CN115544725A - Aerosol profile inversion method based on dual-wavelength Mie-Scattering lidar data - Google Patents
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Abstract
The invention provides an aerosol profile inversion method and device based on dual-wavelength Mie-scattering laser radar data, wherein the method comprises the following steps: acquiring an initial value of aerosol composition profile; constructing a forward model of the optical characteristics of the mixed-mode aerosol; inputting the initial values of the aerosol component profiles into a forward model to obtain an aerosol laser radar ratio and a simulated aerosol extinction coefficient profile; based on the aerosol laser radar ratio, obtaining an inverted aerosol extinction coefficient profile by adopting Fernald method inversion; obtaining an aerosol extinction coefficient profile ratio according to the simulated aerosol extinction coefficient profile and the inverted aerosol extinction coefficient profile, and adjusting the total aerosol concentration by using the aerosol extinction coefficient profile ratio; adjusting the aerosol component ratio based on the aerosol optical thickness value; obtaining a new aerosol component profile according to the adjusted total concentration of the aerosol and the proportion of each component; repeating the steps until the difference of the result of the simulation aerosol extinction coefficient profile and the inversion aerosol extinction coefficient profile is smaller than a threshold value, and completing the inversion of the aerosol profile.
Description
Technical Field
The disclosure relates to the technical field of aerosol measurement, in particular to an aerosol profile inversion method and device based on dual-wavelength Mie-Scattering lidar data, electronic equipment and a storage medium.
Background
Atmospheric aerosol refers to a considerable amount of solid or liquid particles suspended in the atmosphere, with dimensions between 0.001 μm and 100 μm, which play an important role in the earth's climate, radiation, environment, etc. For example, the aerosol is a condition for forming cloud precipitation and can provide condensation nuclei or frozen nuclei for condensation or freezing of water vapor in the atmosphere; secondly, the solar radiation and the long-wave radiation emitted by the atmosphere and the ground can be absorbed and scattered, and the radiation balance of the earth is changed; the aerosol is also one of important causes of the dust haze phenomenon, and directly influences the visibility of air and the health of people. In addition, changes in the composition and content of atmospheric aerosols are also important indicators of geological movements, ecological changes, and human activities. Therefore, monitoring the total amount, vertical distribution and composition of the aerosol in the atmosphere can provide important research data for a plurality of fields.
The atmospheric detection laser radar can realize the inversion of information such as aerosol extinction coefficient profile, optical thickness and the like by actively transmitting laser to the earth atmosphere and receiving backscattering echo signals of aerosol particles, atmospheric molecules and cloud particles in the atmosphere, and is the only means for acquiring long-time-sequence high-vertical-resolution aerosol profile information at present.
The method is characterized in that a laser radar meter scattering echo signal is decomposed into two parts of sources of atmospheric molecules and aerosol based on a laser radar equation, and after an aerosol extinction coefficient boundary value and a laser radar ratio are assumed, the equation is solved to obtain an aerosol extinction coefficient profile. However, due to the influence of factors such as complexity of atmospheric physicochemical changes and aerosol component diversity, the existence of the aerosol lidar ratio depending on the empirical value, the setting of single-mode aerosol in the atmosphere and other assumed conditions can introduce a large error to the inversion of the aerosol extinction coefficient profile. Meanwhile, no mature laser radar signal inversion algorithm is available at present, which can extract the component composition information of the aerosol while acquiring the aerosol extinction coefficient profile. This brings a bottleneck for the research and application of atmospheric detection laser radar inversion aerosol in the fields of ecology, environment, climate, etc.
Disclosure of Invention
In order to solve the problems in the prior art, the aerosol profile inversion method, the aerosol profile inversion device, the electronic device and the storage medium based on the dual-wavelength Mie scattering laser radar data are provided in the embodiments of the present disclosure, and the method performs iterative optimization on the laser radar ratios of the aerosol at different wavelengths by establishing a relationship between the aerosol component profile and the two-channel laser radar echo signals, thereby realizing high-precision detection of the atmospheric aerosol extinction coefficient profile and accurate evaluation of the aerosol component composition information.
A first aspect of the present disclosure provides an aerosol profile inversion method based on dual-wavelength mie scattering lidar data, including: s1, acquiring corresponding aerosol component profile initial values according to atmospheric detection laser radar data; s2, constructing a forward model of the optical characteristics of the mixed-mode aerosol; s3, inputting the initial values of the aerosol component profiles into a mixed-mode aerosol optical characteristic forward model for simulation to obtain an aerosol laser radar ratio of a dual-wavelength channel and a simulated aerosol extinction coefficient profile; s4, based on the aerosol laser radar ratio, obtaining an inversion aerosol extinction coefficient profile of the atmospheric detection laser radar data by adopting Fernald method inversion; s5, obtaining an aerosol extinction coefficient profile ratio of the dual-wavelength channel according to the simulated aerosol extinction coefficient profile and the inverted aerosol extinction coefficient profile, adjusting the total aerosol concentration based on the aerosol extinction coefficient profile ratio, calculating an aerosol optical thickness value, and adjusting the aerosol component proportion based on the aerosol optical thickness value; s6, calculating to obtain a new aerosol component profile according to the adjusted total aerosol concentration and the component proportion; s7, judging whether the result difference between the simulated aerosol extinction coefficient profile obtained in the S3 and the inverted aerosol extinction coefficient profile obtained in the S4 is smaller than a threshold value or not; if not, repeating the steps S3-S6 until the difference between the simulated aerosol extinction coefficient profile obtained in the step S3 and the inverted aerosol extinction coefficient profile obtained in the step S4 is smaller than a threshold value, finishing iteration and finishing the inversion of the aerosol profile.
Further, constructing a mixed-mode aerosol optical characteristic forward model in S2, including: s21, obtaining aerosol size spectrum distribution and aerosol complex refractive index according to the aerosol component profile; s22, based on the meter scattering theory, obtaining optical characteristic parameters of the aerosol with the mixed mode according to the aerosol size spectrum distribution and the aerosol complex refractive index; the mixed-mode aerosol optical characteristic parameters are used for realizing a forward model of the mixed-mode aerosol optical characteristic.
Further, the obtaining of the aerosol scale spectrum distribution according to the aerosol composition profile in S21 includes: s211, calculating the volume concentration of each component according to the mass mixing ratio profile of the aerosol components; s212, obtaining the volume spectrum distribution of the aerosol by adopting a lognormal distribution function according to the volume concentration of each component of the aerosol; and S213, calculating to obtain aerosol number concentration spectrum distribution according to the aerosol volume spectrum distribution.
Further, the mixed mode aerosol optical characteristic parameters include: extinction coefficients, backscattering coefficients, aerosol lidar ratios, and optical thicknesses of the different types of aerosols.
Further, according to the atmospheric detection laser radar data, obtain corresponding aerosol composition profile initial value in S1, include: s11, acquiring atmospheric detection laser radar data; s12, obtaining corresponding time information and longitude and latitude information according to the atmospheric detection laser radar data; s13, acquiring corresponding aerosol profile historical data from a fourth generation atmospheric component global re-analysis database in a European middle-term weather forecast center according to the time information and the longitude and latitude information; and S14, analyzing and evaluating according to the aerosol profile data to obtain an aerosol component profile initial value corresponding to the atmospheric sounding laser radar data.
Further, in S4, based on the aerosol laser radar ratio, an inversion aerosol extinction coefficient profile of the atmospheric sounding laser radar data is obtained by adopting a Fernald method for inversion, which includes: s41, selecting an aerosol backscattering coefficient corresponding to the position of the clean atmosphere near the top of the convection layer as an inverted boundary value; and S42, based on the aerosol laser radar ratio and the boundary value, performing inversion by adopting a Fernald method to obtain an inversion aerosol extinction coefficient profile of the atmospheric sounding laser radar data.
Further, the aerosol profile data includes one or more of sand dust, sea salt, sulfate, organics, and black carbon aerosol.
A second aspect of the present disclosure provides an aerosol profile inversion apparatus based on dual-wavelength mie scattering lidar data, comprising: the data acquisition module is used for acquiring corresponding aerosol component profile initial values according to the atmospheric detection laser radar data; the optical characteristic forward model building module is used for building a mixed-mode aerosol optical characteristic forward model; the data simulation module is used for inputting the initial values of the aerosol component profiles into the mixed-mode aerosol optical characteristic forward model for simulation to obtain aerosol laser radar ratios corresponding to the dual-wavelength channels and simulated aerosol extinction coefficient profiles; the data inversion module is used for obtaining an inversion aerosol extinction coefficient profile of the atmospheric detection laser radar data by adopting Fernald method inversion based on the aerosol laser radar ratio; the aerosol profile optimization module is used for obtaining the aerosol extinction coefficient profile ratio of the dual-wavelength channel according to the simulated aerosol extinction coefficient profile and the inverted aerosol extinction coefficient profile, adjusting the total concentration of the aerosol profile based on the aerosol extinction coefficient profile ratio, and calculating the optical aerosol thickness value; adjusting the aerosol component ratio based on the aerosol optical thickness value; the aerosol component profile updating module is used for calculating to obtain a new aerosol component profile according to the adjusted total aerosol concentration and the component proportion; the inversion iteration module is used for judging whether the result difference between the simulated aerosol extinction coefficient profile obtained in the data simulation module and the inversion aerosol extinction coefficient profile obtained in the data inversion module is smaller than a threshold value or not; and if not, performing data inversion iteration until the difference between the simulated aerosol extinction coefficient profile obtained in the data simulation module and the inverted aerosol extinction coefficient profile obtained in the data inversion module is smaller than a threshold value, and finishing the aerosol profile inversion after the iteration is finished.
A third aspect of the present disclosure provides an electronic device including: a memory, a processor and a computer program stored on the memory and executable on the processor, when executing the computer program, implementing the method for aerosol profile inversion based on dual wavelength mie-scattering lidar data provided by the first aspect of the disclosure.
A fourth aspect of the present disclosure provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for aerosol profile inversion based on dual-wavelength mie-scattering lidar data provided by the first aspect of the present disclosure.
Compared with the prior art, the method has the following beneficial effects:
(1) According to the aerosol profile inversion method based on the dual-wavelength Mie scattering laser radar data, the problem that the laser radar ratio in the current algorithm is set depending on empirical statistics is solved, and the laser radar ratio is not determined by the empirical value.
(2) Compared with a single-mode aerosol and a forward relation database between the extinction coefficient and the backscattering coefficient of the single-mode aerosol, the method can obtain the aerosol laser radar ratio and the extinction characteristic parameter profile which are closer to the real state by constructing the optical characteristic forward model of the mixed-mode aerosol.
(3) More constraints are introduced into the solution of the laser radar equation according to the observation information of the laser radar on the double channels, and the aerosol inversion result which is closer to the real condition is obtained through iterative adjustment of the aerosol laser radar ratio, the composition profile and the extinction coefficient profile on different wavelengths. The acquisition of the aerosol composition profile can provide more visual basic information for relevant researches and policy specification of climate, environment and the like.
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For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
figure 1 schematically illustrates a flow diagram of a method for aerosol profile inversion based on dual wavelength mie-scattering lidar data, in accordance with an embodiment of the present disclosure;
FIG. 2 is a flow chart schematically illustrating a process for obtaining an initial value of an aerosol composition profile corresponding to atmospheric sounding lidar data according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow diagram for constructing a mixed mode aerosol optical property forward model according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow chart for generating an inverted aerosol extinction coefficient profile according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of an apparatus for inverting an aerosol profile based on dual wavelength Mie-Scattering lidar data, in accordance with an embodiment of the present disclosure;
fig. 6 schematically shows a block diagram of an electronic device adapted to implement the above described method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that these descriptions are illustrative only and are not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
In those instances where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). Where a convention analogous to "at least one of A, B, or C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, or C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.).
Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon for use by or in connection with an instruction execution system.
The following describes the technical solution of the present disclosure in detail with reference to a specific flow of an aerosol profile inversion method based on dual-wavelength mie scattering lidar data in a specific embodiment of the present disclosure. It should be understood that the flow, calculation structure, and the like of the aerosol profile inversion method based on the dual-wavelength mie-scattering lidar data shown in the drawings are only exemplary to help those skilled in the art understand the technical solution of the present disclosure, and are not intended to limit the scope of the present disclosure.
Fig. 1 schematically illustrates a flow chart of a method for aerosol profile inversion based on dual wavelength mie-scattering lidar data according to an embodiment of the present disclosure. As shown in fig. 1, the method includes: and S1 to S7.
In operation S1, a corresponding aerosol composition profile initial value is obtained according to the atmospheric sounding lidar data.
In embodiments of the present disclosure, an aerosol in a real atmospheric environment is formed by mixing particles of multiple components, each component having its own distribution and being mixed in different compositions. The atmosphere of a local area is taken as a main research object, and aerosol profile data of the local area needs to be collected as prior information of atmospheric aerosol profile inversion.
According to the embodiment of the present disclosure, as shown in fig. 2, in S1, according to the obtained atmospheric sounding lidar data, obtaining an initial value of a corresponding aerosol component profile, specifically including: s11, acquiring atmospheric detection laser radar data; s12, obtaining corresponding time information and longitude and latitude information according to the atmospheric detection laser radar data; s13, acquiring corresponding aerosol profile historical data from a fourth generation Atmospheric Composition global Reanalysis database (ECMWF) in an European middle-term Weather forecast center (ECMWF) according to time information and the longitude and latitude information; and S14, analyzing and evaluating according to the aerosol profile data to obtain an aerosol component profile initial value corresponding to the atmospheric sounding laser radar data.
Specifically, the aerosol profile historical data includes at least one or more of sand dust, sea salt, sulfate, organics, and black carbon aerosol. The type composition and concentration level of the local atmospheric aerosol can be analyzed and evaluated by carrying out month/season averaging on at least ten years of data, and the aerosol multi-year month average profile is used as an aerosol component profile initial value inverted by laser radar atmospheric detection data, wherein the aerosol component profile initial value comprises local atmospheric aerosol profile data and local atmospheric aerosol optical thickness data.
In operation S2, a mixed-mode aerosol optical property forward model is constructed.
In the embodiment of the disclosure, an optical characteristic forward model of the mixed-mode aerosol is constructed based on the Mie scattering theory and the complex refractive index of the aerosol so as to obtain an extinction coefficient profile corresponding to the aerosol component profile.
Specifically, as shown in fig. 3, constructing a forward model of optical characteristics of the mixed-mode aerosol in S2 specifically includes: steps S21 to S22.
In operation S21, an aerosol scale spectrum distribution and an aerosol complex refractive index are obtained according to the aerosol composition profile.
In embodiments of the present disclosure, the complex refractive index of the particles is composition dependent and varies with wavelength. The Hitran database is a commonly used atmospheric molecular spectrum database in the world at present and provides the birefringence indexes of aerosol particles corresponding to 0.2-40 mu m wave bands of aerosols such as sodium chloride, sea salt, water-soluble aerosol, ammonium sulfate, black carbon, volcanic dust, sulfuric acid, atmospheric dust, quartz, hematite, sand-like dust and the like. And selecting proper complex refractive index data corresponding to different types of aerosols prevailing in the atmosphere of the local area to obtain the complex refractive index of the aerosols.
According to the embodiment of the present disclosure, the obtaining of the aerosol scale spectrum distribution according to the aerosol composition profile in S21 specifically includes: s211, calculating the volume concentration of each component according to the mass mixing ratio profile of the aerosol components; s212, obtaining the volume spectrum distribution of the aerosol by adopting a lognormal distribution function according to the volume concentration of each component of the aerosol; and S213, calculating to obtain aerosol number concentration spectrum distribution according to the aerosol volume spectrum distribution.
Specifically, a lognormal distribution function is used to describe the volume spectral distribution of aerosol particles, which satisfies the following relationship:
wherein r represents a particle radius of the aerosol particles; v (r) represents the aerosol volume in relation to the aerosol radius; c v Represents the volume concentration of the aerosol; r is a radical of hydrogen v Represents the median radius of the aerosol particle size distribution, and σ represents the mean square error of the aerosol particle radius.
Can be distributed by aerosol volume spectrum
To calculate the aerosol number concentration spectrum distribution
The relationship between the two can be expressed as:
wherein N (r) represents the aerosol particle number density.
In operation S22, based on the meter scattering theory, the optical characteristic parameters of the mixed-mode aerosol are obtained according to the aerosol size spectrum distribution and the aerosol complex refractive index. The mixed-mode aerosol optical characteristic parameters are used for realizing a forward model of the mixed-mode aerosol optical characteristic.
In the embodiment of the disclosure, based on the meter scattering theory, the optical characteristic parameters of the mixed-mode aerosol are obtained according to the aerosol size spectrum distribution, the aerosol complex refractive index and the geometric cross-sectional relation between the meter scattering cross section and the spherical particles. Wherein the mixed mode aerosol optical characteristic parameters include: extinction coefficients, backscattering coefficients, aerosol lidar ratios, and optical thicknesses of the different types of aerosols.
The rice scattering theory refers to the phenomenon of scattering by uniform spherical particles of arbitrary composition over a certain scale, and describes the scattering effect between light wavelengths and particle scales when they are comparable. Based on the meter scattering theory, the extinction efficiency factor Q can be calculated according to the relation between the meter scattering cross section and the geometric cross section of the spherical particles ext Scattering efficiency factor Q sca Absorption efficiency factor Q abs And a backscattering efficiency factor Q back The following relationships are satisfied:
Q abs ＝Q ext -Q sca
wherein,
x＝ka＝2πr/λ
h n (x)＝j n (x)+iy n (x)
in the above formula, x represents a scale parameter of the scattering particles; λ is the wavelength of the incident light in the medium surrounding the particle; m represents the complex refractive index of the scattering particles with respect to the surrounding medium, m = m r +im i ，m r Is the real part of the complex refractive index and represents the scattering effect of the particles on the light; m is i And the imaginary part of the complex index represents the absorption. a is n And b n Is a function related to a scale parameter x and a complex refractive index m of the particle; j is a function of n (x) And h n (x) Respectively representing an n-th-order Bessel function and a first-class Hankel function, and the derivative recurrence relation of the Bessel function and the first-class Hankel function is as follows:
[xj n (x)]′＝xj n-1 (x)-nj n (x)
[xh n (x)]′＝xh n-1 (x)-nh n (x)
wherein, J n+0.5 、Y n+0.5 The Bessel functions of the first type and the second type are represented, and the initial values of the Bessel functions are as follows:
it should be noted that the infinite number n in all formulas represents the superposition of the internal field of the scattering particle and the scattering field generated by forced oscillation of the incident field and the divergent field. In the practical calculation process, finite terms must be taken, and the maximum value n of series terms max Can be calculated from the following formula:
the extinction efficiency factor can be obtained through step-by-step iteration.
Thus, the mie scattering extinction coefficient beta e Can be expressed as:
wherein, the integration limits r1, r2 are the minimum and maximum characteristic radius of the particle system, respectively, and can be 0.005 μm and 20 μm, respectively. It is known that the aerosol extinction coefficient is a function of the birefringence index m, the particle radius r and the number (concentration) of particles in the dr particle size range.
In the embodiment of the disclosure, on the basis of obtaining the aerosol spectral distribution and the complex refractive index of each component, the extinction coefficient, the backscattering coefficient, the lidar ratio (the ratio of the extinction coefficient to the backscattering coefficient) and the optical thickness of the aerosol of different types can be obtained by using the method, so that the construction of the mixed-mode aerosol optical characteristic forward model is realized.
In operation S3, the initial values of the aerosol component profiles are input to the forward model of the mixed-mode aerosol optical characteristics for simulation, so as to obtain the aerosol laser radar ratio and the simulated aerosol extinction coefficient profile of the dual-wavelength channel.
In the embodiment of the disclosure, the initial values of the aerosol component profiles are input into the forward model of the optical characteristics of the mixed-mode aerosol for simulation, and the aerosol laser radar ratio and the simulated aerosol extinction coefficient profile of the dual-wavelength channel can be obtained. Wherein the lidar ratio S of the mixed mode aerosol total The calculation method is as follows:
S total ＝p 1 S 1 +p 2 S 2 +…+p n S n
wherein n represents the number of aerosol types; p is a radical of 1 、p 2 、...、p n Representing the mass ratio of the aerosol to the total aerosol; s. the 1 、S 2 、...、S n Indicating the lidar ratio for this type of aerosol.
In operation S4, based on the aerosol lidar ratio, an inversion aerosol extinction coefficient profile of the atmospheric sounding lidar data is obtained by adopting a Fernald method for inversion.
In the embodiment of the disclosure, the lidar equation can quantitatively describe the propagation process of the laser pulse signal in the atmosphere, is a mathematical expression of the working principle of the lidar, and describes the energy of atmosphere backscatter echo signals at different heights received by the lidar when a laser beam vertically emitted by the lidar passes through the atmosphere. By applying a meter scattering theory and combining a laser radar working principle, atmospheric backscattering echo power P (R) at a detection distance R received by the laser radar can be expressed as follows by using a meter scattering laser radar equation:
wherein C represents a system constant, E represents the energy of the emitted laser pulse, and is a known parameter; β (R) represents the atmospheric backscattering coefficient at the probe distance R; σ represents the atmospheric extinction coefficient. The Fernald method considers the light scattering of two components, namely atmospheric molecules and aerosol particles, and can be expressed as follows:
wherein, because the equation contains two unknowns: detecting the backscattering coefficient beta of aerosol particles at a distance R a (R) and extinction coefficient σ a (R), backscattering coefficient of atmospheric molecules beta m (R) and extinction coefficient σ m (R) is available according to the American Standard atmospheric model. The Fernald method defines the aerosol lidar ratio as the ratio S of the extinction coefficient and the backscattering coefficient of the aerosol a (R)＝σ a (R)/β a (R) and is assumed to be a constant that does not vary with height. At the same time, the backscattering/extinction coefficient of the aerosol at a certain reference height, i.e. the boundary value, needs to be known in advance.
Therefore, atmospheric aerosol extinction characteristic parameters at corresponding detection distances can be obtained by solving a laser radar equation by utilizing the backscatter signals at different heights or detection distances detected by the laser radar, and a flow chart of acquiring aerosol extinction coefficients by laser radar observation data inversion is shown in fig. 4.
Assuming the extinction coefficient of air molecules to the backscattering ratio
Based on the equation of the Mie scattering laser radar, after a series of deformations such as integration, natural logarithm taking, derivation and the like, a certain reference height R is known in advance f The reference height R is determined based on the backscattering coefficients of the aerosol particles and the air molecules f The backscattering coefficient of aerosol particles at each height above can be expressed as (near-end solution, forward integral):
thus, the reference height R f Extinction coefficient sigma of aerosol particles at above each height a (R) is:
similarly, the reference height R can be obtained f The extinction coefficient σ of the aerosol at each height a (R) (backward integration):
wherein X (R) = P (R) R 2 。
In an embodiment of the disclosure, the lidar ratio is obtained based on a mixed mode aerosol optical characteristic forward model. Obtaining an inversion aerosol extinction coefficient profile of the atmospheric sounding laser radar data by adopting Fernald method inversion, comprising the following steps: s41, selecting an aerosol backscattering coefficient corresponding to the position of the clean atmosphere near the top of the convection layer as an inverted boundary value; and S42, based on the aerosol laser radar ratio and the boundary value, performing inversion by adopting a Fernald method to obtain an inversion aerosol extinction coefficient profile of the atmospheric sounding laser radar data.
Further, the boundary value is determined using a clean layer method, which selects the reference layer as the location of a clean atmosphere near the top of the convective layer that is almost free of aerosol particles
Height r corresponding to minimum value c . According to the molecular backscattering beta at the reference height m (r c ) The boundary value beta can be determined a (r c ) From the aerosol backscattering ratio R (R) c ) To determine:
it is generally assumed that the aerosol scattering ratio at the reference height is a small constant (e.g., 1.01).
In operation S5, an aerosol extinction coefficient profile ratio of the dual-wavelength channel is obtained according to the simulated aerosol extinction coefficient profile and the inverted aerosol extinction coefficient profile; the total aerosol concentration is adjusted based on the aerosol extinction coefficient profile ratio and the optical aerosol thickness value is calculated, based on which the aerosol component ratios are adjusted.
In the embodiment of the disclosure, the optical characteristic forward model and the Fernald inversion method of the mixed-mode aerosol are utilized to respectively obtain the two-channel extinction coefficient profiles, the aerosol extinction coefficient profile ratio K of the two-wavelength channel is obtained, and the total aerosol concentration is adjusted based on the aerosol component profile. Meanwhile, the optical thickness values of the aerosol respectively obtained by comparing the optical characteristic forward model of the mixed-mode aerosol and the Fernald solution are used for adjusting the proportion of the aerosol components.
In operation S6, a new aerosol composition profile is calculated according to the adjusted total aerosol concentration and the adjusted composition ratio.
In operation S7, it is determined whether a difference between the simulated aerosol extinction coefficient profile obtained in S3 and the inverted aerosol extinction coefficient profile obtained in S4 is less than a threshold. If not, repeating the steps S3-S6 until the difference between the simulated aerosol extinction coefficient profile obtained in the step S3 and the inverted aerosol extinction coefficient profile obtained in the step S4 is smaller than a threshold value, finishing iteration and finishing the inversion of the aerosol profile.
In the embodiment of the present disclosure, it is determined whether a difference between results of the simulated aerosol extinction coefficient profile obtained in S3 and the inverted aerosol extinction coefficient profile obtained in S4 is smaller than a threshold. If not, repeating the steps S3-S6, and finishing iteration when the difference between the simulated aerosol extinction coefficient profile obtained in the step S3 and the inverted aerosol extinction coefficient profile obtained in the step S4 is smaller than a threshold value, thereby finishing the inversion of the aerosol profile. Wherein, the threshold value can be 20% of the aerosol extinction coefficient profile or other values.
According to the aerosol profile inversion method based on the dual-wavelength Mie scattering laser radar data, the acquisition of parameters such as a high-precision aerosol extinction coefficient profile, a component concentration profile, an optical thickness and a laser radar ratio can be realized, the purpose of accurately detecting the atmospheric environment is achieved, the method can be better applied to an atmospheric detection laser radar system, and meanwhile, the method is beneficial to determining the regional atmospheric aerosol mode.
Fig. 5 schematically illustrates a block diagram of an aerosol profile inversion apparatus based on dual wavelength mie-scattering lidar data according to an embodiment of the present disclosure.
As shown in fig. 5, the apparatus 500 for inverting aerosol profile based on dual-wavelength mie-scattering lidar data includes: the system comprises a data acquisition module 510, an optical property forward model construction module 520, a data simulation module 530, a data inversion module 540, an aerosol profile optimization module 550, an aerosol composition profile update module 560 and an inversion iteration module 570. The apparatus 500 may be used to implement the method for aerosol profile inversion based on dual wavelength mie-scattering lidar data described with reference to fig. 1.
And the data acquisition module 510 is configured to acquire a corresponding aerosol composition profile initial value according to the atmospheric detection lidar data. The data obtaining module 510 may be configured to perform the step S1 described above with reference to fig. 1, for example, and is not described herein again.
An optical characteristic forward model constructing module 520, configured to construct a mixed-mode aerosol optical characteristic forward model. The forward optical property model building module 520 may be used to perform the step S2 described above with reference to fig. 1, for example, and will not be described herein again.
And the data simulation module 530 is used for inputting the initial values of the aerosol component profiles into the mixed-mode aerosol optical characteristic forward model for simulation to obtain the aerosol laser radar ratio and the simulated aerosol extinction coefficient profile corresponding to the dual-wavelength channel. The data simulation module 530 may be used to perform the step S3 described above with reference to fig. 1, for example, and is not described herein again.
And the data inversion module 540 is used for obtaining the inversion aerosol extinction coefficient profile of the atmospheric detection laser radar data by adopting Fernald method inversion based on the aerosol laser radar ratio. The data inversion module 540 may be used to perform the step S4 described above with reference to fig. 1, for example, and will not be described herein again.
The aerosol profile optimization module 550 is configured to obtain an aerosol extinction coefficient profile ratio of the dual-wavelength channel according to the simulated aerosol extinction coefficient profile and the inverted aerosol extinction coefficient profile, adjust the total concentration of the aerosol profile based on the aerosol extinction coefficient profile ratio, and calculate an optical aerosol thickness value; the aerosol component ratios are adjusted based on the aerosol optical thickness values. The aerosol profile optimization module 550 may be used, for example, to perform the step S5 described above with reference to fig. 1, which is not described herein again.
And the aerosol component profile updating module 560 is used for calculating to obtain a new aerosol component profile according to the adjusted total aerosol concentration and the component proportion. The aerosol composition profile updating module 560 may be used, for example, to perform the step S6 described above with reference to fig. 1, and will not be described herein again.
The inversion iteration module 570 is used for judging whether the result difference between the simulated aerosol extinction coefficient profile obtained in the data simulation module and the inversion aerosol extinction coefficient profile obtained in the data inversion module is smaller than a threshold value or not; and if not, performing data inversion iteration until the difference between the simulated aerosol extinction coefficient profile obtained in the data simulation module and the inverted aerosol extinction coefficient profile obtained in the data inversion module is smaller than a threshold value, and finishing the aerosol profile inversion after the iteration is finished. The inversion iteration module 570 may be used, for example, to perform the step S7 described above with reference to fig. 1, and is not described here again.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least partially as a hardware circuit, e.g., a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), an on-chip device, a device on a substrate, a device on a package, an Application Specific Integrated Circuit (ASIC), or by any other reasonable means of hardware or firmware for integrating or packaging a circuit, or by any one of or a suitable combination of any of software, hardware, and firmware. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be implemented at least partly as a computer program module, which when executed, may perform a corresponding function.
For example, any of the data acquisition module 510, the optical property forward model construction module 520, the data simulation module 530, the data inversion module 540, the aerosol profile optimization module 550, the aerosol composition profile update module 560, and the inversion iteration module 570 may be combined and implemented in one module, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the data acquisition module 510, the optical property forward model construction module 520, the data simulation module 530, the data inversion module 540, the aerosol profile optimization module 550, the aerosol composition profile update module 560, and the inversion iteration module 570 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a device on a chip, a device on a substrate, a device on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in any one of three implementations of software, hardware, and firmware, or in any suitable combination of any of them. Alternatively, at least one of the data acquisition module 510, the optical property forward model building module 520, the data simulation module 530, the data inversion module 540, the aerosol profile optimization module 550, the aerosol composition profile update module 560 and the inversion iteration module 570 may be at least partially implemented as a computer program module that, when executed, may perform a corresponding function.
Fig. 6 schematically shows a block diagram of an electronic device adapted to implement the above described method according to an embodiment of the present disclosure. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, the electronic device 600 described in this embodiment includes: a processor 601 which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. Processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 601 may also include onboard memory for caching purposes. Processor 601 may include a single processing unit or multiple processing units for performing different actions of a method flow according to embodiments of the disclosure.
In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or RAM 603. Note that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the processor 601, performs the above-described functions defined in the apparatus of the embodiments of the present disclosure. According to an embodiment of the present disclosure, the above-described apparatuses, devices, apparatuses, modules, units, and the like may be realized by computer program modules.
The embodiments of the present disclosure also provide a computer-readable storage medium, which may be included in the apparatus/device/apparatus described in the above embodiments; or may exist alone without being assembled into the apparatus/device/arrangement. The computer readable storage medium carries one or more programs which, when executed, implement a method for aerosol profile inversion based on dual wavelength mie-scattering lidar data according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 602 and/or RAM 603 described above and/or one or more memories other than the ROM 602 and RAM 603.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated by the flow chart. When the computer program product is run in a computer device, the program code is configured to cause the computer device to implement the method for inverting an aerosol profile based on dual wavelength mie-scattering lidar data provided by an embodiment of the disclosure.
The computer program performs the above-described functions defined in the apparatus/devices of the embodiments of the present disclosure when executed by the processor 601. The above described apparatuses, devices, modules, units etc. may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of signals over a network medium, downloaded and installed via the communication section 609, and/or installed from a removable medium 611. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the processor 601, performs the above-described functions defined in the apparatus of the embodiments of the present disclosure. The above described apparatuses, devices, apparatuses, modules, units etc. may be realized by computer program modules according to embodiments of the present disclosure.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to external computing devices (e.g., through the internet using an internet service provider).
It should be noted that each functional module in each embodiment of the present disclosure may be integrated into one processing module, or each module may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present disclosure may be embodied in the form of software products, in part or in whole, which substantially contributes to the prior art.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based apparatus that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
While the disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (10)
1. An aerosol profile inversion method based on dual-wavelength Mie-Scattering lidar data is characterized by comprising the following steps:
s1, acquiring corresponding aerosol component profile initial values according to atmospheric detection laser radar data;
s2, constructing a forward model of the optical characteristics of the mixed-mode aerosol;
s3, inputting the initial values of the aerosol component profiles into the mixed-mode aerosol optical characteristic forward model for simulation to obtain an aerosol laser radar ratio and a simulated aerosol extinction coefficient profile of a dual-wavelength channel;
s4, based on the aerosol laser radar ratio, obtaining an inversion aerosol extinction coefficient profile of the atmospheric sounding laser radar data by adopting Fernald method inversion;
s5, obtaining an aerosol extinction coefficient profile ratio of a dual-wavelength channel according to the simulated aerosol extinction coefficient profile and the inverted aerosol extinction coefficient profile, adjusting the total aerosol concentration based on the aerosol extinction coefficient profile ratio, calculating an aerosol optical thickness value, and adjusting aerosol component proportion based on the aerosol optical thickness value;
s6, calculating to obtain a new aerosol component profile according to the adjusted total aerosol concentration and the component proportion;
s7, judging whether the result difference between the simulated aerosol extinction coefficient profile obtained in the S3 and the inversion aerosol extinction coefficient profile obtained in the S4 is smaller than a threshold value or not; if not, repeating the steps S3-S6 until the difference between the result obtained in the S3 and the result obtained in the S4 is smaller than a threshold value, finishing iteration and finishing aerosol profile inversion.
2. The aerosol profile inversion method based on dual-wavelength Mie-Scattering lidar data according to claim 1, wherein the constructing of the mixed-mode aerosol optical property forward model in S2 comprises:
s21, obtaining aerosol size spectrum distribution and aerosol complex refractive index according to the aerosol component profile;
s22, based on the meter scattering theory, obtaining optical characteristic parameters of the mixed-mode aerosol according to the aerosol scale spectrum distribution and the aerosol complex refractive index; the optical characteristic parameters of the mixed-mode aerosol are used for realizing the forward model of the optical characteristic of the mixed-mode aerosol.
3. The method of claim 2, wherein obtaining an aerosol size spectrum distribution from the aerosol composition profile in S21 comprises:
s211, calculating the volume concentration of each component according to the mass mixing ratio profile of the aerosol components;
s212, obtaining the volume spectral distribution of the aerosol by adopting a lognormal distribution function according to the volume concentration of each component of the aerosol;
and S213, calculating to obtain aerosol number concentration spectrum distribution according to the aerosol volume spectrum distribution.
4. The method of claim 2, wherein the mixed-mode aerosol optical characteristic parameters include: extinction coefficients, backscattering coefficients, aerosol lidar ratios, and optical thicknesses of the different types of aerosols.
5. The aerosol profile inversion method based on dual-wavelength Mie-Scattering lidar data according to claim 1, wherein the step S1 of obtaining corresponding aerosol composition profile initial values according to atmospheric sounding lidar data comprises:
s11, acquiring atmospheric detection laser radar data;
s12, obtaining corresponding time information and longitude and latitude information according to the atmospheric detection laser radar data;
s13, acquiring corresponding aerosol profile historical data from a fourth-generation atmospheric composition global reanalysis database in a European middle-term weather forecast center according to the time information and the latitude and longitude information;
and S14, analyzing and evaluating according to the aerosol profile data to obtain an aerosol component profile initial value corresponding to the atmospheric detection laser radar data.
6. The aerosol profile inversion method based on dual-wavelength meter-scattering laser radar data according to claim 1, wherein the step S4 of obtaining the inversion aerosol extinction coefficient profile of the atmospheric sounding laser radar data by adopting Fernald inversion based on the aerosol laser radar ratio comprises the steps of:
s41, selecting an aerosol backscattering coefficient corresponding to the position of the clean atmosphere near the top of the convection layer as an inverted boundary value;
and S42, based on the aerosol laser radar ratio and the boundary value, carrying out inversion by adopting the Fernald method to obtain an inversion aerosol extinction coefficient profile of the atmospheric sounding laser radar data.
7. The method of claim 5, wherein the aerosol profile data comprises one or more of sand dust, sea salt, sulfate, organics, and black carbon aerosol.
8. An aerosol profile inversion device based on dual-wavelength Mie-Scattering lidar data, comprising:
the data acquisition module is used for acquiring corresponding aerosol component profile initial values according to the atmospheric detection laser radar data;
the optical characteristic forward model building module is used for building a mixed-mode aerosol optical characteristic forward model;
the data simulation module is used for inputting the initial values of the aerosol component profiles into the mixed-mode aerosol optical characteristic forward model for simulation to obtain aerosol laser radar ratios and simulated aerosol extinction coefficient profiles corresponding to the dual-wavelength channels;
the data inversion module is used for obtaining an inversion aerosol extinction coefficient profile of the atmospheric detection laser radar data by adopting Fernald method inversion based on the aerosol laser radar ratio;
the aerosol profile optimization module is used for obtaining an aerosol extinction coefficient profile ratio of a dual-wavelength channel according to the simulated aerosol extinction coefficient profile and the inversion aerosol extinction coefficient profile, adjusting the total concentration of the aerosol profile based on the aerosol extinction coefficient profile ratio, and calculating an aerosol optical thickness value; adjusting aerosol composition ratios based on the aerosol optical thickness values;
the aerosol component profile updating module is used for calculating to obtain a new aerosol component profile according to the adjusted total aerosol concentration and the component proportion;
the inversion iteration module is used for judging whether the result difference between the simulated aerosol extinction coefficient profile obtained in the data simulation module and the inversion aerosol extinction coefficient profile obtained in the data inversion module is smaller than a threshold value or not; and if not, performing data inversion iteration until the difference between the simulated aerosol extinction coefficient profile obtained in the data simulation module and the inverted aerosol extinction coefficient profile obtained in the data inversion module is smaller than a threshold value, and finishing the aerosol profile inversion after the iteration is finished.
9. An electronic device, comprising:
one or more processors;
a storage device to store one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of aerosol profile inversion based on dual wavelength meter scatter lidar data according to any of claims 1 to 7.
10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform a method of aerosol profile inversion based on dual wavelength mie-scattering lidar data according to any of claims 1 to 7.
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| CN116466368A (en) * | 2023-06-16 | 2023-07-21 | 成都远望科技有限责任公司 | Dust extinction coefficient profile estimation method based on laser radar and satellite data |
| CN118033676A (en) * | 2024-04-11 | 2024-05-14 | 长春理工大学 | Correction method of laser radar extinction coefficient profile based on BP neural network |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116466368A (en) * | 2023-06-16 | 2023-07-21 | 成都远望科技有限责任公司 | Dust extinction coefficient profile estimation method based on laser radar and satellite data |
| CN116466368B (en) * | 2023-06-16 | 2023-08-22 | 成都远望科技有限责任公司 | Dust extinction coefficient profile estimation method based on laser radar and satellite data |
| CN118033676A (en) * | 2024-04-11 | 2024-05-14 | 长春理工大学 | Correction method of laser radar extinction coefficient profile based on BP neural network |
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