CN119310551A - A method for correcting power spectrum of laser wind radar based on multi-source observation data - Google Patents
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Abstract
本发明涉及一种基于多源观测资料的激光测风雷达功率谱校正方法，属于无线电领域；基于高频次的探空气球观测数据和组网的微波辐射计反演产品，利用两者数据的偏差，设计了偏差与微波辐射计观测量间的校正策略，生成了高时空分辨率高质量的大气温湿廓线；基于多源垂直观测资料，能够实时的反映大气状态和气溶胶的微物理固有属性，以及计算大气和气溶胶粒子的后向散射系数和消光系数；考虑了大气、气溶胶的复杂性和目标物散射特性、微物理特征等众多因素，采用先进的激光雷达回波信号模拟器输出的回波信号作真值是可靠的。将功率谱按谱偏度分成三类，分别利于梯度强化模型进行训练，并实现了激光测风雷达实测功率谱信息的精准和高效的校正。
The present invention relates to a laser wind radar power spectrum correction method based on multi-source observation data, belonging to the field of radio; based on high-frequency sounding balloon observation data and networked microwave radiometer inversion products, the deviation of the data of the two is utilized to design a correction strategy between the deviation and the microwave radiometer observation quantity, and a high-temporal and high-quality atmospheric temperature and humidity profile with high spatial and temporal resolution is generated; based on multi-source vertical observation data, it can reflect the atmospheric state and the microphysical inherent properties of aerosols in real time, and calculate the backscattering coefficient and extinction coefficient of the atmosphere and aerosol particles; considering the complexity of the atmosphere and aerosols and the scattering characteristics of the target, microphysical characteristics and many other factors, it is reliable to use the echo signal output by the advanced laser radar echo signal simulator as the true value. The power spectrum is divided into three categories according to the spectral skewness, which is conducive to the training of the gradient enhancement model, and realizes the accurate and efficient correction of the power spectrum information measured by the laser wind radar.
Description
Technical Field
The invention relates to the field of radio, in particular to a laser wind-finding radar power spectrum correction method based on multi-source observation data.
Background
The accuracy of the power spectrum of the laser wind-finding radar is important in a laser radar system because the accuracy directly influences the quality, interpretation and subsequent application of data, and particularly, the accuracy of the power spectrum of the laser wind-finding radar can influence the improvement of measurement accuracy, wherein the power spectrum of the laser wind-finding radar reflects the attenuation, scattering and reflection characteristics of a laser signal in atmospheric propagation, the accuracy of distance measurement, scattering coefficient calculation and extinction coefficient estimation can be ensured by the accurate power spectrum, and the possibility of influencing data analysis and interpretation is influenced, and the power spectrum is used for analyzing the characteristics of the laser radar signal such as intensity, frequency distribution and the like, and the analysis directly influences the interpretation of atmospheric components, ground structure and target characteristics. Inaccurate power spectrum may lead to misreading or misinterpretation of data, which in turn affects decision and research results, for example, in meteorology, erroneous power spectrum analysis may lead to erroneous decisions on atmospheric conditions, (3) affect system performance and robustness, laser wind lidar power spectrum is used to accurately measure and model atmospheric, earth's surface or other environmental features, and the accuracy of the power spectrum is directly related to the reliability of research data and the reliability of conclusions. Erroneous power spectrum data may lead to inconsistent or misleading research results, affecting the quality and progress of scientific research. Therefore, the accuracy of the power spectrum of the laser wind-finding radar has key effects on target identification, measurement accuracy, data analysis, system performance, scientific research and advanced application, and inaccurate power spectrum can cause a series of chain reactions from incorrect data interpretation to unreliable application results.
In order to ensure the correctness of the power spectrum signal of the laser wind lidar, various means are generally adopted to optimize the system performance, reduce errors and improve the accuracy of signal processing. The prior art generally adopts the following means that (1) the laser radar system is calibrated regularly, so that the accuracy and consistency of the laser source, the detector, the scanning mechanism and other components are ensured. The method comprises the steps of (1) calibrating laser wavelength, calibrating detector sensitivity and calibrating time delay, (2) denoising, namely, eliminating noise components in a power spectrum by using advanced signal denoising algorithms such as adaptive filtering, wavelet transformation, fourier transformation and the like, so as to improve definition and accuracy, and (3) carrying out time-space averaging on a power spectrum signal measured for multiple times, so that the influence of random noise is reduced, and the signal to noise ratio is improved. And (4) a high-quality laser source is used, and stable high-quality laser sources are used to ensure the stability of the frequency and power of laser output, thereby ensuring the wind measuring precision. The accuracy and the reliability of the laser wind-finding radar system are guaranteed through hardware and software, but power spectrum deviation caused by the influence of factors such as atmosphere, cloud particles, aerosol, atmospheric environment and the like on laser scattering characteristics and beam propagation is rarely considered. In addition, laser wind-finding radars are often compared with other wind-finding devices, and the purpose of such comparison is to correct the accuracy of wind, a product, without correcting the most primitive power spectrum signal.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a laser wind-finding radar power spectrum correction method based on multi-source observation data, and solves the defects in the prior art.
The invention aims at realizing the following technical scheme that the laser wind-finding radar power spectrum correction method based on multi-source observation data comprises the following steps:
S1, acquiring the space position and the observation time of each observation data in a high-frequency sounding balloon observation profile according to collected data, calculating the space distance difference and the observation time difference, and selecting microwave radiation profile data which are matched with the sounding balloon observation profile in a space-time manner;
S2, calculating the difference value of the temperature and the relative humidity of the matched sounding balloon observation and the profile of the inversion product of the microwave radiometer on the same point by taking the sounding balloon observation profile as a reference, and constructing a correction model based on an error multiplication model to realize correction of the temperature and the relative humidity profile of the microwave radiometer reaction;
S3, constructing a data set M 1, inputting the data set M 1 into a Rayleigh scattering model and a discrete coordinate radiation transmission model respectively to obtain a molecular backscattering coefficient and a molecular extinction coefficient at different height layers z, constructing a data set M 2, and inputting the data set M 2 into a Mie scattering model and an optical parameter library model respectively to obtain a backscattering coefficient and an extinction coefficient of aerosol at different height layers z;
S4, calculating the atmospheric transmittance and the total backscattering coefficient according to the data obtained in the step S3, and inputting the collected data into a laser radar echo signal simulator to obtain a laser radar simulation echo signal corresponding to real laser radar observation;
S5, processing the obtained laser radar simulated echo signals to obtain simulated power spectrum signals P M (f), finding power spectrum signals P S (f) which are consistent with the time and space of the power spectrum signals P M (f) from the collected data, forming two data sets P MA (f) and P SA (f), calculating the spectrum skewness of the two data sets, and dividing the two data sets to obtain three training sets;
S6, inputting the obtained three training sets into a selected laser radar power spectrum correction model for model optimization, and inputting the actually measured power spectrum and the calculated spectrum deviation of the laser wind-finding radar into the optimized model to obtain a corrected laser wind-finding radar power spectrum.
The step of S1 comprises the following steps:
the method comprises the steps of collecting inversion product profile data of a networking microwave radiometer, high-frequency sounding balloon observation profile data, observation data of aerosol particle diameter spectrometers placed on different high-level layers on a plurality of observation towers, aerosol type data observed by the aerosol mass spectrometers, laser wind-finding radar instrument observation parameters and high-elevation angle detection power spectrum data sets;
And inverting the product profile data and the high-frequency sounding balloon observation profile data according to the collected networking microwave radiometers, obtaining the space position and the observation time of each observation data in the high-frequency sounding solution observation profile, respectively calculating the space distance difference and the observation time difference of each observation point and all observation points in the inversion product profile of the microwave radiometers in the adjacent space, and picking out the microwave radiation profile data which are matched with the sounding balloon observation profile in time and space by taking the minimum product of the space distance difference and the observation time difference as a selection basis.
The step of S2 comprises the following steps:
According to the matched sounding balloon observation and the inverted product profile of the microwave radiometer, calculating the difference value of the temperature and the relative humidity of the sounding balloon observation profile on the same point by taking the sounding balloon observation profile as a reference;
Based on the error multiplication model, a correction model between the difference value of the temperature and the relative humidity and the corresponding meteorological variable of the inversion of the microwave radiometer is respectively established, a temperature and relative humidity sequence of a plurality of space matching points is input, nonlinear fitting is carried out based on the correction model, specific numerical values of parameters in the correction model are determined according to fitting results, and based on the parameter-specific correction model, inversion product data of the microwave radiometer is taken as input, so that correction of the inversion temperature and relative humidity profile of the microwave radiometer is realized.
The step of S3 comprises the following steps:
The method comprises the steps of forming a data set M 1 by collected laser wind-finding radar instrument observation parameters, corrected temperature and relative humidity profile data inverted by a microwave radiometer, air pressure profile along with height under the condition of standard atmospheric assumption, air molecular density, refractive index and gas main component proportion, inputting the data set M 1 into a Rayleigh scattering model, operating the Rayleigh scattering model to obtain molecular backscattering coefficients beta m (z) at different height layers z, inputting the data set M 1 into a discrete coordinate radiation transmission model, and operating the discrete coordinate radiation transmission model to obtain molecular extinction coefficients a m (z) at different height layers z;
meanwhile, the collected laser wind-finding radar instrument observation parameters, the aerosol particle size spectrometer observation data, the corrected high-space-time resolution temperature and relative humidity profile data, the set particle size distribution function and the aerosol shape are combined into a dataset M 2, the dataset M 2 is input into a meter scattering module, the meter scattering module is operated to obtain the backscattering coefficients beta a (z) of the aerosols at different height layers z, and the dataset M 2 is input into an aerosol and cloud particle optical parameter library module to obtain the extinction coefficients a a (z) of the aerosols at different height layers z.
The step of S4 includes:
According to the obtained molecular and aerosol backscattering coefficients and extinction coefficients at different height layers z, calculating the atmospheric transmittance T (z) through a beer-lambert law, and according to the sum of the atmospheric and aerosol backscattering systems, obtaining a total backscattering system beta (z) =beta m(z)+ βa (z) of the atmospheric and aerosol;
And inputting the collected laser wind-finding radar instrument observation parameters, the wind speed and wind direction observed by the high-frequency sounding balloon, and the elevation angle and azimuth angle of the spatial position of the wind speed and wind direction observation relative to the laser radar wave beam at the moment into a laser radar echo signal simulator to obtain a laser radar simulation echo signal corresponding to real laser radar observation.
The step of S5 includes:
Performing Fourier transform on a laser radar simulation echo signal of a certain spatial position point at a certain moment to generate a corresponding frequency spectrum signal, and then performing modulo calculation on the frequency spectrum signal to obtain a power spectrum signal P M (f) simulated at the spatial position point at the moment;
According to the time and space position information generated by the power spectrum signal P M (f), finding a power spectrum signal P S (f) with consistent time and space from the collected power spectrum data actually detected by the laser wind-finding radar at a high elevation angle;
The time and space positions generated by all the analog power spectrum signals are matched one by one to form two data sets P MA (f) and P SA (f) which are in one-to-one correspondence;
Based on the data set P SA (f), the spectrum bias S K is calculated, the corresponding power spectrum is divided into three categories of positive bias, normal distribution and negative bias according to the positive value, the negative value and 0 of S K, and the corresponding data set P MA (f) is equally divided into three categories according to the three categories of the data set P SA (f), so that three training sets [ P SA1(f), PMA1(f)]、[PSA2(f),PMA2(f)]、[PSA3(f),PMA3 (f) ] are combined.
The step of S6 includes:
the gradient strength regression model is selected as a laser radar power spectrum correction model, three training sets are respectively input into the laser radar power spectrum correction model, and the minimum model loss function and the optimal correction performance are realized through a segmentation optimization strategy;
And taking the actually measured power spectrum and the calculated spectrum deviation of the laser wind-finding radar as the input of an optimized laser radar power spectrum correction model to obtain a corrected laser wind-finding radar power spectrum.
The inversion product profile data of the networking microwave radiometer comprises temperature and humidity profile data with high space-time resolution;
the high-frequency sounding balloon observation profile data comprise high-space-time resolution temperature, relative humidity, air pressure, air speed and wind direction profile data;
the observation data of the aerosol particle size spectrometer comprises aerosol particle radius and aerosol concentration data;
The laser wind-finding radar instrument observation parameters comprise wavelength, frequency, pulse energy, divergence angle of a transmitter, aperture of a receiver, effective receiving efficiency and area, angle of view, observation elevation angle, observation azimuth, space-time resolution and noise level.
The calculation formula of the spectrum deviation S K comprises:
,
Wherein the method comprises the steps of 、、The average Doppler velocity, the Doppler velocities of the left and right endpoints of the signal, S i、PN, the echo signal and the noise level, and sigma, respectively, are the spectral widths measured by the target.
The laser wind-finding radar power spectrum correction method based on the multisource observation data has the advantages that a correction strategy between deviation and observed quantity of a microwave radiometer is designed by utilizing deviation of high-frequency sounding balloon observation data and a networked microwave radiometer inversion product, an atmospheric temperature wet profile with high space-time resolution and high quality is generated, based on the multisource vertical observation data, the micro-physical inherent properties of atmospheric states and aerosol can be reflected in real time, a scattering model, a radiation transmission model, an aerosol cloud particle optical parameter library and the like are facilitated, backscattering coefficients and extinction coefficients of atmospheric and aerosol particles can be calculated accurately, the complexity of the atmospheric and aerosol, scattering characteristics of targets, micro-physical characteristics and other factors are considered, and an echo signal output by an advanced laser radar echo signal simulator is adopted as a true value to be reliable. The power spectrum is divided into three types according to spectrum deflection, which is beneficial to the training of the gradient reinforcement model, and the accurate and efficient correction of the actually measured power spectrum information of the laser wind-finding radar is realized.
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FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a flow chart diagram of a microwave radiometer temperature and humidity profile calibration;
FIG. 3 is a schematic flow chart of molecular back-scattering and extinction coefficient generation for layers of different heights;
FIG. 4 is a schematic flow chart of the back scattering and extinction coefficient generation of aerosol particles of different height layers.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Accordingly, the following detailed description of the embodiments of the application, as presented in conjunction with the accompanying drawings, is not intended to limit the scope of the application as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application. The application is further described below with reference to the accompanying drawings.
As shown in fig. 1, the invention specifically relates to a laser wind-finding radar power spectrum correction method based on multi-source observation data, which specifically comprises the following steps:
The method comprises the steps of 1, collecting inversion product profile data of a networking microwave radiometer, including high-space-time resolution temperature and humidity profile data, collecting observation data of a high-frequency sounding balloon, including high-space-time resolution temperature, relative humidity, air pressure, wind speed, wind direction and other profile data, collecting observation data of aerosol particle size spectrometers placed on different layers on a plurality of observation towers and aerosol type data observed by an aerosol mass spectrometer, and collecting laser wind-finding radar instrument observation parameters and a high-elevation detection power spectrum data set.
Wherein, the observation data of the aerosol particle size spectrometer comprises aerosol particle radius and aerosol concentration data;
the observation parameters of the laser wind-finding radar instrument mainly refer to data with the wavelength of 1550 nm, the frequency of 10KHz, the pulse energy of 300 mu J@1200ns, the divergence angle of a transmitter of less than or equal to 0.1mrad, the aperture of a receiver of 100mm, the effective receiving efficiency and the area of more than or equal to 95% and 7850mm 2 respectively, the field angle of view of less than or equal to 0.1mrad, the observation elevation angle, the observation azimuth, the space-time resolution of 10s multiplied by 30m, the noise level of 10mVpp and the like.
And 2, according to the inversion product profile data of the networking microwave radiometer and the high-frequency sounding balloon observation profile data collected in the step 1, acquiring the spatial position (longitude, latitude and height) and the observation time of each observation data in the high-frequency sounding balloon observation profile, and respectively calculating the spatial distance difference and the observation time difference between each observation point and all observation points in the inversion product profile of the microwave radiometer in the adjacent space. And selecting microwave radiation profile data which are matched with the space-time of the sounding balloon observation profile by taking the minimum product of the space-time distance difference and the observation time difference as a selection basis.
The sounding balloon observation profile data refer to temperature and relative humidity profiles, the temperature and the relative humidity profiles are respectively represented by T tk,i,Qtk,i, 30 layers of profile data are added in the height range of 0-5 km, and the value of i is 1, 2.
The space-time matched microwave radiometric profile data refer to temperature, relative humidity, air pressure, wind speed and wind direction profiles, wherein the temperature and relative humidity profiles are respectively represented by T wb,i,Qwb,i.
The space-distance difference and the observation time difference are respectively indicated by lat_a, lon_a, h_a and time_a at a space point a, and are indicated by (lat_a, lon_a, h_a, time_a), the space point B (lat_b, lon_b, h_b, time_b) is indicated as similar to the point a, and the distance difference Δr and the observation time difference Δt between the two points are respectively:
,Δt=time_a- time_b。
Step 3, calculating the difference value of the temperature or the relative humidity of the sounding balloon observation and the microwave radiometer on the same point by taking the sounding balloon observation profile as a reference according to the profile of the sounding balloon observation and the microwave radiometer inversion product obtained in the step 2, wherein the difference value is respectively And. And then, based on an error multiplication model, respectively establishing a correction model between the difference value of the temperature and the relative humidity and the corresponding meteorological variable of inversion of the microwave radiometer. Inputting temperature and relative humidity sequences of a plurality of space matching points, carrying out nonlinear fitting based on a correction model, and determining parameters in the correction model according to fitting results、、、、AndSpecific values of (2). Based on the correction model after parameter materialization, the inversion product data of the microwave radiometer is used as input, so that correction of inversion temperature and relative humidity profile of the microwave radiometer is realized.
Wherein the correction model refers to a temperature profile correction modelAnd a relative humidity profile correction model。
And 4, the observed parameters of the laser wind-finding radar instrument collected in the step 1, the temperature and relative humidity profile data inverted by the microwave radiometer corrected in the step 3 and parameters such as the distribution profile of air pressure along with the height, the air molecular density, the refractive index, the gas main component proportion and the like under the condition of standard atmospheric assumption form a data set M 1, the data set M 1 is input into a Rayleigh scattering model together, and the molecular backscattering coefficients beta m (z) at different height layers z can be obtained by operating the model. The data set M 1 is simultaneously input into a discrete-coordinate radiation transmission model, and the molecular extinction coefficients a m (z) at different height layers z can be obtained by running the model.
And 5, forming a data set M 2 by the laser wind-finding radar instrument observation parameters collected in the step 1, the aerosol particle size spectrometer observation data, the aerosol mass spectrometer observation data, the corrected high-space-time resolution temperature and relative humidity profile data in the step 3, the set particle size distribution function, the set aerosol shape and other parameters, inputting the data set M 2 into a meter scattering model, and operating the model to obtain aerosol backscattering coefficients beta a (z) at different height layers z. At the same time, the data set M 2 is input into an aerosol and cloud particle optical parameter library model, so that the extinction coefficients a a (z) of the aerosols at different height layers z can be obtained.
And 6, calculating the atmospheric transmittance T (z) according to the molecular and aerosol backscattering coefficients and the extinction coefficients at the layers z with different heights obtained in the step 4 and the step 5 by using the beer-lambert law. From the sum of the atmospheric and aerosol backscattering coefficients, the total atmospheric and aerosol backscattering coefficient β (z) =β m(z)+ βa (z) can be obtained. And then, combining the laser wind-finding radar instrument observation parameters collected in the step 1, the wind speed and the wind direction observed by the high-frequency sounding balloon, and the elevation angle and the azimuth angle of the spatial position of the wind speed and the wind direction observation relative to the laser radar wave beam at the moment, inputting the parameters into a laser radar echo signal simulator (LidarSim), and operating the simulator to obtain an echo signal corresponding to the real laser radar observation.
Wherein, the formula for calculating the atmospheric transmittance by using the beer-lambert law is as followsWhere s represents a propagation path.
And 7, performing Fourier transform on the laser radar simulated echo signal of the spatial position point at the moment obtained in the step 6 to generate a corresponding spectrum signal, and then modulo and squaring the spectrum signal to obtain a simulated power spectrum signal P M (f) at the spatial position point at the moment. And according to the time and space position information generated by the P M (f), finding a power spectrum signal P S (f) with consistent time and space from the power spectrum data actually detected by the high elevation angle of the laser wind-finding radar collected in the step 1. Then, the time and space positions generated by all the analog power spectrum signals are matched one by one, so that two data sets P MA (f) and P SA (f) which are in one-to-one correspondence are formed. Based on the data set P SA (f), the spectrum bias S K is calculated, and the corresponding power spectrum is divided into three categories of positive bias, normal distribution and negative bias according to the positive value, the negative value and 0 of S K. The data sets P SA (f) are divided into three types according to numbers, and the corresponding P M (f) is also divided into three types, thereby combining three types of training sets [ P SA1(f), PMA1(f)]、[PSA2(f), PMA2(f)]、[PSA3(f), PMA3 (f) ]. The calculation formula of the spectrum skewness S K is as follows:
,
Wherein the method comprises the steps of 、、The average Doppler velocity and the Doppler velocities of the left and right end points of the signal are respectively, S i、PN is the echo signal and the noise level respectively, and sigma is the spectrum width measured by the target.
And 8, selecting a gradient reinforcement regression model as a laser radar power spectrum correction model, respectively inputting the three training sets obtained in the step 7 into the model, and realizing minimum model loss function and optimal correction performance through a segmentation optimization strategy.
The sectional tuning strategy refers to dynamically adjusting the main parameters of the model, and then dynamically adjusting other secondary parameters of the model. When a set of better secondary parameters is determined, the primary parameters of the model are finely adjusted again, and finally the setting of the model optimization parameters is realized.
And 9, according to the optimized laser wind-finding radar power spectrum optimization model in the step 8, taking the actually measured power spectrum and the calculated spectrum deviation parameter of the laser wind-finding radar as the input of the model, and outputting to obtain the corrected laser wind-finding radar power spectrum.
The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and adaptations, and of being modified within the scope of the inventive concept described herein, by the foregoing teachings or by the skilled person or knowledge of the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Claims (9)
1. A laser wind-finding radar power spectrum correction method based on multi-source observation data is characterized by comprising the following steps:
S1, acquiring the space position and the observation time of each observation data in a high-frequency sounding balloon observation profile according to collected data, calculating the space distance difference and the observation time difference, and selecting microwave radiation profile data which are matched with the sounding balloon observation profile in a space-time manner;
S2, calculating the difference value of the temperature and the relative humidity of the matched sounding balloon observation and the profile of the inversion product of the microwave radiometer on the same point by taking the sounding balloon observation profile as a reference, and constructing a correction model based on an error multiplication model to realize correction of the temperature and the relative humidity profile of the microwave radiometer reaction;
S3, constructing a data set M 1, inputting the data set M 1 into a Rayleigh scattering model and a discrete coordinate radiation transmission model respectively to obtain a molecular backscattering coefficient and a molecular extinction coefficient at different height layers z, constructing a data set M 2, and inputting the data set M 2 into a Mie scattering model and an optical parameter library model respectively to obtain a backscattering coefficient and an extinction coefficient of aerosol at different height layers z;
S4, calculating the atmospheric transmittance and the total backscattering coefficient according to the data obtained in the step S3, and inputting the collected data into a laser radar echo signal simulator to obtain a laser radar simulation echo signal corresponding to real laser radar observation;
S5, processing the obtained laser radar simulated echo signals to obtain simulated power spectrum signals P M (f), finding power spectrum signals P S (f) which are consistent with the time and space of the power spectrum signals P M (f) from the collected data, forming two data sets P MA (f) and P SA (f), calculating the spectrum skewness of the two data sets, and dividing the two data sets to obtain three training sets;
S6, inputting the obtained three training sets into a selected laser radar power spectrum correction model for model optimization, and inputting the actually measured power spectrum and the calculated spectrum deviation of the laser wind-finding radar into the optimized model to obtain a corrected laser wind-finding radar power spectrum.
2. The method for correcting the power spectrum of the laser wind-finding radar based on the multi-source observation data according to claim 1, wherein the step of S1 comprises the following steps:
the method comprises the steps of collecting inversion product profile data of a networking microwave radiometer, high-frequency sounding balloon observation profile data, observation data of aerosol particle diameter spectrometers placed on different high-level layers on a plurality of observation towers, aerosol type data observed by the aerosol mass spectrometers, laser wind-finding radar instrument observation parameters and high-elevation angle detection power spectrum data sets;
And inverting the product profile data and the high-frequency sounding balloon observation profile data according to the collected networking microwave radiometers, obtaining the space position and the observation time of each observation data in the high-frequency sounding solution observation profile, respectively calculating the space distance difference and the observation time difference of each observation point and all observation points in the inversion product profile of the microwave radiometers in the adjacent space, and picking out the microwave radiation profile data which are matched with the sounding balloon observation profile in time and space by taking the minimum product of the space distance difference and the observation time difference as a selection basis.
3. The method for correcting the power spectrum of the laser wind-finding radar based on the multi-source observation data according to claim 2, wherein the step of S2 comprises the following steps:
according to the matched sounding balloon observation and microwave radiometer inversion product profile, calculating the difference value of the sounding balloon observation profile and the microwave radiometer inversion product profile in the same point position in temperature and relative humidity by taking the sounding balloon observation profile as a reference;
Based on the error multiplication model, a correction model between the difference value of the temperature and the relative humidity and the corresponding meteorological variable of the inversion of the microwave radiometer is respectively established, a temperature and relative humidity sequence of a plurality of space matching points is input, nonlinear fitting is carried out based on the correction model, specific numerical values of parameters in the correction model are determined according to fitting results, and based on the parameter-specific correction model, inversion product data of the microwave radiometer is taken as input, so that correction of the inversion temperature and relative humidity profile of the microwave radiometer is realized.
4. The method for correcting the power spectrum of the laser wind-finding radar based on the multi-source observation data according to claim 3, wherein the step of S3 comprises the following steps:
The method comprises the steps of forming a data set M 1 by collected laser wind-finding radar instrument observation parameters, corrected temperature and relative humidity profile data inverted by a microwave radiometer, air pressure profile along with height under the condition of standard atmospheric assumption, air molecular density, refractive index and gas main component proportion, inputting the data set M 1 into a Rayleigh scattering model, operating the Rayleigh scattering model to obtain molecular backscattering coefficients beta m (z) at different height layers z, inputting the data set M 1 into a discrete coordinate radiation transmission model, and operating the discrete coordinate radiation transmission model to obtain molecular extinction coefficients a m (z) at different height layers z;
meanwhile, the collected laser wind-finding radar instrument observation parameters, the aerosol particle size spectrometer observation data, the corrected high-space-time resolution temperature and relative humidity profile data, the set particle size distribution function and the aerosol shape are combined into a dataset M 2, the dataset M 2 is input into a meter scattering module, the meter scattering module is operated to obtain the backscattering coefficients beta a (z) of the aerosols at different height layers z, and the dataset M 2 is input into an aerosol and cloud particle optical parameter library module to obtain the extinction coefficients a a (z) of the aerosols at different height layers z.
5. The method for correcting the power spectrum of the laser wind-finding radar based on the multi-source observation data according to claim 4, wherein the step of S4 comprises the following steps:
According to the obtained molecular and aerosol backscattering coefficients and extinction coefficients at different height layers z, calculating the atmospheric transmittance T (z) through a beer-lambert law, and according to the sum of the atmospheric and aerosol backscattering systems, obtaining a total backscattering system beta (z) =beta m(z)+βa (z) of the atmospheric and aerosol;
And inputting the collected laser wind-finding radar instrument observation parameters, the wind speed and wind direction observed by the high-frequency sounding balloon, and the elevation angle and azimuth angle of the spatial position of the wind speed and wind direction observation relative to the laser radar wave beam at the moment into a laser radar echo signal simulator to obtain a laser radar simulation echo signal corresponding to real laser radar observation.
6. The method for correcting the power spectrum of the laser wind-finding radar based on the multi-source observation data according to claim 5, wherein the step of S5 comprises the following steps:
Performing Fourier transform on a laser radar simulation echo signal of a certain spatial position point at a certain moment to generate a corresponding frequency spectrum signal, and then performing modulo calculation on the frequency spectrum signal to obtain a power spectrum signal P M (f) simulated at the spatial position point at the moment;
According to the time and space position information generated by the power spectrum signal P M (f), finding a power spectrum signal P S (f) with consistent time and space from the collected power spectrum data actually detected by the laser wind-finding radar at a high elevation angle;
The time and space positions generated by all the analog power spectrum signals are matched one by one to form two data sets P MA (f) and P SA (f) which are in one-to-one correspondence;
Based on the data set P SA (f), the spectrum bias S K is calculated, the corresponding power spectrum is divided into three categories of positive bias, normal distribution and negative bias according to the positive value, the negative value and 0 of S K, and the corresponding data set P MA (f) is equally divided into three categories according to the three categories of the data set P SA (f), so that three training sets [ P SA1(f), PMA1(f)]、[PSA2(f),PMA2(f)]、[PSA3(f),PMA3 (f) ] are combined.
7. The method for correcting the power spectrum of the laser wind-finding radar based on the multi-source observation data according to claim 6, wherein the step of S6 comprises the following steps:
the gradient strength regression model is selected as a laser radar power spectrum correction model, three training sets are respectively input into the laser radar power spectrum correction model, and the minimum model loss function and the optimal correction performance are realized through a segmentation optimization strategy;
And taking the actually measured power spectrum and the calculated spectrum deviation of the laser wind-finding radar as the input of an optimized laser radar power spectrum correction model to obtain a corrected laser wind-finding radar power spectrum.
8. The method for correcting the power spectrum of the laser wind-finding radar based on the multi-source observation data according to claim 2, wherein the inversion product profile data of the networking microwave radiometer comprises temperature and humidity profile data with high space-time resolution;
the high-frequency sounding balloon observation profile data comprise high-space-time resolution temperature, relative humidity, air pressure, air speed and wind direction profile data;
the observation data of the aerosol particle size spectrometer comprises aerosol particle radius and aerosol concentration data;
The laser wind-finding radar instrument observation parameters comprise wavelength, frequency, pulse energy, divergence angle of a transmitter, aperture of a receiver, effective receiving efficiency and area, angle of view, observation elevation angle, observation azimuth, space-time resolution and noise level.
9. The method for correcting the power spectrum of the laser wind-finding radar based on the multi-source observation data according to claim 6, wherein the calculation formula of the spectrum deviation S K comprises the following steps:
,
Wherein the method comprises the steps of 、、The average Doppler velocity, the Doppler velocities of the left and right endpoints of the signal, S i、PN, the echo signal and the noise level, and sigma, respectively, are the spectral widths measured by the target.
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