A bow echo-associated convection early warning method

Abstract

The present invention discloses a bow echo-associated convection early warning method, comprising receiving multidimensional meteorological forecast data input; calculating vertical shear rate data grid by grid using the central difference method; analyzing the vertical shear rate data of each horizontal grid point and detecting qualified vertical shear layers using the sliding window method; coupling and verifying the screened shear rate sequences with thermal parameters; selecting the optimal window for multiple qualified shear rate sequences that may appear in each grid, and selecting the sequence with the largest product as the final output; and plotting the convection triggering intensity of the final output shear sequence as scattered points on the forecast reflectivity image. The present invention is based on theoretically innovative mathematical module construction and method implementation; introducing a shear rate distribution normality test to eliminate abnormal disturbance layers; quantifying the convection triggering potential through the product form of physical quantities; and realizing automated layered feature recognition of three-dimensional meteorological fields.

Description

Technical Field

[0001] The invention belongs to the technical field of meteorological data reprocessing, and in particular relates to a bow echo-associated convection early warning method. Background Art

[0002] A bow echo is a typical type of severe convective weather system, named for its bow-shaped structure on radar reflectivity images. Bow echoes can trigger extreme winds and may be accompanied by short bursts of heavy rainfall, hail, and even tornadoes. Therefore, they are a key focus in severe convective warnings and forecasts. Quasi-linear convective systems sometimes emerge upstream or downstream of a bow echo. These systems, closely related to the parent bow echo storm, are often referred to as bow echo-associated convection (hereinafter referred to as "associated convection"). The development mechanisms of this type of associated convection are complex, resulting in extremely low forecast accuracy (approximately 20%). They often bring hazardous weather such as hail and thunderstorms.

[0003] The mechanism of associated convection is complex. Foreign scholars believe that its occurrence and development are related to low-level convergence, such as Figure 1 As shown in (a), the triggering of the convection associated with the bow echo upstream is due to the convergence of the mesoscale convective vortex of the bow echo parent body and the environmental low-level jet upstream; while the triggering of the convection associated with the downstream is mainly caused by the convergence of the environmental front and the low-level jet downstream, as shown in (a). Figure 1 As shown in (b). However, due to significant differences in domestic climate and environmental characteristics compared to those abroad, the mechanisms for the occurrence and development of associated convection in China exhibit regional characteristics. Domestic scholars have suggested that the occurrence and development of bow echo-associated convection in my country is closely related to rapid changes in mid- and low-level vertical shear. For example, based on a case study of associated convection upstream of a bow echo in Anhui on June 5, 2016, it was suggested that the positive and negative vertical shear variations with altitude (i.e., the rapid vertical shear variation zone, hereinafter referred to as the variation zone) generated by the stacking of the low-level jet and the mesoscale convective vortex of the bow echo itself at different altitudes was a key trigger for associated convection. Further research based on a case study of associated convection downstream of a bow echo in the Bohai Bay on July 25, 2022, found that the anticyclonic vortex formed by the high-level outflow of the bow echo's parent storm modulated the downstream wind field, causing rapid changes in vertical shear, which in turn generated negative pressure disturbances and had a significant impact on the occurrence and development of associated convection downstream. Based on multi-sample simulation, the study further found that the change zone is widely present in the associated convection and has a certain indicative significance for the occurrence and development of the associated convection; the formation of the change zone is related to the interaction of multiple multi-scale elements at different altitudes, such as: the interaction between the northwest airflow behind the large-scale high-altitude trough and the southwest airflow at low altitude; the interaction of the non-uniform flow fields at different altitudes of the northeastern cold vortex, etc.

[0004] Although relevant research has made breakthrough progress, showing that the occurrence and development mechanism of associated convective systems in my country has significant regional characteristics, its triggering and evolution are mainly controlled by the rapid changes in vertical wind shear in the middle and low layers, while the contribution of the ground convergence field is relatively limited. However, the innovative theoretical system has not yet been mathematically implemented. The current numerical forecast analysis products are unable to characterize the key factors of rapid changes in vertical wind shear that affect the occurrence and development of associated convection. There is a lack of specialized diagnostic modules for the precursor signals triggered by such associated convection, resulting in the absence of precursor signals and the inability to provide forecasters with effective warning lead times. These technical bottlenecks directly lead to low forecast accuracy for associated convective systems, seriously restricting the warning capabilities of severe convective weather. Summary of the Invention

[0005] The present invention aims to solve one of the technical problems existing in the related art at least to a certain extent.

[0006] The purpose of the present invention is to provide a bow echo associated convection warning method based on an innovative theoretical system and a mathematical model, generate relevant potential forecast products, and provide early warning and forecast of associated convection triggering.

[0007] In order to achieve the above-mentioned object, the present invention provides, on one hand, a bow echo-associated convection early warning method, comprising the following steps:

[0008] S100, receiving multi-dimensional weather forecast data input;

[0009] S200, using the central difference method, calculates the radial wind vertical shear rate, zonal wind vertical shear rate, accumulated convective instability energy, and free lift height on a grid-by-grid basis;

[0010] S300, analyzing the vertical shear rate data of each horizontal grid point, and detecting a vertical shear layer that meets the conditions using a sliding window method;

[0011] S400, performing thermodynamic parameter coupling and verification on the screened shear rate sequence, wherein the thermodynamic parameters include accumulated convective instability energy and the top height of the shear rate sequence;

[0012] S500, for each grid, performing optimal window selection for multiple eligible shear rate sequences, eliminating intervals completely contained by other shear rate sequences, and selecting the sequence with the largest product as the final output;

[0013] S600: Plot the final output shear sequence convective triggering intensity on the forecast reflectivity image as scattered points to form an associated convective warning forecast product.

[0014] A further preferred technical solution of the present invention is that the multidimensional meteorological forecast data in step S100 is a multidimensional element of the mesoscale model forecast field of the meteorological department, including at least three-dimensional radial wind, zonal wind, temperature, humidity, air pressure, convective instability energy and two-dimensional free lift height.

[0015] Preferably, in step S200, the second-order derivative matrix of the radial wind speed in the vertical direction is represented by the radial wind vertical shear rate, and the calculation formula is:

[0016]

[0017] The zonal wind vertical shear rate represents the second-order derivative matrix of the zonal wind speed in the vertical direction, and the calculation formula is:

[0018]

[0019] The accumulated convective instability energy is a physical quantity matrix reflecting the instability of the atmospheric structure and is expressed as ; The free lift height is used to characterize the two-dimensional field of the critical height of convection triggering, which is expressed as ;

[0020] in, is the partial derivative symbol, is the radial wind speed, is the zonal wind speed, express The accumulated convective instability energy at the altitude level, For free lifting height, represents the radial wind vertical shear rate, represents the zonal wind vertical shear rate, is the latitudinal grid, For radial grid, For the altitude layer.

[0021] Preferably, the step S300 analyzes the vertical shear rate data of each horizontal grid point and uses a sliding window method to detect a vertical shear layer that meets the conditions, specifically including:

[0022] S310, grid by grid (y, x), starting from level i, expand the window upward, the sliding window is represented as [i, j], where i starts from the bottom to the highest level, j ≥ i;

[0023] S320: Calculate the radial wind average shear rate within the window , the zonal mean wind shear rate ;

[0024] S330: When the window thickness reaches a minimum threshold m, the validity of the shear rate sequence in the window is verified.

[0025] Preferably, the validity verification of the shear rate sequence within the window in step S330 includes:

[0026] S331. Perform a normal distribution test on the shear rate sequence in the window area. The calculation formula is:

[0027]

[0028]

[0029] The passing condition is or ;

[0030] represents the Shapiro-Wilk normality test; and are the Shapiro-Wilk normality test statistics and significance probability values ​​of the radial wind within the window; and are the Shapiro-Wilk normality test statistics and significance probability values ​​of the zonal wind within the window; is the significance level;

[0031] S332, verify the average shear rate of the shear rate sequence in the window area, and pass the condition , where n is the minimum value of the average shear rate of the shear rate sequence;

[0032] S333. Verify the thickness of the shear rate sequence in the window area. The pass condition is , where Z is the vertical height of the terrain.

[0033] Preferably, the step S400 of performing thermodynamic parameter coupling and verification on the screened shear rate sequence specifically includes:

[0034] S410, verify the cumulative unstable energy of the screened shear rate sequence, and pass the condition ,in express Convective instability energy at altitude, is the minimum value of the accumulated convective instability energy;

[0035] S420, verify the top height of the screened shear rate sequence layer, and the conditions are as follows: .

[0036] Preferably, step S500 performs optimal window selection for multiple eligible shear rate sequences that may appear in each grid, eliminates intervals that are completely included in other shear rate sequences, and selects the sequence with the largest product as the final output; specifically, the process includes:

[0037] S510, from all valid window collection Remove the intervals that are completely contained by other shear sequences, expressed as:

[0038]

[0039] in, is the height index of any sequence in the set R except the target sequence, and are the bottom and top height indexes respectively;

[0040] S520: Select the sequence with the largest product of average shear rate and accumulated convective instability energy from the remaining candidate sequences as the final output, expressed as:

[0041]

[0042]

[0043] in, is the convective triggering intensity value of the optimal shear rate sequence, is the height index corresponding to the convective triggering intensity value of the optimal shear rate sequence.

[0044] Preferably, step S600 plots the final output shear sequence convective triggering intensity on the forecast reflectivity image as scattered points to form an associated convective warning forecast product; specifically, the step S600 includes:

[0045] S610: Output the associated convective warning and forecast product elements, including the vertical index range of all valid shear rate sequences at each grid point, the average shear rate value of the corresponding shear rate sequence, the cumulative unstable energy value of each shear rate sequence, the vertical index of the optimal shear rate sequence, and the convective triggering intensity value of the optimal shear rate sequence; wherein, the vertical index range of all valid shear rate sequences at each grid point is expressed as ; The convective triggering intensity value of the optimal shear rate sequence is expressed as ;

[0046] S620: When a valid shear rate sequence index exists at a grid point, it indicates that the grid point has the possibility of associated convection triggering; the shear sequence convection triggering intensity value indicates the quality of the associated convection triggering condition of the grid;

[0047] S630: Plot the shear sequence convection triggering intensity on the forecast reflectivity image as scattered points as an associated convection warning precursor signal.

[0048] Another aspect of the present invention provides a non-transitory computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions enable a computer to execute the above-mentioned bow echo-associated convection early warning method.

[0049] Another aspect of the present invention provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus, and the processor calls the logic instructions in the memory to execute the above-mentioned bow echo associated convection warning method.

[0050] Another aspect of the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer executes the above-mentioned bow echo-associated convection warning method.

[0051] Beneficial Effects: The bow echo-associated convection early warning method, based on a self-developed theoretical framework for associated convection triggering, quantitatively characterizes the dynamic process by establishing a mathematical model describing the characteristics of sudden changes in mid- and low-level vertical wind shear. Through theoretical testing and verification of multiple sample cases, critical thresholds for dynamic and thermal parameters are determined. Through comprehensive analysis of dynamic and thermal conditions, an innovative model for the potential for thermal-dynamic coupled convection is established to generate an early warning and forecast product. This product demonstrates excellent predictive effectiveness for the occurrence and development of associated convection.

[0052] The product generated by this invention has a good forecast effect on the target convection. After improvement, it can be used in business applications, achieving a leap from the original to the current forecast of bow echo-associated convection, and further improving the accuracy of related convection warning forecasts. This invention is based on theoretical innovations in mathematical module construction and method implementation; introduces a shear rate distribution normality test to eliminate abnormal disturbance layers; quantifies the convection triggering potential through the product of physical quantities; and realizes automated layered feature recognition in three-dimensional meteorological fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A conceptual model for the occurrence and development mechanism of bow echo-associated convection abroad;

[0054] Figure 1 Middle (a) shows the convection associated with the bow echo upstream; Figure 1 Middle (b) shows the convection associated with the bow echo downstream;

[0055] Figure 2 This is a flow chart of the bow echo-associated convection early warning method of the present invention;

[0056] Figure 3 This is a diagram showing the prediction effect for the case of convection associated with the bow echo upstream in Example 1;

[0057] Figure 3 (a) is the radar combined reflectivity and associated convective potential distribution of the model forecast field at 19:00. Figure 3 (b) is the radar combined reflectivity and associated convective potential distribution of the model forecast field at 20:00. Figure 3 (c) is the radar combined reflectivity and associated convective potential distribution of the model forecast field at 21:00. Figure 3 (d) in the middle is the radar combined reflectivity and associated convective potential distribution of the model forecast field at 22:00;

[0058] Figure 4 This is a diagram showing the prediction effect for the case of convection associated with the bow echo downstream in Example 1;

[0059] Figure 4 (a) is the radar combined reflectivity and associated convective potential distribution of the model forecast field at 03:00. Figure 4 (b) is the radar combined reflectivity and associated convective potential distribution of the model forecast field at 03:30. Figure 4 (c) is the radar combined reflectivity and associated convective potential distribution of the model forecast field at 04:00. Figure 4 (d) is the radar combined reflectivity and associated convective potential distribution of the model forecast field at 04:30. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0061] The following combination Figures 1-4 The present invention describes the bow echo-associated convection early warning method.

[0062] Example 1: This example provides a bow echo-associated convection early warning method, comprising the following steps:

[0063] S100: Receive multi-dimensional weather forecast data input.

[0064] In this example, the input data consists of multidimensional elements from a mesoscale model forecast field from a meteorological agency (e.g., the PWRFS forecast data from the Jiangsu Provincial Meteorological Agency). These include three-dimensional radial wind, zonal wind, temperature, humidity, pressure, convective instability energy, and two-dimensional free lift height. The data structure, using zonal wind U as an example, is represented as U[z,y,x], where z represents the altitude, y represents the radial grid, and x represents the zonal grid.

[0065] S200: Using the central difference method, the radial wind vertical shear rate, the zonal wind vertical shear rate, the accumulated convective instability energy, and the free lift height are calculated grid by grid.

[0066] The radial wind vertical shear rate represents the second-order derivative matrix of the radial wind speed in the vertical direction, with the dimension of [altitude layer, longitude, latitude], and the calculation formula is:

[0067]

[0068] The zonal wind vertical shear rate represents the second-order derivative matrix of the zonal wind speed in the vertical direction, and the calculation formula is:

[0069]

[0070] The accumulated convective instability energy is a physical quantity matrix reflecting the instability of the atmospheric structure and is expressed as ; The free lift height is used to characterize the two-dimensional field of the critical height of convection triggering, which is expressed as ;

[0071] in, is the partial derivative symbol, is the radial wind speed, is the zonal wind speed, express The accumulated convective instability energy at the altitude level, For free lifting height, represents the radial wind vertical shear rate, represents the zonal wind vertical shear rate, is the latitudinal grid, For radial grid, For the altitude layer.

[0072] S300: Analyze the vertical shear rate data of each horizontal grid point and use a sliding window method to detect a vertical shear layer that meets the conditions.

[0073] Specifically include:

[0074] S310, grid by grid (y, x), starting from level i, expand the window upward, the sliding window is represented as [i, j], where i starts from the bottom to the highest level, j ≥ i;

[0075] S320: Calculate the radial wind average shear rate within the window , the zonal mean wind shear rate ;

[0076] S330: When the window thickness reaches the minimum threshold m, the validity of the shear rate sequence in the window is verified, including:

[0077] S331. Perform a normal distribution test on the shear rate sequence in the window area. The calculation formula is:

[0078]

[0079]

[0080] The passing condition is or ;

[0081] represents the Shapiro-Wilk normality test, and are the Shapiro-Wilk normality test statistics and significance probability values ​​of the radial wind within the window; and are the Shapiro-Wilk normality test statistics and significance probability values ​​of the zonal wind within the window; is the significance level;

[0082] S332, verify the average shear rate of the shear rate sequence in the window area, and pass the condition , where n is the minimum value of the average shear rate of the shear rate sequence. In this embodiment, n is 2.e -5 m -1 s -2 ;

[0083] S333. Verify the thickness of the shear rate sequence in the window area. The pass condition is , where Z is the vertical height of the terrain, and m is 2000 meters in this embodiment.

[0084] In addition, in this step, a nested list structure matching the spatial dimension of the input data needs to be created to store the output results such as the shear layer index range and trigger intensity value of each grid point.

[0085] S400: performing thermodynamic parameter coupling and verification on the screened shear rate sequence.

[0086] Thermal conditions include: energy condition - cumulative convective instability energy ≥ preset threshold; height condition - the height of the top layer of the shear rate sequence exceeds the free convection height. Therefore, this step specifically includes:

[0087] S410, verify the cumulative unstable energy of the screened shear rate sequence, and pass the condition ,in express Convective instability energy at altitude, is the minimum value of the accumulated convective instability energy;

[0088] S420, verify the top height of the screened shear rate sequence layer, and the conditions are as follows: .

[0089] S500: For each grid, multiple shear rate sequences that meet the conditions are selected, and the optimal window is selected. The intervals that are completely included in other shear rate sequences are eliminated, and the sequence with the largest product is selected as the final output. Specifically, it includes:

[0090] S510, from all valid window collection Remove the intervals that are completely contained by other shear sequences, expressed as:

[0091]

[0092] in, is the height index of any sequence in the set R except the target sequence, and are the bottom and top height indexes respectively;

[0093] S520: Select the sequence with the largest product of average shear rate and accumulated convective instability energy from the remaining candidate sequences as the final output, expressed as:

[0094]

[0095]

[0096] in, is the convective triggering intensity value of the optimal shear rate sequence, is the height index corresponding to the convective triggering intensity value of the optimal shear rate sequence.

[0097] S600: Plot the final output shear sequence convective triggering intensity on the forecast reflectivity image as scattered points to form an associated convective warning forecast product. Specifically, it includes:

[0098] S610: Output the associated convective warning and forecast product elements, including the vertical index range of all valid shear rate sequences at each grid point, the average shear rate value of the corresponding shear rate sequence, the cumulative unstable energy value of each shear rate sequence, the vertical index of the optimal shear rate sequence, and the convective triggering intensity value of the optimal shear rate sequence; wherein, the vertical index range of all valid shear rate sequences at each grid point is expressed as ; The convection triggering intensity value of the optimal shear rate sequence is expressed as ;

[0099] S620: When a valid shear rate sequence index exists at a grid point, it indicates that the grid point has the possibility of associated convection triggering; the shear sequence convection triggering intensity value indicates the quality of the associated convection triggering condition of the grid;

[0100] S630: Plot the shear sequence convection triggering intensity on the forecast reflectivity image as scattered points as an associated convection warning precursor signal.

[0101] In this example, the effectiveness of the bow echo-associated convection warning method of the present invention is verified using two case studies: the upstream convection associated with the bow echo on June 5, 2016, and the downstream convection associated with the bow echo on June 13, 2018. The color-coded area represents the forecast combined reflectivity, and the grayscale scattered points represent the associated convection potential forecast product.

[0102] The case of convection associated with the bow echo upstream on June 5, 2016 shows that from 19:00 to 20:00, Figure 3 (a) and Figure 3 As shown in (b), the scattered convection behind the bow echo has a clear dissipation trend, while the potential forecast product represented by the scattered point distribution has an enhancement trend. Figure 3 (c) and Figure 3 As shown in (d), the convection behind the bow echo begins to reorganize and develop into a linear convection pattern, whose location coincides with the scattered point distribution between 7:00 PM and 8:00 PM. At this time, the convective potential represented by the scattered point distribution further intensifies. Therefore, it can be seen that the potential forecast product of the present invention has a predictive effect of approximately one hour on the occurrence and development of associated convection.

[0103] The case of convection associated with the bow echo downstream on June 13, 2018 shows that from 03:00 to 03:30, Figure 4 (a) and Figure 4 As shown in (b), during the bow echo system's eastward movement from Bohai Bay, there is a large-scale potential distribution downstream, indicating that the environment in the region is conducive to the triggering of associated convection. Figure 4 (c) and Figure 4As shown in (d), two linear convection currents are triggered downstream of the system, and the triggering locations are basically consistent with the potential forecast locations. This shows that the early warning method has a good early warning and forecast effect on the triggering of convection associated with the bow echo downstream.

[0104] Embodiment 2: This embodiment provides a non-transitory computer-readable storage medium having computer instructions stored thereon. The computer instructions enable a computer to execute a bow echo-associated convection early warning method, the method comprising the following steps:

[0105] S100, receiving multi-dimensional weather forecast data input;

[0106] S200, using the central difference method, calculates the radial wind vertical shear rate, zonal wind vertical shear rate, accumulated convective instability energy, and free lift height on a grid-by-grid basis;

[0107] S300, analyzing the vertical shear rate data of each horizontal grid point, and detecting a vertical shear layer that meets the conditions using a sliding window method;

[0108] S400, performing thermodynamic parameter coupling and verification on the screened shear rate sequence, wherein the thermodynamic parameters include accumulated convective instability energy and the top height of the shear rate sequence;

[0109] S500, for each grid, performing optimal window selection for multiple eligible shear rate sequences, eliminating intervals completely contained by other shear rate sequences, and selecting the sequence with the largest product as the final output;

[0110] S600: Plot the final output shear sequence convective triggering intensity on the forecast reflectivity image as scattered points to form an associated convective warning forecast product.

[0111] Example 3: This embodiment provides an electronic device, which may include: a processor, a communications interface, a memory, and a communications bus, wherein the processor, the communications interface, and the memory communicate with each other via the communications bus. The processor may invoke logic instructions in the memory to execute a bow echo-associated convection early warning method, which includes the following steps:

[0112] S100, receiving multi-dimensional weather forecast data input;

[0113] S200, using the central difference method, calculates the radial wind vertical shear rate, zonal wind vertical shear rate, accumulated convective instability energy, and free lift height on a grid-by-grid basis;

[0114] S300, analyzing the vertical shear rate data of each horizontal grid point, and detecting a vertical shear layer that meets the conditions using a sliding window method;

[0115] S400, performing thermodynamic parameter coupling and verification on the screened shear rate sequence, wherein the thermodynamic parameters include accumulated convective instability energy and the top height of the shear rate sequence;

[0116] S500, for each grid, performing optimal window selection for multiple eligible shear rate sequences, eliminating intervals completely contained by other shear rate sequences, and selecting the sequence with the largest product as the final output;

[0117] S600: Plot the final output shear sequence convective triggering intensity on the forecast reflectivity image as scattered points to form an associated convective warning forecast product.

[0118] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0119] Embodiment 4: This embodiment provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform a bow echo-associated convection early warning method, which includes the following steps:

[0120] S100, receiving multi-dimensional weather forecast data input;

[0121] S200, using the central difference method, calculates the radial wind vertical shear rate, zonal wind vertical shear rate, accumulated convective instability energy, and free lift height on a grid-by-grid basis;

[0122] S300, analyzing the vertical shear rate data of each horizontal grid point, and detecting a vertical shear layer that meets the conditions using a sliding window method;

[0123] S400, performing thermodynamic parameter coupling and verification on the screened shear rate sequence, wherein the thermodynamic parameters include accumulated convective instability energy and the top height of the shear rate sequence;

[0124] S500, for each grid, performing optimal window selection for multiple eligible shear rate sequences, eliminating intervals completely contained by other shear rate sequences, and selecting the sequence with the largest product as the final output;

[0125] S600: Plot the final output shear sequence convective triggering intensity on the forecast reflectivity image as scattered points to form an associated convective warning forecast product.

[0126] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0127] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A bow echo-associated convection early warning method, characterized in that: The following steps are involved: S100, receiving multi-dimensional weather forecast data input; S200, using the central difference method, calculates the radial wind vertical shear rate, zonal wind vertical shear rate, accumulated convective instability energy, and free lift height on a grid-by-grid basis; S300, analyzing the vertical shear rate data of each horizontal grid point, and detecting a vertical shear layer that meets the conditions using a sliding window method; S400, performing thermodynamic parameter coupling and verification on the screened shear rate sequence, wherein the thermodynamic parameters include accumulated convective instability energy and the top height of the shear rate sequence; S500, for each grid, performing optimal window selection for multiple eligible shear rate sequences, eliminating intervals completely contained by other shear rate sequences, and selecting the sequence with the largest product as the final output; S600: Plot the final output shear sequence convective triggering intensity on the forecast reflectivity image as scattered points to form an associated convective warning forecast product.

2. The bow echo-associated convection early warning method according to claim 1, characterized in that: The multidimensional meteorological forecast data in step S100 are multidimensional elements of the mesoscale model forecast field of the meteorological department, including at least three-dimensional radial wind, zonal wind, temperature, humidity, air pressure, convective instability energy and two-dimensional free lift height.

3. The bow echo-associated convection early warning method according to claim 1, characterized in that: In step S200, the second-order derivative matrix of the radial wind speed in the vertical direction is represented by the radial wind vertical shear rate, and the calculation formula is: ； The zonal wind vertical shear rate represents the second-order derivative matrix of the zonal wind speed in the vertical direction, and the calculation formula is: ； The accumulated convective instability energy is a physical quantity matrix reflecting the instability of the atmospheric structure and is expressed as ; The free lift height is used to characterize the two-dimensional field of the critical height of convection triggering, which is expressed as ; in, is the partial derivative symbol, is the radial wind speed, is the zonal wind speed, express The accumulated convective instability energy at the altitude level, For free lifting height, represents the radial wind vertical shear rate, represents the zonal wind vertical shear rate, is the latitudinal grid, For radial grid, For the altitude layer.

4. The bow echo-associated convection early warning method according to claim 3, characterized in that: Step S300 analyzes the vertical shear rate data of each horizontal grid point and uses a sliding window method to detect vertical shear layers that meet the conditions, specifically including: S310, grid by grid (y, x), starting from level i, expand the window upward, the sliding window is represented as [i, j], where i starts from the bottom to the highest level, j ≥ i; S320: Calculate the radial wind average shear rate within the window , the zonal mean wind shear rate ; S330: When the window thickness reaches a minimum threshold m, the validity of the shear rate sequence in the window is verified.

5. The bow echo-associated convection early warning method according to claim 4, characterized in that: In step S330, the validity of the shear rate sequence in the window is verified, including: S331. Perform a normal distribution test on the shear rate sequence in the window area. The calculation formula is: ； ； The passing condition is or ; represents the Shapiro-Wilk normality test; and are the Shapiro-Wilk normality test statistics and significance probability values ​​of the radial wind within the window; and are the Shapiro-Wilk normality test statistics and significance probability values ​​of the zonal wind within the window; is the significance level; S332, verify the average shear rate of the shear rate sequence in the window area, and pass the condition , where n is the minimum value of the average shear rate of the shear rate sequence; S333. Verify the thickness of the shear rate sequence in the window area. The pass condition is , where Z is the vertical height of the terrain.

6. The bow echo-associated convection early warning method according to claim 4, characterized in that: Step S400 performs thermodynamic parameter coupling and verification on the screened shear rate sequence, specifically including: S410, verify the cumulative unstable energy of the screened shear rate sequence, and pass the condition ,in express Convective instability energy at altitude, is the minimum value of the accumulated convective instability energy; S420, verify the top height of the screened shear rate sequence layer, and the conditions are as follows: .

7. The bow echo-associated convection early warning method according to claim 4, characterized in that: Step S500 performs optimal window selection for multiple eligible shear rate sequences that may appear in each grid, eliminates intervals that are completely included in other shear rate sequences, and selects the sequence with the largest product as the final output. Specifically, it includes: S510, from all valid window collection Remove the intervals that are completely contained by other shear sequences, expressed as: ； in, is the height index of any sequence in the set R except the target sequence, and are the bottom and top height indexes respectively; S520: Select the sequence with the largest product of average shear rate and accumulated convective instability energy from the remaining candidate sequences as the final output, expressed as: ； ； in, is the convective triggering intensity value of the optimal shear rate sequence, is the height index corresponding to the convective triggering intensity value of the optimal shear rate sequence.

8. The bow echo-associated convection early warning method according to claim 7, characterized in that: Step S600 plots the final output shear sequence convective triggering intensity on the forecast reflectivity image as scattered points to form an associated convective warning forecast product. Specifically, it includes: S610: Output the associated convective warning and forecast product elements, including the vertical index range of all valid shear rate sequences at each grid point, the average shear rate value of the corresponding shear rate sequence, the cumulative unstable energy value of each shear rate sequence, the vertical index of the optimal shear rate sequence, and the convective triggering intensity value of the optimal shear rate sequence; wherein, the vertical index range of all valid shear rate sequences at each grid point is expressed as ; The convective triggering intensity value of the optimal shear rate sequence is expressed as ; S620: When a valid shear rate sequence index exists at a grid point, it indicates that the grid point has the possibility of associated convection triggering; the shear sequence convection triggering intensity value indicates the quality of the associated convection triggering condition of the grid; S630: Plot the shear sequence convection triggering intensity on the forecast reflectivity image as scattered points as an associated convection warning precursor signal.

9. A non-transitory computer-readable storage medium, characterized in that Computer instructions are stored thereon, and the computer instructions enable the computer to execute the bow echo-associated convection early warning method according to any one of claims 1 to 8.

10. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus, and the processor calls the logic instructions in the memory to execute the bow echo-associated convection warning method described in any one of claims 1-8.

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