An airborne weather radar wind shear identification method combined with a weather numerical model

Abstract

The present application relates to the technical field of wind shear, the field of numerical weather prediction and the technical field of airborne weather radar, and specifically relates to an airborne weather radar wind shear identification method combined with numerical weather prediction, which is based on the existing airborne weather radar detection and identification of wind shear, uses the open source numerical weather prediction model WRF model for numerical simulation, establishes applicable models for a plurality of different typical wind shear events, statistically analyzes the structural characteristics of different wind shear samples, divides the wind shear samples into multiple groups according to the characteristics, and forms a typical wind shear event database. In the actual flight process, the numerical model prior information and the airborne weather radar measured data are combined to accurately determine and identify the type and stage of the current wind shear event, make targeted alarms, improve the identification accuracy of the airborne weather radar for wind shear events, and enhance the alarm capability for different types of wind shear events.

Description

Technical Field

[0001] This invention relates to the fields of wind shear technology, meteorological numerical modeling, and airborne meteorological radar technology, specifically to a wind shear identification method for airborne meteorological radar that combines meteorological numerical models. Background Technology

[0002] Wind shear refers to the sudden change in the wind speed vector at any two points in space over a period of time. This includes changes in the wind speed vector or its components along the vertical or horizontal direction. Wind shear is a vector value that reflects the changes in wind direction and speed between the two points under study. In the field of aviation meteorology, numerous observational and statistical results show that wind shear frequently occurs in the low-to-medium altitude areas where aircraft take off and land, and this is called low-altitude wind shear, which usually occurs below an altitude of about 600 meters above the ground.

[0003] Low-level wind shear poses a significant danger and threat to the safe operation of aircraft; even slight mishandling can result in substantial casualties and property damage. Therefore, low-level wind shear is often referred to as the "invisible aircraft killer." Timely identification and warning of wind shear are crucial aspects of flight safety. There are various causes of low-level wind shear, but investigations and meteorological studies of low-level wind shear accidents indicate that microbursts are the primary cause of wind shear-related flight accidents.

[0004] The theoretical foundation for this aviation hazard assessment and basic measurement process was established between 1991 and 1993. Because the F-factor is easy to calculate and comprehensively considers the horizontal and vertical components of the wind vector shear, as well as the performance of the aircraft itself, it is now widely used by the FAA in the verification process of airborne wind shear radar detection systems. Generally, when the average F-factor over a radial distance of one kilometer is greater than 0.1, the wind shear threat level is considered high. According to the DO-220A standard, the First-Instant Wind Shear Alert (FBAR) should be equal to or greater than 0.13, while the First-Instant Wind Shear Alert (FBAR) should be less than or equal to 0.085.

[0005] However, low-level wind shear manifests in a variety of forms. In particular, the formation mechanisms, characteristics, and impacts on flight safety of different types of dry and wet wind shear are significantly different. Relying solely on airborne weather radar to detect the F-factor to identify and warn of complex low-level wind shear events is clearly insufficient for accurate wind shear identification and for providing targeted warnings for different types of wind shear, such as dry and wet wind shear. Summary of the Invention

[0006] In view of this, this invention provides an airborne weather radar wind shear identification method that combines meteorological numerical models. Building upon existing airborne weather radar detection and identification of wind shear, it employs the open-source Weather Research and Forecasting Model (WRF) for numerical simulation, establishing applicable models for various typical wind shear events. The structural characteristics of different wind shear samples are statistically analyzed, and based on these characteristics, the wind shear samples are categorized into multiple types, forming a database of typical wind shear events. During actual flight, by combining prior information from the numerical model with measured data from the airborne weather radar, the method accurately determines the type and stage of the current wind shear event, issuing targeted warnings. This improves the accuracy of airborne weather radar in identifying wind shear events and enhances its warning capabilities for different types of wind shear events.

[0007] To achieve the above-mentioned technical objectives, the specific technical solution adopted by the present invention is as follows:

[0008] A method for wind shear identification using airborne weather radar combined with meteorological numerical models includes the following steps:

[0009] Step 1: Construct multiple meteorological models applicable to typical wind shear events by simulating different types of typical wind shear events that have occurred in history;

[0010] Step 2: Statistically analyze the structural characteristics of different meteorological models; establish a database including various types of wind shear;

[0011] Step 3: During the operation of the airborne weather radar, compare the measured data of the airborne weather radar with the structural features in the database;

[0012] Step 4: Identify the specific type of the current wind shear event measured by the airborne weather radar; issue targeted warnings for specific types of wind shear events.

[0013] Furthermore, the structural features include convergence zone depth, outflow depth, central radar reflectivity, maximum outflow velocity, outflow radius, maximum outflow velocity radius, and lifetime.

[0014] Furthermore, wind shear can be categorized into dry wind shear events and wet wind shear events.

[0015] Furthermore, wind shear types include symmetrical wind shear events, asymmetrical wind shear events, single-body wind shear events, and / or multi-body wind shear events.

[0016] Furthermore, the database is stored in the data storage and processing device of the airborne weather radar system or the ground data storage and processing device;

[0017] Step 4 specifically involves: when the airborne weather radar is in operation, if the F factor is greater than the no-alarm threshold, based on the radar reflectivity and wind field measured by the airborne weather radar, calculating the matching degree between the current wind field and radar reflectivity and the wind shear model samples in the database, finding the sample and time that best matches the measured data, identifying the type and stage of the current wind shear event, and issuing a targeted alarm.

[0018] Furthermore, the data factors of the meteorological model include: geopotential height, temperature, wind components in different directions, relative humidity, specific humidity, vertical velocity, vorticity, divergence, particle number concentration of condensate, particle mixing ratio of condensate, and radar reflectivity.

[0019] Furthermore, in step 2, the type of wind shear is determined based on the structural features using a clustering analysis algorithm;

[0020] Samples with a reflectance ≥20dBZ at the wind shear center are defined as wet wind shear; otherwise, they are defined as dry wind shear.

[0021] Furthermore, in step 2, the method for establishing the database is as follows: the structural features of wind shear events with the same type and the same time parameter are subjected to a significance test, and all features with a significance level α < 0.05 are taken as the significant feature set under that time parameter to form the database.

[0022] Furthermore, in step 3, the method for comparing the measured data of the airborne weather radar with the structural features in the database is specifically as follows:

[0023] By comparing the measured data with the structural features of the computer-borne meteorological radar measured data and the wind shear model data of different types in the database, the matching degree of the measured data is determined.

[0024] and / or

[0025] Compare the spatial correlation coefficient, structural similarity, and peak signal-to-noise ratio of the measured data with those in the database.

[0026] Furthermore, in step 4, the method for generating targeted alarms is as follows:

[0027] Based on the intensity, development stage, and potential impact on aircraft flight safety of current wind shear events, targeted warnings are issued for different types of wind shear events.

[0028] By adopting the above technical solution, the present invention can bring the following beneficial effects:

[0029] This invention obtains a model database of various types of wind shear events through numerical simulation in WRF mode. By combining the measured data of airborne weather radar with the different types of wind shear database obtained by WRF mode during actual flight, it is possible to accurately identify different types of wind shear events under complex weather conditions and make targeted warnings for different types of wind shear events.

[0030] This invention can be applied to the fields of civilian and military airborne weather radar, improving the ability of airborne weather radar to identify and warn of wind shear events, and further enhancing flight safety. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram of the flowchart of an airborne meteorological radar wind shear identification and alarm method combining meteorological numerical models according to an embodiment of the present invention.

[0033] Figure 2 This is a schematic diagram of the multi-layer nested scheme for simulating wind shear events using WRF mode in this invention;

[0034] Figure 3 This is a schematic diagram of the process structure for constructing different types of wind shear event databases using the WRF pattern in an embodiment of the present invention.

[0035] Figure 4 This is a schematic diagram of the process structure for wind shear identification and alarm based on measured data from airborne meteorological radar and prior information from numerical models in an embodiment of the present invention. Detailed Implementation

[0036] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0037] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0038] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using other structures and / or functionalities besides one or more of the aspects set forth herein.

[0039] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0040] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0041] In one embodiment of the present invention, a method for identifying wind shear using airborne weather radar combined with meteorological numerical models is proposed, comprising the following steps:

[0042] Step 1: Construct multiple meteorological models applicable to typical wind shear events by simulating different types of typical wind shear events that have occurred in history;

[0043] Step 2: Statistically analyze the structural characteristics of different meteorological models; establish a database including various types of wind shear;

[0044] Step 3: During the operation of the airborne weather radar, compare the measured data of the airborne weather radar with the structural features in the database;

[0045] Step 4: Identify the specific type of the current wind shear event measured by the airborne weather radar; issue targeted warnings for specific types of wind shear events.

[0046] In this embodiment, the structural features include convergence zone depth, outflow depth, central radar reflectivity, maximum outflow velocity, outflow radius, maximum outflow velocity radius, and lifetime.

[0047] In this embodiment, the types of wind shear include dry wind shear events and wet wind shear events.

[0048] In this embodiment, the types of wind shear include symmetrical wind shear events, asymmetrical wind shear events, single-body wind shear events, and / or multi-body wind shear events.

[0049] In this embodiment, the database is stored in the data storage and processing device of the airborne weather radar system or the ground data storage and processing device;

[0050] Step 4 specifically involves: when the airborne weather radar is in operation, if the F factor is greater than the no-alarm threshold, based on the radar reflectivity and wind field measured by the airborne weather radar, calculating the matching degree between the current wind field and radar reflectivity and the wind shear model samples in the database, finding the sample and time that best matches the measured data, identifying the type and stage of the current wind shear event, and issuing a targeted alarm.

[0051] In this embodiment, the data factors of the meteorological model include: geopotential height, temperature, wind components in different directions, relative humidity, specific humidity, vertical velocity, vorticity, divergence, particle number concentration of condensate, particle mixing ratio of condensate, and radar reflectivity.

[0052] In this embodiment, in step 2, the type of wind shear is determined based on the structural features using a clustering analysis algorithm;

[0053] Samples with a reflectance ≥30dBZ at the wind shear center are defined as wet wind shear; otherwise, they are defined as dry wind shear.

[0054] In this embodiment, the method for establishing the database in step 2 is as follows: the structural features of wind shear events with the same type and the same time parameter are subjected to significance testing, and all features with a significance level α < 0.05 are taken as the significant feature set under that time parameter to form the database.

[0055] In this embodiment, the method for comparing the measured data of the airborne weather radar with the structural features in the database in step 3 is specifically as follows:

[0056] By comparing the measured data with the structural features of the computer-borne meteorological radar measured data and the wind shear model data of different types in the database, the matching degree of the measured data is determined.

[0057] and / or

[0058] Compare the spatial correlation coefficient, structural similarity, and peak signal-to-noise ratio of the measured data with those in the database.

[0059] In this embodiment, the method for generating targeted alarms in step 4 is as follows:

[0060] Based on the intensity, development stage, and potential impact on aircraft flight safety of current wind shear events, targeted warnings are issued for different types of wind shear events.

[0061] like Figure 1 As shown, this embodiment of the invention provides a method for wind shear identification and warning using airborne meteorological radar combined with meteorological numerical models. The method begins in step S100, constructing a meteorological model for wind shear events. In this embodiment, the wind shear event model is constructed using WRF model version 4.3. The WRF model is a unified mesoscale weather forecasting model developed by US research institutions such as the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). The model is divided into two types: ARW (the Advanced Research WRF) and NMM (the Nonhydrostatic Mesoscale Model), namely research and operational forms, managed and maintained by NCEP and NCAR respectively. This embodiment uses the WRF-ARW module. WRF is written in Fortran, and its software design and development fully consider the effectiveness of adapting to visible parallel platforms in large-scale parallel computing environments, enabling parallel computation of processes on both distributed memory and shared memory computers. WRF models offer horizontal resolutions ranging from tens of meters to tens of kilometers, with a wide range of parameterization schemes to choose from. They are portable, easy to maintain, expandable, and highly efficient, and are widely used in various meteorological studies, including numerical weather prediction, idealized atmospheric simulation, data assimilation development, and coupling with other numerical models.

[0062] In this embodiment of the invention, when constructing a wind shear event model using the WRF mode, it is based on typical wind shear events that have occurred in history. The initial field, boundary field, spatiotemporal resolution, physical parameterization scheme, etc., are determined according to the typical historical wind shear events, forming a series of refined wind shear event models.

[0063] Figure 2 This diagram illustrates a multi-layered nested scheme for simulating wind shear events using the WRF model in this invention. Low-altitude wind shear events, due to their strong randomness and uncertainty, often occur within a specific, relatively small airspace, requiring extremely high precision and resolution for simulation to achieve satisfactory results. However, directly increasing the spatiotemporal resolution of the WRF model over a large airspace would lead to a surge in numerical simulation computation, severely reducing computational efficiency and operational practicality. Therefore, to ensure the refinement and accuracy of simulations for typical aviation meteorological threat scenarios while improving computational efficiency, this embodiment of the invention employs a multi-layered nested approach using mesoscale and Large Eddy Simulation (LES) methods, as illustrated in the diagram below. Figure 2 As shown, a mesoscale resolution WRF model is used as the outer nested system to better reflect the real atmospheric environment. This approach considers the influence of large-scale surrounding factors, accelerates the overall algorithm convergence, and supports research on the weather mechanisms of large-scale circulation in typical meteorological threat scenarios. Then, a smaller-scale LES simulation is used in key areas, such as airports. The results of the outer mesoscale simulation are used as the background field and boundary conditions for the inner LES simulation, allowing for a more accurate description of the instantaneous atmospheric motion through high-precision LES simulation. By using a nested scheme of WRF mesoscale grids and LES, computational efficiency is improved while simulation accuracy is effectively enhanced.

[0064] Three-dimensional time-varying meteorological field data is output through WRF mode. The meteorological field data includes at least the following variables: geopotential height, temperature, u-component and v-component of wind, relative humidity, specific humidity, vertical velocity, vorticity, divergence, particle number concentration and mixing ratio of water vapor, cloud water, rainwater, ice water, snow water, graupel, hail, and other water condensates, and radar reflectivity. The meteorological field data storage format in this embodiment of the invention can be netCDF format, GRIB2 format, MICAPS4 format, HDF5 format, etc., and this embodiment of the invention does not impose specific limitations on this.

[0065] In step S200, the structural characteristics of different types of wind shear events are statistically analyzed.

[0066] In this embodiment of the invention, the structural characteristics of wind shear samples are statistically analyzed by calculating meteorological field data output from a series of wind shear event models. These structural characteristics include at least: convergence zone depth, outflow depth, central radar reflectivity, maximum outflow velocity, outflow radius, maximum outflow velocity radius, and lifespan. Then, based on different structural characteristics, wind shear event models are classified using algorithms such as cluster analysis. The specific types of wind shear events should at least include dry wind shear events and wet wind shear events, and may also include symmetrical wind shear events and asymmetrical wind shear events, single-body wind shear events and multi-body wind shear events, and combinations of the above different types of wind shear events. Samples with a central meteorological reflectivity ≥20 dBZ are classified as wet wind shear, and samples <20 dBZ are classified as dry wind shear.

[0067] In step S300, a database of different types of wind shear events is formed.

[0068] In this embodiment of the invention, the structural features of wind shear events with the same type and time parameters are subjected to significance testing. For example, each structural feature of the dry / wet wind shear type sample feature set is subjected to statistical significance testing, and all features with a significance level α < 0.05 are taken as the significant feature set of the dry / wet wind shear type sample feature set under that time parameter, forming a dry / wet wind shear type event database. A database of symmetrical and asymmetrical wind shear events can also be formed, as well as a database of single-body and multi-body wind shear events, and a database of combinations of the above-mentioned different types of wind shear events can also be included.

[0069] Figure 3 A flowchart illustrating the construction of different types of wind shear event databases using the WRF pattern in an embodiment of the present invention is shown. Figure 3As shown, in this embodiment of the invention, a wind shear event WRF meteorological model is constructed based on typical historical wind shear events. First, the initial field, boundary field, spatiotemporal resolution, and physical parameterization scheme for WRF model simulation are determined based on historical typical wind shear events, forming a series of refined wind shear event models and outputting three-dimensional time-varying meteorological field data. Then, this embodiment calculates the meteorological field data output by a series of wind shear event models to statistically analyze the structural characteristics of the wind shear samples. These structural characteristics include at least: convergence zone depth, outflow depth, central radar reflectivity, maximum outflow velocity, outflow radius, maximum outflow velocity radius, and lifespan. Then, based on different structural characteristics, algorithms such as cluster analysis are used to classify the wind shear event models. The specific types of wind shear events should at least include dry wind shear events and wet wind shear events, and may also include symmetrical wind shear events and asymmetrical wind shear events, single-body wind shear events and multi-body wind shear events, and combinations of the above different types of wind shear events. In this embodiment of the invention, the structural features of wind shear events with the same type and the same time parameters are subjected to significance tests. For example, each structural feature of the dry / wet wind shear type sample feature set is subjected to a statistical significance test, and all features with a significance level α < 0.05 are taken as the significant feature set of the dry / wet wind shear type sample feature set under that time parameter, thus forming a dry / wet wind shear type event database.

[0070] In step S400, the measured data from the airborne weather radar is compared with the model data.

[0071] In this embodiment of the invention, the model database of different types of wind shear events formed in the above steps is stored in the data storage and processing device of the airborne meteorological radar system or the ground data storage and processing device. During actual flight, when the FBAR is greater than the no-fire warning threshold of 0.085, the matching degree between the current wind field, radar reflectivity and each group of wind shear model samples in the database is calculated based on the radar reflectivity and wind field measured by the airborne meteorological radar. The sample and time parameters that best match the measured data are then found. When comparing airborne meteorological radar data and model data, this embodiment of the invention can calculate the matching degree between the measured airborne meteorological radar data and different types of wind shear model data by comparing structural features such as convergence zone depth, outflow depth, central radar reflectivity, maximum outflow velocity, outflow radius, and maximum outflow velocity radius. Alternatively, the matching degree between the measured airborne meteorological radar data and different types of wind shear model data can be characterized by calculating characteristic parameters such as spatial correlation coefficient, structural similarity, and peak signal-to-noise ratio.

[0072] In step S500, the specific type of the current wind shear event is identified.

[0073] In this embodiment of the invention, by comparing the current measured data from the airborne weather radar with different types of wind shear model databases, the wind shear event type that best matches the current airborne weather radar in different types of wind shear model databases is found, and the specific type of the current wind shear event is identified. The specific type of wind shear event should at least include dry wind shear events and wet wind shear events, and may also include symmetrical wind shear events and asymmetrical wind shear events, single-body wind shear events and multi-body wind shear events, and may also include combinations of the above different types of wind shear events.

[0074] In step S600, a targeted alarm is issued.

[0075] In this embodiment of the invention, after identifying the specific type of the current wind shear event, the intensity, development stage, and potential impact on the flight safety of the current wind shear event are determined by combining the F-factor, radar reflectivity, and wind shear event model database obtained from airborne meteorological radar observations, thereby making targeted warnings for different types of wind shear events.

[0076] Figure 4 This is a flowchart illustrating wind shear identification and alarm processing based on measured data from airborne meteorological radar and prior information from numerical models, as described in this embodiment of the invention. In this embodiment, when the FBAR (Front-Off Radar Range) exceeds the no-alarm threshold of 0.085, the matching degree between the current wind field, radar reflectivity, and various wind shear model samples in the database is calculated based on the current measured data from the airborne meteorological radar. The sample and time parameters that best match the measured data are then identified. When comparing airborne meteorological radar data with model data, this embodiment can calculate the matching degree between the measured data and different types of wind shear model data by comparing structural features such as convergence zone depth, outflow depth, central radar reflectivity, maximum outflow velocity, outflow radius, and maximum outflow velocity radius. Alternatively, it can characterize the matching degree between the measured data and different types of wind shear model data by calculating feature parameters such as spatial correlation coefficient, structural similarity, and peak signal-to-noise ratio. In this embodiment of the invention, by comparing the current measured data from the airborne weather radar with different types of wind shear model databases, the wind shear event type that best matches the current airborne weather radar in the different types of wind shear model databases is found, thus identifying the specific type of the current wind shear event. In this embodiment of the invention, after identifying the specific type of the current wind shear event, the F-factor, radar reflectivity obtained from airborne weather radar observations are combined with the wind shear event model database to determine the intensity, development stage, and potential impact on aircraft flight safety of the current wind shear event, thereby issuing targeted warnings for different types of wind shear events.

[0077] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for wind shear identification using airborne meteorological radar combined with meteorological numerical models, characterized in that, Includes the following steps: Step 1: By simulating different types of typical wind shear events that have occurred in history, multiple meteorological models suitable for typical wind shear events are constructed using a multi-layered nested scheme of mesoscale WRF model and large eddy simulation LES. Step 2: Statistically analyze the structural characteristics of different meteorological models; establish a database including various types of wind shear. The database is established by performing a significance test on the structural characteristics of wind shear events with the same type and time parameters, and setting the significance level... All features with a value <0.05 are taken as the salient feature set under this time parameter, forming the database; Step 3: During the operation of the airborne weather radar, compare the measured data of the airborne weather radar with the structural features in the database; Step 4: Identify the specific type of the current wind shear event measured by the airborne weather radar; issue targeted warnings for specific types of wind shear events.

2. The wind shear identification method according to claim 1, characterized in that, The structural features include convergence zone depth, outflow depth, central radar reflectivity, maximum outflow velocity, outflow radius, maximum outflow velocity radius, and lifetime.

3. The wind shear identification method according to claim 2, characterized in that, Wind shear can be categorized into dry wind shear events and wet wind shear events.

4. The wind shear identification method according to claim 3, characterized in that, Wind shear types include symmetrical wind shear events, asymmetrical wind shear events, single-body wind shear events, and / or multi-body wind shear events.

5. The wind shear identification method according to claim 4, characterized in that, The database is stored in the data storage and processing device of the airborne meteorological radar system or the ground data storage and processing device; Step 4 specifically involves: when the airborne weather radar is in operation, if the F factor is greater than the no-alarm threshold, based on the radar reflectivity and wind field measured by the airborne weather radar, calculating the matching degree between the current wind field and radar reflectivity and the wind shear model samples in the database, finding the sample and time that best matches the measured data, identifying the type and stage of the current wind shear event, and issuing a targeted alarm.

6. The wind shear identification method according to claim 5, characterized in that, The data factors of the meteorological model include: geopotential height, temperature, wind components in different directions, relative humidity, specific humidity, vertical velocity, vorticity, divergence, particle number concentration of condensate, particle mixing ratio of condensate, and radar reflectivity.

7. The wind shear identification method according to claim 6, characterized in that, In step 2, the type of wind shear is determined based on the structural features using a clustering analysis algorithm; Samples with a reflectance ≥30dBZ at the wind shear center are defined as wet wind shear; otherwise, they are defined as dry wind shear.

8. The wind shear identification method according to claim 7, characterized in that, In step 3, the method for comparing the measured data from the airborne weather radar with the structural features in the database is as follows: By comparing the measured data with the structural features of the computer-borne meteorological radar measured data and the wind shear model data of different types in the database, the matching degree of the measured data is determined. and / or Compare the spatial correlation coefficient, structural similarity, and peak signal-to-noise ratio of the measured data with those in the database.

9. The wind shear identification method according to claim 1, characterized in that, In step 4, the specific method for generating targeted alarms is as follows: Based on the intensity, development stage, and potential impact on aircraft flight safety of current wind shear events, targeted warnings are issued for different types of wind shear events.

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