CN117473878B - Ground flash intensity inversion method based on stationary satellite data - Google Patents
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Abstract
The invention discloses a ground flash intensity inversion method based on static satellite data, which relates to the technical field of ground flash return intensity inversion and comprises the steps of utilizing matched group characteristic data and lightning type data to establish a cloud/ground flash characteristic data set; establishing a satellite/ground synchronous lightning data set by using the matched group data, ground flash intensity data and static satellite meteorological observation data selected according to influence factors; and training by using a cloud/ground flash characteristic data set and a star/ground synchronous lightning data set by using a machine learning method, respectively constructing a cloud/ground flash distinguishing model and a ground flash intensity inversion model, and realizing ground flash intensity inversion based on static satellite data. The invention fills the blank of inversion of the ground flash intensity by using the static satellite data, realizes deeper application of the static satellite data, and can provide better data support for the work of lightning monitoring and early warning and the like.
Description
Technical Field
The invention relates to the technical field of ground flash strength inversion, in particular to a ground flash strength inversion method based on static satellite data.
Background
Lightning detection is divided into foundation detection and satellite detection according to the different positions of the detector. The static satellite lightning detection is an important component of the current lightning detection, can realize the large-scale all-weather real-time monitoring of lightning, and does not have the problem of poor detection effect when the number of detection stations is rare like foundation detection. The lightning imager (LMI) carried by the stationary satellite FY-4A has realized continuous observation of lightning activity in China and surrounding areas. In lightning research, lightning intensity is a very important physical quantity, is obtained by inversion of foundation electromagnetic radiation data at present, and lacks a key technical method for inversion of the ground flash intensity by using stationary satellite data. The satellite data can be used alone for inversion, and is particularly important in the case of lack of foundation detection data. Meanwhile, as various factors have influence on lightning light radiation characteristics, different types of meteorological data provided by satellites are comprehensively applied in inversion for better effect.
Disclosure of Invention
In order to overcome the problems in the prior art, the invention provides a ground flash intensity inversion method based on stationary satellite data.
The technical scheme adopted for solving the technical problems is as follows: a ground flash intensity inversion method based on stationary satellite data utilizes stationary satellite multi-source data to realize ground flash intensity inversion, comprising the following steps:
step 1, establishing and perfecting a foundation three-dimensional lightning detection network and a foundation wide area lightning positioning network, and preprocessing lightning data acquired by the foundation three-dimensional lightning detection network and the foundation wide area lightning positioning network;
step 2, establishing a cloud/ground flash characteristic data set and a star/ground synchronous lightning data set;
step 3, using a machine learning method, and respectively constructing a cloud/ground flash distinguishing model and a ground flash intensity inversion model by using a cloud/ground flash characteristic data set and a star/ground synchronous lightning data set;
and 4, screening out new stationary satellite data which are relevant to the ground flashover and correspond to the characteristic quantity adopted by the model by using a cloud/ground flashover distinguishing model, inputting the data into a ground flashover intensity inversion model, outputting the ground flashover intensity, and realizing ground flashover intensity inversion based on the stationary satellite data.
According to the ground flash intensity inversion method based on the stationary satellite data, in the step 1, the foundation wide area lightning positioning network comprises a plurality of base stations covering the whole country, and each base station comprises a very low frequency vertical polarization antenna and a set of fast electric field change detector, wherein the very low frequency vertical polarization antenna is directly used for positioning.
According to the ground flash intensity inversion method based on the static satellite data, the three-dimensional ground lightning detection network in the step 1 comprises base stations for carrying out small-range networking observation in a part of areas and used for distinguishing the types of lightning, and each base station adopts a set of fast electric field change detectors to detect the lightning.
The ground flash intensity inversion method based on the stationary satellite data, wherein in the step 1, the lightning data preprocessing method obtained by the three-dimensional lightning detection network and the wide area lightning positioning network of the foundation is as follows: preprocessing lightning waveform data by using an MEWT method, decomposing a signal into a plurality of modes by using empirical wavelet transformation, setting a retaining condition according to the characteristics of a lightning signal, and leaving the mode containing the lightning signal for reconstruction output to obtain higher-quality data.
The specific method for establishing the cloud/ground flash characteristic data set in the step 2 is as follows: and matching the ground three-dimensional lightning detection network data with the static satellite lightning detection data, and taking the lightning type determined by the ground three-dimensional lightning detection network as a tag and the matched group characteristic data as characteristic quantity to form a cloud/ground flash characteristic data set, wherein the group characteristic data is group area and group total energy.
The above-mentioned earth flash intensity inversion method based on static satellite data, the specific steps of establishing the satellite/earth synchronous lightning data set in the step 2 are as follows:
step 2.1, establishing a lightning light radiation transmission optimization model which simultaneously considers the structure of a lightning light source and the irregular shape and the uneven characteristics of cloud, and extracting factors influencing the characteristics of the lightning light radiation through the model;
and 2.2, determining static satellite meteorological observation data to be selected according to influence factors, and forming a satellite/ground lightning synchronous data set by the matched group data, ground lightning intensity data acquired by the foundation wide area lightning positioning network and the selected static satellite meteorological observation data.
The above-mentioned earth flash intensity inversion method based on stationary satellite data, the step 2.1 includes: calculating the whole process of multiple scattering of each photon released by a lightning light source in the cloud by adopting a Monte Carlo method in a lightning light radiation transmission model, and repeating the calculation until the set simulated photon number is reached;
wherein the simulation process for a single photon is: giving an initial weight to the photon, calculating the scattering direction and distance of the next step according to the characteristic of the position of the photon, gradually tracking the scattering path of the photon, and generating a random number between 0 and 1-omega before each scattering occurs 0 Comparing, the random number is less than 1-omega 0 Photons are considered to be absorbed, wherein ω 0 Is the single scattering albedo; when photons are absorbed or escape from the cloud, the simulation process is terminated, and after the position and state information of the photons are recorded, the simulation of the next photon is started; the scattering direction and distance of each photon are calculated by the following formula, the scattering direction is a function of the scattering phaseAnd (3) determining:
;
wherein g represents an asymmetry factor, which is an average value of all scattering angles, related to wavelength and size of cloud particles;alpha represents the scattering of photon transmissionA corner;
；
wherein α represents the scattering angle of photon transmission; r represents a random number uniformly distributed between 0 and 1;
；
where s represents the distance that the photon travels between the two scattering events;representing the mean free path of photons in the cloud;
；
wherein a represents Yun Zhongli sub-average radius and N represents particle average concentration.
According to the ground flash intensity inversion method based on the stationary satellite data, the loadable light source form of the lightning light radiation transmission model comprises a point light source and a linear light source; the linear light source consists of a plurality of equally spaced point light sources, and each point light source forming the linear light source equally divides the total emitted photons, so that the light source is extended in space;
in order to consider the irregular shape and the non-uniform characteristic of the cloud, a cube grid with the side length of 200m is utilized to form a three-dimensional cloud with any shape; the properties of the cloud in each small cube are the same, and different parameter values are given to each small cube according to the requirement to represent the non-uniformity of the cloud; the parameters added to the small cubes are derived from actual observation data of the cloud and accord with common distribution trend of cloud water particles;
the method for extracting the influencing factors comprises the following steps: and researching the influence of different light source shapes, positions, cloud geometric shapes, cloud non-uniformity and observation angles on the lightning light radiation characteristics by a variable control method in the lightning light radiation transmission optimization model, and extracting factors with large influence degree.
The above-mentioned earth flash intensity inversion method based on stationary satellite data, the step 2.2 includes: matching the foundation wide area lightning positioning network data with the static satellite lightning detection data according to the standard that the time interval is less than 10ms and the space interval is less than 20km, and determining commonly observed ground flashes; selecting static satellite meteorological observation data at the same time within the 100km range around lightning according to the occurrence time and the position of lightning matched with a static satellite by a foundation wide area lightning positioning network and extracted influence factors; the matched group data, the ground lightning intensity data acquired by the foundation wide area lightning positioning network and the selected static satellite meteorological observation data are combined into a satellite/ground lightning synchronous data set; the ground flash intensity data acquired by the foundation wide area lightning positioning network are used as labels.
The above-mentioned ground flash intensity inversion method based on stationary satellite data, the said step 3 specifically includes:
step 3.1, establishing a cloud/ground flash distinguishing model by using a random forest method and a cloud/ground flash characteristic data set;
and 3.2, establishing a ground flash intensity inversion model by using the convolutional neural network and the star/ground synchronous lightning data set.
The invention has the beneficial effects that the invention discloses a ground flash intensity inversion method based on static satellite data, fills the blank of ground flash intensity inversion by using the static satellite data, realizes deeper application of the static satellite data, and can provide better data support for research in lightning monitoring and early warning and the like.
Drawings
The invention will be further described with reference to the drawings and examples.
FIG. 1 is a schematic diagram of a general flow chart for implementing the present invention;
Detailed Description
The present invention will be described in detail below with reference to the drawings and detailed description to enable those skilled in the art to better understand the technical scheme of the present invention.
The embodiment discloses a ground flash intensity inversion method based on stationary satellite data, which specifically comprises the following steps as shown in fig. 1: step 1, establishing and perfecting a foundation three-dimensional lightning detection network and a foundation wide area lightning positioning network, and preprocessing lightning data acquired by the foundation three-dimensional lightning detection network and the foundation wide area lightning positioning network.
The step 1 specifically comprises the following steps:
six foundation three-dimensional lightning detection nets including Nanjing, guangdong bead triangle, fujian Jiuxian mountain, bohai Bay, hainan and Xinjiang Wulu mu are built, each foundation three-dimensional lightning detection net covers about 100km multiplied by 100km, each detection station comprises 7-8 three-dimensional detection stations (each detection station comprises data of a fast electric field change measuring instrument, a slow electric field change measuring instrument and a magnetic field change measuring instrument), and the distance between the detection stations is 20-40 km. The sampling rate of the instrument is 1MHz, a continuous and uninterrupted acquisition mode is adopted, one file per second is stored locally in real time, and then pulse data exceeding the signal to noise ratio of 1.2 is reserved and transmitted in real time. Time synchronization is realized among different observation substations through a high-precision GPS clock with time service precision of 50 ns.
And constructing a foundation wide area lightning positioning network covering the national range, wherein each base station comprises a very low frequency vertical polarized antenna (with the bandwidth of within 30 kHz) directly used for positioning and an additionally arranged set of fast electric field change detectors (with the 3dB bandwidth of 1 kHz-400 kHz). The data collected by the fast electric field change detector can be used for inversion of ground bounce strength (charge moment) after being filtered by MEWT. All base stations continuously collect signals from the sensor at a sampling rate of 1MHz, the data recording length is 1ms, the pre-trigger time is 300 mu s, and the original waveform data which is larger than the lowest threshold value is stored. The existing foundation wide area lightning positioning network has good positioning quality, and has good positioning results for single thunderstorm, multi-single thunderstorm, wire and other processes at different moments inside and outside the station network. The waveform library established by the typical daytime ionosphere conditions and the typical nighttime ionosphere conditions can meet all-weather positioning requirements. By analyzing the position deviation between lightning positioned by using equivalent propagation speed and lightning positioned by using fixed light speed in different distance groups inside and outside the station network, the result shows that the accuracy improvement effect brought by the equivalent propagation speed method is less than 2 km in the network and about 4 km outside the network.
Lightning waveform data is preprocessed using the MEWT method. The MEWT method is improved on the basis of empirical wavelet transformation. According to the method, firstly, an empirical wavelet transformation is used for decomposing a signal into a plurality of modes, then, a reservation condition is set according to the characteristics of a lightning signal, and the mode containing the lightning signal is left for reconstruction output, so that higher-quality data is obtained.
And 2, establishing a cloud/ground flash characteristic data set and a star/ground synchronous lightning data set.
The step 2 specifically comprises the following steps:
the specific process for establishing the cloud/ground flash characteristic data set is as follows: and acquiring the pulse position of the lightning three-dimensional radiation source in real time by using a foundation three-dimensional lightning detection network with the coverage range of about 100km multiplied by 100km, and determining the type of lightning. And matching the foundation three-dimensional lightning detection network data with the static satellite lightning detection data according to the standard that the time interval is less than 10ms and the space interval is less than 20km, taking the lightning type (cloud flash/ground flash) determined by the foundation three-dimensional lightning detection network as a tag, and taking the matched group characteristic data as characteristic quantity to form a cloud/ground flash characteristic data set, wherein the group characteristic data is group area, group total energy and the like.
The specific process of establishing the star/ground synchronous lightning data set is as follows:
step 2.1, establishing a lightning light radiation transmission optimization model capable of simultaneously considering the structure of a lightning light source and the irregular shape and the uneven characteristics of cloud, and extracting factors influencing the characteristics of the lightning light radiation through the model;
step 2.1 specifically includes: the lightning light radiation transmission model adopts a Monte Carlo method to calculate the whole process of multiple scattering of each photon released by the lightning light source in the cloud, and the process is repeated until the set simulated photon number (10 in the embodiment is reached 5 ）。
Wherein the simulation process for a single photon is: giving an initial weight value (1 in the embodiment) to the photon, calculating the scattering direction and distance of the next step according to the characteristic of the position of the photon, and gradually tracking the scattering pathAnd generating a random number between 0 and 1-omega before each scatter occurs 0 For comparison, ω 0 For single scattering albedo (0.99998 in this example), when the random number is less than 1- ω 0 Photons are considered to be absorbed. The simulation process is terminated when a photon is absorbed or escapes from the cloud, and the simulation of the next photon begins after the photon's position and state information (absorption or escape) is recorded. The direction and distance of scattering of each photon is calculated by the following equation, where the direction of scattering is determined by the scattering phase function, and the heney-Greenstein function is still used as the scattering phase function in this example：
;
Wherein g represents an asymmetry factor, which is an average value of all scattering angles, related to wavelength and size of cloud particles;alpha represents the scattering angle of photon transmission;
；
wherein α represents the scattering angle of photon transmission; r represents a random number uniformly distributed between 0 and 1;
；
where s represents the distance that the photon travels between the two scattering events;representing the mean free path of photons in the cloud;
；
wherein a represents Yun Zhongli sub-average radius and N represents particle average concentration.
In order to consider the structure of a lightning light source, the light source capable of being loaded by a lightning light radiation transmission model comprises a point light source and a line light source. The linear light source is formed by a series of point light sources at certain intervals, and each point light source forming the linear light source bisects the total emitted photons, so that the light source is extended in space.
To consider irregular shapes and non-uniform characteristics of the cloud, a cube grid with a side length of 200m is utilized to form a three-dimensional cloud with any shape. The nature of the cloud within each small cube is the same, and different parameter values are assigned to each small cube as needed to represent the cloud non-uniformity. The parameters added to the microcubes are derived from actual observations of the cloud and conform to the general distribution trend of cloud water particles, such as a denser distribution of particles at the cloud core.
The method for extracting the influencing factors comprises the following steps: and researching the influence of different light source shapes, positions, cloud geometric shapes, cloud non-uniformity, observation angles and the like on the lightning light radiation characteristics by a variable control method in an optimized lightning light radiation transmission model, and extracting factors with large influence degree.
And 2.2, determining static satellite meteorological observation data to be selected according to influence factors, and forming a satellite/ground lightning synchronous data set by the matched group (group) multidimensional feature data, the selected static satellite meteorological observation data and ground lightning intensity data acquired by a foundation wide area lightning positioning network.
Step 2.2 specifically comprises: matching the foundation wide area lightning positioning network data with the static satellite lightning detection data according to the standard that the time interval is less than 10ms and the space interval is less than 20km, and determining commonly observed ground flashes; and selecting static satellite meteorological observation data at the same time within the range of 100km around lightning according to the occurrence time and the position of lightning matched with the static satellite and the extracted influence factors of the lightning on the foundation wide area lightning positioning network. And forming a star/ground lightning synchronous data set by the matched group (group) multidimensional characteristic data, the ground lightning intensity data acquired by the foundation wide area lightning positioning network and the selected stationary satellite meteorological observation data. The ground flash intensity data acquired by the foundation wide area lightning positioning network are used as labels.
And 3, respectively constructing a cloud/ground flash distinguishing model and a ground flash intensity inversion model by using a machine learning method and utilizing a cloud/ground flash characteristic data set and a star/ground synchronous lightning data set.
Step 3.1, training a cloud/ground flash distinguishing model by using the random forest method and the cloud/ground flash characteristic data set in step 2, and specifically comprising the following steps: and randomly selecting 70% of data in the cloud/ground flash characteristic data set to form a training set, and the other 30% of data to form a verification set for verifying the effect of the generated model. And evaluating the identification model by adopting accuracy and F-score evaluation indexes, and reserving the model with the best effect.
Step 3.2, building a ground flash intensity inversion model by using the convolutional neural network and the star/ground synchronous lightning data set in step 2, and specifically comprising the following steps: 70% of the data in the star/ground synchronous lightning dataset are randomly selected for training a ground lightning intensity inversion model, the rest 30% of the data are used for inspection, an inspection evaluation index adopts a square root of mean error (RMSE), and the model with the best effect is reserved.
And 4, screening out new stationary satellite data which are relevant to the ground flashover and correspond to the characteristic quantity adopted by the model through a cloud/ground flashover distinguishing model, inputting the data into a ground flashover intensity inversion model, outputting the ground flashover intensity, and realizing ground flashover intensity inversion based on the stationary satellite data.
The above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the present invention. Various modifications and equivalent arrangements of this invention will occur to those skilled in the art, and are intended to be within the spirit and scope of the invention.
Claims (5)
1. The earth flash intensity inversion method based on the stationary satellite data is characterized by utilizing the stationary satellite multi-source data to realize earth flash intensity inversion, and comprises the following steps:
step 1, establishing and perfecting a foundation three-dimensional lightning detection network and a foundation wide area lightning positioning network, and preprocessing lightning data acquired by the foundation three-dimensional lightning detection network and the foundation wide area lightning positioning network;
step 2, establishing a cloud/ground flash characteristic data set and a star/ground synchronous lightning data set;
step 3, using a machine learning method, and respectively constructing a cloud/ground flash distinguishing model and a ground flash intensity inversion model by using a cloud/ground flash characteristic data set and a star/ground synchronous lightning data set;
step 4, screening out new stationary satellite data which are relevant to the ground flashover and correspond to the characteristic quantity adopted by the model by utilizing a cloud/ground flashover distinguishing model, inputting the data into a ground flashover intensity inversion model, outputting the ground flashover intensity, and realizing ground flashover intensity inversion based on the stationary satellite data;
the specific method for establishing the cloud/ground flash characteristic data set in the step 2 is as follows: matching the ground three-dimensional lightning detection network data with the static satellite lightning detection data, taking the lightning type determined by the ground three-dimensional lightning detection network as a tag, and taking the matched group characteristic data as characteristic quantity to form a cloud/ground flash characteristic data set, wherein the group characteristic data is group area and group total energy;
the specific steps for establishing the star/ground synchronous lightning data set in the step 2 are as follows:
step 2.1, establishing a lightning light radiation transmission optimization model which simultaneously considers the structure of a lightning light source and the irregular shape and the uneven characteristics of cloud, and extracting factors influencing the characteristics of the lightning light radiation through the model;
step 2.2, determining stationary satellite meteorological observation data to be selected according to influence factors, and forming a satellite/ground lightning synchronous data set by the matched group data, ground lightning intensity data acquired by a foundation wide area lightning positioning network and the selected stationary satellite meteorological observation data;
the step 2.1 comprises the following steps: calculating the whole process of multiple scattering of each photon released by a lightning light source in the cloud by adopting a Monte Carlo method in a lightning light radiation transmission model, and repeating the calculation until the set simulated photon number is reached;
wherein the simulation process for a single photon is: giving an initial weight to the photon, calculating the scattering direction and distance of the next step according to the characteristic of the position of the photon, gradually tracking the scattering path of the photon, and generating a random number between 0 and 1-omega before each scattering occurs 0 Comparing, the random number is less than 1-omega 0 Photons are considered to be absorbed, wherein ω 0 Is the single scattering albedo; when photons are absorbed or escape from the cloud, the simulation process is terminated, and after the position and state information of the photons are recorded, the simulation of the next photon is started; the scattering direction and distance of each photon is calculated by the following equation, the scattering direction being determined by the scattering phase function p (μ):
wherein g represents an asymmetry factor, which is an average value of all scattering angles, related to wavelength and size of cloud particles; μ=cos α, α represents the scattering angle of photon transmission;
wherein α represents the scattering angle of photon transmission; r represents a random number uniformly distributed between 0 and 1;
s＝-ln(r)·Λ
where s represents the distance that the photon travels between the two scattering events; Λ represents the mean free path of photons in the cloud;
wherein a represents Yun Zhongli sub-average radius and N represents particle average concentration;
the loadable light source form of the lightning light radiation transmission model comprises a point light source and a line light source; the linear light source consists of a plurality of equally spaced point light sources, and each point light source forming the linear light source equally divides the total emitted photons, so that the light source is extended in space;
in order to consider the irregular shape and the non-uniform characteristic of the cloud, a cube grid with the side length of 200m is utilized to form a three-dimensional cloud with any shape; the properties of the cloud in each small cube are the same, and different parameter values are given to each small cube according to the requirement to represent the non-uniformity of the cloud; the parameters added to the small cubes are derived from actual observation data of the cloud and accord with common distribution trend of cloud water particles;
the method for extracting the influencing factors comprises the following steps: the influence of the shape, the position, the cloud geometry, the cloud non-uniformity and the observation angle of different light sources on the lightning light radiation characteristics is researched by a variable control method in a lightning light radiation transmission optimization model, and the factors with large influence degree are extracted;
the step 2.2 includes: matching the foundation wide area lightning positioning network data with the static satellite lightning detection data according to the standard with the time interval smaller than 10ms and the space interval smaller than 20km, and determining commonly observed ground flashes; selecting static satellite meteorological observation data at the same time within the 100km range around lightning according to the occurrence time and the position of lightning matched with a static satellite by a foundation wide area lightning positioning network and extracted influence factors; the matched group data, the ground lightning intensity data acquired by the foundation wide area lightning positioning network and the selected static satellite meteorological observation data are combined into a satellite/ground lightning synchronous data set; the ground flash intensity data acquired by the foundation wide area lightning positioning network are used as labels.
2. The method of claim 1, wherein the wide area lightning location network comprises a plurality of base stations covering a nationwide area, each of the base stations comprising a very low frequency vertically polarized antenna and a set of fast electric field change detectors for direct positioning.
3. The earth flash intensity inversion method based on stationary satellite data according to claim 1, wherein the three-dimensional lightning detection network of the foundation in step 1 comprises base stations for carrying out small-range networking observation in a partial area, and the base stations are used for distinguishing the types of lightning, and each base station adopts a set of fast electric field change detectors to detect the lightning.
4. The ground flash intensity inversion method based on stationary satellite data according to claim 1, wherein the lightning data preprocessing method in step 1 for the ground three-dimensional lightning detection network and the ground wide area lightning positioning network is as follows: preprocessing lightning waveform data by using an MEWT method, decomposing a signal into a plurality of modes by using empirical wavelet transformation, setting a retaining condition according to the characteristics of a lightning signal, and leaving the mode containing the lightning signal for reconstruction output to obtain higher-quality data.
5. The method for inversion of earth flash intensity based on stationary satellite data according to claim 1, wherein said step 3 specifically comprises:
step 3.1, establishing a cloud/ground flash distinguishing model by using a random forest method and a cloud/ground flash characteristic data set;
and 3.2, establishing a ground flash intensity inversion model by using the convolutional neural network and the star/ground synchronous lightning data set.
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