Method for inverting aerogel microphysical characteristics based on multi-wavelength laser radar

A technology of lidar and physical characteristics, applied in the field of lidar atmospheric detection and inversion, can solve the problems of reducing the dependence of data lookup table values, time-consuming, low execution efficiency, etc., to achieve real-time automatic processing and analysis, and improve execution efficiency. , the effect of improving the accuracy

Active Publication Date: 2019-08-23
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AI-Extracted Technical Summary

Problems solved by technology

This process is not only time-consuming, but also not automated enough, and may be influenced by the subjective experience of data analysis experts
The inversion method based on minimizing the deviation is not only greatly affected by the look-up table itself, but also sensitive to noise
NASA scientist E. Chemyakin et al. proposed an aerosol microphysical property inversion method ba...
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Method used

From n=1~14, that is to say successively according to optical variable A1→B1→S3, 2→…→S1, 2 calculates and sorts, only retains the minimum ω=55% of distance as possible alternative solution at every turn The search range means that the range of alternative solutions can be narrowed down to K*ω14=50000*0.5514≈12 individuals. For the last one-dimensional optical v...
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The invention discloses a method for inverting aerogel microphysical characteristics based on a multi-wavelength laser radar. The method comprises the following steps: inputting optical characteristicparameters, and performing normalization processing on the optical characteristic parameter to obtain an optical characteristic parameter; establishing a correlative lookup table between the microphysical characteristic parameter of the aerosol and the multi-wavelength optical characteristic parameter; computing the distance between the input optical characteristic parameter and each individual in the look-up table at an optical characteristic domain, and only reserving K individuals with the minimum distance as the range of a feasible solution; according to a Monte Carlo random sampling theory, generating NMC different search execution orders according to the probability sampling, and orderly solving to obtain NMC alternative solutions; and averaging NMC alternative solutions to obtain afinal inversion result of the aerogel microphysical characteristic parameter. Compared with the prior art, an operation of additionally inputting the auxiliary judgment information is avoided, the execution efficiency is high, and an application demand for laser radar mass data automatic processing can be satisfied.

Application Domain

Electromagnetic wave reradiationICT adaptation +1

Technology Topic

Automatic processingMulti wavelength +9


  • Method for inverting aerogel microphysical characteristics based on multi-wavelength laser radar
  • Method for inverting aerogel microphysical characteristics based on multi-wavelength laser radar
  • Method for inverting aerogel microphysical characteristics based on multi-wavelength laser radar


  • Experimental program(1)

Example Embodiment

[0033] The present invention will be described in detail below in conjunction with embodiments and drawings, but the present invention is not limited to this.
[0034] figure 1 It is the overall flow chart of the aerosol microphysical property inversion algorithm proposed by the present invention, including:
[0035] Step S1: It is assumed that the input optical characteristic parameters (backscatter coefficient β and extinction coefficient α) come from three wavelengths (using Nd:YAG laser as laser light source, detection wavelengths are 355nm, 532nm and 1064nm) high spectral resolution lidar/Raman Lidar, that is, n=m=3. Then N=15 normalized optical characteristic parameters can be obtained, which are:
[0036] B 1 = Β(λ 1 )/β 3 ,B 2 = Β(λ 2 )/β 3 ,B 3 = Β(λ 3 )/β 3 ,
[0037] A 1 =α(λ 1 )/α 3 ,A 2 =α(λ 2 )/α 3 ,A 3 =α(λ 3 )/α 3 ,
[0038] S 1,1 =α(λ 1 )/β(λ 1 ),S 1,2 =α(λ 2 )/β(λ 1 ),S 1,3 =α(λ 3 )/β(λ 1 ),
[0039] S 2,1 =α(λ 1 )/β(λ 2 ),S 2,2 =α(λ 2 )/β(λ 2 ),S 2,3 =α(λ 3 )/β(λ 2 ),
[0040] S 3,1 =α(λ 1 )/β(λ 3 ),S 3,2 =α(λ 2 )/β(λ 3 ),S 3,3 =α(λ 3 )/β(λ 3 ),
[0041] Where λ 1 =355nm, λ 2 = 532nm, λ 1064 = 1064nm, Then, the set of normalized optical characteristics can be obtained as Ω={B 1 ,B 2 ,B 3 ,A 1 ,A 2 ,A 3 ,S 1,1 ,S 1,2 ,S 1,3 ,S 2,1 ,S 2,2 ,S 2,3 ,S 3,1 ,S 3,2 ,S 3,3 }.
[0042] Step S2: Establish a look-up table of correlation between aerosol microphysical characteristic parameters and optical characteristic parameters. The look-up table stores the multi-wavelength backscattering coefficient, extinction coefficient and normalized optical characteristic parameters of individual aerosols. Its microphysical properties are mainly determined by the complex refractive index and particle spectral distribution of the particles. In this embodiment, look up The construction of the table considers that the particle size distribution of aerosol particles is a unimodal logarithmic normal distribution, namely
[0044] In the formula, r is the particle size of the aerosol, r med Is the mean value of the particle size distribution mode, and σ is the mode width of the particle size distribution mode. The number concentration of aerosols is fixed at 1cm -3. In this implementation, the range of aerosol complex refractive index and particle size distribution parameters in the lookup table is the real part of the complex refractive index m r The value range of is 1.29~1.71, the value interval is 0.02; the imaginary part of the complex refractive index m i The range of values ​​is 0 and 0.25×10 -3 ~50×10 -3 , The value interval is 10 -3;Particle size parameter r med The value range is 15~305nm, and the value interval is 10nm; the value range of the particle size distribution width parameter σ is 1.475~2.525, and the value interval is 0.05. There are 755,040 aerosol cases in the lookup table, and their corresponding effective particle size, surface area concentration and volume concentration can be calculated according to the following formula:
[0045] Effective particle size: r eff =∫r 3 n(r)dr/∫r 2 n(r)dr=r med exp(-2.5ln 2 σ),
[0046] Surface area concentration: s t =4π∫r 2 n(r)dr=4πr med exp(-2ln 2 σ),
[0047] Volume concentration: v t =4π/3∫r 3 n(r)dr=4π/3r med exp(-4.5ln 2 σ).
[0048] The corresponding backscattering coefficient and extinction coefficient can be calculated according to the T-matrix or Mie scattering theory, and the normalized optical characteristic parameters are calculated by the method described in step S1. In this embodiment, the particles are assumed to be spherical equivalent particles, and Mie scattering is used to calculate their optical properties
[0051] Where K β And K α Are the kernel functions of the backscattering coefficient and extinction coefficient, r is the aerosol particle size, λ is the laser wavelength, m r +im i Is the aerosol complex refractive index, m r And m i Are the real and imaginary parts of the complex refractive index. K β (r,λ,m r +im i )=πr 2 Q bsc (r,λ,m r +im i ), K α (r,λ,m r +im i )=πr 2 Q ext (r,λ,m r +im i ), Q bsc And Q ext They are the backscattering coefficient and extinction efficiency factor calculated by Mie scattering.
[0052] Step S3: Calculate the distance between the input case and each individual in the lookup table in the normalized optical characteristic domain Ω And sort them in ascending order, and select the K=50,000 individuals with the smallest distance in the lookup table as the range of feasible solutions.
[0053] Step S4: figure 2 The specific process of finding alternative solutions in step S4 is given, and the search path constructed according to the optical characteristics is shared Here, a random search path is generated according to the Monte Carlo sampling idea. In this embodiment, a method for generating a random search path is introduced, but the method for generating a random path by MC sampling is not limited to this method. First generate 15 uniformly distributed random numbers [0,1],
[0054] 1 2 3 4 5 6 7 8 0.1270 0.9134 0.6324 0.0975 0.2785 0.5469 0.9575 0.9649 9 10 11 12 13 14 15 0.1576 0.9706 0.9572 0.4854 0.8003 0.1419 0.4218
[0055] Sort these 15 random numbers from smallest to largest:
[0056] 4 1 14 9 5 15 12 6 0.0975 0.1270 0.1419 0.1576 0.2785 0.4218 0.4854 0.5469 3 13 2 11 7 8 10 0.6324 0.8003 0.9134 0.9572 0.9575 0.9649 0.9706
[0057] Then we can get the random sequence of sampling as 4→1→14→…→8→10, that is, the search order of this candidate solution is G 4 →G 1 →G 14 →…→G 8 →G 10 , Which is A 1 →B 1 →S 3,2 →…→S 1,2 →S 2,1.
[0058] From n = 1 to 14, that is, follow the optical variable A in turn 1 →B 1 →S 3,2 →…→S 1,2 Calculation And sort, only keep the distance each time The smallest ω = 55% is used as the search range of possible alternative solutions, which can narrow the range of alternative solutions to K*ω 14 =50000*0.55 14 ≈12 individuals, for the last one-dimensional optical variable (n=15)S 2,1 Calculate the distance parameter And choose The smallest individual is the candidate solution obtained this time. Repeat the process, get N MC = 500 alternative solutions. It is worth noting that the solving process of each candidate solution is independent of each other, so it is easy to design parallel algorithms.
[0059] Step S5: Calculate the corresponding to each candidate solution #p
[0060] Number concentration:
[0061] Surface area concentration:
[0062] Volume concentration:
[0063] The aerosol complex refractive index corresponding to the alternative solution And effective particle size It can be obtained directly from the lookup table. Furthermore, 500 candidate solutions are averaged to obtain the final aerosol complex refractive index, effective particle size, number concentration, surface area concentration, volume concentration and other microphysical characteristic parameters inversion results. image 3 The statistical results of the relative error of the inversion of the effective aerosol particle size for 2880 different input optical characteristic parameters are given. The histogram represents the number of statistical cases, corresponding to the y coordinate on the right, and the curve represents the integral probability of the inversion relative error. Density, corresponding to the y coordinate on the left.
[0064] The above are only examples of preferred implementations of the present invention and are not used to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention within.


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