Three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data

Abstract

The invention relates to the technical field of meteorological prediction, in particular to a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data. According to the system, the cloud bottom height of a target cloud cluster is determined based on a laser ceilometer, radiation corresponding to the cloud top height of the target cloud cluster is collected based on a meteorological satellite, the cloud top temperature of the target cloud cluster is calculated based on a radiation transmission mode, and the cloud top height of the target cloud cluster is determined based on the cloud top temperature. And detecting precipitation particles of the marked segmented cloud layer, deducing the water content density of the marked segmented cloud layer according to the precipitation particles, obtaining an aerosol particle extinction coefficient and a backscattering coefficient of the marked segmented cloud layer, and obtaining a meteorological characterization value of the target cloud cluster based on the water content density, the aerosol particle extinction coefficient and the backscattering coefficient. The reasonability of the target cloud cluster is analyzed based on the meteorological characterization value of the target cloud cluster, the cloud layer meteorological prediction precision can be improved, and the meteorological prediction timeliness is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological forecasting, and in particular to a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data. Background Art

[0002] With the continuous development of atmospheric science and information technology, the study of the characteristics of the atmosphere and clouds has gradually shifted from traditional two-dimensional analysis to three-dimensional analysis. The fusion analysis of three-dimensional atmosphere and three-dimensional cloud can not only provide more comprehensive and accurate information on the state of the atmosphere, but also reveal the interaction mechanism between the atmosphere and clouds, which is of great significance for improving the accuracy of weather forecasts, assessing climate change trends, and ensuring aviation safety.

[0003] Traditional 3D atmosphere and 3D cloud analysis methods have many limitations. The data source is single and often relies on limited data sources such as ground observation stations and satellite remote sensing, resulting in incomplete data coverage and low accuracy of meteorological forecasts. On the other hand, data processing and analysis methods are relatively backward, making it difficult to effectively integrate and utilize big data resources, limiting the accuracy and timeliness of analysis results.

[0004] Therefore, a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data is urgently needed to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data: to solve the technical problem that when conducting meteorological analysis on clouds in the prior art, the meteorological forecast is not accurate and has low timeliness because it only relies on limited data sources such as ground observation stations and satellite remote sensing.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A 3D atmosphere and 3D cloud fusion analysis system based on big data, including: The height measurement module is used to determine the cloud base height of the target cloud cluster based on the laser ceilometer, collect the radiation corresponding to the cloud top height of the target cloud cluster based on the meteorological satellite, calculate the cloud top temperature of the target cloud cluster based on the radiation transmission model, and determine the cloud top height of the target cloud cluster based on the cloud top temperature; The cloud segmentation module is used to obtain the vertical height of the target cloud according to the cloud base height and cloud top height of the target cloud, and to mark the cloud segmentation according to the vertical height to obtain the marked segmented cloud layer; The meteorological characterization value calculation module is used to detect precipitation particles in the marked segmented cloud layer, infer the water density of the marked segmented cloud layer based on the precipitation particles, obtain the aerosol particle extinction coefficient and backscattering coefficient of the marked segmented cloud layer, and obtain the meteorological characterization value of the target cloud cluster based on the water density, aerosol particle extinction coefficient and backscattering coefficient; The fusion analysis module is used to analyze the rationality of the existence of the target cloud based on the meteorological characterization value of the target cloud.

[0007] Furthermore, determining the cloud base height of the target cloud based on the laser ceilometer specifically includes the following processes: The cloud base height of the target cloud is calculated based on the radar equation of the laser ceilometer: ; in, The cloud base height of the target cloud cluster is the laser with wavelength λ. The backscattered power at the distance is measured by a laser ceilometer, is the laser pulse energy value, C is a constant, is the total atmospheric backscatter coefficient, is the structural integrity coefficient of the target cloud, is the effective receiving area of ​​the telescope.

[0008] Furthermore, the steps for obtaining the structural integrity coefficient of the target cloud specifically include the following processes: Collect the reflected radar waves after the radar waves emitted by the laser ceilometer reach the target cloud, and process and analyze the reflected radar waves: When the incident radar wave propagates to the target cloud, the impedance of the preset complete target cloud is , the impedance obtained by collecting the area where the incident radar wave is reflected is , then according to The reflection coefficient is calculated as , the transmission coefficient is ; If the reflected radar wave waveform changes only in position with time but not in phase, and the reflection coefficient is is 0, and the transmission coefficient is =1, the structural integrity coefficient of the target cloud is 1, if the reflected radar wave waveform changes phase, and / Greater than 1 or / When it is less than 1, the structural integrity coefficient of the target cloud is 0.

[0009] Furthermore, the cloud top temperature of the target cloud cluster is calculated based on the radiation transfer model, and the cloud top height of the target cloud cluster is determined based on the cloud top temperature, which specifically includes the following processes: The cloud top temperature of the target cloud is calculated based on the radiation transfer model: ; in, is the emissivity of the target cloud, is the cloud top temperature Planck blackbody radiation, The ground temperature is Planck blackbody radiation, It is the radiation corresponding to the cloud top height of the target cloud group collected by the meteorological satellite; Based on temperature lapse rate, cloud top temperature and ground temperature The cloud top height of the target cloud is calculated, where the temperature lapse rate is the rate at which the temperature decreases with height.

[0010] Furthermore, the vertical height of the target cloud is obtained according to the cloud base height and cloud top height of the target cloud, and the cloud layer is segmented and labeled according to the vertical height. The labeled segmented cloud layer specifically includes the following process: Calculate the height difference between the cloud top and the cloud bottom, record the difference as the vertical height, divide the vertical height into several height segments, obtain the relative humidity value of each height segment, calculate the relative humidity difference between the relative humidity values ​​of adjacent height segments, set the relative humidity difference threshold, and determine whether the relative humidity difference between the relative humidity values ​​of two adjacent height segments exceeds the relative humidity difference threshold. If not, record the cloud layer corresponding to the two adjacent height segments as a labeled segmented cloud layer until i labeled segmented cloud layers are obtained.

[0011] Furthermore, obtaining the relative humidity value of each height segment specifically includes the following steps: collecting the air pressure of each height segment based on the three-dimensional atmospheric acquisition system and temperature T, the relative humidity value HP for each altitude segment is calculated based on the correlation formula: ,in, is the relative humidity coefficient, is the saturated water vapor pressure value.

[0012] Furthermore, the precipitation particles of the marked segmented cloud layer are detected, and the water density of the marked segmented cloud layer is inferred based on the precipitation particles, which specifically includes the following processes: Based on the detection of the microphysical characteristics of precipitation particles in the marked segmented cloud layer by meteorological radar, the matrix method is used to calculate the single particle scattering matrix of different precipitation particle types: Construct a scattering coordinate system L, select any precipitation particle as a reference particle, place the origin of the coordinate inside the reference particle, and fix its direction in space. Calculate the spatial distance values ​​between other precipitation particles and the reference particle, and use the spatial distance values ​​as elements of the scattering matrix, where precipitation particles include cloud droplets, snow crystals, graupel, hail, and graupel particles; The similarity between the single particle scattering matrix and the standard scattering matrix is ​​calculated to obtain the similarity, and based on the similarity, the water density table of the annotated segmented cloud layer corresponding to the standard scattering matrix is ​​queried to obtain the water density of the annotated segmented cloud layer, and the water density of the annotated segmented cloud layer is obtained; The Euclidean distance method is used to calculate the similarity between the single particle scattering matrix and the standard scattering matrix to obtain the similarity.

[0013] Furthermore, obtaining the meteorological characterization value of the target cloud cluster based on the water density, the extinction coefficient of the aerosol particles and the backscattering coefficient specifically includes the following process: adding the sum of the water density, the extinction coefficient of the aerosol particles and the backscattering coefficient to obtain the characterization index of the marked segmented cloud layer until i characterization indexes are obtained, establishing a rectangular coordinate system with the number of marked segmented cloud layers as the X-axis and the characterization index as the Y-axis, drawing a meteorological characterization curve by plotting points, calculating a first area enclosed by a line segment of the meteorological characterization curve located above the preset meteorological characterization curve and the preset meteorological characterization curve, then calculating a second area enclosed by the meteorological characterization curve and the X-axis, calculating the product between the first area and the second area, then calculating the acute angle formed by the first intersection of the meteorological characterization curve and the preset meteorological characterization curve, and then multiplying the product by the product to obtain a product value, and recording the product value as the meteorological characterization value of the target cloud cluster.

[0014] Furthermore, the rationality of the existence of the target cloud cluster is analyzed based on the meteorological characterization value of the target cloud cluster, which includes the following process: The meteorological characterization value threshold is loaded, wherein the meteorological characterization value threshold is stored in the system and its value is set by the system, and whether the meteorological characterization value of the target cloud cluster is greater than the meteorological characterization value threshold is judged. If so, it is judged that the target cloud cluster is unreasonable, and a signal of increased rainfall possibility in the corresponding area of ​​the target cloud cluster is generated. If not, it is judged that the target cloud cluster is reasonable, and a signal of increased rainfall possibility in the corresponding area of ​​the target cloud cluster is not generated.

[0015] Compared with the existing solutions, the present invention achieves the following beneficial effects: The present invention determines the cloud base height of a target cloud cluster based on a laser ceilometer, collects radiation corresponding to the cloud top height of the target cloud cluster based on a meteorological satellite, calculates the cloud top temperature of the target cloud cluster based on a radiation transmission mode, determines the cloud top height of the target cloud cluster based on the cloud top temperature, detects precipitation particles of a marked segmented cloud layer, infers the water density of the marked segmented cloud layer according to the precipitation particles, obtains the aerosol particle extinction coefficient and the backscattering coefficient of the marked segmented cloud layer, obtains the meteorological characterization value of the target cloud cluster based on the water density, the aerosol particle extinction coefficient and the backscattering coefficient, analyzes the rationality of the existence of the target cloud cluster based on the meteorological characterization value of the target cloud cluster, and can improve the accuracy of meteorological prediction of cloud layers and improve the timeliness of meteorological prediction.

[0016] Furthermore, the system can integrate multiple data sources such as ground observation stations, satellite remote sensing, radar detection, etc. to form a comprehensive three-dimensional atmosphere and cloud data set, which significantly improves the data coverage and accuracy, and provides a basis for more accurate analysis and prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a system block diagram of a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data according to an embodiment of the present invention; Figure 2 This is a workflow diagram of a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data according to an embodiment of the present invention; Figure 3 This is another workflow diagram of a big data-based three-dimensional atmosphere and three-dimensional cloud fusion analysis system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0021] This embodiment provides a 3D atmosphere and 3D cloud fusion analysis system based on big data. Figure 1 is a system block diagram of a 3D atmosphere and 3D cloud fusion analysis system based on big data according to an embodiment of the present invention. Figure 1 As shown, the system includes: The height measurement module is used to determine the cloud base height of the target cloud cluster based on the laser ceilometer, collect the radiation corresponding to the cloud top height of the target cloud cluster based on the meteorological satellite, calculate the cloud top temperature of the target cloud cluster based on the radiation transmission model, and determine the cloud top height of the target cloud cluster based on the cloud top temperature; The cloud segmentation module is used to obtain the vertical height of the target cloud according to the cloud base height and cloud top height of the target cloud, and to mark the cloud segmentation according to the vertical height to obtain the marked segmented cloud layer; The meteorological characterization value calculation module is used to detect precipitation particles in the marked segmented cloud layer, infer the water density of the marked segmented cloud layer based on the precipitation particles, obtain the aerosol particle extinction coefficient and backscattering coefficient of the marked segmented cloud layer, and obtain the meteorological characterization value of the target cloud cluster based on the water density, aerosol particle extinction coefficient and backscattering coefficient; The fusion analysis module is used to analyze the rationality of the existence of the target cloud based on the meteorological characterization value of the target cloud.

[0022] The present invention determines the cloud base height of a target cloud cluster based on a laser ceilometer, collects radiation corresponding to the cloud top height of the target cloud cluster based on a meteorological satellite, calculates the cloud top temperature of the target cloud cluster based on a radiation transmission mode, determines the cloud top height of the target cloud cluster based on the cloud top temperature, detects precipitation particles of a marked segmented cloud layer, infers the water density of the marked segmented cloud layer according to the precipitation particles, obtains the aerosol particle extinction coefficient and the backscattering coefficient of the marked segmented cloud layer, obtains the meteorological characterization value of the target cloud cluster based on the water density, the aerosol particle extinction coefficient and the backscattering coefficient, analyzes the rationality of the existence of the target cloud cluster based on the meteorological characterization value of the target cloud cluster, and can improve the accuracy of meteorological prediction of cloud layers and improve the timeliness of meteorological prediction.

[0023] In some embodiments, determining the cloud base height of the target cloud cluster based on the laser ceilometer specifically includes the following process: The cloud base height of the target cloud is calculated based on the radar equation of the laser ceilometer: ; in, The cloud base height of the target cloud cluster is the laser with wavelength λ. The backscattered power at the distance is measured by a laser ceilometer, is the laser pulse energy value, C is a constant, is the total atmospheric backscatter coefficient, is the structural integrity coefficient of the target cloud, is the effective receiving area of ​​the telescope.

[0024] Furthermore, the steps for obtaining the structural integrity coefficient of the target cloud specifically include the following processes: Collect the reflected radar waves after the radar waves emitted by the laser ceilometer reach the target cloud, and process and analyze the reflected radar waves: When the incident radar wave propagates to the target cloud, the impedance of the preset complete target cloud is , the impedance obtained by collecting the area where the incident radar wave is reflected is , then according to The reflection coefficient is calculated as , the transmission coefficient is ; If the reflected radar wave waveform changes only in position with time but not in phase, and the reflection coefficient is is 0, and the transmission coefficient is =1, the structural integrity coefficient of the target cloud is 1, if the reflected radar wave waveform changes phase, and / Greater than 1 or / When it is less than 1, the structural integrity coefficient of the target cloud is 0.

[0025] In some embodiments, obtaining the cloud top temperature of the target cloud cluster based on the radiation transmission model and determining the cloud top height of the target cloud cluster based on the cloud top temperature specifically include the following processes: The cloud top temperature of the target cloud is calculated based on the radiation transfer model: ; in, is the emissivity of the target cloud, is the cloud top temperature Planck blackbody radiation, The ground temperature is Planck blackbody radiation, It is the radiation corresponding to the cloud top height of the target cloud group collected by the meteorological satellite; Based on temperature lapse rate, cloud top temperature and ground temperature The cloud top height of the target cloud is calculated, where the temperature lapse rate is the rate at which the temperature decreases with height.

[0026] In some embodiments, the vertical height of the target cloud is obtained according to the cloud base height and cloud top height of the target cloud, and the cloud layer is segmented and labeled according to the vertical height to obtain the labeled segmented cloud layer, which specifically includes the following process: Calculate the height difference between the cloud top and the cloud bottom, record the difference as the vertical height, divide the vertical height into several height segments, obtain the relative humidity value of each height segment, calculate the relative humidity difference between the relative humidity values ​​of adjacent height segments, set the relative humidity difference threshold, and determine whether the relative humidity difference between the relative humidity values ​​of two adjacent height segments exceeds the relative humidity difference threshold. If not, record the cloud layer corresponding to the two adjacent height segments as a labeled segmented cloud layer until i labeled segmented cloud layers are obtained.

[0027] In some embodiments, obtaining the relative humidity value of each height segment specifically includes the following steps: collecting the air pressure of each height segment based on the three-dimensional atmosphere collection system; and temperature T, the relative humidity value HP for each altitude segment is calculated based on the correlation formula: ,in, is the relative humidity coefficient, is the saturated water vapor pressure value.

[0028] In some embodiments, Figure 2 is a workflow diagram of a 3D atmosphere and 3D cloud fusion analysis system based on big data according to an embodiment of the present invention. Figure 2 As shown, detecting precipitation particles in the marked segmented cloud layer and inferring the water density of the marked segmented cloud layer based on the precipitation particles specifically includes the following steps: Step S201: Based on the detection of microphysical characteristics of precipitation particles in the marked segmented cloud layer by the meteorological radar, a matrix method is applied to calculate the single particle scattering matrix of different precipitation particle types; Specifically, a scattering coordinate system L is constructed, and any precipitation particle is selected as a reference particle. The origin of the coordinate is located inside the reference particle, and its direction is fixed in space. The spatial distance values ​​between other precipitation particles and the reference particle are calculated, and the spatial distance values ​​are used as elements of the scattering matrix, where precipitation particles include cloud droplets, snow crystals, graupel, hail, and graupel particles; Step S202: Calculate the similarity between the single particle scattering matrix and the standard scattering matrix to obtain the similarity, and query the water density table of the annotated segmented cloud layer corresponding to the standard scattering matrix based on the similarity to obtain the water density of the annotated segmented cloud layer; Specifically, each similarity range interval corresponds to a specific water density in the water density table.

[0029] Among them, the Euclidean distance method is used to calculate the similarity between the single particle scattering matrix and the standard scattering matrix to obtain the similarity.

[0030] In some embodiments, Figure 3 FIG. 1 is another workflow diagram of a 3D atmosphere and 3D cloud fusion analysis system based on big data according to an embodiment of the present invention. Figure 3 As shown, obtaining the meteorological characterization value of the target cloud based on the water density, the aerosol particle extinction coefficient and the backscattering coefficient specifically includes the following steps: Step S301: Add the sum of the water density, the aerosol particle extinction coefficient and the backscattering coefficient to obtain a characterization index of the marked segmented cloud layer until i characterization indices are obtained; Step S302: a rectangular coordinate system is established with the number of marked segmented cloud layers as the X-axis and the characterization index as the Y-axis, and a meteorological characterization curve is drawn by plotting points; Step S303: Calculate a first area enclosed by a line segment of the meteorological characterization curve located above the preset meteorological characterization curve and the preset meteorological characterization curve, then calculate a second area enclosed by the meteorological characterization curve and the X-axis, and calculate the product of the first area and the second area; Step S304: Calculate the acute angle formed by the first intersection of the meteorological characterization curve and the preset meteorological characterization curve, and then multiply it by the product to obtain a product value, and record the product value as the meteorological characterization value of the target cloud.

[0031] It is worth explaining that the aerosol extinction coefficient refers to the comprehensive reflection of the absorption and scattering of light by aerosol particles in the atmosphere. It is an important parameter to measure the influence of aerosol particles in the atmosphere on light propagation, while the backscattering coefficient refers to the measure of the backscattering ability of aerosol particles in the atmosphere to light. Regarding the specific values ​​of the extinction coefficient and backscattering coefficient of aerosol particles in the cloud layer, the parameters usually need to be obtained through field measurement and analysis using professional measuring equipment and methods. In actual applications, researchers will use advanced equipment such as lidar and solar radiometers to measure aerosol particles in the cloud layer, and obtain the specific values ​​of the extinction coefficient and backscattering coefficient by analyzing the measurement data.

[0032] In some embodiments, analyzing the rationality of the existence of the target cloud based on the meteorological characteristic value of the target cloud includes the following process: The meteorological characterization value threshold is loaded, wherein the meteorological characterization value threshold is stored in the system and its value is set by the system, and whether the meteorological characterization value of the target cloud cluster is greater than the meteorological characterization value threshold is judged. If so, it is judged that the target cloud cluster is unreasonable, and a signal of increased rainfall possibility in the corresponding area of ​​the target cloud cluster is generated. If not, it is judged that the target cloud cluster is reasonable, and a signal of increased rainfall possibility in the corresponding area of ​​the target cloud cluster is not generated.

[0033] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0034] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0035] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0036] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0037] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0038] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A 3D atmosphere and 3D cloud fusion analysis system based on big data, characterized by: The system includes: The height measurement module is used to determine the cloud base height of the target cloud cluster based on the laser ceilometer, collect the radiation corresponding to the cloud top height of the target cloud cluster based on the meteorological satellite, calculate the cloud top temperature of the target cloud cluster based on the radiation transmission model, and determine the cloud top height of the target cloud cluster based on the cloud top temperature; The cloud segmentation module is used to obtain the vertical height of the target cloud according to the cloud base height and cloud top height of the target cloud, and to mark the cloud segmentation according to the vertical height to obtain the marked segmented cloud layer; The meteorological characterization value calculation module is used to detect precipitation particles in the marked segmented cloud layer, infer the water density of the marked segmented cloud layer based on the precipitation particles, obtain the aerosol particle extinction coefficient and backscattering coefficient of the marked segmented cloud layer, and obtain the meteorological characterization value of the target cloud cluster based on the water density, aerosol particle extinction coefficient and backscattering coefficient; The fusion analysis module is used to analyze the rationality of the existence of the target cloud based on the meteorological characterization value of the target cloud.

2. The big data-based three-dimensional atmosphere and three-dimensional cloud fusion analysis system according to claim 1 is characterized in that: The specific process of determining the cloud base height of the target cloud based on the laser ceilometer is as follows: The cloud base height of the target cloud is calculated based on the radar equation of the laser ceilometer: ； in, The cloud base height of the target cloud cluster is the laser with wavelength λ. The backscattered power at the distance is measured by a laser ceilometer, is the laser pulse energy value, C is a constant, is the total atmospheric backscatter coefficient, is the structural integrity coefficient of the target cloud, is the effective receiving area of ​​the telescope.

3. The big data-based 3D atmosphere and 3D cloud fusion analysis system according to claim 2 is characterized in that: The steps for obtaining the structural integrity coefficient of the target cloud include the following processes: Collect the reflected radar waves after the radar waves emitted by the laser ceilometer reach the target cloud, and process and analyze the reflected radar waves: When the incident radar wave propagates to the target cloud, the impedance of the preset complete target cloud is , the impedance obtained by collecting the area where the incident radar wave is reflected is , then according to The reflection coefficient is calculated as , the transmission coefficient is ; If the reflected radar wave waveform changes only in position with time but not in phase, and the reflection coefficient is is 0, and the transmission coefficient is =1, the structural integrity coefficient of the target cloud is 1, if the reflected radar wave waveform changes phase, and / Greater than 1 or / When it is less than 1, the structural integrity coefficient of the target cloud is 0.

4. The big data-based 3D atmosphere and 3D cloud fusion analysis system according to claim 1 is characterized in that: The cloud top temperature of the target cloud cluster is calculated based on the radiation transfer model, and the cloud top height of the target cloud cluster is determined based on the cloud top temperature. The specific process includes the following: The cloud top temperature of the target cloud is calculated based on the radiation transfer model: ； in, is the emissivity of the target cloud, is the cloud top temperature Planck blackbody radiation, The ground temperature is Planck blackbody radiation, It is the radiation corresponding to the cloud top height of the target cloud group collected by the meteorological satellite; Based on temperature lapse rate, cloud top temperature and ground temperature The cloud top height of the target cloud is calculated, where the temperature lapse rate is the rate at which the temperature decreases with height.

5. The big data-based 3D atmosphere and 3D cloud fusion analysis system according to claim 1 is characterized in that: The vertical height of the target cloud is obtained according to the cloud base height and cloud top height of the target cloud, and the cloud layer is segmented and annotated according to the vertical height. The specific process of obtaining the annotated segmented cloud layer includes the following steps: Calculate the height difference between the cloud top and the cloud bottom, record the difference as the vertical height, divide the vertical height into several height segments, obtain the relative humidity value of each height segment, calculate the relative humidity difference between the relative humidity values ​​of adjacent height segments, set the relative humidity difference threshold, and determine whether the relative humidity difference between the relative humidity values ​​of two adjacent height segments exceeds the relative humidity difference threshold. If not, record the cloud layer corresponding to the two adjacent height segments as a labeled segmented cloud layer until i labeled segmented cloud layers are obtained.

6. The big data-based 3D atmosphere and 3D cloud fusion analysis system according to claim 1 is characterized in that: The specific steps of obtaining the relative humidity value of each height segment include: collecting the air pressure of each height segment based on the three-dimensional atmospheric acquisition system; and temperature T, the relative humidity value HP for each altitude segment is calculated based on the correlation formula: ,in, is the relative humidity coefficient, is the saturated water vapor pressure value.

7. The big data-based 3D atmosphere and 3D cloud fusion analysis system according to claim 1 is characterized in that: The process of detecting precipitation particles in the marked segmented cloud layer and inferring the water density of the marked segmented cloud layer based on the precipitation particles specifically includes the following steps: Based on the detection of the microphysical characteristics of precipitation particles in the marked segmented cloud layer by meteorological radar, the matrix method is used to calculate the single particle scattering matrix of different precipitation particle types: Construct a scattering coordinate system L, select any precipitation particle as a reference particle, place the origin of the coordinate inside the reference particle, and fix its direction in space. Calculate the spatial distance values ​​between other precipitation particles and the reference particle, and use the spatial distance values ​​as elements of the scattering matrix, where precipitation particles include cloud droplets, snow crystals, graupel, hail, and graupel particles; The similarity between the single particle scattering matrix and the standard scattering matrix is ​​calculated to obtain the similarity. Based on the similarity, the water density table of the annotated segmented cloud layer corresponding to the standard scattering matrix is ​​queried to obtain the water density of the annotated segmented cloud layer: The Euclidean distance method is used to calculate the similarity between the single particle scattering matrix and the standard scattering matrix to obtain the similarity, wherein each similarity range interval corresponds to a specific water density in the water density table.

8. The big data-based three-dimensional atmosphere and three-dimensional cloud fusion analysis system according to claim 1 is characterized in that: Obtaining the meteorological characterization value of the target cloud based on the water density, the extinction coefficient of the aerosol particles and the backscattering coefficient specifically includes the following process: adding the sum of the water density, the extinction coefficient of the aerosol particles and the backscattering coefficient to obtain the characterization index of the marked segmented cloud layer until i characterization indexes are obtained, establishing a rectangular coordinate system with the number of marked segmented cloud layers as the X-axis and the characterization index as the Y-axis, drawing a meteorological characterization curve by plotting points, calculating a first area enclosed by a line segment of the meteorological characterization curve located above the preset meteorological characterization curve and the preset meteorological characterization curve, then calculating a second area enclosed by the meteorological characterization curve and the X-axis, calculating the product between the first area and the second area, then calculating the acute angle formed by the first intersection of the meteorological characterization curve and the preset meteorological characterization curve, and then multiplying the product by the product to obtain a product value, and recording the product value as the meteorological characterization value of the target cloud.

9. The big data-based three-dimensional atmosphere and three-dimensional cloud fusion analysis system according to claim 1, characterized in that: The rationality of the target cloud cluster's existence is analyzed based on the meteorological characterization value of the target cloud cluster, which includes the following processes: The meteorological characterization value threshold is loaded, wherein the meteorological characterization value threshold is stored in the system and its value is set by the system, and whether the meteorological characterization value of the target cloud cluster is greater than the meteorological characterization value threshold is judged. If so, it is judged that the target cloud cluster is unreasonable, and a signal of increased rainfall possibility in the corresponding area of ​​the target cloud cluster is generated. If not, it is judged that the target cloud cluster is reasonable, and a signal of increased rainfall possibility in the corresponding area of ​​the target cloud cluster is not generated.

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