Cloud condensation nucleus number concentration inversion method, device, medium and product

Abstract

The invention discloses a cloud condensation nucleus number concentration inversion method and device, a medium and a product, and relates to the field of atmospheric environment detection.The method comprises the steps that particulate matter parameters observed by a multi-wavelength polarization Raman laser radar are obtained; the particulate matter parameters comprise a radar ratio, a depolarization ratio and a depolarization color ratio; determining the aerosol type according to the particulate matter parameters; based on the aerosol type, extracting an extinction coefficient and a backscattering coefficient of each aerosol; performing relative humidity correction on the extinction coefficient and the backscattering coefficient, and determining a corrected optical parameter; matching the corrected optical parameters with optical parameters in an aerosol optical parameter database to determine aerosol parameters; calculating the critical radius of each aerosol under different supersaturation ratios; and for each aerosol, according to the critical radius and the aerosol parameter, inverting the CCN number concentration under different supersaturation ratios, and according to the method, the CCN number concentration under different supersaturation ratios can be accurately inverted.

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 and subsequently evolve into cloud droplets as cloud condensation nuclei (CCN). Anthropogenic emissions are the main source of CCN, promoting the formation of cloud droplets, thereby changing cloud properties, precipitation patterns, and climate forcing. Therefore, assessing the CCN number concentration 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 CCN is primarily controlled by the particle size distribution, followed by the 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 a high humidity environment.

[0003] Multi-wavelength polarization Raman lidar is powerful and can simultaneously measure the atmospheric backscattering coefficient, depolarization ratio, radar ratio, extinction color ratio and depolarization color ratio, thereby classifying aerosols and clouds in the atmosphere. Generally, the time resolution of polarization lidar is 10 minutes and the spatial resolution is 7.5m, which meets the needs of high temporal and spatial resolution detection of aerosol profiles.

[0004] Based on this, previous researchers have also established CCN number concentration inversion schemes, but they have 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 components 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] Determine the aerosol type according to the particle parameters; the aerosol type includes a single aerosol and a mixed aerosol; the mixed aerosol type includes 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;

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

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

[0014] For each aerosol, the CCN number concentration under 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, including 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 the aerosol type, so as to accurately invert the influence of each type of aerosol in the mixed aerosol on the CCN number concentration; in addition, the extinction coefficient and the backscattering coefficient are corrected for relative humidity, and the corrected optical parameters are matched with the optical parameters in the aerosol optical parameter database, fully considering the influence of aerosol hygroscopic growth on the size distribution, and for each aerosol, the CCN number concentration under different supersaturation ratios is accurately inverted according to 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 drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

[0021] Figure 1 A flow chart 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 the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In order to make the above-mentioned objects, 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 present application embodiment provides a cloud condensation nucleus number concentration inversion method, which is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the present application embodiment, 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 a radar ratio, a depolarization ratio, and a depolarization color ratio.

[0029] S2: Determine the aerosol type according to the particle matter parameters; the aerosol type includes single aerosol and mixed aerosol; the mixed aerosol types 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 for each aerosol at different supersaturation ratios.

[0034] S7: For each aerosol, invert the CCN number concentration under 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 three aerosol optical parameter thresholds: Figure 2 The thresholds in are empirical values ​​and may vary to a certain extent in different regions. The precise 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 aerosol is usually mixed in; when the DCR value is between 1 and 2, biomass burning aerosol is usually mixed in; when sea salt aerosol is mixed in, the LR value will drop rapidly. According to the difference in the threshold values ​​of the optical parameters of the three aerosols, eight mixed aerosols can be identified: ① dust and urban haze, ② biomass burning and urban haze, ③ biomass burning and dust, ④ biomass burning, urban haze and dust aerosol, ⑤ dust and sea salt aerosol, ⑥ biomass burning and sea salt aerosol, ⑦ dust, biomass burning and sea salt aerosol.

[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 the 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 backscatter coefficient (BAC) and extinction coefficient (EXT) of each aerosol in the mixed aerosol are separated by the depolarization ratio. The BAC calculation formula of 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 of urban haze, biomass burning or sea salt aerosol. The prior value is usually obtained based on the long-term observation statistics of lidar. The extinction coefficient is calculated by multiplying the backscatter coefficient by the radar ratio.

[0045] When the type of 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 calculation formula for 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 the separated urban haze or sea salt aerosol, BAC b is the backscattering coefficient of the separated biomass burning aerosol. DCR 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 obtained based on the long-term observation statistics of lidar.

[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, firstly, the dust aerosol backscattering coefficient is separated by applying formulas (1) and (2), and then the radar ratio is used to separate the urban haze and biomass burning aerosol, or the biomass burning aerosol and sea salt aerosol backscattering coefficient. 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 the separated urban haze or sea salt aerosol, BAC b is the backscattering coefficient of separated biomass burning 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 simulated by Mie scattering theory.

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

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

[0061] According to the diffusion theory and the aerosol parameters of different types of aerosols, the extinction coefficient and the 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 are collected. The long-term data comes from the AERONET observation network, mainly including the coarse and fine mode aerosol particle number concentrations (N), aerosol particle effective radius (r) and geometric standard deviation (σ) of urban haze, dust aerosol, biomass burning aerosol and sea salt aerosol.

[0063] According to Mie scattering theory, the coarse and fine mode number concentrations (N), effective radius (r), and geometric standard deviation (σ) of different types of aerosols collected are used as input to calculate the corresponding BAC or EXT, and establish an aerosol optical parameter database with one-to-one correspondence between N, r, σ and BAC, EXT. The establishment of this aerosol optical parameter database directly affects the inversion accuracy. As many stations as possible and as long as possible observation time should be selected, and the separation of N, r, σ should be dynamically adjusted according to 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] In the formula, 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 short axis length and b is the long axis length; 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] The long-term data of sun photometer observations are collected, mainly for the number concentration (N), effective radius (r), and geometric standard deviation (σ) of the coarse and fine modes of urban haze, dust aerosol, biomass burning aerosol, and sea salt aerosol, to build a lookup table. In order to reduce the size of the lookup table and reduce the amount of calculation, the lookup table is simplified into 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 radius r is limited to 0.01-10 μm in the calculation, and the logarithmic interval of r is 0.002. Aerosol particle number concentration N i Range: 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 log-normal 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 the 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 in 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 The CCN number concentration caused by each type of aerosol after separation was calculated, and then the CCN number concentrations caused by all types of aerosols were added together 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 As 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 using long-term data from sun photometer observations combined with 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 by 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 for short) 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, wherein a computer program is stored in the memory, and the above method is implemented when the processor executes 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 can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ReadOnlyMemory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (Magnetoresistive RandomAccess Memory, MRAM), ferroelectric random access memory (Ferroelectric RandomAccess Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (RandomAccess 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 database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0092] The technical features of the above embodiments may 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 article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will 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 comprises: Obtaining particle parameters observed by a multi-wavelength polarization Raman lidar; the particle parameters include radar ratio, depolarization ratio, and depolarization color ratio; Determine the aerosol type according to the particle parameters; the aerosol type includes a single aerosol and a mixed aerosol; the mixed aerosol type includes 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; Based on the aerosol type, extracting the extinction coefficient and the backscattering coefficient of each 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 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; Calculate the critical radius of each aerosol at different supersaturation ratios; For each aerosol, the CCN number concentration under 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, including: When the aerosol type is a single type aerosol, the extinction coefficient and backscattering coefficient of the single type aerosol are directly extracted; When the aerosol type is a mixed aerosol, extracting the extinction coefficient of each aerosol in the mixed aerosol; When the type of 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, a depolarization ratio is used to separate the backscattering coefficient of each aerosol in the mixed aerosol; When the type of 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 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.

3. The cloud condensation nucleus number concentration inversion method according to claim 1, characterized in that: The extinction coefficient and the backscattering coefficient are corrected for relative humidity to determine corrected optical parameters, specifically including: 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: The corrected optical parameters are matched with the optical parameters in the aerosol optical parameter database to determine the aerosol parameters, and the method also includes: Based on long-term data observed by a sun photometer; the long-term data include aerosol parameters of coarse and fine modes of urban haze, dust aerosol, biomass burning aerosol and sea salt aerosol; According to the diffusion theory and the aerosol parameters of different types of aerosols, the extinction coefficient and the 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 the optical parameters in the aerosol optical parameter database to determine the 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 under 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 a is the geometric standard deviation of the fine mode, N a is the aerosol particle number concentration in the fine mode, is the median radius of aerosol particles in the fine mode; when i = c, σ a is the geometric standard deviation of the coarse mode, N a is the aerosol particle number concentration in the coarse mode; 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 described in 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 described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the cloud condensation nucleus number concentration inversion method described in any one of claims 1 to 6 is implemented.

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