An optimization method of lightning stroke positioning data considering path lengthening factor

Abstract

This application relates to an optimization method for lightning return stroke location data considering path extension factors. First, it determines the level of influence of different characteristic parameters of observation substations on lightning return stroke location accuracy. Then, it grids the location accuracy distribution of the observation substations according to the determined levels. Next, it determines the location error of different grids based on the propagation time delay of the electromagnetic field of lightning return stroke caused by complex terrain. Then, based on different requirements for lightning return stroke location error, lightning data in grids with large location errors are removed. Finally, based on preset comparison data, it determines the difference between the lightning location data before and after optimization and evaluates the quality control effect of the lightning location data. The method provided in this application adds an additional time deviation caused by the terrain propagation path to the inherent time deviation of the observation substation hardware, and uses the Monte Carlo algorithm to achieve real-time optimization and processing of the location deviation problem of the observation substations.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lightning detection and warning service of severe convective weather process, and particularly relates to a lightning stroke positioning data optimization method considering path extension factor. BACKGROUND

[0002] The multi-station time difference (TOA) lightning positioning technology based on GPS technology is a commonly used positioning algorithm. In recent years, with the development of GPS technology, the high-precision GPS time has greatly improved the positioning accuracy of TOA. For a long baseline time difference lightning positioning system, the main error source is the uncertainty of time measurement. The complex terrain and non-uniform conductivity in the propagation process of lightning electromagnetic waves have a certain influence on the time of the pulses arriving at each observation substation. For lightning electromagnetic waves propagating over a long distance, the arrival time will also be affected by the curvature of the earth and the refractive index of the atmosphere. In addition, the better the time synchronization between the observation substations, the more observation substations involved in positioning, and the higher the positioning accuracy. Finally, the electromagnetic environment in which the observation substation is located will be coupled with the electromagnetic wave arriving at the observation substation, causing the observation substation to record the distortion of the lightning electromagnetic wave, thereby affecting the identification of the pulse waveform and positioning.

[0003] In the TOA positioning technology, the distance between the observation substations is fixed. The time difference of the lightning signal arriving at two observation substations can be used to construct a hyperbola. Multiple observation substations construct multiple hyperbolas, which must intersect at a point, which is the position of the lightning radiation origin. Three-station time difference can calculate the two-dimensional latitude and longitude coordinates of the radiation source point through the hyperbolic method. Four-station time difference can calculate the three-dimensional spatial coordinates of the radiation source point through the hyperboloid method. For five-station time difference data, the three-dimensional spatial coordinates and occurrence time of the radiation source point are calculated by solving a nonlinear equation set. More synchronous time information is used for optimization of the positioning result. The accuracy of TOA positioning is directly related to the error of time measurement. The influence of terrain or environmental interference may cause the error of TOA positioning to be in the order of meters to kilometers.

[0004] The error of time measurement mainly comes from the following sources: (1) the prolongation of electromagnetic signal arrival time caused by terrain; (2) the accuracy of GPS timing; (3) the time of arrival / time difference calculated by different methods. Among them, the time measurement error caused by terrain is related to the degree of terrain undulation. For long-wave signals propagating along the ground surface, statistical results show that the time measurement error caused by terrain is about 1 μs per 100 km. At the same time, due to the dispersion and interference effects in the process of electromagnetic wave propagation, the waveform will appear distortion, and the rising edge parameters and peak time of the waveform will change to varying degrees. The accuracy of GPS timing is affected by the installed timing system, and the time measurement error may be from 50 ns to 200 ns. It can be seen that the time error caused by terrain has a greater impact on the positioning results, which also leads to the fact that the TOA positioning results in mountainous areas with rugged terrain often have large deviations. For example, Schulz and Diendorfer used the real terrain propagation path to replace the straight path in the IMPACT algorithm, and the distance between the corrected lightning location point and the radio tower as a reference was reduced, and the time deviation was also reduced to a certain extent. Li et al. further compared the correction effect of two path extension methods, terrain-envelope method and tight-terrain-fit method, on two-dimensional TOA positioning algorithm, and also proved that it is necessary to consider the influence of the roughness and undulation characteristics of mountainous terrain on lightning location.

[0005] The mountainous terrain has a great impact on the TOA algorithm positioning results based on ideal assumptions, i.e. assuming that the signal propagates along the straight path to the observation substation at the speed of light without loss. It may have an adverse effect on lightning protection in mountainous areas, so it is necessary to conduct more in-depth research on the impact of mountainous terrain on lightning location, and improve the lightning location algorithm combined with real terrain data.

[0006] The ground-based lightning detection network currently used locates the position of the radiation source point based on the correlation theory of electromagnetic field propagation, and inverses the current amplitude and other physical parameters. In the propagation process of lightning electromagnetic waves, the waveform parameters such as rising edge time, rate of change and waveform peak value of the electromagnetic signal arriving at a remote observation substation may deviate from the theoretical value to different degrees due to the influence of various factors, affecting the positioning accuracy of the detection network and the accuracy of the inversion result. Within a relatively short distance, the electromagnetic radiation field generated by the lightning return stroke channel mainly propagates to the observation substation in the form of ground wave, and the influence of rugged terrain such as mountains on the propagation of the return stroke electromagnetic field cannot be ignored. In the positioning algorithm, it is generally assumed that the electromagnetic signal generated by lightning propagates along a straight line to the observation substation at the speed of light, and the surface is smooth and the conductivity is infinite, while in the real environment, the propagation of lightning electromagnetic field will be affected by dispersion and diffraction effects, which will reduce the accuracy of lightning positioning, and thus the lightning parameter inversion result will have a large error. Therefore, it is necessary to conduct in-depth research and discussion on the propagation of lightning electromagnetic waves in complex terrain, and to correct the waveform changes caused by the undulating terrain such as mountains, so as to improve the accuracy of the positioning and inversion results of the ground-based lightning detection network in the real environment.

[0007] However, at present, the evaluation of the positioning accuracy of the detection network and the optimization of false signals and large deviation data are usually evaluated by using common methods such as Monte Carlo, and there is no good optimization method for actual observation data.

[0008] However, in fact, due to the influence of high mountains on the propagation of lightning return stroke electromagnetic pulse signal, there is a certain lag in the arrival of lightning return stroke electromagnetic pulse at the observation substation. SUMMARY

[0009] The application provides a lightning return stroke positioning data optimization method considering path lengthening factors to solve the problem that the traditional method cannot better optimize lightning return stroke positioning data.

[0010] The technical scheme adopted by the application to solve the above technical problems is as follows:

[0011] A lightning return stroke positioning data optimization method considering path lengthening factors, comprising the following steps:

[0012] determining the level of the influence of different characteristic parameters of the observation substation on the lightning return stroke positioning accuracy of the observation substation;

[0013] grid processing the observation substation positioning accuracy distribution in the detection area according to the level;

[0014] determining the positioning error of different grids according to the time delay of lightning return stroke electromagnetic field propagation caused by the complex terrain in the detection network area.

[0015] According to different requirements of lightning stroke positioning error, lightning data in the grid with positioning error greater than a preset error requirement is eliminated;

[0016] According to preset comparison data, differences before and after optimization of lightning positioning data are determined, and effects of lightning positioning data quality control are evaluated.

[0017] Further, the step of griding the positioning accuracy distribution of observation sub-stations in the detection area according to the grades comprises the following steps:

[0018] According to a preset range, the detection area is determined;

[0019] The detection area is grided with a preset grid length;

[0020] A lightning radiation source point is set at the center of each grid.

[0021] Further, the step of determining the positioning error of different grids comprises the following steps:

[0022] The time of arrival of lightning electromagnetic pulses of the lightning radiation source point at each observation sub-station is simulated and calculated, and the time error of the lightning electromagnetic pulses arriving at each observation sub-station is determined;

[0023] According to the time error, the simulated position of the lightning radiation source point is calculated;

[0024] The plane error between the simulated position of the lightning radiation source point and the real position of the lightning radiation source point is determined;

[0025] The root mean square positioning error of each grid is determined.

[0026] Further, the step of determining the positioning error of different grids further comprises the following steps:

[0027] The ideal propagation time of lightning electromagnetic pulses of the lightning radiation source point propagating at the speed of light over a flat ground to each observation sub-station is determined;

[0028] The equivalent propagation time of lightning electromagnetic pulses of the lightning radiation source point propagating at the equivalent path of the real terrain to each observation sub-station is determined;

[0029] The difference between the equivalent propagation time and the ideal propagation time is set as a compensation time.

[0030] Further, the plane error comprises horizontal direction error and vertical direction error, wherein,

[0031]

[0032] D V =|Z s -Z t |

[0033] In the formula, D H represents horizontal direction error, D V represents vertical direction error, X s represents X coordinate value of the radiation source in the simulated positioning in three-dimensional space coordinates, Y s represents Y coordinate value of the radiation source in the simulated positioning in three-dimensional space coordinates, Z s represents Z coordinate value of the radiation source in the simulated positioning in three-dimensional space coordinates, X t represents X coordinate value of the real radiation source in three-dimensional space coordinates, Y t represents Y coordinate value of the real radiation source in three-dimensional space coordinates, Z t represents Z coordinate value of the real radiation source in three-dimensional space coordinates.

[0034] Further, the time for the lightning electromagnetic pulse to propagate to the observation substation is the sum of the ideal propagation time and the compensation time.

[0035] Further, the characteristic parameters include the longitude and latitude of the observation substation, the number of the observation substation, and the time deviation inherent to the GPS of the observation substation.

[0036] Further, the level of the influence of the different characteristic parameters of the observation substation on the lightning return stroke positioning accuracy of the observation substation is determined by using the Monte Carlo algorithm.

[0037] The technical scheme provided in the application has the following beneficial technical effects:

[0038] The application provides a lightning return stroke positioning data optimization method considering path extension factors, which first determines the level of the influence of the different characteristic parameters of the observation substation on the lightning return stroke positioning accuracy of the observation substation, then performs grid processing on the positioning accuracy distribution of the observation substation in the detection area according to the determined different levels, then determines the positioning error of different grids according to the delay of the lightning return stroke electromagnetic field propagation caused by the complex terrain in the detection grid area, then removes the lightning data in the grid with positioning error greater than the preset error requirement according to the different requirements of the lightning return stroke positioning error, and finally determines the difference before and after the lightning positioning data optimization according to the preset comparison data, and evaluates the effect of lightning positioning data quality control. The lightning return stroke positioning data optimization method provided in the application adds the additional time deviation caused by the terrain propagation path to the inherent time deviation of the observation substation hardware, and realizes real-time optimization and processing of the positioning deviation problem of the observation substation by using the Monte Carlo algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The main step flow chart of the optimization method of lightning stroke positioning data considering path extension factor provided by the embodiment of the application is shown in the figure;

[0040] Figure 2 The specific implementation step flow chart of the optimization method of lightning stroke positioning data considering path extension factor provided by the embodiment of the application is shown in the figure;

[0041] Figure 3 The path extension equivalent schematic diagram of the real terrain provided by the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0042] In order to describe and understand the technical scheme of the application, the technical scheme of the application is further described in combination with the drawings and embodiments.

[0043] Referring to Figure 1 The optimization method of lightning stroke positioning data considering path extension factor provided by the embodiment of the application is shown in the figure. The main implementation steps of the method are as follows: first, the observation substation layout and the time deviation inherent in the observation substation GPS, and the lag of the arrival of lightning stroke time caused by the terrain are used to analyze the positioning accuracy distribution of the observation substation by using the Monte Carlo algorithm; then, according to different requirements of different industries and different departments on the lightning stroke positioning accuracy, the lightning data of the error area analyzed by using the Monte Carlo algorithm is removed, so as to achieve the effect of data quality control; finally, the radar combined reflectivity or the cloud top temperature (CTT, Cloud Top Temperature) data of the Fengsi satellite are used to compare and analyze the difference before and after the optimization of the lightning positioning data, and to evaluate the effect of data quality control.

[0044] Specifically, referring to Figure 2 The specific implementation steps of the optimization method of lightning stroke positioning data considering path extension factor provided by the embodiment of the application are as follows: first, the influence of the observation substation layout, the number of observation substations and the inconsistency of observation substation GPS time on the lightning stroke positioning accuracy is analyzed by using the Monte Carlo algorithm, and the positioning accuracy distribution of the observation substation in the detection area is processed by gridding; then, the influence of the delay of the electromagnetic field propagation time caused by the complex terrain in the detection network area on the gridding positioning accuracy is analyzed by using the Monte Carlo algorithm, and the positioning deviation of different grid points is determined; according to different requirements of different industries and different departments on the lightning stroke positioning accuracy, the lightning data of the error grid area analyzed by using the Monte Carlo algorithm is removed, so as to achieve the effect of data quality control; the radar combined reflectivity or the cloud top temperature data of the Fengsi satellite are used to compare and analyze the difference before and after the optimization of the lightning positioning data, and to evaluate the effect of data quality control.

[0045] The optimization method of lightning stroke positioning data considering path extension factor provided by the embodiments of the application is described below by taking a specific embodiment as an example.

[0046] When the Monte Carlo algorithm is used to simulate and analyze the positioning accuracy of observation sub-stations, the following steps can be mainly divided:

[0047] S1: Perform grid processing on the detection area. The size of the simulation analysis area is 200kmx200km, and the center of the simulation analysis area is the geometric center of the positions of the seven observation sub-stations. The space of the simulation analysis area is grid processed, the lightning radiation source point position is the center of each grid, the grid size is 5kmx5km, and the radiation source is at a height of 0km, 1km, 5km and 10km.

[0048] S2: Simulate and calculate the time of the radiation source reaching each observation sub-station, and superimpose the time error. When calculating the arrival time, the influence of complex terrain and other factors on the timing is ignored, and it is assumed that the radiation source pulse propagates in a straight line at the speed of light; random timing errors with a mean value of 0μs and standard deviations SD of 100ns, 200ns and 300ns are selected to simulate the arrival time error of the real lightning electromagnetic pulse.

[0049] S3: Calculate the radiation source position after superimposing the timing error, and calculate the plane error D H and D V between the simulated positioning radiation source and the real radiation source. The calculation method is:

[0050]

[0051] D V =|Z s -Z t |

[0052] In the formula, D H represents the horizontal direction error, D V represents the vertical direction error, X s represents the X coordinate value of the simulated positioning radiation source in the three-dimensional space coordinates, Y s represents the Y coordinate value of the simulated positioning radiation source in the three-dimensional space coordinates, Z s represents the Z coordinate value of the simulated positioning radiation source in the three-dimensional space coordinates, X t represents the X coordinate value of the real radiation source in the three-dimensional space coordinates, Y t represents the Y coordinate value of the real radiation source in the three-dimensional space coordinates, and Z t represents the Z coordinate value of the real radiation source in the three-dimensional space coordinates.

[0053] S4: repeat steps S1-S3 100 times to obtain 100 positioning errors corresponding to the grid, and then calculate the root mean square positioning error on each grid.

[0054] Time compensation considering the terrain delay effect.

[0055] Ignoring the error of the GPS timing accuracy of the observation substation, the time t of the signal propagating from the lightning strike point to the observation substation will change due to the influence of the terrain, electrical conductivity, etc. The propagation time t of the signal propagating on an ideal flat surface with the same horizontal propagation distance will be flat There is a certain deviation, that is,

[0056] t=t flat +Δt

[0057] In the formula, t flat is the time of the signal propagating at the speed of light on a flat surface, and Δt is the deviation of the propagation time between the actual rugged terrain and the ideal flat surface.

[0058] The equivalent path diagram for time compensation is given in Figure 1 . Figure 1 The curve of the rugged terrain in the figure is the terrain profile from the lightning location point to the observation substation, and the envelope approximate dashed line is the maximum envelope of the terrain obtained by the convex hull function. The straight line of the straight line path is the straight line connecting the lightning point and the observation point.

[0059] When performing time compensation, the time difference of the signal propagating along the envelope approximate dashed line and the straight line of the straight line path at the speed of light is used to estimate Δt in the above formula, so as to reduce the deviation of the propagation time t from t flat in the ideal case.

[0060] Through the above steps, the inherent time deviation of the observation substation hardware is increased by the additional time deviation caused by the terrain propagation path. Then, the Monte Carlo algorithm can be used to optimize and process the positioning deviation problem of the observation substation in real time.

Claims

1. An optimization method of cloud flash return stroke location data considering path lengthening factors, characterized in that, The method comprises the following steps: determining the level of influence of different characteristic parameters of observation sub-stations on the positioning accuracy of cloud-to-ground lightning strokes; grid processing the positioning accuracy distribution of observation sub-stations in a detection area according to the level; determining the positioning error of different grids according to the delay of electromagnetic field propagation of cloud-to-ground lightning strokes caused by complex terrain in the detection grid area; the determination of the positioning error of different grids comprises: simulating the time of lightning electromagnetic pulses of a lightning radiation source point reaching each observation sub-station and determining the time error of the lightning electromagnetic pulses reaching each observation sub-station; calculating the simulated position of the lightning radiation source point according to the time error; determining the plane error of the simulated position of the lightning radiation source point and the real position of the lightning radiation source point; determining the root mean square positioning error of each grid; the plane error comprises horizontal direction error and vertical direction error, wherein, D V =|Z s -Z t | wherein D H represents the horizontal direction error, D V represents the vertical direction error, X s represents the X coordinate value of the analog located radiation source in three-dimensional space coordinates, Y s represents the Y coordinate value of the analog located radiation source in three-dimensional space coordinates, Z s represents the Z coordinate value of the analog located radiation source in three-dimensional space coordinates, X t represents the X coordinate value of the real radiation source in three-dimensional space coordinates, Y t represents the Y coordinate value of the real radiation source in three-dimensional space coordinates, Z t represents the Z coordinate value of the real radiation source in three-dimensional space coordinates; according to different requirements of cloud-to-ground lightning stroke positioning error, the lightning data in the grid with positioning error greater than the preset error requirement is removed; according to preset comparison data, determining the difference between lightning positioning data before optimization and after optimization, and evaluating the effect of lightning positioning data quality control; determining the positioning error of different grids further comprises the following steps: determining the ideal propagation time of lightning electromagnetic pulses of the lightning radiation source point propagating to each observation sub-station at the speed of light through a flat surface; determining the equivalent propagation time of lightning electromagnetic pulses of the lightning radiation source point propagating to each observation sub-station through the equivalent path of real terrain; setting the difference between the equivalent propagation time and the ideal propagation time as the compensation time.

2. The optimization method of cloud flash location data considering path lengthening factor according to claim 1, wherein, The grid processing of the positioning accuracy distribution of observation sub-stations in a detection area according to the level comprises the following steps: determining the detection area according to a preset range; grid processing the detection area with a preset grid side length; setting the lightning radiation source point at the center of each grid.

3. The optimization method of cloud flash location data considering path lengthening factor according to claim 1, wherein, The time of lightning electromagnetic pulses propagating to observation sub-stations is the sum of the ideal propagation time and the compensation time.

4. The optimization method of cloud flash location data considering path lengthening factor according to claim 1, wherein, The characteristic parameters comprise the latitude and longitude of the observation sub-stations, the number of observation sub-stations, and the time deviation inherent to GPS of the observation sub-stations.

5. The optimization method of cloud flash location data considering path lengthening factor according to claim 1, wherein, The determination of the level of influence of different characteristic parameters of observation sub-stations on the positioning accuracy of cloud-to-ground lightning strokes is determined by using the Monte Carlo algorithm.

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