CN116819490B - Cloud and aerosol classification method based on cloud radar and laser radar - Google Patents
Cloud and aerosol classification method based on cloud radar and laser radar Download PDFInfo
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Abstract
The invention discloses a cloud and aerosol classification method based on cloud radar and laser radar, which relates to the technical field of radar aerosol and cloud classification and comprises the following steps: s1, data quality control: inputting a cloud radar reflectivity factor, a laser radar backscattering coefficient and a laser radar depolarization ratio into a system, and performing quality control on the data; s2, dynamically updating the threshold value required by identification: defining an initial data pool in the system, wherein initial content is empty; the data after quality control in the step S1 is put into an initial data pool, and the data pool can continuously generate a new threshold value along with continuous data updating in the data pool; s3, identifying the current latest threshold value in the step S2 by library, and outputting a classification result. The method is reasonable in design, combines the observation advantages of strong cloud penetration capability of the cloud radar and strong sensitivity of the laser radar to tiny particles, and well realizes classification of cloud and aerosol.
Description
Technical Field
The invention relates to the technical field of radar aerosol and cloud classification, in particular to the technical field of cloud and aerosol classification methods based on cloud radar and laser radar.
Background
Radar aerosol and cloud classification is a method of distinguishing between aerosols and clouds in the atmosphere by using radar technology. Radar aerosol classification relies primarily on the characteristics of the scattered signal. The scattering properties of aerosol particles are related to their particle size, shape, refractive index, etc. By analyzing the information of the intensity, frequency, phase, etc. of the reflected signal, the type and concentration of the aerosol can be determined. For example, for airborne particulates, such as dust, smoke, etc., they typically have specific scattering characteristics that can be detected and classified by radar signals. The radar aerosol and cloud classification have important applications in the fields of meteorology, environmental monitoring, air quality assessment and the like. The system can provide information about the distribution, properties and evolution of aerosols and clouds in the atmosphere, and support weather forecast, climate research and environmental protection. In addition, the radar aerosol and cloud classification can be used in the fields of aviation, astronomical observation and the like, and helps to analyze the distribution and movement rules of substances in the atmosphere. The prior patent discloses the following technology:
the patent with publication number CN114296103B entitled "method for inverting extinction coefficient of airborne high-spectrum resolution laser radar" discloses the following: the method comprises the steps of obtaining original data; reducing signal noise using a plurality of noise removal methods; calculating a backscattering coefficient and a scattering ratio of the laser radar based on the denoising signal; performing primary level identification by using a scattering ratio threshold method; preliminary classification of cloud, aerosol, earth surface and clean atmosphere is realized through fine screening conditions; determining an effective inversion region through hierarchical processing and earth surface removal; preliminary inverting the extinction coefficient to radar ratio by using a conventional inversion method; based on the preliminary classification, sub-class classification and treatment of the cloud and the aerosol are realized; and (3) taking the radar ratio of the preliminary inversion as an initial value, carrying out radar ratio classification iteration, and calculating a final extinction coefficient result. The invention can improve inversion accuracy and inversion integrity of extinction coefficient of airborne high-spectrum resolution laser radar, and is beneficial to research in fields of cloud-aerosol interaction, atmospheric pollution prevention and control and the like.
Patent publication number CN112698354B, entitled "method and system for identifying atmospheric aerosols and clouds", discloses the following: the method comprises the following steps: s1, acquiring laser radar original data; s2, correcting data; s3, analyzing and obtaining a color ratio and a backscattering coefficient of the attenuated laser radar; s4, cloud and aerosol identification judgment. The technology can acquire laser radar original data of aerosol and cloud types based on a foundation dual-band polarized laser radar system, obtain specific type classification of the aerosol and the cloud, comprehensively obtain six types of ice cloud, mixed phase cloud, water cloud, dust aerosol, artificial pollutants and mixed layers of the two, and improve accuracy of identifying the aerosol and the cloud types.
The classification of the cloud and the aerosol is based on a single observation device of the laser radar, and Yun Lei is partially used for the single observation device in the market; however, cloud radars are insensitive to tiny particles although they have strong cloud penetration capability; although the laser radar has strong sensitivity to tiny particles, the cloud penetration capability is poor; therefore, the existing single observation equipment has the defect of low precision in classifying the cloud and the aerosol.
Disclosure of Invention
The invention aims at: in order to solve the technical problem that the accuracy of the existing single observation equipment on cloud and aerosol classification is low, the invention provides a cloud and aerosol classification method based on cloud radar and laser radar.
The invention adopts the following technical scheme for realizing the purposes:
the invention provides a cloud and aerosol classification method based on cloud radar and laser radar, which comprises the following steps:
s1, data quality control: inputting a cloud radar reflectivity factor, a laser radar backscattering coefficient and a laser radar depolarization ratio into a system, and performing quality control on the data;
s2, dynamically updating the threshold value required by identification: defining an initial data pool in the system, wherein initial content is empty; the data after quality control in the step S1 is put into an initial data pool, and the data pool can continuously generate the latest threshold value along with continuous data updating in the data pool;
s3, identifying the current latest threshold value in the step S2 by library, and outputting a classification result.
In one embodiment, in step S1, the specific process of quality control of the cloud radar reflectivity factor is as follows:
filling holes in the echo by using a 11011 radial hole filling method, so that errors in the subsequent classification process are reduced;
the cloud radar pulse pressure side lobe clutter filtering method Yun Lei adopts a top scanning mode, the scanning rays are radial rays perpendicular to the top of the cloud radar, and the effective reflectivity factors traversed during scanning of the scanning rays are completely filtered.
In one embodiment, 11011 is a radial hole filling method comprising the following steps: yun Lei by adopting the top scanning mode, traversing each ray emitted by the cloud radar from the beginning in the radial direction, marking a distance library with effective reflectivity value as 1, marking a distance library with ineffective reflectivity value as 0, and if 11011 condition is met, accumulating the other four effective reflectivity values, averaging and assigning the average value to the distance library with the ineffective value originally in the middle.
Specifically, the distance library is a small distance unit divided into distances along the ray direction in the cloud radar echo signal processing, if 11011 is met, namely, two adjacent effective values are adjacent up and down in the middle of the invalid value, the other four effective values of reflectivity are accumulated, averaged and assigned to the distance library with the original invalid value in the middle, and the operation can effectively fill the cavity in the echo, and reduce errors brought in the subsequent classification process.
In one embodiment, the cloud radar pulse-pressure sidelobe clutter filtering method specifically comprises the following steps:
firstly, determining the height H of pulse pressure side lobe clutter, and calculating a distance library Y corresponding to the height H by knowing the distance resolution of a cloud radar;
yun Lei the scanning ray is a radial ray perpendicular to the top of the cloud radar, and the scanning ray traverses upwards from the distance library Y until the reflectivity is larger than the empirical value obtained by actual observation, and the effective reflectivity factors traversed by the scanning ray are completely filtered out.
Specifically, cloud radar pulse-pressure side lobe clutter (also referred to as side lobe spurs or side lobe interference) is one type of interference signal in Yun Lei-arrival systems. Sidelobe clutter is typically due to non-ideal characteristics of pulse compression filters, system noise or other interference factors. Z_limit is defined as the empirical value that is derived in connection with the actual observation.
In one embodiment, in step S1, the specific process of quality control of the laser radar backscatter coefficient is as follows:
obtaining a preliminary laser radar backscattering coefficient through calibration with a reflected signal of a ground target;
comparing the preliminary laser radar backscatter coefficients of the ground targets with the signals measured by the laser radar by placing the ground targets within the measurement range of the laser radar, and calculating the calibration coefficients of the laser radar backscatter coefficients (using ground characteristic calibration methods: calibration using the reflection characteristics of the ground;
and multiplying the calibration coefficient by the actually measured laser radar backscatter coefficient to obtain the quality-controlled laser radar backscatter coefficient.
Specifically, the surface target may be accomplished using a surface target of known reflectivity (e.g., a calibration plate).
In one embodiment, in step S1, the specific process of quality control of the laser radar depolarization ratio is as follows:
the calibration target selects a standard reflector with a known lidar depolarization ratio.
Comparing the known laser radar depolarization ratio of the calibration target with the laser radar measured signal by placing the calibration target in the measurement range of the laser radar, and calculating the laser radar depolarization ratio calibration coefficient of the laser radar (using a standard target calibration method: calibration using standard targets of known reflectivity or scattering characteristics, such as a reflectivity sphere, a prism, etc.. Measuring the scattering signals of these standard targets at different angles and distances, and then comparing with known physical quantities, calculating the laser radar depolarization ratio calibration coefficient.);
multiplying the laser radar depolarization ratio calibration coefficient by the actually measured laser radar depolarization ratio to obtain the laser radar depolarization ratio after quality control.
Specifically, the laser radar depolarization ratio refers to the ratio of horizontally polarized and vertically polarized signals received by the laser radar. It can be used to evaluate the vertical polarization resolution of a lidar system and the accuracy of the lidar. Lidar depolarization ratio quality control generally involves calibrating a radar system using known lidar depolarization ratio calibration targets.
In one embodiment, in step S2, the specific procedure for dynamically updating the threshold value required for identification is as follows:
firstly, judging whether input data is rainy weather or not according to cloud radar reflectivity factors after quality control in an input data pool;
if the input data is rainfall weather, the rainfall data is removed (rainfall has great influence on the product of aerosol observation, so when the threshold value is counted, the rainfall data is removed);
if the input data are not rainy weather, counting the input cloud radar reflectivity factors, the laser radar backscattering coefficients and the laser radar depolarization ratio, counting three types of data upper limits when the local aerosol is below the average height, setting the three types of data upper limits to be M1, M2 and M3 respectively, obtaining three types of data output threshold values to be M1, M2 and M3, adding corresponding threshold coefficients k1, k2 and k3 on the basis of M1, M2 and M3 according to the identification tendency (hope that the method is more sensitive to aerosol identification or more sensitive to cloud), wherein the input result is k 1M 1, k 2M 2 and k3, and updating the original threshold values, wherein k1, k2 and k 3E (0, 1);
specifically, cloud radar reflectivity factors, laser radar backscattering coefficients and laser radar depolarization ratio data after quality control are input into a data pool are all in a top scanning mode, namely vertical opposite scanning is performed, scanning periods of the cloud radar and the laser radar are different, the time is generally Yun Lei and reaches 5 seconds, the time is generally 5 minutes, the scanning result of each time is complete radial data, and the method adopts hour data, generally 720 cloud radar data and 12 laser radar data.
In one embodiment, according to the cloud radar reflectivity factor after quality control in the input data pool, judging whether the input data is rainy weather or not, the specific process is as follows:
judging whether the single moment is rainfall weather or not in the vertical direction: traversing from the 0 th distance library to the n th distance library, and if the echo which is larger than Xdbz accounts for more than 90% of 0~n distance libraries, determining that rainfall exists at the moment, wherein n represents the number of distance libraries corresponding to the average height of the local aerosol, and X is a threshold value of the echo intensity under clear sky data; (cloud radar range bin resolution is 30 meters, e.g. the average height of local aerosol is 2km, then n=2000/30=66; where X is the statistic, the threshold value of echo intensity under clear sky data is counted).
Judging whether the hour data is rainy weather or not in the horizontal direction: counting the number of times when rainfall exists in the vertical direction in a small period, and if the number of rainfall times exceeds 30% of the total number of times, considering the small period as rainfall weather. (however, we use the data of hours, and 720 pieces of Yun Lei data are obtained every hour (each piece of radar data is one moment, and two adjacent moments are separated by 5 s), so that further judgment is needed on the level).
In one embodiment, in step S3, the current latest threshold value in step S2 is used to identify the database by database, and the specific steps of outputting the classification result are as follows: for input data, using a newly generated threshold value of a data pool to identify a library by library, classifying (identifying a library by library, classifying each radial distance library by one), and based on a small rule, assuming that a cloud radar reflectivity factor < k1 x M1, a laser radar backscattering coefficient < k2 x M2, and a laser radar depolarization ratio < k3 x M3 are input, the distance library position is considered as aerosol particles, otherwise, the distance library position is cloud particles.
The beneficial effects of the invention are as follows:
a method for realizing cloud and aerosol classification based on multi-equipment and combined observation is provided. According to the method, the observation advantages of the cloud radar with strong cloud penetration capability (but insensitive to tiny particles) and the laser radar with strong sensitivity to tiny particles (but poor cloud penetration capability) are combined, so that the classification of the cloud and the aerosol is better realized.
Drawings
FIG. 1 is a method flow diagram of a cloud and aerosol classification method based on cloud radar and lidar of the present invention.
Fig. 2 is a graph of cloud radar reflectivity statistics.
Fig. 3 is a statistical plot of the backscatter coefficients of lidar.
Fig. 4 is a statistical plot of laser radar depolarization ratios.
Fig. 5 is a graph of the raw reflectivity of the test data.
Fig. 6 is a graph of test data output results.
Fig. 7 is a graph of Yun Lei pre-quality control reflectivity.
Fig. 8 is a graph of Yun Lei post-quality control reflectance.
Fig. 9 is a graph of the raw reflectivity of the test data.
Fig. 10 is a graph of test data output results.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
In describing embodiments of the present invention, it should be noted that the directions or positional relationships indicated by the terms "inner", "outer", "upper", etc. are directions or positional relationships based on those shown in the drawings, or those that are conventionally put in place when the inventive product is used, are merely for convenience of description and simplification of description, and are not indicative or implying that the apparatus or element in question must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
Example 1
As shown in fig. 1 to 8, the present embodiment provides a method for cloud and aerosol classification of a base Yu Yunlei and a lidar, comprising the steps of:
s1, data quality control: the cloud radar reflectivity factor, the laser radar backscattering coefficient and the laser radar depolarization ratio are input into the system, and the data are subjected to quality control (the sensitivity of the laser radar to particles is higher than that of the cloud radar, wherein the observation information of aerosol particles is mostly provided by the laser radar, so that the quality control of the laser radar data is important, and the accuracy of the calculation of the following threshold value is directly determined.)
S11, the specific process of quality control of the cloud radar reflectivity factor is as follows:
filling holes in the echo by using a 11011 radial hole filling method, so that errors in the subsequent classification process are reduced; 11011 radial cavity filling method comprises the following specific processes: yun Lei by adopting the top scanning mode, traversing each ray emitted by the cloud radar from the beginning in the radial direction, marking a distance library with effective reflectivity value as 1, marking a distance library with ineffective reflectivity value as 0, and if 11011 condition is met, accumulating the other four effective reflectivity values, averaging and assigning the average value to the distance library with the ineffective value originally in the middle.
Specifically, the distance library is a small distance unit divided into distances along the ray direction in the cloud radar echo signal processing, if 11011 is met, namely, two adjacent effective values are adjacent up and down in the middle of the invalid value, the other four effective values of reflectivity are accumulated, averaged and assigned to the distance library with the original invalid value in the middle, and the operation can effectively fill the cavity in the echo, and reduce errors brought in the subsequent classification process.
The cloud radar pulse pressure side lobe clutter filtering method Yun Lei adopts a top scanning mode, the scanning rays are radial rays perpendicular to the top of the cloud radar, and the effective reflectivity factors traversed during scanning of the scanning rays are completely filtered. The cloud radar pulse-pressure sidelobe clutter filtering method specifically comprises the following steps:
firstly, determining the height H of pulse pressure side lobe clutter, and calculating a distance library Y corresponding to the height H by knowing the distance resolution of a cloud radar;
yun Lei the scanning ray is a radial ray perpendicular to the top of the cloud radar, and the scanning ray traverses upwards from the distance library Y until the reflectivity is larger than the empirical value obtained by actual observation, and the effective reflectivity factors traversed by the scanning ray are completely filtered out.
Specifically, cloud radar pulse-pressure side lobe clutter (also referred to as side lobe spurs or side lobe interference) is one type of interference signal in Yun Lei-arrival systems. Sidelobe clutter is typically due to non-ideal characteristics of pulse compression filters, system noise or other interference factors. Z_limit is defined as the empirical value that is derived in connection with the actual observation.
FIG. 2 is a graph of cloud radar reflectivity statistics.
S12, the specific process of quality control of the laser radar backscattering coefficient is as follows:
obtaining a preliminary laser radar backscattering coefficient through calibration with a reflected signal of a ground target;
the method comprises the steps of comparing a preliminary laser radar backscattering coefficient of a ground target with a laser radar measured signal by placing the ground target in a measuring range of the laser radar, and calculating a calibration coefficient of the laser radar backscattering coefficient;
and multiplying the calibration coefficient by the actually measured laser radar backscatter coefficient to obtain the quality-controlled laser radar backscatter coefficient.
Specifically, the surface target may be accomplished using a surface target of known reflectivity (e.g., a calibration plate). Fig. 3 is a statistical plot of the backscatter coefficients of lidar.
S13, the specific process of quality control of the laser radar depolarization ratio is as follows:
the calibration target selects a standard reflector with a known laser radar depolarization ratio;
the method comprises the steps of placing a calibration target in a measurement range of a laser radar, comparing a known laser radar depolarization ratio of the calibration target with a signal measured by the laser radar, and calculating a laser radar depolarization ratio calibration coefficient of the laser radar;
multiplying the laser radar depolarization ratio calibration coefficient by the actually measured laser radar depolarization ratio to obtain the laser radar depolarization ratio after quality control.
Specifically, the laser radar depolarization ratio refers to the ratio of horizontally polarized and vertically polarized signals received by the laser radar. It can be used to evaluate the vertical polarization resolution of a lidar system and the accuracy of the lidar. Lidar depolarization ratio quality control generally involves calibrating a radar system using known lidar depolarization ratio calibration targets. Fig. 4 is a statistical plot of laser radar depolarization ratios.
S2, dynamically updating the threshold value required by identification: defining an initial data pool in the system, wherein initial content is empty; the data after quality control in the step S1 is put into an initial data pool, and the data pool can continuously generate the latest threshold value along with continuous data updating in the data pool; the specific process of dynamically updating the threshold value required for identification is as follows:
firstly, judging whether input data is rainy weather or not according to cloud radar reflectivity factors after quality control in an input data pool;
if the input data is rainfall weather, the rainfall data is removed (rainfall has great influence on the product of aerosol observation, so when the threshold value is counted, the rainfall data is removed);
if the input data are not rainy weather, counting the input cloud radar reflectivity factors, the laser radar backscattering coefficients and the laser radar depolarization ratio, counting three types of data upper limits when the local aerosol is below the average height, setting the three types of data upper limits to be M1, M2 and M3 respectively, obtaining three types of data output threshold values to be M1, M2 and M3, adding corresponding threshold coefficients k1, k2 and k3 on the basis of M1, M2 and M3 according to the identification tendency (hope that the method is more sensitive to aerosol identification or more sensitive to cloud), wherein the input result is k 1M 1, k 2M 2 and k3, and updating the original threshold values, wherein k1, k2 and k 3E (0, 1);
specifically, cloud radar reflectivity factors, laser radar backscattering coefficients and laser radar depolarization ratio data after quality control are input into a data pool are all in a top scanning mode, namely vertical opposite scanning is performed, scanning periods of the cloud radar and the laser radar are different, the time is generally Yun Lei and reaches 5 seconds, the time is generally 5 minutes, the scanning result of each time is complete radial data, and the method adopts hour data, generally 720 cloud radar data and 12 laser radar data.
Judging whether the input data is rainy weather or not through the input cloud radar reflectivity factors, wherein the specific process is as follows:
judging whether the single moment is rainfall weather or not in the vertical direction: traversing from the 0 th distance library to the n th distance library, and if the echo which is larger than Xdbz accounts for more than 90% of 0~n distance libraries, determining that rainfall exists at the moment, wherein n represents the number of distance libraries corresponding to the average height of the local aerosol, and X is a threshold value of the echo intensity under clear sky data; (cloud radar range bin resolution is 30 meters, e.g. the average height of local aerosol is 2km, then n=2000/30=66; where X is the statistic, the threshold value of echo intensity under clear sky data is counted).
Judging whether the hour data is rainy weather or not in the horizontal direction: counting the number of times when rainfall exists in the vertical direction in a small period, and if the number of rainfall times exceeds 30% of the total number of times, considering the small period as rainfall weather. (however, we use the data of hours, and 720 pieces of Yun Lei data are obtained every hour (each piece of radar data is one moment, and two adjacent moments are separated by 5 s), so that further judgment is needed on the level).
Fig. 5 is a graph of the original reflectance of the test data, fig. 7 and Yun Lei are graphs of the reflectance before quality control, and fig. 8 is a graph of the reflectance after quality control of Yun Lei.
S3, identifying the current latest threshold value in the step S2 by library, and outputting a classification result. The method comprises the following specific steps: for input data, using a newly generated threshold value of a data pool to identify a library by library, classifying (identifying a library by library, classifying each radial distance library by one), and based on a small rule, assuming that a cloud radar reflectivity factor < k1 x M1, a laser radar backscattering coefficient < k2 x M2, and a laser radar depolarization ratio < k3 x M3 are input, the distance library position is considered as aerosol particles, otherwise, the distance library position is cloud particles. Fig. 6 is a graph of the output results after classification of test data.
Claims (8)
1. The cloud and aerosol classification method based on the cloud radar and the laser radar is characterized by comprising the following steps of:
s1, data quality control: inputting a cloud radar reflectivity factor, a laser radar backscattering coefficient and a laser radar depolarization ratio into a system, and performing quality control on the data;
s2, dynamically updating the threshold value required by identification: defining an initial data pool in the system, wherein initial content is empty; the data after quality control in the step S1 is put into an initial data pool, and the data pool can continuously generate a new threshold value along with continuous data updating in the data pool; the specific process of dynamically updating the threshold value required for identification is as follows:
firstly, judging whether input data is rainy weather or not according to cloud radar reflectivity factors after quality control in an input data pool;
if the input data is rainfall weather, removing the rainfall data;
if the input data are not rainy days, counting the input cloud radar reflectivity factors, the laser radar backscattering coefficients and the laser radar depolarization ratios, counting three types of data upper limits when the local aerosol is below the average height, setting the three types of data upper limits to be M1, M2 and M3 respectively, obtaining three types of data output threshold values to be M1, M2 and M3, and increasing corresponding threshold coefficients k1, k2 and k3 on the basis of M1, M2 and M3 according to the recognition tendency, wherein the input results are k1, k2, M2 and k3, and updating the original threshold values, wherein k1, k2 and k 3E (0, 1);
s3, identifying the current latest threshold value in the step S2 by library, and outputting a classification result.
2. The cloud and aerosol classification method based on cloud radar and lidar according to claim 1, wherein in step S1, the specific process of quality control of the cloud radar reflectivity factor is as follows:
filling holes in the echo by using a 11011 radial hole filling method, so that errors in the subsequent classification process are reduced;
the cloud radar pulse pressure side lobe clutter filtering method Yun Lei adopts a top scanning mode, the scanning rays are radial rays perpendicular to the top of the cloud radar, and the effective reflectivity factors traversed during scanning of the scanning rays are completely filtered.
3. The cloud and aerosol classification method based on cloud radar and lidar according to claim 2, wherein the 11011 radial hole filling method comprises the following specific procedures: yun Lei by adopting the top scanning mode, traversing each ray emitted by the cloud radar from the beginning in the radial direction, marking a distance library with effective reflectivity value as 1, marking a distance library with ineffective reflectivity value as 0, and if 11011 condition is met, accumulating the other four effective reflectivity values, averaging and assigning the average value to the distance library with the ineffective value originally in the middle.
4. The cloud and aerosol classification method based on cloud radar and laser radar according to claim 2, wherein the cloud radar pulse-pressure side lobe clutter filtering method comprises the following specific steps:
firstly, determining the height H of pulse pressure side lobe clutter, and calculating a distance library Y corresponding to the height H by knowing the distance resolution of a cloud radar;
yun Lei the scanning ray is a radial ray perpendicular to the top of the cloud radar, and the scanning ray traverses upwards from the distance library Y until the reflectivity is larger than the empirical value obtained by actual observation, and the effective reflectivity factors traversed by the scanning ray are completely filtered out.
5. The cloud and aerosol classification method based on cloud radar and lidar according to claim 1, wherein in step S1, the specific process of quality control of the backscatter coefficient of the lidar is as follows:
obtaining a preliminary laser radar backscattering coefficient through calibration with a reflected signal of a ground target;
the method comprises the steps of comparing a preliminary laser radar backscattering coefficient of a ground target with a laser radar measured signal by placing the ground target in a measuring range of the laser radar, and calculating a calibration coefficient of the laser radar backscattering coefficient;
and multiplying the calibration coefficient by the actually measured laser radar backscatter coefficient to obtain the quality-controlled laser radar backscatter coefficient.
6. The cloud and aerosol classification method based on cloud radar and lidar according to claim 1, wherein in step S1, the specific process of quality control of the lidar depolarization ratio is as follows:
the calibration target selects a standard reflector with a known laser radar depolarization ratio;
the method comprises the steps of placing a calibration target in a measurement range of a laser radar, comparing a known laser radar depolarization ratio of the calibration target with a signal measured by the laser radar, and calculating a laser radar depolarization ratio calibration coefficient of the laser radar;
multiplying the laser radar depolarization ratio calibration coefficient by the actually measured laser radar depolarization ratio to obtain the laser radar depolarization ratio after quality control.
7. The cloud and aerosol classification method based on cloud radar and lidar according to claim 1, wherein the determining whether the input data is rainy weather is performed according to the cloud radar reflectivity factor after quality control in the input data pool comprises the following steps:
judging whether the single moment is rainfall weather or not in the vertical direction: traversing from the 0 th distance library to the n th distance library, and if the echo which is larger than Xdbz accounts for more than 90% of 0~n distance libraries, determining that rainfall exists at the moment, wherein n represents the number of distance libraries corresponding to the average height of the local aerosol, and X is a threshold value of the echo intensity under clear sky data;
judging whether the hour data is rainy weather or not in the horizontal direction: counting the number of times when rainfall exists in the vertical direction in a small period, and if the number of rainfall times exceeds 30% of the total number of times, considering the small period as rainfall weather.
8. The cloud and aerosol classification method based on cloud radar and lidar according to claim 7, wherein in step S3, the current latest threshold value in step S2 is used for identifying by library, and the specific step of outputting the classification result is as follows: and for input data, identifying a database by using a newly generated threshold value of a data pool, classifying, and based on a small rule, assuming that a cloud radar reflectivity factor < k1 x M1 is input, a laser radar backscattering coefficient < k2 x M2, and a laser radar depolarization ratio < k3 x M3 are considered as aerosol particles, otherwise, the distance database is cloud particles.
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