CN112698354B - Atmospheric aerosol and cloud identification method and system - Google Patents

Atmospheric aerosol and cloud identification method and system Download PDF

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CN112698354B
CN112698354B CN202011415954.0A CN202011415954A CN112698354B CN 112698354 B CN112698354 B CN 112698354B CN 202011415954 A CN202011415954 A CN 202011415954A CN 112698354 B CN112698354 B CN 112698354B
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cloud
aerosol
ratio
laser radar
data
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CN112698354A (en
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黄忠伟
黄建平
齐思齐
周天
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Lanzhou University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/95—Lidar systems specially adapted for specific applications for meteorological use
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G01N21/49—Scattering, i.e. diffuse reflection within a body or fluid
    • G01N21/53—Scattering, i.e. diffuse reflection within a body or fluid within a flowing fluid, e.g. smoke
    • G01N21/538—Scattering, i.e. diffuse reflection within a body or fluid within a flowing fluid, e.g. smoke for determining atmospheric attenuation and visibility
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G01N2021/4704—Angular selective
    • G01N2021/4709—Backscatter
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The application discloses a method and a system for identifying atmospheric aerosol and cloud, wherein the method comprises the following steps: s1, acquiring laser radar original data; s2, correcting data; s3, analyzing and obtaining the color ratio and the attenuation backscattering coefficient; and S4, cloud and aerosol identification judgment. The technology can acquire the original data of the laser radar of the aerosol and cloud types based on a foundation dual-band polarization laser radar system, obtain the specific type classification of the aerosol and the cloud, and comprehensively obtain six types of ice cloud, mixed phase cloud, water cloud, sand aerosol, artificial pollutants and mixed layers of the ice cloud, the mixed phase cloud, the water cloud, the sand aerosol and the artificial pollutants, so that the accuracy of identifying the aerosol and the cloud types is improved.

Description

Atmospheric aerosol and cloud identification method and system
Technical Field
The application relates to the technical field of atmospheric data analysis, in particular to a method and a system for identifying atmospheric aerosol and cloud.
Background
Aerosols and clouds play an important role in regional and global climate systems. The aerosol can change the radiation balance of the earth atmospheric system by scattering and absorbing short-wave and long-wave radiation, and can also be used as a cloud condensation nucleus to influence the generation and the service life of the cloud and change the micro-physical properties of the cloud. The cloud layer has a strong regulating effect on the radiation balance of the earth by reflecting solar radiation and absorbing long-wave thermal radiation from the earth. The classification of aerosols and clouds is the basis for studying their impact on climate systems and the environment. In recent decades, polarized lidar measurements have been widely used to identify different aerosol and cloud types. The polarization laser radar has unique detection capability, can clearly distinguish the phase of a cloud layer, and has near ideal sensitivity to the cirrus cloud.
The type of atmospheric aerosols and clouds remains one of the uncertainties in current atmospheric climate research. Therefore, accurate observation and identification of atmospheric aerosols and clouds is crucial to climate research. Noh et al (2017) have recognized that if the dust in the mixed dust stream can be separated from the artificially contaminated aerosol, it would be helpful to improve the understanding of the aerosol mixing layer and to provide a comprehensive understanding of the changes in the optical and microscopic physical properties of the dust mixed with the artificially contaminated aerosol particles. In the past decades, polarized lidar measurements have been widely used to identify different aerosol and cloud types.
Polarized lidar has unique detection capabilities in terms of well-defined cloud phase, which is nearly ideal in terms of sensitivity to cloud.
Liu et al (2004) introduced a three-dimensional algorithm used in the calipo observation task to identify clouds and aerosols in a dual wavelength backscatter lidar profile using layer-averaged attenuated backscatter at 532nm, layer-averaged color ratio (1064nm/532nm), and mid-layer height, thereby completing the theoretical basis of the calipo lidar cloud and aerosol identification (CAD) algorithms. Wang and Sassen (2001) distinguish various atmospheric targets such as ice clouds, flag clouds, precipitation and aerosols based on laser radar measurements.
Zhao et al (2014) propose a method and a system for identifying atmospheric aerosol and cloud based on a new algorithm for measuring aerosol and cloud based on micro-pulse laser radar data. Gro β et al (2013) suggest that the combined use of lidar ratio and particle linear depolarization ratio can be used to distinguish continental europe contaminated aerosols from other types of aerosols.
Burton et al (2014) propose a method and system for identifying atmospheric aerosols and clouds using a parametric solution for classifying aerosols into two or more types using polarized lidar measurements, which incorporates three factors, a lidar ratio, a backscatter ratio, and a depolarization ratio, for identifying the aerosol type. Zhou et al (2013) determined the relationship between the layer-by-layer attenuation backscattering coefficient and the layer-by-layer integral depolarization ratio of the airborne dust flow over tachrama dry desert using CALIPO lidar data.
However, the existing method for identifying aerosol and cloud by active remote sensing is complex and inefficient, is difficult to be applied to actual online observation work, and is not beneficial to the fields of weather and climate forecast, environmental early warning, disaster prevention and reduction and the like.
Disclosure of Invention
The method and the system for identifying the aerosol and the cloud based on the dual-band polarized laser radar observation quantity are mainly used for solving the problems of misjudgment and complexity of the existing laser radar cloud and aerosol identification algorithm and improving the accuracy of identifying the aerosol and the cloud type.
In order to achieve the above object, the present application provides the following techniques:
the invention provides a method for identifying atmospheric aerosol and cloud in a first aspect, which comprises the following steps:
s1, acquiring laser radar original data: acquiring aerosol and cloud type laser radar original data based on a foundation dual-band polarization laser radar system, and uploading the laser radar original data to a cloud end;
s2, data correction: receiving laser radar original data, and performing data correction processing on the received laser radar original data to obtain and upload laser radar correction data;
s3, analyzing and obtaining the color ratio and the attenuation backscattering coefficient: receiving laser radar correction data, and analyzing according to the laser radar correction data to obtain color ratio CR of 532nm and 355nm wave bands of the particles to be identified532nm/355nmAnd attenuation backscattering coefficient ABC532Uploading;
s4, cloud and aerosol identification and judgment: receiving color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532Comparing the color ratio with a preset threshold condition of the attenuation back scattering coefficient to obtain the analysis result of the aerosol and cloud types。
Preferably, in step S2, the data correction includes background noise correction, distance correction, geometric overlap correction, and polarization correction.
Preferably, in step S4, the preset threshold condition of the color ratio and the attenuation backscatter coefficient is:
color ratio CR532nm/355nmLess than 3.0, and
attenuation backscattering coefficient ABC532Less than 0.015/sr/km.
Preferably, in step S4, the reception color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532And comparing with a preset threshold value to obtain the analysis result of the aerosol and the cloud type, which specifically comprises the following steps:
identifying and judging the aerosol: when color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532When the preset threshold value is met, judging that the aerosol and the cloud type are aerosol;
cloud identification and judgment: when color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532And when the preset threshold value is not met, judging that the aerosol and the cloud type are cloud.
Preferably, in step S4, after obtaining the analysis result of the aerosol and cloud types, the method further includes:
s5: secondary classification is carried out according to the depolarization ratio: receiving laser radar correction data, and analyzing to obtain the depolarization ratio delta of 532nm and 355nm of the particles to be identified according to the laser radar correction data532/δ355Uploading;
obtaining the ratio delta of the depolarization ratio532/δ355And comparing the deviation ratio with a preset threshold value condition of the deviation reduction ratio to respectively obtain the specific types of the aerosol and the cloud type.
The invention provides a system for identifying atmospheric aerosol and cloud, which comprises a laser radar original data acquisition module, a data correction module, a color ratio and attenuation backscattering coefficient analysis acquisition module and a cloud and aerosol identification judgment module which are sequentially in communication connection, wherein,
the laser radar original data acquisition module: the system is used for collecting aerosol and cloud type laser radar original data based on a foundation dual-band polarization laser radar system and uploading the laser radar original data to a cloud end;
the data correction module: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for receiving laser radar original data, performing data correction processing on the received laser radar original data, acquiring and uploading laser radar corrected data;
the color ratio and attenuation backscattering coefficient analysis and acquisition module comprises: used for receiving the laser radar correction data and analyzing and obtaining the color ratio CR of 532nm and 355nm wave bands of the particles to be identified according to the laser radar correction data532nm/355nmAnd attenuation backscattering coefficient ABC532Uploading;
the cloud and aerosol identification and judgment module: for receiving colour ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532And comparing the color ratio with a preset threshold condition of the attenuation backscattering coefficient to obtain an analysis result of the aerosol and cloud types.
Preferably, the data correction module includes a background noise correction module, a distance correction module, a geometric overlap correction module and a polarization correction module.
Preferably, the cloud and aerosol identification determination module comprises:
the color ratio and attenuation backscattering coefficient preset threshold setting module: the method is used for setting a threshold condition, and specifically comprises the following steps:
color ratio CR532nm/355nmLess than 3.0, and
attenuation backscattering coefficient ABC532Less than 0.015/sr/km.
Preferably, the cloud and aerosol identification determination module comprises:
the aerosol identification and judgment module: for judging color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532When the preset threshold value is met, judging that the aerosol and the cloud type are aerosol;
cloud discernment judge module: for judging color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532When the preset threshold value is not satisfied, the judgment is madeAerosol and cloud types are clouds.
Preferably, the cloud and aerosol identification determining module further comprises:
a secondary classification module of the depolarization ratio: used for receiving laser radar correction data and analyzing and obtaining the depolarization ratio delta of 532nm and 355nm of the particles to be identified according to the laser radar correction data532/δ355Uploading; and
obtaining the ratio delta of the depolarization ratio532/δ355And comparing the deviation ratio with a preset threshold value condition of the deviation reduction ratio to respectively obtain the specific types of the aerosol and the cloud type.
Compared with the prior art, this application can bring following technological effect:
1. the technology can collect aerosol and cloud type laser radar original data based on a foundation dual-waveband polarization laser radar system, and upload the laser radar original data to a cloud end; receiving laser radar correction data, and analyzing according to the laser radar correction data to obtain color ratio CR of 532nm and 355nm wave bands of the particles to be identified532nm/355nmAnd attenuation backscattering coefficient ABC532The aerosol cloud data detected by the radar can be analyzed and judged to obtain the analysis result of the aerosol and the cloud type;
2. on the analysis results of the aerosol and cloud types, the technology can be used for specifically classifying and judging, receiving laser radar correction data, and analyzing according to the laser radar correction data to obtain the 532nm and 355nm depolarization ratio delta of the particles to be identified532/δ355Uploading; and obtaining a depolarization ratio value delta532/δ355And comparing the deviation ratio with a preset threshold value condition of the deviation rejection ratio to respectively obtain specific types of the aerosol and the cloud, so that specific types of the aerosol and the cloud can be classified, six types of ice cloud, mixed phase cloud, water cloud, sand dust aerosol, artificial pollutants and mixed layers of the ice cloud, the mixed phase cloud, the water cloud, the sand dust aerosol and the artificial pollutants are comprehensively obtained, and the accuracy of identifying the types of the aerosol and the cloud is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a schematic flow chart of an implementation of the method for identifying atmospheric aerosol and cloud according to the present invention;
FIG. 2 is a flow chart of a particular lidar atmospheric aerosol and cloud identification method of the present invention;
FIG. 3 is a schematic diagram of an application of a vertical distribution diagram of laser radar atmospheric aerosol and cloud type recognition results in embodiment 1 of the present invention
Fig. 4 is a schematic structural diagram of the identification system of atmospheric aerosol and cloud according to the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
In addition, the term "plurality" shall mean two as well as more than two.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Example 1
The technology of the invention is mainly based on the aerosol and cloud type identification method of the dual-band polarization laser radar observed quantity, so as to solve the problems of misjudgment and complexity of the existing laser radar cloud and aerosol identification algorithm, and improve the accuracy of identifying the aerosol and cloud type.
As shown in the attached figure 1, a set of recognition methods of the atmospheric aerosol and the cloud are developed on the basis of a ground-based dual-band polarization laser radar system which is independently developed, the polarization measurement difference of ultraviolet and visible light bands of the laser radar is fully utilized, and a more efficient and reliable recognition method of the atmospheric aerosol and the cloud is provided compared with the conventional method.
First, it is necessary to clarify each technical term of the present technology:
in the technique of the present application, the particle volume depolarization ratio is the total depolarization ratio of atmospheric molecules and particles, and the value thereof is a parameter of the particle shape. The Volume Depolarization Ratio (VDR) is defined by the Ratio of the parallel and perpendicular components of the backscattered signal:
the depolarization ratio is as follows:
Figure BDA0002816186330000091
wherein, beta⊥Is the attenuation backscattering coefficient, beta, of the vertical channel//Is the attenuated backscatter coefficient of the parallel channel signal. And C is the correction coefficient of the polarization measurement of the laser radar system.
In the technique of the application, the ratio delta of the depolarization ratios of the two bands532/δ355Is defined as a parameter that distinguishes between aerosol and cloud.
The Color Ratio (CR (532/355)) is a parameter related to particle size, where a large Color Ratio corresponds to coarse particles.
The color ratio is defined by the ratio of the Attenuation Backscattering Coefficients (ABC) at 532nm and 355 nm:
color ratio:
Figure BDA0002816186330000092
wherein, beta532,//、β532,⊥、β355,//And beta355,⊥Denotes the horizontal and vertical components of the attenuated backscatter coefficients of the atmospheric aerosol particles at a given wavelength.
The invention provides a method for identifying atmospheric aerosol and cloud in a first aspect, which comprises the following steps:
s1, acquiring laser radar original data: acquiring aerosol and cloud type laser radar original data based on a foundation dual-band polarization laser radar system, and uploading the laser radar original data to a cloud end;
the technology adopts a foundation dual-band polarization laser radar system to obtain aerosol and cloud type laser radar original data, and the laser radar original data is required to be uploaded to a cloud end in order to facilitate data lookup and calling;
s2, data correction: receiving laser radar original data, and performing data correction processing on the received laser radar original data to obtain and upload laser radar correction data;
and the laser radar original data needs to be subjected to correction processing, and the received laser radar original data is subjected to data correction processing to obtain and upload the laser radar correction data.
Preferably, in step S2, the data correction includes background noise correction, distance correction, geometric overlap correction, and polarization correction.
In the technology of the application, background noise correction, distance correction, geometric overlapping correction and polarization correction are carried out on laser radar data.
According to the technical scheme, through a large number of experiments, six types of ice cloud, mixed phase cloud, water cloud, sand dust aerosol, artificial pollutants and mixed layers of the ice cloud, the mixed phase cloud, the water cloud, the sand dust aerosol and the artificial pollutants are synthesized, and the laser radar atmospheric aerosol and the cloud identification method in the application technology are obtained.
S3, analyzing and obtaining the color ratio and the attenuation backscattering coefficient: receiving laser radar correction data, and analyzing according to the laser radar correction data to obtain color ratio CR of 532nm and 355nm wave bands of the particles to be identified532nm/355nmAnd attenuation backscattering coefficient ABC532Uploading;
the analysis results in the color ratio CR of 532nm and 355nm wave bands of the particles to be identified532nm/355nmAnd attenuation backscattering coefficient ABC532；
As shown in fig. 2, the present technique performs two classifications,
for the first time, the atmospheric aerosol and the cloud are firstly distinguished, and the following steps are adopted:
s4, cloud and aerosol identification and judgment: receiving color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532And comparing the color ratio with a preset threshold condition of the attenuation backscattering coefficient to obtain an analysis result of the aerosol and cloud types.
Setting a preset threshold condition of color ratio and attenuation backscattering coefficient, and comparing the color ratio CR of 532nm and 355nm wave bands of the particles to be identified532nm/355nmAnd attenuation backscattering coefficient ABC532The value is compared with a preset threshold value.
Preferably, in step S4, the preset threshold condition of the color ratio and the attenuation backscatter coefficient is:
color ratio CR532nm/355nmLess than 3.0, and
attenuation backscattering coefficient ABC532Less than 0.015/sr/km.
As shown in the attached figure 2 of the drawings,
preferably, in step S4, the reception color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532And comparing with a preset threshold value to obtain the analysis result of the aerosol and the cloud type, which specifically comprises the following steps:
identifying and judging the aerosol: when color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532When the preset threshold value is met, judging that the aerosol and the cloud type are aerosol;
cloud identification and judgment: when color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532And when the preset threshold value is not met, judging that the aerosol and the cloud type are cloud.
I.e. the received color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532Satisfy the color ratio CR of the preset threshold value532nm/355nmLess than 3.0 and an attenuated backscattering coefficient ABC532When the air volume is less than 0.015/sr/km, the laser radar atmosphere is judged to be aerosol;
receiving color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532Color ratio CR not satisfying preset threshold532nm/355nmLess than 3.0 and an attenuated backscattering coefficient ABC532When the air volume is less than 0.015/sr/km, the laser radar atmosphere is judged to be cloud;
after aerosol and cloud are judged and analyzed for the first time, accurate judgment is needed, and each specific type is identified, so that the technology has secondary judgment:
preferably, in step S4, after obtaining the analysis result of the aerosol and cloud types, the method further includes:
s5: secondary classification is carried out according to the depolarization ratio: receiving laser radar correction data, and correcting the number according to the laser radarThe deviation ratio delta of 532nm to 355nm of the particles to be identified is obtained by analysis532/δ355Uploading;
obtaining the ratio delta of the depolarization ratio532/δ355And comparing the deviation ratio with a preset threshold value condition of the deviation reduction ratio to respectively obtain the specific types of the aerosol and the cloud type.
Analyzing according to the laser radar correction data to obtain the ratio delta of the depolarization ratios of 532nm and 355nm of the particles to be identified532/δ355Will be the depolarization ratio delta532/δ355And comparing the deviation reduction ratio with a preset threshold value condition, and respectively classifying and identifying different aerosol and cloud types.
The preset threshold values for the de-deflection ratio for aerosols and clouds are different, as shown in figure 2,
aerosol: ratio of depolarization factor delta532/δ355The method is divided into three ranges, and the limit values are 0.8, 1.1 and 2.5 respectively. Analyzing according to the laser radar correction data to obtain the ratio delta of the depolarization ratios of 532nm and 355nm of the particles to be identified532/δ355When the ratio is in accordance with the corresponding ratio, the sand dust, the mixed layer and the artificial pollutant can be respectively judged.
Cloud: ratio of depolarization factor delta532/δ355Three ranges are also included, with limits of 0.4, 0.8 and 1.5, respectively. Analyzing according to the laser radar correction data to obtain the ratio delta of the depolarization ratios of 532nm and 355nm of the particles to be identified532/δ355When the ratio is in accordance with the corresponding ratio, the cloud, the mixed phase cloud and the ice cloud can be respectively judged.
Therefore, the technical scheme can analyze and obtain the color ratio CR of 532nm and 355nm wave bands of the particles to be identified532nm/355nmAnd attenuation backscattering coefficient ABC532；
Analyzing to obtain the ratio delta of the depolarization ratios of 532nm and 355nm of the particles to be identified532/δ355；
Analyzing to obtain the depolarization ratio value delta of sand dust, artificial pollutants, sand dust + artificial pollutants, water cloud, ice cloud and mixed phase cloud532/δ355A threshold value;
and analyzing and obtaining specific types of the laser radar atmospheric aerosol and the cloud in the application by integrating six types of ice cloud, mixed phase cloud, water cloud, sand dust aerosol and artificial pollutants and mixed layers of the ice cloud, the mixed phase cloud, the water cloud and the artificial pollutants.
As shown in fig. 3, for the specific application of the embodiment,
applying the proposed method to lidar measurements at 11 days 3 month and 11 days 4 month in Linze county, Lanzhou, the corresponding vertical profiles of atmospheric aerosol type and cloud layer type were obtained.
Ice clouds of day 3, 11 and water clouds of day 4, 11 were successfully identified.
In the aspect of identification of atmospheric aerosols, not only are sand dust aerosols and artificial atmospheric pollutants separated, but also mixtures thereof are identified.
Example 2
Corresponding to the application scheme of the identification method of the atmospheric aerosol and the cloud in embodiment 1, this embodiment provides an application system, and functions of the respective modules are different, and the application system may be implemented on a storage or a storage medium, so as to implement the identification method in embodiment 1.
As shown in fig. 4, a second aspect of the present invention provides an atmospheric aerosol and cloud identification system, which includes a laser radar raw data acquisition module, a data correction module, a color ratio and attenuation backscattering coefficient analysis acquisition module, and a cloud and aerosol identification determination module, which are sequentially connected in a communication manner,
the laser radar original data acquisition module: the system is used for collecting aerosol and cloud type laser radar original data based on a foundation dual-band polarization laser radar system and uploading the laser radar original data to a cloud end;
the data correction module: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for receiving laser radar original data, performing data correction processing on the received laser radar original data, acquiring and uploading laser radar corrected data;
the color ratio and attenuation backscattering coefficient analysis and acquisition module comprises: used for receiving the laser radar correction data and analyzing and obtaining the color ratio CR of 532nm and 355nm wave bands of the particles to be identified according to the laser radar correction data532nm/355nmAnd declineReduced backscattering coefficient ABC532Uploading;
the cloud and aerosol identification and judgment module: for receiving colour ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532And comparing the color ratio with a preset threshold condition of the attenuation backscattering coefficient to obtain an analysis result of the aerosol and cloud types.
Preferably, the data correction module includes a background noise correction module, a distance correction module, a geometric overlap correction module and a polarization correction module.
Preferably, the cloud and aerosol identification determination module comprises:
the color ratio and attenuation backscattering coefficient preset threshold setting module: the method is used for setting a threshold condition, and specifically comprises the following steps:
color ratio CR532nm/355nmLess than 3.0, and
attenuation backscattering coefficient ABC532Less than 0.015/sr/km.
Preferably, the cloud and aerosol identification determination module comprises:
the aerosol identification and judgment module: for judging color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532When the preset threshold value is met, judging that the aerosol and the cloud type are aerosol;
cloud discernment judge module: for judging color ratio CR532nm/355nmAnd attenuation backscattering coefficient ABC532And when the preset threshold value is not met, judging that the aerosol and the cloud type are cloud.
Preferably, the cloud and aerosol identification determining module further comprises:
a secondary classification module of the depolarization ratio: used for receiving laser radar correction data and analyzing and obtaining the depolarization ratio delta of 532nm and 355nm of the particles to be identified according to the laser radar correction data532/δ355Uploading; and
obtaining the ratio delta of the depolarization ratio532/δ355And comparing the deviation ratio with a preset threshold value condition of the deviation reduction ratio to respectively obtain the specific types of the aerosol and the cloud type.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (8)

1. An atmospheric aerosol and cloud identification method is characterized by comprising the following steps:
s1, acquiring laser radar original data: acquiring aerosol and cloud type laser radar original data based on a foundation dual-band polarization laser radar system, and uploading the laser radar original data to a cloud end;
s2, data correction: receiving laser radar original data, and performing data correction processing on the received laser radar original data to obtain and upload laser radar correction data;
s3, analyzing and obtaining the color ratio and the backscattering coefficient: receiving laser radar correction data, and analyzing according to the laser radar correction data to obtain color ratio CR of 532nm and 355nm wave bands of the particles to be identified532nm/355nmAnd coefficient of backscattering ABC532Uploading;
s4, cloud and aerosol identification and judgment: receiving color ratio CR532nm/355nmAnd coefficient of backscattering ABC532And comparing the color ratio with a preset threshold condition of the backscattering coefficientObtaining the analysis result of the aerosol and the cloud type; coefficient of backscattering ABC532Less than 0.015;
s5: secondary classification is carried out according to the depolarization ratio: receiving laser radar correction data, and analyzing to obtain the depolarization ratio values of 532nm and 355nm of the particles to be identified according to the laser radar correction data
Figure 795397DEST_PATH_IMAGE001
Uploading; obtaining a depolarization ratio value
Figure 936528DEST_PATH_IMAGE002
Comparing the deviation ratio with a preset threshold value condition of the deviation reduction ratio to respectively obtain specific types of aerosol and cloud types; specifically, the method comprises the following steps:
aerosol: ratio of depolarization
Figure 903347DEST_PATH_IMAGE001
The method comprises three ranges with the limit values of 0.8, 1.1 and 2.5 respectively, and obtaining the depolarization ratio values of 532nm and 355nm of the particles to be identified according to the laser radar correction data analysis
Figure 19202DEST_PATH_IMAGE001
When the corresponding ratio is met, the sand dust, the mixed layer and the artificial pollutant can be respectively judged;
cloud: ratio of depolarization
Figure 704261DEST_PATH_IMAGE001
The particle size is also divided into three ranges, the limit values are respectively 0.4, 0.8 and 1.5, and the retrogradation ratio values of 532nm and 355nm of the particles to be identified are obtained according to the analysis of the laser radar correction data
Figure 180242DEST_PATH_IMAGE001
When the ratio is in accordance with the corresponding ratio, the cloud, the mixed phase cloud and the ice cloud can be respectively judged.
2. The method for identifying atmospheric aerosols and clouds of claim 1 wherein in step S2, the data corrections include background noise corrections, distance corrections, geometric overlay corrections and polarization corrections.
3. The method for identifying atmospheric aerosol and cloud according to claim 1 or 2, wherein in step S4, the predetermined threshold conditions of the color ratio and the backscattering coefficient are:
color ratio CR532nm/355nmLess than 3.0.
4. The method as claimed in claim 3, wherein in step S4, the CR is the ratio of received colors532nm/355nmAnd coefficient of backscattering ABC532And comparing with a preset threshold value to obtain the analysis result of the aerosol and the cloud type, which specifically comprises the following steps:
identifying and judging the aerosol: when color ratio CR532nm/355nmAnd backscattering coefficient ABC532When the preset threshold value is met, judging that the aerosol and the cloud type are aerosol;
cloud identification and judgment: when color ratio CR532nm/355nmAnd backscattering coefficient ABC532And when the preset threshold value is not met, judging that the aerosol and the cloud type are cloud.
5. An identification system for atmospheric aerosol and cloud is characterized by comprising a laser radar original data acquisition module, a data correction module, a color ratio and backscattering coefficient analysis acquisition module and a cloud and aerosol identification judgment module which are sequentially in communication connection, wherein,
the laser radar original data acquisition module: the system is used for collecting aerosol and cloud type laser radar original data based on a foundation dual-band polarization laser radar system and uploading the laser radar original data to a cloud end;
the data correction module: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for receiving laser radar original data, performing data correction processing on the received laser radar original data, acquiring and uploading laser radar corrected data;
the color ratio and backscatteringA coefficient analysis acquisition module: used for receiving the laser radar correction data and analyzing and obtaining the color ratio CR of 532nm and 355nm wave bands of the particles to be identified according to the laser radar correction data532nm/355nmAnd coefficient of backscattering ABC532Uploading;
the cloud and aerosol identification and judgment module: for receiving colour ratio CR532nm/355nmAnd coefficient of backscattering ABC532Comparing the obtained value with a preset threshold condition of the color ratio and the backscattering coefficient to obtain an analysis result of aerosol and cloud types; coefficient of backscattering ABC532Less than 0.015;
a secondary classification module of the depolarization ratio: used for receiving the laser radar correction data and analyzing and obtaining the depolarization ratio values of 532nm and 355nm of the particles to be identified according to the laser radar correction data
Figure 80196DEST_PATH_IMAGE001
Uploading; and obtaining a depolarization ratio value
Figure 491586DEST_PATH_IMAGE003
Comparing the deviation ratio with a preset threshold value condition of the deviation reduction ratio to respectively obtain specific types of aerosol and cloud types; specifically, the method comprises the following steps:
aerosol: ratio of depolarization
Figure 788575DEST_PATH_IMAGE001
The method comprises three ranges with the limit values of 0.8, 1.1 and 2.5 respectively, and obtaining the depolarization ratio values of 532nm and 355nm of the particles to be identified according to the laser radar correction data analysis
Figure 678034DEST_PATH_IMAGE001
When the corresponding ratio is met, the sand dust, the mixed layer and the artificial pollutant can be respectively judged;
cloud: ratio of depolarization
Figure 494811DEST_PATH_IMAGE001
Also divided into three ranges, with limits of 0.4, 0.8 and 1.5, respectively, according to lidarThe correction data analysis obtains the deviation ratio values of 532nm and 355nm of the particles to be identified
Figure 77102DEST_PATH_IMAGE001
When the ratio is in accordance with the corresponding ratio, the cloud, the mixed phase cloud and the ice cloud can be respectively judged.
6. The atmospheric aerosol and cloud identification system of claim 5, wherein said data correction module comprises a background noise correction module, a distance correction module, a geometric overlap correction module, and a polarization correction module.
7. An atmospheric aerosol and cloud identification system according to claim 5 or 6, wherein the cloud and aerosol identification determining module comprises:
the color ratio and backscattering coefficient preset threshold setting module: the method is used for setting a threshold condition, and specifically comprises the following steps:
color ratio CR532nm/355nmLess than 3.0.
8. The atmospheric aerosol and cloud identification system of claim 7, wherein the cloud and aerosol identification determination module comprises:
the aerosol identification and judgment module: for judging color ratio CR532nm/355nmAnd backscattering coefficient ABC532When the preset threshold value is met, judging that the aerosol and the cloud type are aerosol;
cloud discernment judge module: for judging color ratio CR532nm/355nmAnd backscattering coefficient ABC532And when the preset threshold value is not met, judging that the aerosol and the cloud type are cloud.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7580127B1 (en) * 2006-07-21 2009-08-25 University Corporation For Atmospheric Research Polarization lidar for the remote detection of aerosol particle shape
CN104268423A (en) * 2014-10-11 2015-01-07 武汉大学 Large-scale dynamic evolution dust type aerosol retrieval method
US8984939B1 (en) * 2011-01-07 2015-03-24 Weather Decision Technologies Inc. Dual-polarization weather radar data system and method
CN105158819A (en) * 2015-07-03 2015-12-16 兰州大学 Integrated observation platform for semi-arid climate
CN105974397A (en) * 2016-07-12 2016-09-28 南京信息工程大学 Polarization laser radar calibration method based on Jones matrix, and polarization laser radar
CN111736176A (en) * 2020-06-23 2020-10-02 中国人民解放军陆军炮兵防空兵学院 Atmospheric environment data sharing method based on laser radar
CN211741577U (en) * 2020-01-15 2020-10-23 大连理工大学 Self-calibration polarized atmospheric laser radar device

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7755041B2 (en) * 2005-11-15 2010-07-13 University Of South Florida Optical and laser differential absorption remote detection of TATP peroxide based explosives
US7656526B1 (en) * 2006-07-21 2010-02-02 University Corporation For Atmospheric Research Lidar system for remote determination of calibrated, absolute aerosol backscatter coefficients
CA2742687C (en) * 2008-11-05 2013-07-30 Neptec Design Group Ltd. Return pulse shape analysis for falling edge object discrimination of aerosol lidar
CN103197305A (en) * 2013-03-21 2013-07-10 武汉大学 Sand-dust type aerosol inversion method based on support vector machine identification
CN107728163B (en) * 2017-09-05 2020-04-07 兰州大学 Atmospheric characteristic layer detection method and device
CN108957474B (en) * 2018-06-12 2022-07-15 西安理工大学 Full-polarization laser radar system for detecting particle morphology and detection method thereof
CN109541639A (en) * 2018-12-25 2019-03-29 天津珞雍空间信息研究院有限公司 A kind of inversion boundary layer height method based on particle cluster
CN111123293A (en) * 2020-01-15 2020-05-08 大连理工大学 Self-calibration polarized atmospheric laser radar device and method
CN111551961B (en) * 2020-06-12 2023-09-26 南通大学 Cloud condensation nuclear number concentration vertical profile inversion method based on multi-wavelength laser radar

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7580127B1 (en) * 2006-07-21 2009-08-25 University Corporation For Atmospheric Research Polarization lidar for the remote detection of aerosol particle shape
US8984939B1 (en) * 2011-01-07 2015-03-24 Weather Decision Technologies Inc. Dual-polarization weather radar data system and method
CN104268423A (en) * 2014-10-11 2015-01-07 武汉大学 Large-scale dynamic evolution dust type aerosol retrieval method
CN105158819A (en) * 2015-07-03 2015-12-16 兰州大学 Integrated observation platform for semi-arid climate
CN105974397A (en) * 2016-07-12 2016-09-28 南京信息工程大学 Polarization laser radar calibration method based on Jones matrix, and polarization laser radar
CN211741577U (en) * 2020-01-15 2020-10-23 大连理工大学 Self-calibration polarized atmospheric laser radar device
CN111736176A (en) * 2020-06-23 2020-10-02 中国人民解放军陆军炮兵防空兵学院 Atmospheric environment data sharing method based on laser radar

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Dual-wavelength High-Spectral-Resolution Lidar for advanced classification and retrieval of aerosols;Tomoaki Nishizawa;Nobuo Sugimoto;Ichiro Matsui;《2011 IEEE International Geoscience and Remote Sensing Symposium》;20111020;第4157-4160页 *
Evaluating the Performance of SVM in Dust Aerosol Discrimination and Testing its Ability in an Extended Area;Yingying Ma;Wei Gong;《IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 》;20120822;第1849-1858页 *
Observations of extinction-to-backscatter ratio and depolarization ratio of tropospheric aerosols and clouds by high-spectral-resolution lidar;B. Tatarov;N. Sugimoto;I. Matsui;A. Shimizu;《2005 Pacific Rim Conference on Lasers & Electro-Optics》;20060110;第1259-1260页 *

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