CN113075754A - Method and device for acquiring raindrop spectrum based on coherent Doppler laser radar - Google Patents

Method and device for acquiring raindrop spectrum based on coherent Doppler laser radar Download PDF

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CN113075754A
CN113075754A CN202110355597.1A CN202110355597A CN113075754A CN 113075754 A CN113075754 A CN 113075754A CN 202110355597 A CN202110355597 A CN 202110355597A CN 113075754 A CN113075754 A CN 113075754A
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CN113075754B (en
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赵自豪
潘宁
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Jiangsu Guangzai Technology Co.,Ltd.
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract

The invention discloses a method and a device for acquiring a raindrop spectrum based on a coherent Doppler laser radar. The method adopts an iterative deconvolution method to obtain a real raindrop reflectivity spectrum, and uses an aerosol signal model considering window effect to construct an aerosol signal spectrum S'air(v) The rainfall reflectivity spectrum can be solved by only carrying out deconvolution operation once; meanwhile, the invention creatively applies the theoretical backscattering cross section of the raindrops to the laser wave band to the raindrop spectrum distribution obtained by the coherent Doppler laser radar, thereby improving the accuracy and the robustness of the raindrop spectrum calculation. The invention obtains the distribution of the rainfall and raindrop spectrum according to the power spectrum of the laser radar on the basis of only using one coherent Doppler laser radar. The invention has important significance for atmospheric weather mode, aviation safety and the likeAnd (5) defining.

Description

Method and device for acquiring raindrop spectrum based on coherent Doppler laser radar
Technical Field
The invention relates to the field of wind measuring laser radars and atmospheric detection, in particular to a method and a device for acquiring a raindrop spectrum based on a coherent Doppler laser radar.
Background
The size distribution of raindrops is an essential microscopic physical property of precipitation. Its vertical profile helps reveal precipitation formation and evolution processes such as collisions, coalescence, melting, cracking and evaporation. Furthermore, in meteorological radar applications, information on raindrop size distribution (DSD for short) can be used to calibrate the rainfall (R) estimate, which is derived from radar reflectivity (Z) based on an equation commonly referred to as the Z-R relationship.
The ground raindrop spectrometer can only provide point measurements of DSD. The DSD vertical distribution can be obtained by the relationship between the terminal velocity of the raindrops and their size using a doppler radar of normal incidence. The K-band micro-rain radar is developed under the assumption of zero vertical wind. The use of UHF and VHF radar wind profilers allows simultaneous measurement of bragg scattering due to atmospheric refractive index inhomogeneities and rayleigh scattering echoes due to water condensation. The motion information of the background air can be used to remove the effects of vertical wind velocity and turbulence on the derived DSD profile by a deconvolution process.
After the research of the inventor, the inventor discovers that: UHF, VHF wind profile radar can simultaneously detect turbulence signals from Bragg scattering and rainfall signals from Rayleigh scattering; microwave weather radar is only sensitive to rainfall particles. Doppler lidar is sensitive to aerosol particles while detecting reflected signals from raindrops when raining. By means of the method in the wind profile radar, the raindrop spectral distribution information of rainfall can be inverted by using the Doppler velocity spectrum of the Doppler laser radar.
Disclosure of Invention
The invention aims to provide a method and a device for acquiring a raindrop spectrum based on a coherent Doppler laser radar. On the basis of only using one coherent Doppler laser radar, the distribution of the rainfall and raindrop spectrum is obtained according to the power spectrum of the laser radar. Has important significance for atmospheric weather mode, aviation safety and the like.
The invention is realized by the following steps: a raindrop spectrum obtaining method based on a coherent Doppler laser radar is characterized by comprising the following steps: the method comprises the following steps:
acquiring original echo data of a coherent Doppler laser radar under a rainfall condition;
performing fast Fourier transform on the original echo data to obtain a power spectrum S (v); wherein the content of the first and second substances,
S(v)＝S′air(v)+S′air(v)*Srain(v)；
of formula (II) S'air(v) Is the aerosol signal spectrum, v is the Doppler velocity, Srain(v) Representing a convolution operation for a rainfall reflectivity spectrum;
calculating Aerosol Signal Spectrum S'air(v) Wherein, in the step (A),
S′air(v)＝Sair(v)*W(v)；
Figure BDA0003002839760000021
in the formula, Sair(v) Is the spectrum of atmospheric turbulence, W (v) is the frequency spectrum of a window function, IairIs the intensity, v0Is the mean wind speed, σairIs the spectral width, indicates the convolution operation;
according to Srain,i＝0＝S-S′airIterative initial value S for calculating rainfall reflectivity spectrumrain,i＝0(ii) a Wherein S is the abbreviation of S (v);
iteration step: according to the formula Si＝S′air(v)+S′air(v)*Srain,iCalculating a simulated signal spectrum SiI is a subscript, corresponding to the number of iterations; from an iteration initial value Srain,i＝0At the beginning according to formula
Figure BDA0003002839760000022
Figure BDA0003002839760000023
Calculating a new rainfall reflectance spectrum Srain,i+1；
Judging whether the result meets the preset iteration termination condition, if so, stopping the iteration to obtain a rainfall reflectivity spectrum Srain(v)＝Srain,i(ii) a If not, adding 1 to i, and using a new iteration variable SiAnd Srain,iContinuing to execute the iteration step;
according to the obtained rainfall reflectivity spectrum Srain(v) Formula (II)
Figure BDA0003002839760000024
And formula
Figure BDA0003002839760000025
Calculating the raindrop spectrum distribution N (D); wherein C is a preset calibration constant, N (D) is the distribution of raindrop spectrum, and σbk(D) Is a back scattering cross section of the raindrop to the detection wavelength; v is terminal rain speed, D is equivalent spherical raindrop diameter, ρ is air density, ρ is0Is the air density at zero altitude.
Further, an iterative initial value S of the rainfall reflectivity spectrumrain,i＝0The effective data range of (a) is 0-10 m/s, which is lower than a preset noise level n0Is also set to 0.
Further according to formula
Figure BDA0003002839760000026
Calculating a new rainfall reflectance spectrum Srain,i+1In, still include:
smoothing using a gaussian-weighted moving average method
Figure BDA0003002839760000027
Further, the preset iteration termination condition is Si+1-Si＜n0Or the iteration times reach the preset maximum iteration steps; wherein n is0Is a preset noise level.
Further, the preset maximum iteration step number is positively correlated with the length of the window of the gaussian-weighted moving smoothing.
Further, σbk(D) Is a backscattering section of raindrops to a detection wavelength, and accurately calculates sigma by using a meter scattering theory for spherical raindrops with the diameter less than 1mmbk(D) (ii) a Calculation of sigma using geometrical optics theory for flat raindrops with a diameter greater than 1mmbk(D) Geometrical optics theory includes the vector complex ray model VCRM.
Further, performing fast fourier transform on the raw echo data to obtain a power spectrum s (v), including:
and cutting off the original beat frequency signals of the coherent Doppler laser radar in the original echo data, and dividing the original beat frequency signals into a plurality of range gates. And performing Fourier transform on the signal of each range gate to obtain a power spectrum.
Further, calculating an aerosol signal spectrum S'air(v) The method comprises the following steps:
determining a peak near zero in the power spectrum as an aerosol signal peak; convolution model S 'using Gaussian function and window function spectrum'air(v)＝Sair(v)*W(v)，
Figure BDA0003002839760000031
Determination of the Aerosol Signal Spectrum S 'by least squares fitting'air。
The invention provides a raindrop spectrum inversion device based on a coherent Doppler laser radar, which comprises:
the system comprises an original signal module, a data acquisition module and a data processing module, wherein the original signal module is used for acquiring original echo data of a coherent Doppler laser radar under a rainfall condition;
the power spectrum calculation module is used for carrying out fast Fourier transform on the original echo data to obtain a power spectrum S (v); wherein the content of the first and second substances,
S(v)＝S′air(v)+S′air(v)*Srain(v)；
of formula (II) S'air(v) Is the aerosol signal spectrum, v is the Doppler velocity, Srain(v) Representing a convolution operation for a rainfall reflectivity spectrum;
an aerosol spectrum calculation module for calculating an aerosol signal spectrum S'air(v) Wherein, in the step (A),
S′air(v)＝Sair(v)*W(v)；
Figure BDA0003002839760000032
in the formula, Sair(v) Is the spectrum of atmospheric turbulence, W (v) is the frequency spectrum of a window function, IairIs the intensity, v0Is the mean wind speed, σairIs the spectral width, indicates the convolution operation;
an iterative initial value calculation module for calculating the initial value according to Srain,i＝0＝S-S′airIterative initial value S for calculating rainfall reflectivity spectrumrain,i＝0(ii) a Wherein S is the abbreviation of S (v);
an iteration module for calculating the equation Si＝S′air(v)+S′air(v)*Srain,iCalculating the total signal spectrum SiI is a subscript, corresponding to the number of iterations; from an iteration initial value Srain,i＝0At the beginning according to formula
Figure BDA0003002839760000033
Figure BDA0003002839760000034
Calculating a new rainfall reflectance spectrum Srain,i+1；
A judging module for judging whether the result meets the preset iteration termination condition, if so, stopping the iteration to obtain the rainfall reflectivity spectrum Srain(v)＝Srain,i(ii) a If not, adding 1 to i, and using a new iteration variable SiAnd Srain,iContinuing to execute the iteration step;
a raindrop spectrum calculation module for calculating the rainfall reflectance spectrum Srain(v) And formula
Figure BDA0003002839760000041
Figure BDA0003002839760000042
Calculating the raindrop spectrum distribution N (D); wherein C is a preset calibration constant, N (D) is the distribution of raindrop spectrum, and σbk(D) Is a back scattering cross section of the raindrop to the detection wavelength; v and D are terminal rain speed and equivalent spherical raindrop diameter, ρ and ρ, respectively0Representing the air density at high altitude and zero altitude, respectively.
Further, the iteration module further includes:
a smoothing unit for smoothing using a Gaussian weighted moving average method
Figure BDA0003002839760000047
In summary, the present invention provides a method for obtaining a raindrop spectrum based on a coherent doppler laser radar, first, obtaining original echo data of the coherent doppler laser radar under a rainfall condition; secondly, performing fast Fourier transform on the original echo data to obtain a power spectrum S (v); calculating Aerosol Signal Spectrum S'air(v) According to Srain,i＝0＝S-S′airIterative initial value S for calculating rainfall reflectivity spectrumrain,i＝0(ii) a According to the formula Si＝S′air(v)+S′air(v)*Srain,iCalculating a simulated signal spectrum SiI is a subscript, corresponding to the number of iterations; from an iteration initial value Srain,i＝0At the beginning according to formula
Figure BDA0003002839760000043
Calculating a new rainfall reflectance spectrum Srain,i+1(ii) a Judging whether the result meets the preset iteration termination condition, if so, stopping the iteration to obtain a rainfall reflectivity spectrum Srain(v)＝Srain,i(ii) a If not, adding 1 to i, and using a new iteration variable SiAnd Srain,iContinuing to execute the iteration step; finally, according to the obtained rainfall reflectivity spectrum Srain(v) Formula (II)
Figure BDA0003002839760000044
And formula
Figure BDA0003002839760000045
Figure BDA0003002839760000046
And calculating the raindrop spectrum distribution N (D).
The acquisition of raindrop size distribution (DSD) using Coherent Doppler Lidar (CDL) requires an accurate backscatter cross section of the raindrops at the operating wavelength and a true reflectance spectrum of the raindrops. Mie scattering theory provides a strict solution to the light scattering of a homogeneous isotropic spherical raindrop. The present invention uses a Vector Complex Ray Model (VCRM) for large oblate raindrops. Obtaining true raindrop reflection by iterative deconvolution methodThe ratio spectrum. By using an aerosol signal model taking into account the window effect, by constructing an aerosol signal spectrum S'air(v) The method can solve the rainfall reflectivity spectrum by only one deconvolution operation, and meanwhile, the method creatively applies the backscattering section calculated theoretically to the coherent Doppler laser radar to obtain the distribution of the raindrop spectrum, so that the accuracy and the robustness of the calculation of the raindrop spectrum are improved. The invention obtains the distribution of the rainfall and raindrop spectrum according to the power spectrum of the laser radar on the basis of only using one coherent Doppler laser radar. Has important significance for atmospheric weather mode, aviation safety and the like.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a rainfall identification method based on a wind lidar according to an embodiment of the present invention;
FIG. 2 shows the backscattering efficiency Qbk＝4σbk/πD2The result of (1);
FIG. 3 is an example of two iterative deconvolutions;
FIG. 4 is a set of measurements provided by an embodiment of the present invention;
FIG. 5 is another set of measurements provided by an embodiment of the present invention;
FIG. 6 is a calculation of a reflectance spectrum provided by an embodiment of the present invention;
FIG. 7 is a calculation of the rainfall speed and the average diameter provided by an embodiment of the present invention;
fig. 8 is a block diagram of a rainfall recognition device based on a wind lidar according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
as shown in fig. 1, the invention discloses a method for obtaining a raindrop spectrum based on a coherent doppler laser radar, comprising:
and S1, acquiring the original echo data of the coherent Doppler laser radar under the rainfall condition.
In particular, the all-fiber coherent Doppler laser radar is applied to precipitation detection in the vertical staring mode. The compact all-fiber CDL operates at a wavelength that is 1.5 μm that is safe for the human eye. It has been used for studies of atmospheric boundary layer height, gravitational waves and turbulence. During precipitation experiments, the CDL was set to run in vertical gaze mode with a time resolution of 1 second. In this application, CDL stands for coherent doppler lidar.
And S2, performing fast Fourier transform on the original echo data to obtain a power spectrum S (v). Wherein the content of the first and second substances,
S(v)＝S′air(v)+S′air(v)*Srain(v)；
of formula (II) S'air(v) Is the aerosol signal spectrum, v is the Doppler velocity, Srain(v) The reflectance spectrum of rainfall represents the convolution operation.
Specifically, performing fast fourier transform on the original echo data to obtain a power spectrum s (v), includes: firstly, cutting off an original beat frequency signal in original echo data of the coherent Doppler laser radar, and dividing the original beat frequency signal into a plurality of range gates. And performing Fourier transform on the signal of each range gate to obtain a power spectrum.
It should be noted that, in this step, the power spectrum s (v) obtained by performing fast fourier transform on the original echo data is a power spectrum corresponding to the actually measured signal.
S3 calculating an aerosol signal spectrum S'air(v) In that respect Wherein the content of the first and second substances,
S′ar(v)＝Sair(v)*W(v)；
Figure BDA0003002839760000061
in the formula, Sair(v) Is the spectrum of atmospheric turbulence, W (v) is the frequency spectrum of a window function, IairIs the intensity, v0Is the mean wind speed, σairIs the spectral width, indicates the convolution operation.
Since the vertical wind speed is near zero and the raindrop velocity is large, the peak near zero in the power spectrum is determined as the aerosol signal peak. Determining an aerosol signal spectrum S 'by least squares fitting using a convolution model of a Gaussian function and a window function spectrum'air(v)。
S4, according to Srain,i＝0＝S-S′airIterative initial value S for calculating rainfall reflectivity spectrumrain,i＝0(ii) a Wherein S is the abbreviation of S (v).
S5, iteration step: according to the formula Si＝S′air(v)+S′air(v)*Srain,iCalculating a simulated signal spectrum SiI is a subscript, corresponding to the number of iterations; from an iteration initial value Srain,i＝0At the beginning according to formula
Figure BDA0003002839760000062
Figure BDA0003002839760000063
Calculating a new rainfall reflectance spectrum Srain,i+1。
It should be noted that the power spectrum S (v), i.e., S, obtained in step S2 is a power spectrum corresponding to the measured signal; and S in step S5iIs the result of the iterative calculation.
S6, judging whether the result meets the preset iteration termination condition, if so, stopping the iteration to obtain the rainfall reflectivity spectrum Srain(v)＝Srain,i(ii) a If not, adding 1 to i, and using a new iteration variable SiAnd Srain,iAnd continuing to execute the iteration steps.
S7, obtaining rainfall reflectivity spectrum Srain(v) Formula (II)
Figure BDA0003002839760000064
And formula
Figure BDA0003002839760000065
Calculating the raindrop spectrum distribution N (D); wherein C is a preset calibration constant, N (D) is the distribution of raindrop spectrum, and σbk(D) The backscattering cross section of the raindrops to the detection wavelength is obtained by solving a coherent laser radar equation; v is terminal rain speed, D is equivalent spherical raindrop diameter, rho is detected height air density, rho0Is the air density at zero altitude.
Specifically, the rainfall reflectance spectrum S is obtained in the previous steprain(v) Then, the S is addedrain(v) Substituted into a formula
Figure BDA0003002839760000071
In (1), wherein,
Figure BDA0003002839760000072
derived from the relationship between terminal drop speed and raindrop size, given by the following empirical equation:
Figure BDA0003002839760000073
further, in step S4, an iteration initial value S of the rainfall reflectivity spectrumrain,i＝0The effective data range of (a) is 0-10 m/s, which is lower than a preset noise level n0Is also set to 0.
Further according to formula
Figure BDA0003002839760000074
Calculating a new rainfall reflectance spectrum Srain,i+1In, still include:
smoothing using a gaussian-weighted moving average method
Figure BDA0003002839760000075
Further, the preset iteration termination condition is | Si+1-Si|＜n0Or the iteration times reach the preset maximum iteration steps; wherein n is0Is a preset noise level.
Further, the preset maximum iteration step number is positively correlated with the length of the window of the gaussian-weighted moving smoothing.
Further, σbk(D) Is a backscattering section of raindrops to a detection wavelength, and accurately calculates sigma by using a meter scattering theory for spherical raindrops with the diameter less than 1mmbk(D) (ii) a Calculation of sigma using geometrical optics theory for flat raindrops with a diameter greater than 1mmbk(D) Geometrical optics theory includes the vector complex ray model VCRM.
Further, calculating an aerosol signal spectrum S'air(v) The method comprises the following steps:
determining a peak near zero in the power spectrum as an aerosol signal peak; convolution model S 'using Gaussian function and window function spectrum'air(v)＝Sair(v)*W(v)，
Figure BDA0003002839760000076
Determination of the Aerosol Signal Spectrum S 'by least squares fitting'air。
In order to more clearly illustrate the aspects of the present invention, the following detailed description is given of the principles and application scenarios of the present invention.
In the wavelength range of lidar applications, echoes from aerosols (mie scattering) and raindrops (geometric scattering) can be detected during precipitation events, which offers the potential for deriving DSD by lidar. In the present application, DSD stands for raindrop spectrum. Two-color lidar measurements have been used to estimate the DSD of hair rain. A combination of doppler lidar and cloud radar is also used to estimate DSD from the different rain velocities measured by the two instruments. Aoki et al introduced the method used in radar wind profilers to coherent doppler lidar and derived the DSD using an empirical backscattering efficiency function.
An accurate backscatter cross section is critical to obtaining DSD. The main difference between microwave radar and lidar in the detection of precipitation is their operating wavelength and the corresponding scattering properties. For long wave radar, rayleigh scattering is applied, which means a 6 th power relation between the backscatter cross-section and the droplet size. In lidar, geometric optical approximation (external reflection), i.e. the dependence of the backscatter cross section on the square of the raindrop size, is often used. The application of theoretical backscattering cross sections in the inversion of DSD by coherent Doppler laser radar and the comparison of inversion results of the laser radar and the microwave radar are not reported.
The rainfall information directly detected by the doppler lidar or radar is a doppler shifted reflectivity spectrum, which is a superposition of raindrop signals from different falling velocities, which depend on the raindrop size. The rainfall reflectance spectrum in the velocity domain can be represented as
Figure BDA0003002839760000081
Where v is the terminal rain speed (m s)-1) And D is the raindrop equivalent spherical diameter (mm). Wherein N (D) is the distribution of raindrop spectrum to be solved by the invention, Srain(v) From CDL probing, σbk(D) The calculation is performed by a theoretical model, which will be described below.
Figure BDA0003002839760000082
Derived from the relationship between terminal drop speed and raindrop size. The relationship is given by an empirical equation
Figure BDA0003002839760000083
Where v and D are in units of m/s and mm, respectively. Rho and rho0Representing the air density at detection altitude and zero altitude, respectively.
Parameter C in equation (1) is a calibration factor that depends on instrument parameters and path attenuation and can be obtained by solving the lidar equation accurately. However, this method is difficult due to the strong attenuation of the laser by the raindrops. In this work, we calibrated using the radar reflectivity Z output by the micro rain radar MRR as reference information. In the present application, MRR stands for a light rain radar. Nevertheless, the shape of the DSD and its derived parameters (e.g. raindrop average velocity and average diameter) are obtained independently by the CDL.
The scattering properties of raindrops depend mainly on the wavelength dependent refractive index and geometry. The exact backscatter cross-section is critical to obtaining the DSD, as shown in equation (1). Mie scattering theory provides a strict solution to electromagnetic wave scattering in a uniform sphere. But the sphere is assumed to be effective only for raindrops with a diameter less than 1mm, otherwise the influence of the non-spherical shape of the raindrops should be considered. On the one hand, we used the MiePlot tool developed by Philip to calculate the backscatter cross-section of a spherical droplet. The MiePlot is optimized for the rice scatter calculation of large scale parameter raindrops and gives a stable result. On the other hand, the VCRM model developed by Ren Kuan Fang was used in view of the larger non-spherical raindrops. Compared to numerical calculation methods such as T-matrices and Finite Difference Time Domains (FDTD), VCRM is not limited to relatively small scale parameters. By introducing the properties of the wavefront into the geometric optical model, the VCRM can accurately calculate the interaction of light with smooth surface objects.
The complex index of refraction of liquid water is required for the calculation. At a wavelength of 1.5 μm, the value is 1.32+0.000135j, the imaginary part representing the absorption. Considering the shape model of the non-spherical raindrop, we approximate the large raindrop to an oblate spheroid, whose axial ratio is expressed by a fourth-order polynomial
Figure BDA0003002839760000091
Where a and b are the radii of the horizontal major axis and the vertical minor axis along the direction of gravity, respectively. When the diameter is equal to 5.0 mm, the axial ratio can reach 0.7. The values of a and b are determined by equation (3) and the volume equation
Figure BDA0003002839760000092
Note that, when calculating the backscatter cross section, an equilibrium state is assumed to be reached between the raindrop surface tension and the hydrostatic pressure. The complex oscillation phenomenon of raindrops and the influence of multiple scattering are not considered here.
Backscattering efficiency QbkThe results are shown in FIG. 2, and are shown in
Figure BDA0003002839760000093
Due to the large size parameter, the backscattering efficiency at a single diameter will exhibit a highly variable behavior. The actual scattering process is however averaged out by a large number of differently sized raindrops. Therefore, the backscattering efficiency at each diameter is averaged 100 times to eliminate high frequency oscillations. The average results are shown in fig. 2. By considering the raindrops as spherical, gray circles in the diameter range of 0.01-6mm were calculated using Mie's (Mie) theory. The backscattering efficiency of spherical raindrops drops rapidly from 0.1mm due to the strong absorption of light within the drop and reaches the geometrical optical limit at a diameter of about 2mm (P ═ 0, external reflection). For the actual oblate spheroid, the results of the VCRM calculation are shown as red squares in the range of 1-6mm in the figure. The backscattering efficiency of the oblate spheroid increases with diameter starting from about 2 mm. This is due to the increase in cross-sectional area on the horizontal axis of the raindrop (perpendicular to the laser beam). We combined the results of Mie (0.1-1mm) and VCRM (1-6mm) and fit the results by a smooth spline curve. This fitted curve was used to calculate the DSD in this study.
Under precipitation conditions, the shape of the power spectrum is the sum of the backscattering spectra from aerosols and raindrops. However, due to the effects of turbulence, wind shear and window effects, the observed rainfall spectrum is broader than the true reflectance spectrum. Thus, the spectrum observed under precipitation conditions can be expressed as:
S(v)＝[Sair(v)+Sair(v)*Srain(v)]*W(v), (6)
where v is the Doppler velocity, Sair(v) For atmospheric turbulence spectrum, Srain(v) For the rainfall reflectance spectrum, w (v) is the window function spectrum, and the asterisks indicate the convolution operation. The turbulence spectrum is generally approximated by a Gaussian distribution
Figure BDA0003002839760000101
Wherein Iair,v0,σairIntensity, mean wind speed and spectral width, respectively. We rewrite equation (4) to
.S(v)＝Sair(v)*W(v)+[Sair(v)*W(v)]*Srain(v)＝S′air(v)+S′air(v)*Srain(v), (8)
Wherein, S'air(v)＝Sair(v) W (v). In some studies, the window effect was not considered. Such an approximation may result in relatively large errors when using short window functions that cannot ignore side lobes. In other work, the window spectrum was first removed by direct deconvolution before the raindrop scattering spectrum was acquired. A second deconvolution is required. In the invention, an aerosol signal model S 'is established by convolving a Gaussian-shaped turbulence spectrum and a window function spectrum'air(v) So as to improve the accuracy and the robustness of the inversion of the real reflectivity spectrum.
We use an iterative deconvolution method to obtain the DSD. In the iterative deconvolution method, no particular DSD shape needs to be assumed. The method specifically comprises the following steps:
1) determining an aerosol signal spectral model S 'by a fitting method'air(v)；
2) By Srain,i＝0＝S-S′airAn initial estimate of the rainfall reflectance spectrum is calculated. Will Srain,i＝0Is limited to
Figure BDA0003002839760000102
To avoid unrealistic raindrops. And, below the noise levelIs set to zero to avoid noise amplification during the iteration process.
3) Calculating the spectrum S using equation (6)i. Calculating a new reflectance spectrum Srain,i+1＝Srain,i×(S/Si). Pre-smoothing S/S using 10 point Gaussian moving averageiTo reduce the noise amplified during the iteration.
4) The iteration continues from step 3) until the change in the reflectance spectrum is negligible or a preset maximum number of iterations is reached. In one embodiment, the maximum number of iterations is set to 10.
Fig. 3 gives an example of two iterative deconvolutions. The data handlers preset in the CDL use a "rectangular" window. The side lobes of the aerosol peak extend into the velocity range of the rainfall signal. By using an aerosol signal model incorporating the window effect, a more accurate reflectance spectrum can be obtained.
According to the method of the present invention, a series of experiments were performed. Two remote sensing instruments were used: CDL and MRR. In addition, a ground optical raindrop spectrometer (Parsivel-2) was also deployed for comparative measurements. All instruments were placed within 30m diameter.
The compact all-fiber CDL operates at a 1.5 μm wavelength that is eye-safe. It has been used for the study of atmospheric boundary layer height, gravitational waves, turbulence, and the like. During precipitation experiments, the CDL was set to run in vertical gaze mode with a time resolution of 1 second.
The micro rain radar is a vertically-directed Frequency Modulated Continuous Wave (FMCW) radar (MRR-2, METEK, Germany). According to the high resolution, the microwave tunable filter can be modulated under the microwave of the frequency of 24.23GHz, and the modulation range is 0.5-15 MHz. In this study, the temporal and spatial resolution of the MRR was set to 1 minute and 100m, respectively.
Fig. 4 shows the results of CDL and MRR measurements on 6/13/2020. Parsivel-2 and a co-located visibility sensor (Vaisala PWD50) record the rate of rainfall and visibility on the ground, respectively. Apart from a brief pause of a few minutes, the rainfall continued from 00:00 to 18:00 with a cumulative rainfall of 64 mm. The wideband noise ratio (CNR) is defined as the ratio of the total signal power to the noise power over the entire spectral bandwidth. As shown in fig. 4(a), CNRs with values greater than 0dB mainly correspond to clouds. The CDL is set to automatically clean the telescope lens every 5 minutes to mitigate the effect of wavefront distortion on reception efficiency. Thus, CNRs show periodic streaking during precipitation. The spectral width and the skewness can be well recognized for precipitation. Under rainfall conditions, the spectral width will be broadened due to the additional signal peaks generated by raindrop reflections. Similarly, if there is a rain signal, the skewness will deviate from zero. Skewness is more sensitive in identifying precipitation than spectral width, but also requires a relatively high CNR. As shown in fig. 4(c), positive values of skewness indicate that the aerosol peak is greater than the rain peak, and vice versa. Fig. 4(d) shows the attenuated radar reflectance of MRR recordings, where high values correspond to heavy rainfall.
The DSDs obtained from the CDL with a resolution of 1-s are then averaged in the time and spatial domains to match the data resolution of the MRR. To verify performance, the DSD is compared to the MRR, as shown in fig. 5. Data were divided into three groups according to rainfall rate, namely light rain (<1mm/h), medium rain (1-10mm/h) and heavy rain (>10 mm/h). The results are plotted at three heights starting from a height of 200 m. As shown in fig. 5(a1) and 5(b1), in light rain, the DSD difference between CDL and MRR becomes large starting from a diameter of 2 mm. This is probably due to the lower concentration of large droplets in light rain, since the detection volume of CDL is much smaller compared to MRR. At moderate rainfall, the average DSD of the CDL is highly consistent with the MRR over a diameter range of 1-5 mm. In heavy rain, the difference is large because of few samples. In each case, the number concentration of small droplets with a diameter of less than 1mm shows a significant difference. MRR gives the maximum concentration and parsivel-2 the minimum concentration. There are related studies that indicate that MRR may overestimate the number of small droplets due to spectral mixing and vertical winds. While parsevel-2 is also indicated to suffer from underestimating the small droplets. CDL gives a more reasonable value.
In fact, the backscatter cross-section function related to the raindrop diameter can cause large errors at small droplets. For a particular instrument noise level s, the inverted DSD error can be expressed as s/σbk(D) In that respect For Rayleigh approximation in radar, σbk∝D6This is true. As D approaches zero, the error increases rapidly at a negative 6 th power. Whereas in lidar the error follows s/D2A relationship of geometric approximation. Thus, the error for small droplets acquired by CDL is much smaller than the error for small droplets acquired from MRR, relative to large droplets.
For further comparison and statistics of the results of CDL and MRR, the reflectance-weighted rain velocity v and the mass-weighted mean diameter D in the sense of light scattering were calculatedm
Figure BDA0003002839760000121
Figure BDA0003002839760000122
The reflectance spectrum in the sense of light scattering was first calculated and compared between CDL and MRR as shown in fig. 6. Although the overestimated small raindrop concentration has little effect on the integrated rainfall parameters, such as Rainfall Rate (RR), Liquid Water Content (LWC) and radar reflectivity (Z), it may greatly affect the calculated reflectivity spectrum in the sense of light scattering. As shown in fig. 6(b) and 6(c), the reflectance spectrum of MRR shows a high value in a small velocity range due to overestimation of the number of small droplets. Therefore, it is necessary to start from a lower speed (or diameter) bound to get a reasonable value when integrating equations (9) and (10). The position of the local minimum of the reflectivity spectrum is used to determine the lower limit below which the MRR data is deemed unreliable. In the MRR inversion, since the wind speed cannot be detected, the vertical wind is assumed to be zero. Any non-zero value will introduce a bias in the parameters obtained by the inversion. We used the wind speed measured by CDL to correct the MRR data. However, due to the difference in detection space between the CDL and MRR, vertical wind miscorrection may further degrade data quality. Thus, if the wind correction gives a poor result, the original result is used.
Average rainfall speed at three elevations between CDL and MRR
Figure BDA0003002839760000123
And average diameter DmThe scatter plot of (a) is shown in FIG. 7. The color of the scatter plot indicates the corresponding rainfall rate. The linear fitting result of each height shows high consistency, and a coefficient (R) is determined2) At least up to 0.93. The average rain speed and average diameter are positively correlated with the rain rate. Wool rain below 0.1mm/h is mainly small raindrops with an average diameter of less than 1 mm. When the rainfall rate exceeds 1mm/h, the positive correlation becomes less significant, meaning that it is the droplet concentration and not the droplet size that determines the rainfall rate.
Despite the large differences in wavelength and scattering properties between CDL and MRR, the derived DSD parameters showed good agreement with the exception of the droplet. According to the error analysis, CDL gives a more reasonable value.
In the present application, the DSD was obtained based on a power spectrum of 1.5 μm CDL using a theoretical backscattering cross-section and an iterative deconvolution method. Accuracy and robustness are improved after accounting for the window effect of the aerosol signal model. The DSD inverted from CDL was compared to the DSD of MRR at the same location. The results showed good consistency. MRR was found to overestimate the concentration of the droplets, while CDL gave a more reasonable value. The radar reflectivity from the MRR is used as reference information to determine the absolute value of the DSD of the CDL. The shape of the DSD and derived parameters (e.g. average rain speed and average diameter of raindrops) are independently obtained by the CDL.
As shown in fig. 8, the present invention further provides a raindrop spectrum inversion apparatus based on a coherent doppler lidar, including:
the system comprises an original signal module, a data acquisition module and a data processing module, wherein the original signal module is used for acquiring original echo data of a coherent Doppler laser radar under a rainfall condition;
the power spectrum calculation module is used for carrying out fast Fourier transform on the original echo data to obtain a power spectrum S (v); wherein the content of the first and second substances,
S(v)＝S′air(v)+S′air(v)*Srain(v)；
of formula (II) S'air(v) Is an aerosol signal spectrum, v is a polypeptideDoppler velocity, Srain(v) Representing a convolution operation for a rainfall reflectivity spectrum;
an aerosol spectrum calculation module for calculating an aerosol signal spectrum S'air(v) Wherein, in the step (A),
S′air(v)＝Sair(v)*W(v)；
Figure BDA0003002839760000131
in the formula, Sair(v) Is the spectrum of atmospheric turbulence, W (v) is the frequency spectrum of a window function, IairIs the intensity, v0Is the mean wind speed, σairIs the spectral width, indicates the convolution operation;
an iterative initial value calculation module for calculating the initial value according to Srain,i＝0＝S-S′airIterative initial value S for calculating rainfall reflectivity spectrumrain,i＝0(ii) a Wherein S is the abbreviation of S (v);
an iteration module for calculating the equation Si＝S′air(v)+S′air(v)*Srain,iCalculating the total signal spectrum SiI is a subscript, corresponding to the number of iterations; from an iteration initial value Srain,i＝0At the beginning according to formula
Figure BDA0003002839760000132
Figure BDA0003002839760000133
Calculating a new rainfall reflectance spectrum Srain,i+1；
A judging module for judging whether the result meets the preset iteration termination condition, if so, stopping the iteration to obtain the rainfall reflectivity spectrum Srain(v)＝Srain,i(ii) a If not, adding 1 to i, and using a new iteration variable SiAnd Srain,iContinuing to execute the iteration step;
a raindrop spectrum calculation module for calculating the rainfall reflectance spectrum Srain(v) And formula
Figure BDA0003002839760000134
Figure BDA0003002839760000135
Calculating the raindrop spectrum distribution N (D); wherein C is a preset calibration constant, N (D) is the distribution of raindrop spectrum, and σbk(D) Is a back scattering cross section of the raindrop to the detection wavelength; v and D are terminal rain speed and equivalent spherical raindrop diameter, ρ and ρ, respectively0Representing the air density at high altitude and zero altitude, respectively.
Further, the iteration module further includes:
a smoothing unit for smoothing using a Gaussian weighted moving average method
Figure BDA0003002839760000141
The present invention uses a Coherent Doppler Lidar (CDL) to obtain raindrop size distribution (DSD) requires an accurate backscatter cross-section of raindrops at the operating wavelength and a true reflectance spectrum. Mie scattering theory provides a strict solution to light scattering from uniform isotropic spherical raindrops. The present invention uses a Vector Complex Ray Model (VCRM) for large oblate raindrops. And obtaining a real rainfall reflectivity spectrum by adopting an iterative deconvolution method. Constructing an aerosol signal spectrum S 'by using an aerosol signal model considering a window effect'air(v) The rainfall reflectivity spectrum can be solved by only one deconvolution operation, and the accuracy and the robustness of the calculation of the raindrop spectrum are improved. The invention obtains the distribution of the rainfall and raindrop spectrum according to the power spectrum of the laser radar on the basis of only using one coherent Doppler laser radar. Has important significance for atmospheric weather mode, aviation safety and the like.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A raindrop spectrum obtaining method based on a coherent Doppler laser radar is characterized by comprising the following steps: the method comprises the following steps:
acquiring original echo data of a coherent Doppler laser radar under a rainfall condition;
performing fast Fourier transform on the original echo data to obtain a power spectrum S (v); wherein the content of the first and second substances,
S(v)＝S′air(v)+S′air(v)*Srain(v)；
of formula (II) S'air(v) Is the aerosol signal spectrum, v is the Doppler velocity, Srain(v) Representing a convolution operation for a rainfall reflectivity spectrum;
calculating Aerosol Signal Spectrum S'air(v) Wherein, in the step (A),
S′air(v)＝Sair(v)*W(v)；
Figure FDA0003002839750000011
in the formula, Sair(v) Is the spectrum of atmospheric turbulence, W (v) is the frequency spectrum of a window function, IairIs the intensity, v0Is the mean wind speed, σairIs the spectral width, indicates the convolution operation;
according to Srain，i＝0＝S-S’airIterative initial value S for calculating rainfall reflectivity spectrumrain，i＝0(ii) a Wherein S is the abbreviation of S (v);
iteration step: according to the formula Si＝S′air(v)+S′air(v)*Srain，iCalculating a simulated signal spectrum SiI is a subscript, corresponding to the number of iterations; from an iteration initial value Srain，i＝0At the beginning according to formula
Figure FDA0003002839750000012
Figure FDA0003002839750000013
Computing newRainfall reflectance spectrum Srain，i+1；
Judging whether the result meets the preset iteration termination condition, if so, stopping the iteration to obtain a rainfall reflectivity spectrum Srain(v)＝Srain，i(ii) a If not, adding 1 to i, and using a new iteration variable SiAnd Srain，iContinuing to execute the iteration step;
according to the obtained rainfall reflectivity spectrum Srain(v) Formula (II)
Figure FDA0003002839750000014
And formula
Figure FDA0003002839750000015
Calculating the raindrop spectrum distribution N (D); wherein C is a preset calibration constant, N (D) is the distribution of raindrop spectrum, and σbk(D) Is a back scattering cross section of the raindrop to the detection wavelength; v is the terminal rain speed, D is the equivalent spherical raindrop diameter, ρ is the air density of the detected altitude, ρ is the terminal rain speed0Is the air density at sea level.
2. The method of claim 1, wherein: iteration initial value S of rainfall reflectivity spectrumrain，i＝0The effective data range of (a) is 0-10 m/s, which is lower than a preset noise level n0Is set to 0.
3. The method according to claim 1 or 2, characterized in that: according to the formula
Figure FDA0003002839750000021
Figure FDA0003002839750000022
Calculating a new rainfall reflectance spectrum Srain，i+1In, still include:
smoothing using a gaussian-weighted moving average method
Figure FDA0003002839750000023
4. A method according to claim 1 or 3, characterized in that: the preset iteration termination condition is | Si+1-Si|＜n0Or the iteration times reach the preset maximum iteration step number; wherein n is0Is a preset noise level.
5. The method of claim 1, wherein: the preset maximum iteration step number is positively correlated with the length of the window of the Gaussian weighted moving smoothing.
6. The method of claim 1, wherein: sigmabk(D) Is a backscattering section of raindrops to a detection wavelength, and accurately calculates sigma by using a meter scattering theory for spherical raindrops with the diameter less than 1mmbk(D) (ii) a Calculation of sigma using geometrical optics theory for flat raindrops with a diameter greater than 1mmbk(D) Geometrical optics theory includes the vector complex ray model VCRM.
7. The method of claim 1, wherein: performing a fast Fourier transform on the raw echo data to obtain a power spectrum S (v), comprising:
and cutting off the original beat frequency signals of the coherent Doppler laser radar in the original echo data, and dividing the original beat frequency signals into a plurality of range gates. And performing Fourier transform on the signal of each range gate to obtain a power spectrum.
8. The method of claim 1, wherein: calculating Aerosol Signal Spectrum S'air(v) The method comprises the following steps:
determining a peak near zero in the power spectrum as an aerosol signal peak; convolution model S 'using Gaussian function and window function spectrum'air(v)＝Sair(v)*W(v)，
Figure FDA0003002839750000024
Determination of aerosol signal by least squares fittingMusic score S'air。
9. The utility model provides a raindrop spectrum inversion device based on coherent Doppler laser radar which characterized in that: the method comprises the following steps:
the system comprises an original signal module, a data acquisition module and a data processing module, wherein the original signal module is used for acquiring original echo data of a coherent Doppler laser radar under a rainfall condition;
the power spectrum calculation module is used for carrying out fast Fourier transform on the original echo data to obtain a power spectrum S (v); wherein the content of the first and second substances,
S(v)＝S′air(v)+S′air(v)*Srain(v)；
of formula (II) S'air(v) Is the aerosol signal spectrum, v is the Doppler velocity, Srain(v) Representing a convolution operation for a rainfall reflectivity spectrum;
an aerosol spectrum calculation module for calculating an aerosol signal spectrum S'air(v) Wherein, in the step (A),
S′air(v)＝Sair(v)*W(v)；
Figure FDA0003002839750000031
in the formula, Sair(v) Is the spectrum of atmospheric turbulence, W (v) is the frequency spectrum of a window function, IairIs the intensity, v0Is the mean wind speed, σairIs the spectral width, indicates the convolution operation;
an iterative initial value calculation module for calculating the initial value according to Srain，i＝0＝S-S′airIterative initial value S for calculating rainfall reflectivity spectrumrain，i＝0(ii) a Wherein S is the abbreviation of S (v);
an iteration module for calculating the equation Si＝S′air(v)+S′air(v)*Srain，iCalculating the total signal spectrum SiI is a subscript, corresponding to the number of iterations; from an iteration initial value Srain，i＝0At the beginning according to formula
Figure FDA0003002839750000032
Figure FDA0003002839750000033
Calculating a new rainfall reflectance spectrum Srain，i+1；
A judging module for judging whether the result meets the preset iteration termination condition, if so, stopping the iteration to obtain the rainfall reflectivity spectrum Srain(v)＝Srain，i(ii) a If not, adding 1 to i, and using a new iteration variable SiAnd Srain，iContinuing to execute the iteration step;
a raindrop spectrum calculation module for calculating the rainfall reflectance spectrum Srain(v) And formula
Figure FDA0003002839750000034
Figure FDA0003002839750000036
Calculating the raindrop spectrum distribution N (D); wherein C is a preset calibration constant, N (D) is the distribution of raindrop spectrum, and σbk(D) Is a back scattering cross section of the raindrop to the detection wavelength; v and D are terminal rain speed and equivalent spherical raindrop diameter, ρ and ρ, respectively0Representing the air density at detection altitude and zero altitude, respectively.
10. The apparatus of claim 9, wherein the iteration module further comprises:
a smoothing unit for smoothing using a Gaussian weighted moving average method
Figure FDA0003002839750000037
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114384539A (en) * 2021-12-21 2022-04-22 中国科学院光电技术研究所 Absorption spectral line phase shift speed measurement method based on background light synchronous difference
CN114396881A (en) * 2021-12-06 2022-04-26 武汉颐光科技有限公司 Method and device for fast Fourier transform fitting in spectral measurement and analysis
CN114488200A (en) * 2022-04-17 2022-05-13 中国科学技术大学 Power spectrum signal processing method for improving wind measurement precision of laser radar
CN115616520A (en) * 2022-12-20 2023-01-17 成都远望探测技术有限公司 Cirrus cloud ice crystal shape recognition method based on laser and millimeter wave cloud radar
CN117930281A (en) * 2024-03-25 2024-04-26 南京信息工程大学 Visibility inversion method based on coherent Doppler laser radar backscattering

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000009857A (en) * 1998-06-26 2000-01-14 Mitsubishi Electric Corp Weather radar device
US20090160700A1 (en) * 2005-07-13 2009-06-25 Hagit Messer-Yaron Monitoring and Mapping of Atmospheric Phenomena
CN104391343A (en) * 2014-11-19 2015-03-04 邓勇 Correcting method for measuring errors of laser raindrop disdrometer
CN107807907A (en) * 2017-09-08 2018-03-16 中国电力科学研究院 A kind of precipitation classification method and system
CN109254291A (en) * 2018-10-22 2019-01-22 中国气象科学研究院 The Doppler power spectra data fusion method and device of cloud radar
CN110082842A (en) * 2019-05-24 2019-08-02 北京敏视达雷达有限公司 A kind of precipitation estimation method and device
CN110488393A (en) * 2019-09-24 2019-11-22 中国科学院大气物理研究所 X-band dual-polarization radar measurement method and system

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000009857A (en) * 1998-06-26 2000-01-14 Mitsubishi Electric Corp Weather radar device
US20090160700A1 (en) * 2005-07-13 2009-06-25 Hagit Messer-Yaron Monitoring and Mapping of Atmospheric Phenomena
CN104391343A (en) * 2014-11-19 2015-03-04 邓勇 Correcting method for measuring errors of laser raindrop disdrometer
CN107807907A (en) * 2017-09-08 2018-03-16 中国电力科学研究院 A kind of precipitation classification method and system
CN109254291A (en) * 2018-10-22 2019-01-22 中国气象科学研究院 The Doppler power spectra data fusion method and device of cloud radar
CN110082842A (en) * 2019-05-24 2019-08-02 北京敏视达雷达有限公司 A kind of precipitation estimation method and device
CN110488393A (en) * 2019-09-24 2019-11-22 中国科学院大气物理研究所 X-band dual-polarization radar measurement method and system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ASHKAN TAREMI ZADEH 等: "ExperimentalResults on Rain Detection at Ka-Band based on Range-Doppler Signal Processing", 《2020 14TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION》 *
郑晨雨 等: "Ka/Ku双波段云雷达探测云降水滴谱和空气垂直运动速度的能力模拟分析", 《高原气象》 *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114396881A (en) * 2021-12-06 2022-04-26 武汉颐光科技有限公司 Method and device for fast Fourier transform fitting in spectral measurement and analysis
CN114384539A (en) * 2021-12-21 2022-04-22 中国科学院光电技术研究所 Absorption spectral line phase shift speed measurement method based on background light synchronous difference
CN114384539B (en) * 2021-12-21 2023-06-30 中国科学院光电技术研究所 Absorption spectrum line phase shift speed measurement method based on background light synchronous difference
CN114488200A (en) * 2022-04-17 2022-05-13 中国科学技术大学 Power spectrum signal processing method for improving wind measurement precision of laser radar
CN114488200B (en) * 2022-04-17 2022-07-15 中国科学技术大学 Power spectrum signal processing method for improving wind measurement precision of laser radar
CN115616520A (en) * 2022-12-20 2023-01-17 成都远望探测技术有限公司 Cirrus cloud ice crystal shape recognition method based on laser and millimeter wave cloud radar
CN115616520B (en) * 2022-12-20 2023-03-14 成都远望探测技术有限公司 Cloud ice crystal shape recognition method based on laser and millimeter wave cloud radar
CN117930281A (en) * 2024-03-25 2024-04-26 南京信息工程大学 Visibility inversion method based on coherent Doppler laser radar backscattering
CN117930281B (en) * 2024-03-25 2024-05-24 南京信息工程大学 Visibility inversion method based on coherent Doppler laser radar backscattering

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