3D Atmospheric and 3D Cloud Fusion Analysis System Based on Big Data

Abstract

The present invention relates to the field of meteorological prediction technology, and particularly to a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data. The system includes: The present invention determines the cloud base height of the target cloud cluster based on a ceilometer, collects the 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 radiative transfer model, and determines the cloud top height of the target cloud cluster based on the cloud top temperature, detects the precipitation particles of the labeled segmented cloud layer, infers the water content density of the labeled segmented cloud layer according to the precipitation particles, obtains the extinction coefficient and backscattering coefficient of the aerosol particles of the labeled segmented cloud layer, obtains the meteorological characterization value of the target cloud cluster based on the water content density, extinction coefficient and backscattering coefficient of the aerosol particles, and analyzes the rationality of the existence of the target cloud cluster based on the meteorological characterization value of the target cloud cluster, which can improve the accuracy of cloud meteorological prediction and improve the timeliness of meteorological prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological prediction, and particularly 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 research on the characteristics of the atmosphere and cloud layers has gradually shifted from traditional two-dimensional analysis to three-dimensional stereoscopic analysis. The fusion analysis of three-dimensional atmosphere and three-dimensional cloud can not only provide more comprehensive and accurate atmospheric state information, but also reveal the interaction mechanism between the atmosphere and cloud layers, which is of great significance for improving the accuracy of weather forecasting, evaluating climate change trends, and ensuring aviation safety.

[0003] Traditional three-dimensional atmosphere and three-dimensional cloud analysis methods have many limitations. The data sources are single, often relying only on limited data sources such as ground observation stations and satellite remote sensing, resulting in incomplete data coverage and low accuracy of meteorological prediction. On the other hand, the data processing and analysis methods are relatively backward, making it difficult to effectively integrate and utilize big data resources, which limits the accuracy and timeliness of the analysis results.

[0004] Therefore, there is an urgent need for a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data 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, which solves the technical problems in the prior art that when performing meteorological analysis on cloud clusters, due to relying only on limited data sources such as ground observation stations and satellite remote sensing, the accuracy of meteorological prediction is not high and the timeliness is low.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data, the system includes:

[0008] An altitude measurement module, which is used to determine the cloud base height of the target cloud cluster based on a laser ceilometer, collect the radiation corresponding to the cloud top height of the target cloud cluster based on a meteorological satellite, calculate the cloud top temperature of the target cloud cluster based on a radiation transfer model, and determine the cloud top height of the target cloud cluster based on the cloud top temperature;

[0009] A cloud layer segmentation module, which is used to obtain the vertical height of the target cloud cluster according to the cloud base height and cloud top height of the target cloud cluster, perform cloud layer segmentation annotation according to the vertical height, and obtain the annotated segmented cloud layers;

[0010] A meteorological characterization value calculation module, which is used to detect precipitation particles in the annotated segmented clouds, infer the water content density of the annotated segmented clouds based on the precipitation particles, obtain the extinction coefficient and backscattering coefficient of aerosol particles in the annotated segmented clouds, and obtain the meteorological characterization value of the target cloud cluster based on the water content density, the extinction coefficient of aerosol particles, and the backscattering coefficient;

[0011] A fusion analysis module, which is used to analyze the rationality of the existence of the target cloud cluster based on the meteorological characterization value of the target cloud cluster.

[0012] Furthermore, determining the cloud base height of the target cloud cluster based on a ceilometer specifically includes the following process:

[0013] Calculating the cloud base height of the target cloud cluster based on the radar equation of the ceilometer:

[0014] ;

[0015] Wherein, is the backscattering power of the laser with wavelength λ at the cloud base height distance of the target cloud cluster, measured by the ceilometer, is the laser pulse energy value, C is a constant, is the total atmospheric backscattering coefficient, is the structural integrity coefficient of the target cloud cluster, is the effective receiving area of the telescope.

[0016] Furthermore, the steps for obtaining the structural integrity coefficient of the target cloud cluster specifically include the following process:

[0017] Collect the reflected radar wave after the radar wave emitted by the ceilometer reaches the target cloud cluster, and process and analyze the reflected radar wave: when the incident radar wave propagates to the target cloud cluster, the impedance of the preset complete target cloud cluster is , and the impedance collected for the area where the incident radar wave is reflected is , then according to calculate the reflection coefficient as , and the transmission coefficient is ; if the waveform of the reflected radar wave only changes in position with time and does not change in phase, and the reflection coefficient is is 0 and the transmission coefficient is is 1, the structural integrity coefficient of the target cloud cluster is 1. If the waveform of the reflected radar wave changes in phase and / is greater than 1 or / is less than 1, the structural integrity coefficient of the target cloud cluster is 0.

[0018] Further, calculating the cloud top temperature of the target cloud mass based on the radiative transfer model, and determining the cloud top height of the target cloud mass based on the cloud top temperature specifically includes the following process:

[0019] Calculating the cloud top temperature of the target cloud mass based on the radiative transfer model:

[0020] ;

[0021] where is the emissivity of the target cloud mass, is the Planck blackbody radiation with the cloud top temperature of , is the Planck blackbody radiation with the ground temperature of , is the radiation corresponding to the cloud top height of the target cloud mass collected by the meteorological satellite;

[0022] Calculating the cloud top height of the target cloud mass based on the lapse rate of temperature, the cloud top temperature and the ground temperature , where the lapse rate of temperature is the rate at which the air temperature decreases with height.

[0023] Further, obtaining the vertical height of the target cloud mass according to the cloud base height and the cloud top height of the target cloud mass, and performing cloud layer segmentation annotation according to the vertical height to obtain the annotated segmented cloud layers specifically includes the following process:

[0024] Calculating the height difference between the cloud top height and the cloud base height, recording the difference as the vertical height, equally dividing the vertical height into several height segments, obtaining the relative humidity value of each height segment, calculating the relative humidity difference between the relative humidity values of adjacent height segments, setting the relative humidity difference threshold, and determining whether the relative humidity difference between the relative humidity values of adjacent two height segments exceeds the relative humidity difference threshold. If not, recording the cloud layers corresponding to the adjacent two height segments as one annotated segmented cloud layer until i annotated segmented cloud layers are obtained.

[0025] Further, obtaining the relative humidity value of each height segment specifically includes the following steps: collecting the air pressure and the air temperature T of each height segment based on the three-dimensional atmosphere acquisition system, and calculating the relative humidity value HP of each height segment based on the correlation formula, and the correlation formula is , where is the relative humidity coefficient, is the saturated water vapor pressure value.

[0026] Further, detecting the precipitation particles of the annotated segmented cloud layers, and inferring the water content density of the annotated segmented cloud layers according to the precipitation particles specifically includes the following process:

[0027] Based on the detection of the microphysical properties of precipitation particles in the labeled segmented cloud layers by a meteorological radar, the matrix method is applied to calculate the single-particle scattering matrix of different precipitation particle types:

[0028] Construct a scattering coordinate system L. Select any precipitation particle as a reference particle. Place the coordinate origin inside the reference particle, with its direction fixed in space. Calculate the spatial distance value between other precipitation particles and the reference particle, and use the spatial distance value as an element of the scattering matrix. Among them, precipitation particles include cloud droplets, snow crystals, graupel, hail, and sleet particles;

[0029] Calculate the similarity between the single-particle scattering matrix and the standard scattering matrix to obtain the similarity. Based on the similarity, query the water content density table of the labeled segmented cloud layer corresponding to the standard scattering matrix to obtain the water content density of the labeled segmented cloud layer, and obtain the water content density of the labeled segmented cloud layer;

[0030] Use the Euclidean distance method to calculate the similarity between the single-particle scattering matrix and the standard scattering matrix to obtain the similarity.

[0031] Furthermore, obtaining the meteorological characterization value of the target cloud cluster based on the water content density, aerosol particle extinction coefficient, and backscattering coefficient specifically includes the following process: Add the sum of the water content density, aerosol particle extinction coefficient, and backscattering coefficient to obtain the characterization index of the labeled segmented cloud layer until i characterization indexes are obtained. Establish a rectangular coordinate system with the number of labeled segmented cloud layers as the X-axis and the characterization index as the Y-axis. Plot the meteorological characterization curve by plotting points. Calculate the first area enclosed by the line segment of the meteorological characterization curve above the preset meteorological characterization curve and the preset meteorological characterization curve, then calculate the second area enclosed by the meteorological characterization curve and the X-axis. Calculate the product between the first area and the second area, then calculate the acute angle degree formed by the first intersection of the meteorological characterization curve and the preset meteorological characterization curve, and multiply it by the product to obtain the product value. Record this product value as the meteorological characterization value of the target cloud cluster.

[0032] Furthermore, analyzing the rationality of the existence of the target cloud cluster based on the meteorological characterization value of the target cloud cluster includes the following process:

[0033] Load the meteorological characterization value threshold. Among them, the meteorological characterization value threshold is stored in the system, and its value is set by the system. Determine whether the meteorological characterization value of the target cloud cluster is greater than the meteorological characterization value threshold. If so, determine that the existence of the target cloud cluster is unreasonable and generate a signal indicating an increased possibility of rainfall in the area corresponding to the target cloud cluster. If not, determine that the existence of the target cloud cluster is reasonable and do not generate a signal indicating an increased possibility of rainfall in the area corresponding to the target cloud cluster.

[0034] Compared with the existing solutions, the beneficial effects achieved by the present invention:

[0035] The present invention determines the cloud base height of a target cloud mass based on a lidar ceilometer, collects the radiation corresponding to the cloud top height of the target cloud mass based on a meteorological satellite, calculates the cloud top temperature of the target cloud mass based on a radiation transfer model, determines the cloud top height of the target cloud mass based on the cloud top temperature, detects precipitation particles in the segmented cloud layers with annotations, infers the water content density of the segmented cloud layers with annotations based on the precipitation particles, obtains the extinction coefficient and backscattering coefficient of aerosol particles in the segmented cloud layers with annotations, obtains the meteorological characterization value of the target cloud mass based on the water content density, extinction coefficient and backscattering coefficient of aerosol particles, and analyzes the rationality of the existence of the target cloud mass based on the meteorological characterization value of the target cloud mass, which can improve the accuracy of cloud meteorological prediction and the timeliness of meteorological prediction.

[0036] Furthermore, the system can integrate multiple data sources such as ground observation stations, satellite remote sensing, and radar detection to form a comprehensive three-dimensional atmosphere and cloud data set, significantly improving the data coverage and accuracy, and providing a basis for more accurate analysis and prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0038] Figure 1 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;

[0039] Figure 2 is a working flowchart of a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data according to an embodiment of the present invention;

[0040] Figure 3 is another working flowchart of a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0042] In addition, the described features, structures, or characteristics may be combined in any suitable manner in one or more example embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the example embodiments of the present disclosure. However, those skilled in the art will realize that one or more of the specific details may be omitted in practicing the technical solutions of the present disclosure, 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.

[0043] This embodiment provides a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data. Figure 1 As shown in the system block diagram of the three-dimensional atmosphere and three-dimensional cloud fusion analysis system according to the embodiment of the present invention, Figure 1 As shown, the system includes:

[0044] An altitude measurement module, configured to determine the cloud base altitude of the target cloud cluster based on a laser ceilometer, collect the radiation corresponding to the cloud top altitude of the target cloud cluster based on a meteorological satellite, calculate the cloud top temperature of the target cloud cluster based on a radiative transfer model, and determine the cloud top altitude of the target cloud cluster based on the cloud top temperature;

[0045] A cloud layer segmentation module, configured to obtain the vertical altitude of the target cloud cluster according to the cloud base altitude and cloud top altitude of the target cloud cluster, perform cloud layer segmentation annotation according to the vertical altitude, and obtain the annotated segmented cloud layers;

[0046] A meteorological characterization value calculation module, configured to detect the precipitation particles in the annotated segmented cloud layers, infer the water content density of the annotated segmented cloud layers according to the precipitation particles, obtain the aerosol particle extinction coefficient and backscattering coefficient of the annotated segmented cloud layers, and obtain the meteorological characterization value of the target cloud cluster based on the water content density, aerosol particle extinction coefficient, and backscattering coefficient;

[0047] A fusion analysis module, configured to analyze the rationality of the existence of the target cloud cluster based on the meteorological characterization value of the target cloud cluster.

[0048] The present invention determines the cloud base altitude of the target cloud cluster based on a laser ceilometer, collects the radiation corresponding to the cloud top altitude of the target cloud cluster based on a meteorological satellite, calculates the cloud top temperature of the target cloud cluster based on a radiative transfer model, and determines the cloud top altitude of the target cloud cluster based on the cloud top temperature, detects the precipitation particles in the annotated segmented cloud layers, infers the water content density of the annotated segmented cloud layers according to the precipitation particles, obtains the aerosol particle extinction coefficient and backscattering coefficient of the annotated segmented cloud layers, obtains the meteorological characterization value of the target cloud cluster based on the water content density, aerosol particle extinction coefficient, and backscattering coefficient, and analyzes the rationality of the existence of the target cloud cluster based on the meteorological characterization value of the target cloud cluster, which can improve the accuracy of cloud layer meteorological prediction and the timeliness of meteorological prediction.

[0049] In some embodiments, determining the cloud base height of the target cloud mass based on a lidar specifically includes the following process:

[0050] Calculating the cloud base height of the target cloud mass based on the radar equation of the lidar:

[0051] ;

[0052] Wherein, is the backscattering power of the laser with wavelength λ at the cloud base height distance of the target cloud mass, measured by the lidar, is the laser pulse energy value, C is a constant, is the total atmospheric backscattering coefficient, is the structural integrity coefficient of the target cloud mass, is the effective receiving area of the telescope.

[0053] Furthermore, the steps for obtaining the structural integrity coefficient of the target cloud mass specifically include the following process:

[0054] Collect the reflected radar wave after the radar wave emitted by the lidar reaches the target cloud mass, and process and analyze the reflected radar wave: When the incident radar wave propagates to the target cloud mass, the impedance of the preset complete target cloud mass is , and the impedance collected from the area where the incident radar wave is reflected is , then according to calculate the reflection coefficient as , and the transmission coefficient as ; If the waveform of the reflected radar wave only changes in position with time and does not change in phase, and the reflection coefficient is is 0, and the transmission coefficient is is 1, the structural integrity coefficient of the target cloud mass is 1. If the waveform of the reflected radar wave changes in phase, and / is greater than 1 or / is less than 1, the structural integrity coefficient of the target cloud mass is 0.

[0055] In some embodiments, calculating the cloud top temperature of the target cloud mass based on the radiative transfer model and determining the cloud top height of the target cloud mass based on the cloud top temperature specifically include the following process:

[0056] Calculating the cloud top temperature of the target cloud mass based on the radiative transfer model:

[0057] ;

[0058] Wherein, is the emissivity of the target cloud cluster, is the Planck blackbody radiation when the cloud top temperature is ; is the Planck blackbody radiation when the ground temperature is ; is the radiation corresponding to the cloud top height of the target cloud cluster collected by the meteorological satellite;

[0059] Based on the lapse rate of temperature, the cloud top temperature and the ground temperature the cloud top height of the target cloud cluster is calculated, where the lapse rate of temperature is the rate at which the air temperature decreases with height.

[0060] In some embodiments, the vertical height of the target cloud cluster is obtained according to the cloud base height and the cloud top height of the target cloud cluster, and the cloud layer is segmented and labeled according to the vertical height. The specific process of obtaining the labeled segmented cloud layer includes the following steps:

[0061] Calculate the height difference between the cloud top height and the cloud base height, record the difference as the vertical height, divide the vertical height into several height segments equally, 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 adjacent two height segments exceeds the relative humidity difference threshold. If not, record the cloud layers corresponding to the adjacent two height segments as a labeled segmented cloud layer until i labeled segmented cloud layers are obtained.

[0062] In some embodiments, obtaining the relative humidity value of each height segment specifically includes the following steps: Based on the three-dimensional atmosphere acquisition system, collect the air pressure and the air temperature T of each height segment, and calculate the relative humidity value HP of each height segment based on the correlation formula. The correlation formula is where is the relative humidity coefficient, is the saturated water vapor pressure value.

[0063] In some embodiments, Figure 2 is a flowchart of the operation of a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data according to an embodiment of the present invention. As Figure 2 shown, detecting precipitation particles in the labeled segmented cloud layer and inferring the water content density of the labeled segmented cloud layer according to the precipitation particles specifically includes the following steps:

[0064] Step S201: Detect the microphysical properties of precipitation particles in the labeled segmented cloud layer based on a meteorological radar, and calculate the single particle scattering matrix of different precipitation particle types by applying the matrix method;

[0065] Specifically, a scattering coordinate system L is constructed. Arbitrarily select a precipitation particle as a reference particle, place the coordinate origin inside the reference particle, with its direction fixed in space, calculate the spatial distance values between other precipitation particles and the reference particle, and use the spatial distance values as the elements of the scattering matrix. Here, precipitation particles include cloud droplets, snow crystals, graupel, hail, and sleet particles;

[0066] Step S202: Calculate the similarity between the single-particle scattering matrix and the standard scattering matrix to obtain a similarity. Based on the similarity, query the water content density table of the labeled segmented cloud layer corresponding to the standard scattering matrix to obtain the water content density of the labeled segmented cloud layer;

[0067] Specifically, each similarity range interval corresponds to a specific water content density in the water content density table.

[0068] 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.

[0069] In some embodiments, Figure 3 is the flowchart of the operation of another three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data according to the embodiments of the present invention. As Figure 3 shown, obtaining the meteorological characterization value of the target cloud mass based on the water content density, aerosol particle extinction coefficient, and backscattering coefficient specifically includes the following steps:

[0070] Step S301: Add the sum of the water content density, aerosol particle extinction coefficient, and backscattering coefficient to obtain the characterization index of the labeled segmented cloud layer until i characterization indices are obtained;

[0071] Step S302: Establish a rectangular coordinate system with the number of labeled segmented cloud layers as the X-axis and the characterization index as the Y-axis, and draw a meteorological characterization curve by plotting points;

[0072] Step S303: Calculate the first area enclosed by the line segment of the meteorological characterization curve above the preset meteorological characterization curve and the preset meteorological characterization curve, then calculate the second area enclosed by the meteorological characterization curve and the X-axis, and calculate the product between the first area and the second area;

[0073] Step S304: Then calculate the acute angle degree formed by the first intersection of the meteorological characterization curve and the preset meteorological characterization curve, and multiply it by the product to obtain a product value, and record this product value as the meteorological characterization value of the target cloud mass.

[0074] It should be noted that the aerosol extinction coefficient is a comprehensive reflection of the absorption and scattering of light by aerosol particles in the atmosphere. It is an important parameter for measuring the impact of aerosol particles in the atmosphere on light propagation. The backscattering coefficient is a 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 clouds, parameters usually need to be obtained through on-site measurement and analysis using professional measurement equipment and methods. In practical applications, researchers will use advanced equipment such as lidar and solar radiometers to measure aerosol particles in clouds, and obtain the specific values of the extinction coefficient and backscattering coefficient by analyzing the measurement data.

[0075] In some embodiments, analyzing the rationality of the existence of a target cloud mass based on the meteorological characterization value of the target cloud mass includes the following process:

[0076] Load the meteorological characterization value threshold. Among them, the meteorological characterization value threshold is stored in the system, and its value is set by the system. Determine whether the meteorological characterization value of the target cloud mass is greater than the meteorological characterization value threshold. If so, determine that the existence of the target cloud mass is unreasonable and generate a signal indicating an increase in the probability of rainfall in the area corresponding to the target cloud mass. If not, determine that the existence of the target cloud mass is reasonable and do not generate a signal indicating an increase in the probability of rainfall in the area corresponding to the target cloud mass.

[0077] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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 wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0078] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0079] 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 foregoing method embodiments, and will not be elaborated herein.

[0080] In several embodiments provided in this 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 merely illustrative. For example, the division of the units is only for some logical function divisions. In actual implementation, there may be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0081] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data, characterized in that, The system includes: A height measurement module, which is used to determine the cloud base height of the target cloud cluster based on a laser ceilometer, collect the radiation corresponding to the cloud top height of the target cloud cluster based on a meteorological satellite, calculate the cloud top temperature of the target cloud cluster based on a radiation transfer model, and determine the cloud top height of the target cloud cluster based on the cloud top temperature; A cloud layer segmentation module, which is used to obtain the vertical height of the target cloud cluster according to the cloud base height and cloud top height of the target cloud cluster, perform cloud layer segmentation annotation according to the vertical height to obtain annotated segmented cloud layers: calculate the height difference between the cloud top height and the cloud base height, record this difference as the vertical height, equally 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 a 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 layers corresponding to these two adjacent height segments as one annotated segmented cloud layer until i annotated segmented cloud layers are obtained; A meteorological characterization value calculation module, which is used to detect the precipitation particles in the annotated segmented cloud layers, infer the water content density of the annotated segmented cloud layers according to the precipitation particles, obtain the extinction coefficient and backscattering coefficient of the aerosol particles in the annotated segmented cloud layers, and obtain the meteorological characterization value of the target cloud cluster based on the water content density, extinction coefficient and backscattering coefficient of the aerosol particles: add the sum of the water content density, extinction coefficient and backscattering coefficient to obtain the characterization index of the annotated segmented cloud layer until i characterization indexes are obtained. Establish a rectangular coordinate system with the number of annotated segmented cloud layers as the X-axis and the characterization index as the Y-axis, draw a meteorological characterization curve by the method of plotting points, calculate the first area enclosed by the line segment of the meteorological characterization curve above the preset meteorological characterization curve and the preset meteorological characterization curve, then calculate the second area enclosed by the meteorological characterization curve and the X-axis, calculate the product between the first area and the second area, then calculate the acute angle degree 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 this product value as the meteorological characterization value of the target cloud cluster; A fusion analysis module, which is used to analyze the rationality of the existence of the target cloud cluster based on the meteorological characterization value of the target cloud cluster: load a meteorological characterization value threshold, determine whether the meteorological characterization value of the target cloud cluster is greater than the meteorological characterization value threshold. If so, determine that the existence of the target cloud cluster is unreasonable and generate a signal indicating an increase in the rainfall possibility in the area corresponding to the target cloud cluster. If not, determine that the existence of the target cloud cluster is reasonable and do not generate a signal indicating an increase in the rainfall possibility in the area corresponding to the target cloud cluster.

2. The three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data according to claim 1, characterized in that The specific process of determining the cloud base height of the target cloud cluster based on a laser ceilometer specifically includes the following process: Calculate the cloud base height of the target cloud cluster based on the radar equation of the laser ceilometer: ； Among them, is the backscattering power at the cloud base height of the target cloud cluster at a distance of measured by a ceilometer, is the laser pulse energy value, C is a constant, is the total atmospheric backscattering coefficient, is the structural integrity coefficient of the target cloud cluster, is the effective receiving area of the telescope.

3. The three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data according to claim 2, characterized in that The specific steps for obtaining the structural integrity coefficient of the target cloud cluster specifically include the following process: Collect the reflected radar wave after the radar wave emitted by the lidar reaches the target cloud cluster, and process and analyze the reflected radar wave: When the incident radar wave propagates to the target cloud cluster, the impedance of the preset complete target cloud cluster is , and the impedance obtained by collecting the area where the incident radar wave is reflected is , then according to calculate the reflection coefficient as , and the transmission coefficient as ; If the waveform of the reflected radar wave only changes in position with time and does not change in phase, and the reflection coefficient is is 0, and the transmission coefficient is is 1, the structural integrity coefficient of the target cloud cluster is 1. If the waveform of the reflected radar wave changes in phase, and / is greater than 1 or / is less than 1, the structural integrity coefficient of the target cloud cluster is 0.

4. The three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data according to claim 1, wherein The specific process of calculating the cloud top temperature of the target cloud cluster based on a radiation transfer model and determining the cloud top height of the target cloud cluster based on the cloud top temperature specifically includes the following process: Calculate the cloud top temperature of the target cloud cluster based on a radiation transfer model: ； Among them, is the emissivity of the target cloud cluster, is the Planck blackbody radiation with the cloud top temperature of , is the Planck blackbody radiation with the ground temperature of , is the radiation corresponding to the cloud top height of the target cloud cluster collected by the meteorological satellite; Based on the lapse rate, cloud top temperature and surface temperature the cloud top height of the target cloud cluster is calculated, where the lapse rate is the rate at which the air temperature decreases with height.

5. The three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data according to claim 1, wherein Obtaining the relative humidity value of each altitude segment specifically includes the following steps: Based on the three-dimensional atmosphere acquisition system, the air pressure of each altitude segment is collected and the temperature T, and the relative humidity value HP of each altitude segment is calculated based on the correlation formula. The correlation formula is , where is the relative humidity coefficient, is the saturated water vapor pressure value.

6. The three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data according to claim 1, characterized in that, Detect precipitation particles in the labeled segmented cloud layer, and infer the water content density of the labeled segmented cloud layer based on the precipitation particles. The specific process includes the following: Based on the detection of the microphysical characteristics of precipitation particles in the labeled segmented cloud layer by a meteorological radar, the single-particle scattering matrix of different precipitation particle types is calculated using the matrix method: Construct a scattering coordinate system L. Select any precipitation particle as a reference particle. Place the coordinate origin inside the reference particle, with its direction fixed in space. Calculate the spatial distance value between other precipitation particles and the reference particle, and use the spatial distance value as an element of the scattering matrix. Among them, precipitation particles include cloud droplets, snow crystals, graupel, hail, and sleet particles; Calculate the similarity between the single-particle scattering matrix and the standard scattering matrix to obtain the similarity. Based on the similarity, query the water content density table of the labeled segmented cloud layer corresponding to the standard scattering matrix to obtain the water content density of the labeled segmented cloud layer, and obtain the water content density of the labeled segmented cloud layer: Use the Euclidean distance method to calculate the similarity between the single-particle scattering matrix and the standard scattering matrix to obtain the similarity. Among them, each similarity range interval corresponds to a specific water content density in the water content density table.

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