CN113325440A - Polarization laser radar data inversion method and system based on image recognition and signal characteristic decomposition - Google Patents

Polarization laser radar data inversion method and system based on image recognition and signal characteristic decomposition Download PDF

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CN113325440A
CN113325440A CN202110491517.5A CN202110491517A CN113325440A CN 113325440 A CN113325440 A CN 113325440A CN 202110491517 A CN202110491517 A CN 202110491517A CN 113325440 A CN113325440 A CN 113325440A
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CN113325440B (en
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殷振平
易帆
张天澈
周军
何芸
柳付超
张云飞
熊梓稀
谢文超
王磊
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Wuhan University WHU
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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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/95—Lidar systems specially adapted for specific applications for meteorological use
    • 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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
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    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • 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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    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract

The invention relates to a method and a system for inverting polarized laser radar data based on image recognition and signal feature decomposition. The method comprises the steps of meteorological data downloading, radar data preprocessing, cloud layer identification, reference height selection and data inversion, wherein the meteorological data downloading automatically downloads radiosonde data or model reanalysis data related to observation time for calculating the scattering intensity of atmospheric molecules, the atmospheric molecule scattering intensity is compared with laser radar data processed by a laser radar data preprocessing module, after a section and an aerosol clean area are respectively removed by cloud layer identification and reference height selection, the residual data enter the radar data inversion, and information such as aerosol extinction coefficient and height distribution of particle depolarization ratio is obtained. The invention solves the difficulty of manually identifying cloud layers and aerosol clean areas in the data processing of the atmospheric sounding laser radar, fully automates the traditional data analysis process, and can meet the requirements of various practical applications on the calculation efficiency and the product quality.

Description

Polarization laser radar data inversion method and system based on image recognition and signal characteristic decomposition
Technical Field
The invention belongs to the field of laser atmospheric remote sensing inversion, and particularly relates to a polarization laser radar data inversion method and system based on image recognition and signal characteristic decomposition.
Background
The atmospheric detection laser radar data inversion needs to input a height area with negligible aerosol concentration and a section for filtering cloud influence. For the identification of aerosol clean zones, there are two main types of processes: (1) directly adopting signals at the height of the stratosphere; (2) the Ra yleigh fit determination method (bairs et al, 2016) utilizes the comparison of the lidar signal and the atmospheric molecular scattering signal. Both methods have their own disadvantages, and method 1 is difficult to use for inversion of daytime lidar data. Because the signal during the day is subject to the signal-to-noise ratio, the signal at stratospheric height is dominated by noise. Method 2 can be used for daytime aerosol clean zone screening, but because method 2 requires a clean zone search window assuming a certain width, the method fails when the clean zone is narrow. Moreover, since method 2 is a height-by-height comparison method, it is computationally inefficient to traverse all height gates.
Typical methods for cloud layer identification are: signal gradient methods (Wang and Sassen,2001), wavelet transform methods (Baars et al, 2016), Douglas-Peucker methods (Gong et al, 2011), machine learning methods (Binietoglou et al, 2018), and signal feature enhanced image recognition methods (Zhao et al, 2014). Wherein, the signal gradient method and the wavelet change method both need the calibrated radar signal profile. In practice, the system efficiency of radar varies with the laser energy, the system transmittance, and the quantum efficiency of the photomultiplier tube, and thus cannot be used in an automated data processing scheme. The Douglas-Peucker method can decompose the aerosol layer and cloud layer in the signal profile, but the decomposition is very susceptible to signal noise, so the detection effect on thin cloud and daytime cloud layer is not good (Mao et al, 2013). The machine learning method can overcome the above difficulties, but the machine learning method needs a pre-classified data set for model training, and thus needs extra workload.
In addition, the laser radar data has important effects on understanding physical processes occurring in the atmosphere, real-time environmental monitoring, later-stage evaluation of aerosol content change and the like. Conventional lidar data processing is typically based on manual analysis, which is time consuming. This solution is not desirable when the data volume is large or when multiple sites are observed jointly, so an automatic inversion method is needed to solve these problems. Foreign atmospheric lidar automatic inversion methods are generally based on raman lidar systems and therefore do not address the inversion of elastic lidar data (Baars et al, 2016; Thorsen and Fu, 2015). The satellite-borne CALIPO data processing algorithm is successfully applied to satellite-borne elastic radar data processing, but the ground-based radar and the satellite-borne radar have different observation angles, so that the inversion algorithm of the satellite-borne radar cannot be directly applied to ground-based data processing (Winker et al, 2009). The processing of ground-based elastic lidar data still needs to be addressed by other methods.
Disclosure of Invention
In order to solve the data inversion problem, the invention provides a laser radar automatic inversion method combining an image recognition algorithm and signal characteristic decomposition, which can solve the problems of low efficiency in manual inversion, difficulty in selecting reference height in low signal-to-noise ratio in daytime, dependence of the traditional cloud layer recognition algorithm on the efficiency of a laser radar system, fixed length of a search window of the traditional reference height recognition algorithm, low calculation efficiency and the like, can realize the characteristics of automatic cloud layer and reference height recognition, noise filtering, intelligent signal characteristic recognition, high calculation efficiency and the like, and has the advantages of no need of any manual intervention, simple setting, strong universality and the like.
In order to achieve the above purpose, the invention adopts the following technical scheme to realize: a structure diagram of the whole method is shown in figure 1, and the method comprises polarized laser radar data preprocessing, cloud layer recognition, non-cloud section segmentation, reference height recognition, data inversion and inversion result output, wherein meteorological data input is required in the reference height recognition and data inversion processes.
Step 1, preprocessing polarization laser radar data to correct a pulse accumulation effect in the laser radar data;
step 2, identifying cloud layers detected in the laser radar single-section data;
step 3, screening out all signal profiles containing cloud layers to obtain profiles without cloud layers, and dividing the non-cloud profiles into continuous subsets with fixed time lengths;
step 4, inputting meteorological data;
step 5, calculating a molecular scattering signal by combining the meteorological data input in the step 4, identifying the reference height of the cloudless profile segmentation radar signal in the step 3, and finding out a characteristic height section closest to the molecular scattering signal as the reference height;
step 6, combining the meteorological data input in the step 4, and performing inversion from the continuous cloud-free signal profile in the step 5 to obtain an aerosol extinction coefficient, a backscattering coefficient and a particle depolarization ratio;
and 7, finally outputting the inversion result into a scientific data format file with a standard format.
Further, in step 1, the pulse pile-up effect in the laser radar data is corrected, and the correction formula is as follows:
Figure BDA0003052433420000031
wherein C' is the corrected photon counting rate; c is the photon count rate affected by the pulse pile-up effect; τ is the dead time; after pulse accumulation effect correction, the two-channel data of photon counting and analog sampling are spliced, and the splicing formula is as follows:
Figure BDA0003052433420000032
where C "represents the spliced signal, m represents the weight of the analog sampled signal, s is the slope, o is the offset, A is the intensity of the analog sampled signal, CminAnd CmaxRespectively the range of the photon counting rate of the splicing;
and finally, removing the background signal from the spliced signal, wherein the calculation formula of the background signal is as follows:
Figure BDA0003052433420000033
wherein P isbgFor background signal, k is the number of range gates, n is the initial range gate of the background signal, P (z)i) Is the signal strength at the ith height gate.
Further, the cloud layer identification method in step 2 is to identify k1The multiple signal uncertainty is used for distinguishing jitter generated by natural change of signals from echo signal jitter generated by a real characteristic layer, and adjacent signal deviation larger than k is reserved from the near end to the far end of the signals in sequence1A point of multiple signal uncertainty, wherein the signal uncertainty is calculated by the formula:
Figure BDA0003052433420000034
the same process is performed from the far end to the near end again in reverse order, and the signals obtained twice are averaged to obtain semi-discretizationAveraging the signals obtained twice to obtain a semi-discretized signal profile; then, a histogram equalization algorithm is adopted to detect a height interval higher than a reference signal as a characteristic layer, and finally, a threshold value is utilized to judge whether the maximum value and the minimum value of the signal in the layer exceed k or not2To distinguish whether the feature layer is a cloud layer.
Further, the meteorological data input in step 4 includes data downloading and data interpolation, wherein the data downloading is used for downloading real-time or historical radiosonde data, 1 ° × 1 ° meteorological field data output by a global data assimilation system GDAS, or a meteorological field output by a re-analysis data set ERA5 of a european mesoscale meteorological forecast model of the fifth generation, and the data interpolation is used for interpolating a global-grid meteorological field onto observation grid points of the laser radar by a bilinear interpolation method.
Further, the reference height identification in the step 5 comprises signal feature decomposition and Rayleigh fit judgment, wherein the signal feature decomposition divides a radar signal vertical section into height sections with the same discrete signal features by using a Douglas-Peucker algorithm, then initial detection is carried out on the divided signal feature height sections, the height sections comprise the vertical thickness of the feature height sections and the positions of the feature height sections, then Rayleigh fit judgment is carried out on the feature height sections meeting the height range and minimum thickness limitation, and the feature height section closest to the molecular scattering signal is found out to be used as the reference height; wherein the molecular scattering signal is calculated according to meteorological data, and the calculation formula is as follows:
Figure BDA0003052433420000041
wherein P ismRepresents the molecular scattering signal, betamAnd alphamRepresenting the molecular backscattering coefficient and extinction coefficient; the molecular backscattering coefficient and extinction coefficient can be calculated by the following formulas:
Figure BDA0003052433420000042
Figure BDA0003052433420000043
wherein p (z) is atmospheric pressure in units of hectopascal, T (z) is atmospheric temperature in units of Kelvin, λ0The wavelength of the excitation light is in nanometers.
Further, the specific implementation manner of obtaining the aerosol extinction coefficient, the backscattering coefficient and the particle depolarization ratio by inversion in the step 6 is as follows;
for an emission wavelength of λ0The radar equation describing the received signal of the elastic scattering lidar of (1) is shown in equation (8):
Figure BDA0003052433420000044
where P (z) is the strength of the radar echo signal at height z, C is the receiving efficiency of the entire system, O (z) is the overlap factor of the system, and βaIs the backscattering coefficient, alpha, of the aerosolaIs the extinction coefficient of the aerosol, z' represents the integral variable; the backscattering coefficient and extinction coefficient of aerosol cannot be solved by an equation, and the traditional solution method is to assume that the backscattering coefficient and the extinction coefficient satisfy a linear relationship, as shown in equation (9):
Figure BDA0003052433420000051
wherein S isaIs the aerosol extinction-backscattering ratio, also known as the lidar ratio; based on this relationship, a numerical solution for the backscattering coefficient of the aerosol can be obtained by solving the bernoulli equation as follows:
Figure BDA0003052433420000052
wherein z isrefFor reference height, obtained by step 5, z "represents the integral variationAn amount;
after obtaining the backscattering coefficient of the aerosol, the particle depolarization ratio can be obtained by inverting the following formula:
Figure BDA0003052433420000053
Figure BDA0003052433420000054
where K is the ratio of the system efficiencies of the parallel and perpendicular channels, calibrated by the Δ 90 method, P||And P⊥Signal strength, delta, for parallel and vertical channels, respectivelymIs the molecular depolarization ratio, a quantity related only to the receive channel filter bandwidth and atmospheric temperature.
The invention also provides a polarization laser radar data inversion system based on image recognition and signal characteristic decomposition, which comprises the following modules:
the preprocessing module is used for preprocessing the polarization laser radar data so as to correct the pulse accumulation effect in the laser radar data;
the cloud layer identification module is used for identifying cloud layers detected in the laser radar single-section data;
the cloud-free section segmentation module is used for screening out all signal sections containing cloud layers to obtain sections without the cloud layers, and segmenting the cloud-free sections into continuous subsets with fixed time lengths;
the meteorological data input module is used for inputting meteorological data;
the reference height identification module is used for calculating a molecular scattering signal by combining the input meteorological data, carrying out reference height identification on the cloudless profile segmentation radar signal and finding out a characteristic height section closest to the molecular scattering signal as a reference height;
and the data inversion module is used for inverting from the continuous non-cloud signal profile by combining the input meteorological data to obtain an aerosol extinction coefficient, a backscattering coefficient and a particle depolarization ratio.
And the inversion result output module is used for outputting the inversion result as a scientific data format file with a standard format.
The invention has the beneficial effects that: compared with the prior art, the method can solve the problems that the efficiency of a manual inversion process is low, the reference height in the daytime cannot be identified, the cloud layer identification algorithm depends on a laser radar calibration result, and the calculation efficiency in the reference height identification process is low. The method has the advantages of obtaining inversion products from the polarization laser radar data in a high-efficiency and automatic mode.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is an algorithmic work flow diagram of the present invention;
FIG. 2 is a laser radar signal pre-processing presentation diagram;
fig. 3 is a diagram showing an identification process of actual data in a cloud layer identification process, (a) the diagram shows typical non-cloud and cloud laser radar signal profiles, wherein a solid line shows an actually measured non-cloud radar signal profile, a dotted line shows a signal profile with a low-layer liquid water cloud and a high-layer rolling cloud, and a signal marked by a thick line segment is a cloud layer identified by the cloud layer identification algorithm; (b) represents the signal profile after each half-discretization, wherein the dotted line represents three times the corresponding signal uncertainty; (c) the solid and dashed implementations represent the signal profile equalized by the value diagram, respectively, and the dashed gray lines represent the corresponding standard signals;
fig. 4 is a diagram showing the actual data processing result of the lidar according to the present invention throughout the day, and (a) is a time height diagram of the range correction signal. Wherein the black solid line is the aerosol backscattering coefficient obtained by inversion, and the bold solid line is the reference height range obtained automatically; (b) the cloud layer identification result is obtained; the signal profile containing the cloud layer consists of a middle strip.
Fig. 5 is a diagram showing the identification process of actual data in the reference height identification process, and (a) represents the measured distance correction signal. The black dot-dash line represents signal uncertainty; (b) the middle dots distinguish new number segments which are identified by a Douglas-Peucker algorithm and have the same characteristics; (c) the signal marked by the medium black is the signal segment closest to the molecular scattering signal;
Detailed Description
In order to make the technical means, features and functions of the invention easy to understand, the invention is further explained below with reference to the specific drawings.
Step 1, preprocessing polarization laser radar data to correct a pulse accumulation effect in the laser radar data, wherein a correction formula is as follows:
Figure BDA0003052433420000071
wherein C' is the corrected photon counting rate; c is the photon count rate affected by the pulse pile-up effect; τ is the dead time. After pulse accumulation effect correction, the two-channel data of photon counting and analog sampling are spliced, and the splicing formula is as follows:
Figure BDA0003052433420000072
where C "represents the spliced signal, m represents the weight of the analog sampled signal, s is the slope, o is the offset, A is the intensity of the analog sampled signal, CminAnd CmaxRespectively the range of the photon counting rate of the splicing. The weights, slopes, and offsets may be calculated from the signal strengths, typically as known quantities. The splicing range is related to the signal of the used acquisition instrument, and is generally selected to be 1-50 MHz. And finally, removing the background signal from the spliced signal, wherein the calculation formula of the background signal is as follows:
Figure BDA0003052433420000073
wherein P isbgFor background signal, k is the number of range gates, n is the initial range gate of the background signal, P (z)i) Is the signal strength at the ith height gate. Typically, n is a very remote height gate, at which heightThe effective echo signals are extremely weak, and almost all received signals are background signals.
And 2, identifying the cloud layer for identifying the cloud layer detected in the laser radar single-section data. The identification method is characterized in that triple signal uncertainty is used for distinguishing jitter generated by natural change of a signal from echo signal jitter generated by a real characteristic layer, the signal is sequentially kept from a near end to a far end at a point of signal uncertainty of which the adjacent signal deviation is more than three times (other values can be taken, generally more than or equal to 3, and if the value is too small, a noise signal is included), wherein the signal uncertainty can be calculated by the following formula:
Figure BDA0003052433420000074
and the same process is performed from the far end to the near end again according to the reverse sequence, the signals obtained twice are averaged to obtain a semi-discretized signal profile, and the signals obtained twice are averaged to obtain the semi-discretized signal profile. And then detecting a height interval higher than the reference signal by adopting a histogram equalization algorithm commonly used in image processing as a characteristic layer. And finally, judging whether the maximum value and the minimum value of the signals in the layer exceed four (other values can be also taken, and 4 is the most reasonable threshold) by utilizing the threshold to distinguish whether the characteristic layer is a cloud layer.
And 3, screening all the signal profiles containing the cloud layer to obtain the profiles without the cloud layer, and dividing the non-cloud profiles into continuous subsets with fixed time lengths.
And 4, meteorological data input, including data downloading and data interpolation, wherein the data downloading is used for downloading real-time or historical radiosonde data, meteorological field data of 1 degree multiplied by 1 degree output by a global data assimilation system GDAS or a meteorological field output by a re-analysis data set ERA5 of a fifth generation European mesoscale meteorological forecasting model (ECMWF), and the data interpolation is to interpolate the global gridded meteorological field to observation grid points of the laser radar by a bilinear interpolation method.
Step 5, calculating a molecular scattering signal by combining the meteorological data input in the step 4, identifying the reference height of the cloudless profile segmentation radar signal in the step 3, and finding out a characteristic height section closest to the molecular scattering signal as the reference height; the reference altitude identification includes signal feature decomposition and Rayleigh fit determination. The signal characteristic decomposition divides a radar signal vertical section into height sections with the same signal characteristics by using a Douglas-Peucker algorithm, then performs initial detection on the height sections of the divided signal characteristics, including the vertical thickness of the height sections of the characteristics and the positions of the height sections of the characteristics, then performs Rayleigh fit judgment on the height sections of the characteristics meeting the limitation of height range and minimum thickness, and finds out the height section of the characteristics closest to the molecular scattering signal as a reference height. Wherein the molecular scattering signal is calculated according to meteorological data, and the calculation formula is as follows:
Figure BDA0003052433420000081
wherein P ismRepresents the molecular scattering signal, betamAnd alphamRepresenting the molecular backscattering coefficient and extinction coefficient. The molecular backscattering coefficient and extinction coefficient can be calculated by the following formulas:
Figure BDA0003052433420000082
Figure BDA0003052433420000083
where p (z) is atmospheric pressure in units of hectopascal. T (z) is the atmospheric temperature in Kelvin. Lambda [ alpha ]0The wavelength of the excitation light is in nanometers.
And 6, performing data inversion to obtain an aerosol extinction coefficient, a backscattering coefficient and a particle depolarization ratio by performing inversion on the continuous cloud-free signal profile obtained in the step 5.
For an emission wavelength of λ0The elastic scattering lidar describedThe radar equation for the received signal is shown in equation (8):
Figure BDA0003052433420000091
where P (z) is the strength of the radar echo signal at height z, C is the receiving efficiency of the entire system, O (z) is the overlap factor of the system, and βaIs the backscattering coefficient, alpha, of the aerosolaIs the aerosol extinction coefficient. The backscattering coefficient and extinction coefficient of aerosol cannot be solved by an equation, and the traditional solution method is to assume that the backscattering coefficient and the extinction coefficient satisfy a linear relationship, as shown in equation (9):
Figure BDA0003052433420000092
wherein S isaIs the extinction-backscattering ratio of the particles, also known as the laser radar ratio. Based on this relationship, a numerical solution for the backscattering coefficient of the aerosol can be obtained by solving the bernoulli equation as follows:
Figure BDA0003052433420000093
wherein z isrefFor reference height, it can be obtained by step 5. After obtaining the aerosol backscattering coefficient, the aerosol extinction coefficient can be calculated by equation (9). Because there is a large difference between the scattering characteristics of clouds in the atmosphere and aerosols (Ansmann et al, 1992), and the scattering signals of cloud layers are affected by multiple scattering of cloud layers (Hogan,2006), in order to satisfy the assumption that the ratio of lidar is constant in the inversion process, all the sections containing cloud layers need to be filtered, and the sections without clouds can be obtained through step 2 and step 3. After obtaining the backscattering coefficient of the aerosol, the particle depolarization ratio deltaa(z) can be obtained by inversion of the following equation:
Figure BDA0003052433420000094
Figure BDA0003052433420000095
where K is the ratio of the system efficiencies of the parallel and vertical channels, and can be calibrated by the Δ 90 ° method (Freudet haler, et al, 2009), P||And P⊥The signal strength of the parallel and vertical channels, respectively. DeltamIs the molecular depolarization ratio, a quantity related only to the receive channel filter bandwidth and atmospheric temperature.
And 7, finally outputting the inversion result into a scientific data format file with a standard format.
The overall working principle of the algorithm of the invention is as follows: the laser radar collected signal is input into the laser radar data processing module for preliminary signal correction and signal splicing, the result is output into the cloud layer identification module, the sections with cloud layer interference are identified and removed from the original sections, then inputting the cloudless profile into a cloudless profile division module, dividing the separated signal profile into a plurality of continuous signal profiles with a fixed time length, the signals of the profiles are accumulated in time and then input into a reference height identification module, the areas closest to the molecular scattering signals are identified as reference heights through comparison with the molecular scattering signals, and then inputting the reference height and the signal into a data inversion module to calculate an aerosol extinction coefficient, a backscattering coefficient and a particle depolarization ratio, and finally outputting the aerosol extinction coefficient, the backscattering coefficient and the particle depolarization ratio as a scientific data format file through an inversion result output module.
The embodiment of the invention also provides a polarization laser radar data inversion system based on image recognition and signal characteristic decomposition, which comprises the following modules:
the preprocessing module is used for preprocessing the polarization laser radar data so as to correct the pulse accumulation effect in the laser radar data;
the cloud layer identification module is used for identifying cloud layers detected in the laser radar single-section data;
the cloud-free section segmentation module is used for screening out all signal sections containing cloud layers to obtain sections without the cloud layers, and segmenting the cloud-free sections into continuous subsets with fixed time lengths;
the meteorological data input module is used for inputting meteorological data;
the reference height identification module is used for calculating a molecular scattering signal by combining the input meteorological data, carrying out reference height identification on the cloudless profile segmentation radar signal and finding out a characteristic height section closest to the molecular scattering signal as a reference height;
and the data inversion module is used for inverting from the continuous non-cloud signal profile by combining the input meteorological data to obtain an aerosol extinction coefficient, a backscattering coefficient and a particle depolarization ratio.
And the inversion result output module is used for outputting the inversion result as a scientific data format file with a standard format.
The specific implementation manner and the steps of each module correspond, and the invention is not described.
The first embodiment is as follows: the collected raw data of the 532nm polarization lidar includes two types of photon counting data and analog sampling data, and the two types of data have different dynamic ranges as shown in fig. 2. Photon count data tends to saturate with low-level echo data because there is a pulse pile-up response when the signal is too strong. The analog sampling data has higher electric background noise, so the display effect of the echo signal which is weak at high altitude is poor. In order to facilitate the subsequent radar signal processing, the original laser radar signal passes through a polarization laser radar data preprocessing module, the photon counting pulse accumulation effect is corrected, and the two paths of output signals are integrated into a path of splicing signal which is saturated and has a wider dynamic range. The integrated signal is subjected to cloud interference detection through a cloud layer identification algorithm module, and typical identification results of sections with clouds and sections without clouds are shown in fig. 3. Fig. 3(a) shows the results of the signal profile affected by the high-level cirrus and the low-level liquid cloud (liquid cloud) and the cloudless signal profile, and after noise filtering and semi-discretization, the noise in the two profiles is filtered, and the result is shown in fig. 3 (b). Finally, the contrast of the signal is improved by a threshold distribution histogram method, and the region where the signal is increased is highlighted in the signal profile, so that a cloud layer and an aerosol layer are highlighted, as shown in fig. 3(c), and finally, the cloud layer is identified by a simple cloud layer and aerosol layer identification algorithm, and the identification result is shown in fig. 4 (b). The results are then divided into successive observation intervals of 1 hour duration, the signal passage times of the individual intervals being accumulated into individual signal profiles of high signal-to-noise ratio, which are fed into a reference height recognition module. In the reference altitude identification module, the altitude segments with signal intensity less than three times the signal uncertainty are first filtered, as shown in fig. 5 (a). The entire signal is then decomposed into different feature height segments using the Dou glas-Peucker algorithm, as shown in fig. 5 (b). The height segment closest to the molecular scattering signal is then identified as the reference height using the Rayleigh fit algorithm, as shown in FIG. 5 (c). The reference height segment and the signal profile are input into an inversion algorithm module, and finally a height profile of the aerosol backscattering coefficient, the extinction coefficient and the particle depolarization ratio is obtained, wherein the backscattering coefficient profile is shown in fig. 4 (a).
It should be understood that portions not set forth in detail in this specification pertain to existing algorithms or techniques.
It should be understood that the above description of the preferred embodiments is given in some detail, and not as a limitation on the scope of the invention, and that various alternatives and modifications can be devised by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (7)

1. A method for inverting polarized laser radar data based on image recognition and signal feature decomposition is characterized by comprising the following steps:
step 1, preprocessing polarization laser radar data to correct a pulse accumulation effect in the laser radar data;
step 2, identifying cloud layers detected in the laser radar single-section data;
step 3, screening out all signal profiles containing cloud layers to obtain profiles without cloud layers, and dividing the non-cloud profiles into continuous subsets with fixed time lengths;
step 4, inputting meteorological data;
step 5, calculating a molecular scattering signal by combining the meteorological data input in the step 4, identifying the reference height of the cloudless profile segmentation radar signal in the step 3, and finding out a characteristic height section closest to the molecular scattering signal as the reference height;
step 6, combining the meteorological data input in the step 4, and performing inversion from the continuous cloud-free signal profile in the step 5 to obtain an aerosol extinction coefficient, a backscattering coefficient and a particle depolarization ratio;
and 7, finally outputting the inversion result into a scientific data format file with a standard format.
2. The method of claim 1, wherein the method comprises the following steps: in step 1, the pulse pile-up effect in the laser radar data is corrected, and the correction formula is as follows:
Figure FDA0003052433410000011
wherein C' is the corrected photon counting rate; c is the photon count rate affected by the pulse pile-up effect; τ is the dead time; after pulse accumulation effect correction, the two-channel data of photon counting and analog sampling are spliced, and the splicing formula is as follows:
Figure FDA0003052433410000012
where C "represents the spliced signal, m represents the weight of the analog sampled signal, s is the slope, o is the offset, A is the intensity of the analog sampled signal, CminAnd CmaxRespectively spliced photon count rate ranges；
And finally, removing the background signal from the spliced signal, wherein the calculation formula of the background signal is as follows:
Figure FDA0003052433410000013
wherein P isbgFor background signal, k is the number of range gates, n is the initial range gate of the background signal, P (z)i) Is the signal strength at the ith height gate.
3. The method of claim 1, wherein the method comprises the following steps: the cloud layer identification method in the step 2 is to identify k1The multiple signal uncertainty is used for distinguishing jitter generated by natural change of signals from echo signal jitter generated by a real characteristic layer, and adjacent signal deviation larger than k is reserved from the near end to the far end of the signals in sequence1A point of multiple signal uncertainty, wherein the signal uncertainty is calculated by the formula:
Figure FDA0003052433410000021
the same process is carried out from the far end to the near end again according to the reverse sequence, the signals obtained twice are averaged to obtain a semi-discretized signal profile, and the signals obtained twice are averaged to obtain a semi-discretized signal profile; then, a histogram equalization algorithm is adopted to detect a height interval higher than a reference signal as a characteristic layer, and finally, a threshold value is utilized to judge whether the maximum value and the minimum value of the signal in the layer exceed k or not2To distinguish whether the feature layer is a cloud layer.
4. The method of claim 1, wherein the method comprises the following steps: the meteorological data input in the step 4 comprises data downloading and data interpolation, wherein the data downloading is used for downloading real-time or historical radiosonde data, 1-degree multiplied by 1-degree meteorological field data output by a global data assimilation system GDAS or a meteorological field output by a re-analysis data set ERA5 of a fifth generation European mesoscale meteorological forecasting model, and the data interpolation is to interpolate the global-grid meteorological field to an observation grid point of the laser radar by a bilinear interpolation method.
5. The method of claim 1, wherein the method comprises the following steps: the reference height identification in the step 5 comprises signal feature decomposition and Rayleigh fit judgment, wherein the signal feature decomposition divides a radar signal vertical section into height sections with the same discrete signal features by using a Douglas-Peucker algorithm, then the initial detection is carried out on the height sections of the divided signal features, the height sections comprise the vertical thickness of the feature height sections and the positions of the feature height sections, then the Rayleigh fit judgment is carried out on the feature height sections meeting the limitation of the height range and the minimum thickness, and the feature height section closest to the molecular scattering signal is found out to be used as the reference height; wherein the molecular scattering signal is calculated according to meteorological data, and the calculation formula is as follows:
Figure FDA0003052433410000031
wherein P ismRepresents the molecular scattering signal, betamAnd alphamRepresenting the molecular backscattering coefficient and extinction coefficient; the molecular backscattering coefficient and extinction coefficient can be calculated by the following formulas:
Figure FDA0003052433410000032
Figure FDA0003052433410000033
where p (z) is the atmospheric pressure,in units of hectopa, T (z) is the atmospheric temperature, in units of Kelvin, λ0The wavelength of the excitation light is in nanometers.
6. The method of claim 1, wherein the method comprises the following steps: the specific implementation mode of obtaining the aerosol extinction coefficient, the backscattering coefficient and the particle depolarization ratio through inversion in the step 6 is as follows;
for an emission wavelength of λ0The radar equation describing the received signal of the elastic scattering lidar of (1) is shown in equation (8):
Figure FDA0003052433410000034
where P (z) is the strength of the radar echo signal at height z, C is the receiving efficiency of the entire system, O (z) is the overlap factor of the system, and βaIs the backscattering coefficient, alpha, of the aerosolaIs the extinction coefficient of the aerosol, z' represents the integral variable; the backscattering coefficient and extinction coefficient of aerosol cannot be solved by an equation, and the traditional solution method is to assume that the backscattering coefficient and the extinction coefficient satisfy a linear relationship, as shown in equation (9):
Figure FDA0003052433410000035
wherein S isaIs the aerosol extinction-backscattering ratio, also known as the lidar ratio; based on this relationship, a numerical solution for the backscattering coefficient of the aerosol can be obtained by solving the bernoulli equation as follows:
Figure FDA0003052433410000036
wherein z isrefFor the reference height, obtained by step 5, z "represents an integral variable;
after obtaining the backscattering coefficient of the aerosol, the particle depolarization ratio can be obtained by inverting the following formula:
Figure FDA0003052433410000041
Figure FDA0003052433410000042
where K is the ratio of the system efficiencies of the parallel and perpendicular channels, calibrated by the Δ 90 method, P||And P⊥Signal strength, delta, for parallel and vertical channels, respectivelymIs the molecular depolarization ratio, a quantity related only to the receive channel filter bandwidth and atmospheric temperature.
7. A polarized laser radar data inversion system based on image recognition and signal feature decomposition is characterized by comprising the following modules:
the preprocessing module is used for preprocessing the polarization laser radar data so as to correct the pulse accumulation effect in the laser radar data;
the cloud layer identification module is used for identifying cloud layers detected in the laser radar single-section data;
the cloud-free section segmentation module is used for screening out all signal sections containing cloud layers to obtain sections without the cloud layers, and segmenting the cloud-free sections into continuous subsets with fixed time lengths;
the meteorological data input module is used for inputting meteorological data;
the reference height identification module is used for calculating a molecular scattering signal by combining the input meteorological data, carrying out reference height identification on the cloudless profile segmentation radar signal and finding out a characteristic height section closest to the molecular scattering signal as a reference height;
and the data inversion module is used for inverting from the continuous non-cloud signal profile by combining the input meteorological data to obtain an aerosol extinction coefficient, a backscattering coefficient and a particle depolarization ratio.
And the inversion result output module is used for outputting the inversion result as a scientific data format file with a standard format.
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