier and more accurately.

[0066] Precise positioning and probability assessment: The present invention can regard continuous data points with a probability greater than a preset threshold as identification results, and accurately locate the area where hail or thunderstorm gale occurs according to its central coordinates, while evaluating the probability of disaster occurrence by calculating the probability mean. This advancement enables emergency response departments to take targeted protective measures and reduce unnecessary waste of resources.

[0067] Improve meteorological disaster prevention capabilities: This invention significantly enhances the early warning capabilities of meteorological disaster prevention, especially for severe convective weather. Through more accurate identification and positioning of hail and thunderstorms, relevant departments can take effective measures in advance to avoid significant losses caused by disasters.

[0068] In summary, the present invention solves the problems of inaccurate identification, inability to accurately locate and lack of intelligent analysis in the prior art by combining multi-parameter analysis and fuzzy logic methods, and has achieved significant technological progress. It has important industrial application value in the field of weather warning and meteorological disaster prevention. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a flow chart of a method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation provided by an embodiment of the present invention;

[0070] Figure 2 is a schematic diagram of obtaining a strong convection identification result from a single point probability provided by an embodiment of the present invention;

[0071] Figure 3 It is a structural diagram of a hail and thunderstorm gale probability identification system based on X-band all-solid-state polarization radar RHI observation provided by an embodiment of the present invention.

[0072] Figure 4 This is a rendering of hail recognition results based on X-band all-solid-state polarization weather radar RHI observation provided by an embodiment of the present invention;

[0073] Figure 5 This is a rendering of thunderstorm and gale identification results based on X-band all-solid-state polarization weather radar RHI observations provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0075] The following are two specific application examples of the method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation:

[0076] Example 1: Thunderstorm and gale warning issued by the city meteorological department

[0077] The meteorological department of a certain city has deployed a monitoring system based on an X-band all-solid-state polarization radar. The system conducts RHI radar observations of the city and surrounding areas every day, and monitors the probability of thunderstorms in real time. In one monitoring, the radar system captured anomalies in parameters such as the radar reflectivity factor and differential reflectivity in a certain area, and combined with fuzzy logic to calculate that the probability of thunderstorms exceeded the set threshold. The system promptly identified the specific location of the thunderstorm and issued an early warning to the city's emergency management department. Relevant departments took measures in advance to evacuate the crowd and ensure the safety of infrastructure based on the early warning, successfully reducing casualties and property losses.

[0078] Example 2: Hail warning system in agricultural areas

[0079] In a large agricultural production area, the meteorological department used an X-band all-solid-state polarization radar system to detect hail weather. The system analyzes the radar echo characteristics of different areas through RHI observation. In a certain monitoring, the system analyzed data such as reflectivity factor and correlation coefficient, and used fuzzy logic to calculate that the probability of hail in a certain agricultural area continued to rise and exceeded the set threshold. The system identified that the probability of hail in the area had reached a dangerous level. The meteorological department quickly issued a hail warning, and farmers took protective measures in advance to reduce crop losses.

[0080] These two embodiments demonstrate the practical application of this method in early warning and prevention of disastrous weather, effectively improving the accuracy and timeliness of the warning.

[0081] like Figure 1 As shown, an embodiment of the present invention provides a method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation, comprising the following steps:

[0082] Step 1: Extract characteristic parameters related to hail, thunderstorm and gale weather processes from weather radar RHI observations, and calculate the distribution of characteristic parameters along the RHI observation azimuth with horizontal distance;

[0083] Step 2: For the two types of weather processes, hail and thunderstorm, the parameters related to the occurrence of hail and thunderstorm are selected from the characteristic parameters calculated in step 1, and the distribution of the probability of occurrence of hail and thunderstorm with horizontal distance is obtained by using fuzzy logic;

[0084] Step three: take the continuous data points whose occurrence probability is greater than the preset threshold as an identification result, take their central coordinates as the occurrence location of hail and thunderstorm, and take the mean of their occurrence probability as the occurrence probability of hail and thunderstorm.

[0085] Step 1 specifically includes the following:

[0086] Based on the reflectivity observed by RHI, the height difference DOH between the center of the strong echo and the zero-degree layer, the echo bottom height EB, the echo top height ET, the echo thickness ED and the vertical accumulated liquid water content VIL at different horizontal distances x are calculated.

[0087] Before calculating the height difference DOH between the center height of the strong echo and the zero-degree layer, the echo bottom height EB, the echo top height ET, the echo thickness ED and the vertical accumulated liquid water content VIL, the reflectivity factor in the polar coordinates is interpolated to the rectangular coordinates Z (x, z). The calculation methods of DOH, EB, ET, ED and VIL are:

[0088] DOH(x)=max({H(x,j)|Z(x,z j )>40})-H 0c (1)

[0089] EB(x)=min({H(x,j)|Z(x,z j )>18}) (2)

[0090] ET(x)=max({H(x,j)|Z(x,z j )>18}) (3)

[0091] ED(x)=ET-EB (4)

[0092]

[0093] Among them, H 0c It represents the height of the zero-degree layer, which can be obtained from the sounding observations or numerical model prediction results of similar time and space. The reflectivity factor Z in equation (5) is in linear units, and the units in other equations are dB.

[0094] The radial shear RS at different horizontal distances x is calculated based on the Doppler velocity observed by RHI. To calculate the radial shear RS, first calculate the radial shear in polar coordinates:

[0095]

[0096] Then the radial shear RS in polar coordinates p Interpolation to rectangular coordinates RS c (x, z), and calculate the average of all radial convergence values ​​at each x position:

[0097]

[0098] For the hail probability recognition in step 2, three characteristic parameters DOH, ED and VIL are selected as the input of the membership function to obtain the fuzzy value of hail recognition.

[0099] V hail(x) = ω DOH MF DOH +ω ED MF ED +ω VIL MF VIL (8)

[0100] For the probability identification of thunderstorms, three characteristic parameters, RS, ED and VIL, are selected as the input of the membership function to obtain the fuzzy value of thunderstorm identification.

[0101] V wind (x) = ω RS MF RS +ω ED MF ED +ω VIL MF VIL (9)

[0102] MF and ω in equations (8) and (9) represent the membership function and weight value of a parameter respectively. Note that the membership function and weight of the same parameter in equations (8) and (9) are different. The membership function is the ladder function commonly used in fuzzy logic:

[0103]

[0104] The parameters x1 and x2 are obtained based on experience or statistics from historical case data.

[0105] For the hail and thunderstorm fuzzy values ​​obtained in step 2, Figure 2 The strong convection identification result is shown as follows. First, take a value greater than a certain preset threshold V T All points M:

[0106] M={x i |V(x i )>V T} (10)

[0107] Assume N = {x j , x j+1 , ..., x j+K} is a subset of M and K data points are continuous in the horizontal direction, then N is a hail, thunderstorm and gale recognition result. The horizontal position and occurrence probability of this recognition result are:

[0108]

[0109]

[0110] like Figure 3As shown, an embodiment of the present invention provides a method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation, and a system for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation, comprising:

[0111] The characteristic parameter extraction module extracts characteristic parameters related to hail, thunderstorm and gale weather processes from the weather radar RHI observations, and calculates the distribution of characteristic parameters along the RHI observation azimuth with horizontal distance;

[0112] The probability recognition module selects parameters related to the occurrence of hail and thunderstorm from the calculated characteristic parameters for the two types of weather processes, and uses fuzzy logic to obtain the distribution of the probability of hail and thunderstorm with horizontal distance;

[0113] The occurrence probability calculation module takes the continuous data points with an occurrence probability greater than a preset threshold as an identification result, takes its central coordinates as the occurrence location for identifying hail and thunderstorms, and takes the mean of its occurrence probability as the occurrence probability for identifying hail and thunderstorms.

[0114] The invention belongs to the field of meteorological observation and forecasting business, and specifically belongs to an application product of probability identification of X-band all-solid-state polarization weather radar RHI observation data in hail and thunderstorm gale weather. Figure 4 , 5 It is the effect diagram of the experiment of implementing the present invention, Figure 4 This is the effect diagram of hail identification results in X-band all-solid-state polarization weather radar RHI observation. The black five-pointed star icon is the area identified as hail. Figure 5 This is a rendering of the thunderstorm and gale identification results in the X-band all-solid-state polarization weather radar RHI observation. The black five-pointed star icon is the area identified as the thunderstorm and gale. Therefore, through this method, based on the X-band all-solid-state polarization weather radar RHI observation data, the occurrence areas of hail and thunderstorm and gale can be basically identified, providing effective technical support for the observation and early warning of these two types of severe convective weather.

[0115] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0116] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation, characterized in that: The following steps are involved: Step 1: extract multiple characteristic parameters related to hail and thunderstorm weather processes from weather radar RHI observations, wherein the characteristic parameters include radar reflectivity factor, differential reflectivity, and correlation coefficient; and calculate the distribution of characteristic parameters along the RHI observation azimuth with horizontal distance by combining a high-performance computing platform for real-time data processing; Step 2: For the two types of weather processes, hail and thunderstorm, the embedded fuzzy logic operation unit is used to automatically select the characteristic parameters highly correlated with hail and thunderstorm from the characteristic parameters calculated in step 1, and the fuzzy logic rules are optimized by combining the machine learning algorithm to calculate the distribution of the probability of hail and thunderstorm with horizontal distance; Step 3: Take the continuous data points with a probability greater than the preset threshold as an identification result, and optimize and adjust them in combination with the dynamic adaptive threshold algorithm. Take their center coordinates and combine them with the high-precision GPS or Beidou system to accurately identify the location of hail and thunderstorms. Take the mean of their probability of occurrence as the probability of identifying hail and thunderstorms. The cloud computing platform integrates data from multiple radar sites, synchronizes and analyzes the identification results in real time, and uses the GIS system to visualize the spatial distribution of disastrous weather, and publishes weather warning information in real time to mobile and web terminals.

2. The method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation as claimed in claim 1, characterized in that: In the step 1, the height difference DOH between the center height of the strong echo and the zero-degree layer, the echo bottom height EB, the echo top height ET, the echo thickness ED and the vertical accumulated liquid water content VIL at different horizontal distances x are calculated based on the reflectivity observed by RHI.

3. The method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation as claimed in claim 2 is characterized in that: Before calculating the height difference DOH between the center height of the strong echo and the zero-degree layer, the echo bottom height EB, the echo top height ET, the echo thickness ED and the vertical accumulated liquid water content VIL, the reflectivity factor in the polar coordinates is interpolated to the rectangular coordinates Z (x, z). The calculation methods of DOH, EB, ET, ED and VIL are: DOH(x)=max({H(x,j)|Z(x,z j )>40})-H 0c (1) EB(x)=min({H(x,j)|Z(x,z j )>18}) (2) AND(x)=max({H(x,j)|Z(x,z j )>18}) (3) ED(x)=ET-EB (4) Among them, H 0c It represents the height of the zero-degree layer, which is obtained from the sounding observations or numerical model prediction results of similar time and space. The reflectivity factor Z in formula (5) is in linear units, and the units in other formulas are dB.

4. The method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation as claimed in claim 1, characterized in that: In the step 1, the radial shear RS at different horizontal distances x is calculated based on the Doppler velocity observed by RHI; First calculate the radial shear in polar coordinates: Then interpolate the radial shear RSp in polar coordinates to the rectangular coordinates RSc(x, z), and calculate the average of all radial convergence values ​​at each x position:

5. The method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation as claimed in claim 1, characterized in that: In step 2, for the probability recognition of hail, three characteristic parameters, DOH, ED and VIL, are selected as the input of the fuzzy logic membership function to obtain the fuzzy value of hail recognition: V hail (x)=ω DOH MF DOH +oh ED MF ED +oh VIL MF VIL (8) For the probability identification of thunderstorms, three characteristic parameters, RS, ED and VIL, are selected as the input of the fuzzy logic membership function to obtain the fuzzy value of thunderstorm identification: V wind (x)=ω RS MF RS +oh ED MF ED +oh VIL MF VIL (9) MF and ω in equations (8) and (9) represent the membership function and weight value of a parameter respectively; the membership function and weight of the same parameter in equations (8) and (9) are different.

6. The method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation as claimed in claim 1, characterized in that: Step 3 specifically includes the following: Based on the hail and thunderstorm fuzzy values ​​obtained in step 2, take all points M that are greater than a pre-set threshold VT: M={x i |V(x i )>V T } (10) Assume N = {x j ,x j+1 ,...,x j+K } is a subset of M and K data points are continuous in the horizontal direction, then N is a hail, thunderstorm and gale recognition result; the horizontal position and occurrence probability of the recognition result are:

7. A method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observations according to any one of claims 1 to 6, characterized in that: include: The characteristic parameter extraction module extracts characteristic parameters related to hail, thunderstorm and gale weather processes from the weather radar RHI observations, and calculates the distribution of characteristic parameters along the RHI observation azimuth with horizontal distance; The probability recognition module selects parameters related to the occurrence of hail and thunderstorm from the calculated characteristic parameters for the two types of weather processes, and uses fuzzy logic to obtain the distribution of the probability of hail and thunderstorm with horizontal distance; The occurrence probability calculation module takes the continuous data points with an occurrence probability greater than a preset threshold as an identification result, takes its central coordinates as the occurrence location for identifying hail and thunderstorms, and takes the mean of its occurrence probability as the occurrence probability for identifying hail and thunderstorms.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for probabilistic identification of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observation as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for identifying the probability of hail and thunderstorm gale based on X-band all-solid-state polarization radar RHI observations as described in any one of claims 1 to 6.

10. An information data processing terminal, characterized in that: The information data processing terminal includes the hail and thunderstorm gale probability identification system based on X-band all-solid-state polarization radar RHI observation as described in claim 7.

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