A cloud condensation nucleus number concentration inversion method, device, medium and product

Abstract

The present application discloses a cloud condensation nucleus number concentration inversion method, equipment, medium and product, which relate to the field of atmospheric environment detection. The method includes obtaining particle parameters observed by a multi-wavelength polarization Raman lidar; the particle parameters include radar ratio, depolarization ratio and depolarization color ratio; determining the aerosol type according to the particle parameters; based on the aerosol type, extracting the extinction coefficient and backscattering coefficient of each aerosol; performing relative humidity correction on the extinction coefficient and backscattering coefficient to determine the corrected optical parameters; matching the corrected optical parameters with the optical parameters in an aerosol optical parameter database to determine the aerosol parameters; calculating the critical radius of each aerosol at different supersaturation ratios; for each aerosol, inverting the CCN number concentration at different supersaturation ratios according to the critical radius and the aerosol parameters. The present application can accurately invert the CCN number concentration at different supersaturation ratios.

Description

Technical Field

[0001] The present application relates to the field of atmospheric environment detection, and in particular to a cloud condensation nucleus number concentration inversion method, equipment, medium and product. Background Art

[0002] Atmospheric aerosols allow water vapor to condense under certain supersaturation conditions, subsequently acting as cloud condensation nuclei (CCN) to evolve into cloud droplets. Anthropogenic emissions are the primary source of CCN, promoting the formation of cloud droplets, thereby changing cloud properties, precipitation patterns, and climate forcing. Therefore, assessing the number concentration of CCN in the atmosphere is central to the ability to accurately quantify the impact of anthropogenic aerosols on cloud properties. The ability of aerosols to act as CCNs is primarily controlled by their particle size distribution, followed by their chemical composition. In addition, in natural environments, the hygroscopicity of aerosols can also affect the size distribution of aerosol particles and their optical properties, especially when they are close to the cloud base or in high humidity environments.

[0003] Multi-wavelength polarization Raman lidar is a powerful tool that can simultaneously measure the atmospheric backscatter coefficient, depolarization ratio, radar ratio, extinction color ratio, and depolarization color ratio, thereby classifying aerosols and clouds in the atmosphere. Typical polarization lidars have a temporal resolution of 10 minutes and a spatial resolution of 7.5 meters, meeting the requirements for high-resolution detection of aerosol profiles.

[0004] Based on this, previous researchers have also established CCN number concentration inversion schemes, but they ignored some key influencing factors: 1. Aerosols in nature are often composed of mixtures of multiple types, not a single type. Different types of aerosols have different chemical compositions and their particle size distributions vary greatly. Therefore, it is necessary to accurately invert the impact of each type of aerosol in the mixed aerosol on the CCN number concentration in order to evaluate the impact of the mixed aerosol on the CCN concentration. However, the existing CCN number concentration inversion scheme cannot accurately invert the CNN number concentration of each type of aerosol in the mixed aerosol; 2. The effect of aerosol hygroscopic growth on size distribution is not considered. In the actual atmosphere, the aerosol particle size distribution is affected by the hygroscopic characteristics of the aerosol, especially when it is under high relative humidity conditions or close to the cloud base, and the CNN number concentration cannot be accurately inverted. Summary of the Invention

[0005] The purpose of this application is to provide a cloud condensation nucleus number concentration inversion method, equipment, medium and product to solve the problem of low accuracy of CNN number concentration inversion.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a cloud condensation nucleus number concentration inversion method, comprising:

[0008] Obtaining particle parameters observed by a multi-wavelength polarization Raman lidar; the particle parameters include radar ratio, depolarization ratio, and depolarization color ratio;

[0009] Determining the aerosol type according to the particulate matter parameters; the aerosol type includes a single aerosol and a mixed aerosol; the mixed aerosol types include a mixture of sand and dust and urban haze, a mixture of biomass burning and urban haze, a mixture of biomass burning and sand and dust, a mixture of biomass burning, urban haze and sand and dust aerosols, a mixture of sand and dust and sea salt aerosols, a mixture of biomass burning and sea salt aerosols, and a mixture of sand and dust, biomass burning and sea salt aerosols;

[0010] Based on the aerosol type, extracting the extinction coefficient and backscattering coefficient of each aerosol;

[0011] Performing relative humidity correction on the extinction coefficient and the backscattering coefficient to determine corrected optical parameters; the corrected optical parameters include the corrected extinction coefficient and the corrected backscattering coefficient;

[0012] Matching the corrected optical parameters with optical parameters in an aerosol optical parameter database to determine aerosol parameters; the aerosol parameters include aerosol particle number concentration, aerosol particle effective radius, and geometric standard deviation;

[0013] Calculate the critical radius of each aerosol at different supersaturation ratios;

[0014] For each aerosol, the CCN number concentration at different supersaturation ratios is inverted according to the critical radius and the aerosol parameters.

[0015] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the cloud condensation nucleus number concentration inversion methods described above.

[0016] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the cloud condensation nucleus number concentration inversion methods described above.

[0017] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the cloud condensation nucleus number concentration inversion methods described above.

[0018] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0019] This application analyzes the extinction coefficient and backscattering coefficient of each aerosol based on aerosol type, thereby accurately inverting the impact of each type of aerosol in a mixed aerosol on the CCN number concentration. In addition, the extinction coefficient and backscattering coefficient are corrected for relative humidity, and the corrected optical parameters are matched with the optical parameters in the aerosol optical parameter database. This fully considers the effect of aerosol hygroscopic growth on the size distribution. For each aerosol, the CCN number concentration at different supersaturation ratios is accurately inverted based on the calculated critical radius and the matched aerosol parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 Flowchart of the cloud condensation nucleus number concentration inversion method provided in this application;

[0022] Figure 2 Aerosol classification flow chart provided for this application;

[0023] Figure 3 A flow chart of another cloud condensation nucleus number concentration inversion method provided in this application;

[0024] Figure 4 Schematic diagram of cloud condensation nucleus number concentration profiles under three supersaturation ratios observed at a certain climate observatory. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0027] The embodiment of the present application provides a cloud condensation nucleus number concentration inversion method, which is executed by a computer device, specifically, it can be executed by a computer device such as a terminal or a server alone, or it can be executed by a terminal and a server together. In the embodiment of the present application, Figure 1 As shown, the method includes the following steps.

[0028] S1: Obtaining particle parameters observed by a multi-wavelength polarization Raman lidar; the particle parameters include radar ratio, depolarization ratio, and depolarization color ratio.

[0029] S2: Determine the aerosol type according to the particulate matter parameters; the aerosol type includes single aerosol and mixed aerosol; the types of the mixed aerosol include a mixture of dust and urban haze, a mixture of biomass burning and urban haze, a mixture of biomass burning and dust, a mixture of biomass burning, urban haze and dust aerosol, a mixture of dust and sea salt aerosol, a mixture of biomass burning and sea salt aerosol, and a mixture of dust, biomass burning and sea salt aerosol.

[0030] S3: Based on the aerosol type, extract the extinction coefficient and backscattering coefficient of each aerosol.

[0031] S4: performing relative humidity correction on the extinction coefficient and the backscattering coefficient to determine corrected optical parameters; the corrected optical parameters include the corrected extinction coefficient and the corrected backscattering coefficient.

[0032] S5: Matching the corrected optical parameters with the optical parameters in the aerosol optical parameter database to determine aerosol parameters; the aerosol parameters include aerosol particle number concentration, aerosol particle effective radius, and geometric standard deviation.

[0033] S6: Calculate the critical radius of each aerosol at different supersaturation ratios.

[0034] S7: For each aerosol, invert the CCN number concentration at different supersaturation ratios according to the critical radius and the aerosol parameters.

[0035] In an illustrative example, Figure 2 As shown in Figure 2, aerosol type identification mainly depends on the thresholds of three aerosol optical parameters: Figure 2 The thresholds in are empirical values ​​and may vary to a certain extent in different regions. Accurate thresholds can be statistically determined through long-term radar observations. The depolarization ratio (DEP), depolarization color ratio (DCR), and radar ratio (LR) are the most powerful particle parameters for identifying mixed aerosols.

[0036] When the DEP value is between 0.1 and 0.28, dust aerosols are usually mixed in. When the DCR value is between 1 and 2, biomass burning aerosols are usually mixed in. When sea salt aerosols are mixed in, the LR value drops rapidly. Based on the differences in the optical parameter thresholds of the three aerosols, eight mixed aerosol types can be identified: ① a mixture of dust and urban haze, ② a mixture of biomass burning and urban haze, ③ a mixture of biomass burning and dust, ④ a mixture of biomass burning, urban haze, and dust aerosols, ⑤ a mixture of dust and sea salt aerosols, ⑥ a mixture of biomass burning and sea salt aerosols, and ⑦ a mixture of dust, biomass burning, and sea salt aerosols.

[0037] In an exemplary embodiment, S3 may be replaced by the following steps.

[0038] When the aerosol type is a single type aerosol, the extinction coefficient and backscattering coefficient of the single type aerosol are directly extracted.

[0039] When the aerosol type is a mixed aerosol, the extinction coefficient of each aerosol in the mixed aerosol is extracted.

[0040] When the mixed aerosol is a mixture of dust and urban haze, a mixture of biomass burning and dust, or a mixture of dust and sea salt aerosol, the depolarization ratio is used to separate the backscattering coefficient of each aerosol in the mixed aerosol.

[0041] Furthermore, for mixed aerosols ①, ③, and ⑤, the depolarization ratio is used to separate the backscatter coefficient (BAC) and extinction coefficient (EXT) of each aerosol in the mixed aerosol. The BAC calculation formula for dust aerosol is:

[0042]

[0043] BAC a =BAC-BAC d (2)

[0044] Where, BAC is the backscatter coefficient of observed ①, ③ or ⑤, BAC d Represents the backscattering coefficient of the separated dust aerosol, BAC a is the backscattering coefficient of separated urban haze, biomass burning or sea salt aerosol, DEP a is the prior value of the depolarization ratio for urban haze, biomass burning, or sea salt aerosols. This prior value is typically derived from long-term lidar observations. The extinction coefficient is calculated by multiplying the backscatter coefficient by the radar ratio.

[0045] When the mixed aerosol is a mixture of biomass burning and urban haze, or a mixture of biomass burning and sea salt aerosol, the backscattering coefficient and extinction coefficient of each aerosol in the mixed aerosol are calculated according to the contribution ratio of each aerosol to the backscattering coefficient.

[0046] Furthermore, for mixed aerosols ② and ⑥, the formula for calculating the proportion of the two aerosols' contribution to the backscattering coefficient is:

[0047]

[0048] BAC u =x×BAC(4)

[0049] BAC b =(1-x)×BAC(5)

[0050] Where x is the backscatter ratio of the two aerosols, DCR is the observed decolorization ratio, BAC is the observed backscatter coefficient, and BAC u is the backscattering coefficient of separated urban haze or sea salt aerosol, BAC b is the backscattering coefficient of the separated biomass burning aerosol. u is the prior value of decolorization ratio of urban haze or sea salt aerosol, DCR b is the prior value of the decolorization ratio of biomass burning aerosol. The prior value is usually derived from long-term lidar observation statistics.

[0051] When the type of the mixed aerosol is a mixture of biomass burning, urban haze and dust aerosol, or a mixture of dust, biomass burning and sea salt aerosol, the depolarization ratio is used to separate the backscattering coefficient of the dust aerosol in the mixed aerosol, and the radar ratio is used to classify the urban haze and biomass fuel aerosol in the mixture of biomass burning, urban haze and dust aerosol, or the backscattering coefficients of biomass burning and sea salt aerosol in the mixture of dust, biomass burning and sea salt aerosol.

[0052] Furthermore, for mixed aerosols ④ and ⑦, first apply formulas (1) and (2) to separate the backscatter coefficient of dust aerosol, and then use radar ratio to separate the backscatter coefficient of urban haze and biomass burning aerosol, or biomass burning aerosol and sea salt aerosol. The calculation formula is:

[0053]

[0054] BAC u =x×BAC(7)

[0055] BAC b =(1-x)×BAC(8)

[0056] Where x is the backscatter ratio of the two aerosols, LR is the observed radar ratio, BAC is the observed backscatter coefficient, and BAC u is the backscattering coefficient of separated urban haze or sea salt aerosol, BAC b is the backscattering coefficient of the separated biomass combustion aerosol.

[0057] In an exemplary embodiment, S4 may be replaced by the following steps.

[0058] use Determine the corrected optical parameters; where, when the extinction coefficient is corrected for relative humidity, P dry is the corrected extinction coefficient, P wet is the extinction coefficient before correction; when the backscattering coefficient is corrected for relative humidity, P dry is the corrected backscatter coefficient, P wet is the backscattering coefficient before correction; RH is the relative humidity; ε is the fitting parameter, which is a function of relative humidity. The ε value for each type of aerosol can be obtained by simulating the Mie scattering theory.

[0059] In an exemplary embodiment, before S5, the step further includes:

[0060] Based on long-term data observed by sun photometers; the long-term data include aerosol parameters of coarse and fine modes of urban haze, dust aerosols, biomass burning aerosols, and sea salt aerosols;

[0061] According to the diffusion theory and the aerosol parameters of different types of aerosols, the extinction coefficient and backscattering coefficient corresponding to the aerosol parameters are determined, and an aerosol optical parameter database is constructed.

[0062] Furthermore, long-term data of sun photometer observations were collected. The long-term data came from the AERONET observation network, mainly including the coarse and fine mode aerosol particle number concentrations (N), the effective radius (r) and geometric standard deviation (σ) of aerosol particles of urban haze, dust aerosol, biomass burning aerosol and sea salt aerosol.

[0063] Based on Mie scattering theory, the coarse and fine mode number concentrations (N), effective radius (r), and geometric standard deviation (σ) of different aerosol types are collected as input to calculate the corresponding BAC or EXT. This database of aerosol optical parameters is then established, with N, r, σ corresponding to BAC and EXT. The establishment of this database directly impacts the accuracy of the inversion. Therefore, it is important to select as many stations and observe for as long a time as possible. Furthermore, the separation of N, r, and σ should be dynamically adjusted based on the amount of data from the sun photometer.

[0064] Furthermore, the calculation process is as follows:

[0065] Each type of aerosol can be considered as a combination of coarse and fine particles with a log-normal distribution, which can be expressed as

[0066]

[0067] Where, is the lognormal distribution function of particle size, σ i and N i denote the number concentration and geometric standard deviation of the coarse mode or fine mode, respectively. The subscript f denotes the fine mode, the subscript c denotes the coarse mode, and r i n Indicates the median radius of the coarse mode or fine mode particle.

[0068] When using the T-matrix scattering model to calculate the optical parameters (BAC, EXT) of spherical particles, the theoretical formula is simplified to the Mie scattering theory model. When using the T-matrix scattering model to calculate the optical parameters of non-spherical aerosol particles, the non-spherical particles are simplified into three typical shapes: ellipsoidal particles, cylindrical particles, and Chebyshev particles. The shape of the ellipsoidal particle is described by the axis ratio a / b, where a is the length of the minor axis and b is the length of the major axis; the shape of the cylindrical particle is described by the axis ratio D / L, where D is the base diameter and L is the generatrix length; the shape of the Chebyshev particle is described by the T-matrix scattering model. n (X), where n and X refer to the deformation degree and deformation parameter, respectively.

[0069] Long-term data from sun photometer observations are collected to construct a lookup table for the number concentration (N), effective radius (r), and geometric standard deviation (σ) of coarse and fine modes of urban haze, dust aerosol, biomass burning aerosol, and sea salt aerosol. To reduce the size of the lookup table and reduce the amount of calculation, the lookup table is simplified to two parts:

[0070]

[0071] Where B is the data matrix pre-calculated based on the range of the fine / coarse modal spectrum distribution parameters (r, σ) of the aerosol, and σ f 、r f and r c The calculation intervals are 0.01, 0.002 and 0.01 μm, σ c The aerosol particle number concentration N is a known quantity. The range of the aerosol particle radius r is limited to 0.01-10 μm, and the logarithmic interval of r is 0.002. i Range is 100-10000 pieces / cm 3 , with an interval of 10 / cm3 .

[0072] In an exemplary embodiment, S5 may be replaced by the following steps.

[0073] use The corrected optical parameters are matched with the optical parameters in the aerosol optical parameter database to determine the aerosol parameters; wherein E is the matching error; when the extinction coefficient is matched, P q is the extinction coefficient of different bands observed by lidar, P' q is the extinction coefficient of the corresponding band in the aerosol optical parameter database; when matching the backscattering coefficient, P q is the backscattering coefficient of different bands observed by lidar, P' q is the backscattering coefficient of the corresponding band in the aerosol optical parameter database; q is the band number; and n is the total number of bands.

[0074] In an exemplary embodiment, S6 may be replaced by the following steps.

[0075] use Calculate the critical radius of each aerosol at different supersaturation ratios; where r c is the critical radius; SS is the supersaturation ratio; M is the molecular weight of water; ρ is the density of water; R is the universal gas constant; T is the temperature; φ is the hygroscopicity factor, usually urban haze φ = 0.3, biomass burning aerosol φ = 0.1, and dust aerosol φ = 0.03.

[0076] In an exemplary embodiment, S7 may be replaced by the following steps.

[0077] use Invert the CCN number concentration under different supersaturation ratios; where N CCN is the CCN number concentration; is the lognormal distribution function of particle size, r is the effective radius of aerosol particles; f is the fine mode; c is the coarse mode; when i = f, σ i is the geometric standard deviation of the fine mode, N i is the aerosol particle number concentration in fine mode, r i n is the median radius of aerosol particles in the fine mode; when i = c, σ i is the geometric standard deviation of the coarse mode, N i is the aerosol particle number concentration under the coarse mode; r i n is the median radius of aerosol particles in the coarse mode.

[0078] In practical applications, for a single type of aerosol, The calculated value is the CCN number concentration caused by this type of aerosol; for mixed aerosols, it is necessary to use Calculate the CCN number concentration caused by each type of separated aerosol, and then add up the CCN number concentrations caused by all types of aerosol to obtain the final CCN number concentration of the mixed aerosol.

[0079] Apply the technical solution of this application to the observation data of a certain climate observatory, such as Figure 3 Shown, including:

[0080] Using multi-wavelength polarization Raman lidar, aerosols are first classified. For mixed aerosols, the backscattering coefficient and extinction coefficient of each type of aerosol are inverted.

[0081] A database of optical properties of different types of aerosols is constructed by applying long-term data from sun photometer observations, combining particle size distribution parameters and Mie scattering theory.

[0082] The backscattering coefficient of each type of aerosol is corrected for humidity and matched with the aerosol optical property database, and then the cloud condensation nucleus number concentration at different supersaturation ratios is inverted.

[0083] For mixed aerosols, the cloud condensation nucleus number concentrations retrieved for each type of aerosol are combined.

[0084] Figure 4 The following is a schematic diagram of the cloud condensation nucleus number concentration profiles under three supersaturation ratios observed at a certain climate observatory. Figure 4 As shown, the present application achieves high temporal and spatial resolution inversion of cloud condensation nucleus number concentration profiles in mixed aerosols; and performs relative humidity correction to improve the inversion accuracy of cloud condensation nucleus number concentration.

[0085] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store cloud condensation nucleus number concentration inversion data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a cloud condensation nucleus number concentration inversion method is implemented.

[0086] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the above method when executing the computer program.

[0087] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0088] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0089] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0090] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0091] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0092] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A cloud condensation nucleus number concentration inversion method, characterized in that: The cloud condensation nucleus number concentration inversion method includes: Obtaining particle parameters observed by a multi-wavelength polarization Raman lidar; the particle parameters include radar ratio, depolarization ratio, and depolarization color ratio; Determining the aerosol type according to the particulate matter parameters; the aerosol type includes a single aerosol and a mixed aerosol; the mixed aerosol types include a mixture of sand and dust and urban haze, a mixture of biomass burning and urban haze, a mixture of biomass burning and sand and dust, a mixture of biomass burning, urban haze and sand and dust aerosols, a mixture of sand and dust and sea salt aerosols, a mixture of biomass burning and sea salt aerosols, and a mixture of sand and dust, biomass burning and sea salt aerosols; Extracting the extinction coefficient and backscattering coefficient of each aerosol based on the aerosol type, specifically including: when the aerosol type is a mixed aerosol, extracting the extinction coefficient of each aerosol in the mixed aerosol; When the mixed aerosol is a mixture of dust and urban haze, a mixture of biomass burning and dust, or a mixture of dust and sea salt aerosol, the backscattering coefficient of each aerosol in the mixed aerosol is separated by using the depolarization ratio; When the mixed aerosol is a mixture of biomass burning and urban haze, or a mixture of biomass burning and sea salt aerosol, the backscattering coefficient and the extinction coefficient of each aerosol in the mixed aerosol are calculated according to the contribution ratio of each aerosol to the backscattering coefficient; When the type of the mixed aerosol is a mixture of biomass burning, urban haze and sand aerosol, or a mixture of sand, biomass burning and sea salt aerosol, the depolarization ratio is used to separate the backscattering coefficient of the sand aerosol in the mixed aerosol; and the radar ratio is used to separate the backscattering coefficients of urban haze and biomass fuel aerosol in the mixture of biomass burning, urban haze and sand aerosol, or the backscattering coefficients of biomass burning and sea salt aerosol in the mixture of sand, biomass burning and sea salt aerosol; Performing relative humidity correction on the extinction coefficient and the backscattering coefficient to determine corrected optical parameters; the corrected optical parameters include the corrected extinction coefficient and the corrected backscattering coefficient; Matching the corrected optical parameters with optical parameters in an aerosol optical parameter database to determine aerosol parameters; the aerosol parameters include aerosol particle number concentration, aerosol particle effective radius, and geometric standard deviation; Calculate the critical radius of each aerosol at different supersaturation ratios; For each aerosol, the CCN number concentration at different supersaturation ratios is inverted according to the critical radius and the aerosol parameters.

2. The cloud condensation nucleus number concentration inversion method according to claim 1, characterized in that: Based on the aerosol type, the extinction coefficient and backscattering coefficient of each aerosol are extracted, specifically including: When the aerosol type is a single type aerosol, the extinction coefficient and backscattering coefficient of the single type aerosol are directly extracted.

3. The cloud condensation nucleus number concentration inversion method according to claim 1, characterized in that: Performing relative humidity correction on the extinction coefficient and the backscattering coefficient to determine corrected optical parameters specifically includes: use Determine the corrected optical parameters; where, when the extinction coefficient is corrected for relative humidity, P dry is the corrected extinction coefficient, P wet is the extinction coefficient before correction; when the backscattering coefficient is corrected for relative humidity, P dry is the corrected backscatter coefficient, P wet is the backscattering coefficient before correction; RH is the relative humidity; ε is the fitting parameter.

4. The cloud condensation nucleus number concentration inversion method according to claim 1, characterized in that: Matching the corrected optical parameters with optical parameters in an aerosol optical parameter database to determine aerosol parameters, the method also includes: Based on long-term data observed by sun photometers; the long-term data include aerosol parameters of coarse and fine modes of urban haze, dust aerosols, biomass burning aerosols, and sea salt aerosols; According to the diffusion theory and the aerosol parameters of different types of aerosols, the extinction coefficient and backscattering coefficient corresponding to the aerosol parameters are determined, and an aerosol optical parameter database is constructed.

5. The cloud condensation nucleus number concentration inversion method according to claim 1, characterized in that: Matching the corrected optical parameters with optical parameters in an aerosol optical parameter database to determine aerosol parameters specifically includes: use The corrected optical parameters are matched with the optical parameters in the aerosol optical parameter database to determine the aerosol parameters; wherein E is the matching error; when the extinction coefficient is matched, P q is the extinction coefficient of different bands observed by lidar, P q ' is the extinction coefficient of the corresponding band in the aerosol optical parameter database; when matching the backscattering coefficient, P q is the backscattering coefficient of different bands observed by lidar, P q ' is the backscattering coefficient of the corresponding band in the aerosol optical parameter database; q is the band number; and n is the total number of bands.

6. The cloud condensation nucleus number concentration inversion method according to claim 1, characterized in that: Calculate the critical radius of each aerosol at different supersaturation ratios, including: use Calculate the critical radius of each aerosol at different supersaturation ratios; where r c is the critical radius; SS is the supersaturation ratio; M is the molecular weight of water; ρ is the density of water; R is the universal gas constant; T is the temperature; φ is the hygroscopicity factor.

7. The cloud condensation nucleus number concentration inversion method according to claim 1, characterized in that: For each aerosol, the CCN number concentration at different supersaturation ratios is inverted according to the critical radius and the aerosol parameters, specifically including: use Invert the CCN number concentration under different supersaturation ratios; where N CCN is the CCN number concentration; is the lognormal distribution function of particle size, r is the effective radius of aerosol particles; f is the fine mode; c is the coarse mode; when i = f, σ i is the geometric standard deviation of the fine mode, N i is the aerosol particle number concentration in fine mode, r i n is the median radius of aerosol particles in the fine mode; when i = c, σ i is the geometric standard deviation of the coarse mode, N i is the aerosol particle number concentration under the coarse mode; r i n is the median radius of aerosol particles in the coarse mode.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cloud condensation nucleus number concentration inversion method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the cloud condensation nucleus number concentration inversion method according to any one of claims 1 to 7 is implemented.

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