Laser wind finding radar power spectrum correction method based on multi-source observation data

Abstract

The invention relates to a laser wind finding radar power spectrum correction method based on multi-source observation data, and belongs to the field of radio. On the basis of high-frequency sounding balloon observation data and a networked microwave radiometer inversion product, a correction strategy between deviation and microwave radiometer observation quantity is designed by utilizing the deviation of the two data, and an atmospheric temperature and humidity profile with high temporal-spatial resolution and high quality is generated; based on multi-source vertical observation data, the atmospheric state and the micro-physical inherent attributes of aerosol can be reflected in real time, and the backscattering coefficients and extinction coefficients of atmospheric and aerosol particles can be calculated; complexity of atmosphere and aerosol, scattering characteristics of a target object, micro-physical characteristics and other factors are considered, and it is reliable to use an echo signal output by an advanced laser radar echo signal simulator as a truth value. Power spectrums are divided into three types according to spectrum skewness, training of a gradient strengthening model is facilitated, and accurate and efficient correction of actual measurement power spectrum information of the laser wind finding radar is achieved.

Description

Technical Field

[0001] The invention relates to the field of radio, and in particular to a laser wind radar power spectrum correction method based on multi-source observation data. Background Art

[0002] The accuracy of the power spectrum of the laser wind radar is crucial in the laser radar system because it directly affects the quality, interpretation and subsequent application of the data. Specifically, the accuracy of the power spectrum of the laser wind radar affects the following aspects: (1) It affects the improvement of measurement accuracy. The power spectrum of the laser wind radar reflects the attenuation, scattering and reflection characteristics of the laser signal in the atmospheric propagation. An accurate power spectrum can ensure the accuracy of distance measurement, scattering coefficient calculation and extinction coefficient estimation. (2) It affects the possibility of data analysis and interpretation. The power spectrum is used to analyze the intensity, frequency distribution and other characteristics of the laser radar signal. These analyses directly affect the interpretation of atmospheric composition, ground structure and target characteristics. Inaccurate power spectrum may lead to misreading or misinterpretation of data, which in turn affects decision-making and research results. For example, in meteorology, incorrect power spectrum analysis may lead to incorrect judgment of atmospheric conditions. (3) It affects system performance and robustness. The power spectrum of the laser wind radar is used to accurately measure and model the characteristics of the atmosphere, surface or other environment. The accuracy of the power spectrum is directly related to the credibility of research data and the reliability of conclusions. Incorrect power spectrum data may lead to inconsistent or misleading research results, affecting the quality and progress of scientific research. Therefore, the accuracy of the laser wind radar power spectrum plays a key role in target identification, measurement accuracy, data analysis, system performance, scientific research, and advanced applications; inaccurate power spectrum can lead to a series of chain reactions, from incorrect data interpretation to unreliable application results.

[0003] In order to ensure the correctness of the power spectrum signal of the laser wind radar, various means are usually adopted to optimize system performance, reduce errors and improve the accuracy of signal processing. The existing technology usually adopts the following means: (1) Regular calibration, by regularly calibrating the laser radar system, ensure the accuracy and consistency of components such as the laser source, detector, scanning mechanism, etc. This includes the calibration of the laser wavelength, the calibration of the detector sensitivity and the calibration of the time delay; (2) De-noising, using advanced signal denoising algorithms such as adaptive filtering, wavelet transform, Fourier transform, etc., to eliminate the noise components in the power spectrum and improve clarity and accuracy; (3) Spatiotemporal averaging, by time averaging the power spectrum signals measured multiple times, reduce the impact of random noise and improve the signal-to-noise ratio. The power spectrum signal is collected at different spatial positions or different scanning angles and averaged to reduce the interference of spatial variability on the signal; (4) High-quality laser source, using a stable high-quality laser source to ensure the stability of the frequency and power of the laser output, thereby ensuring the accuracy of wind measurement. All of the above methods are to ensure the accuracy and reliability of the laser wind radar system from both hardware and software aspects, but rarely consider the power spectrum deviation caused by the influence of factors such as atmosphere, cloud particles, aerosols and atmospheric environment on laser scattering characteristics and beam propagation. In addition, laser wind radar is often compared with other wind measurement equipment. The purpose of this comparison is only to correct the accuracy of the wind product, but not to correct the most original power spectrum signal. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a laser wind radar power spectrum correction method based on multi-source observation data, thereby solving the shortcomings of the prior art.

[0005] The object of the present invention is achieved by the following technical scheme: a laser wind radar power spectrum correction method based on multi-source observation data, the power spectrum correction method comprising: S1. Obtain the spatial position and observation time of each observation data in the high-frequency sounding balloon observation profile based on the collected data and calculate the spatial distance difference and observation time difference, and select the microwave radiometer profile data that matches the sounding balloon observation profile in time and space; S2. Calculate the temperature and relative humidity difference between the matched sounding balloon observation and the profile of the microwave radiometer inversion product at the same point based on the sounding balloon observation profile, and build a correction model based on the error multiplication model to realize the correction of the temperature and relative humidity profile of the microwave radiometer response; S3. Build dataset M 1 , and input them into the Rayleigh scattering model and the discrete coordinate radiation transfer model respectively, and obtain the molecular backscattering coefficient and molecular extinction coefficient at different height layers z respectively, and construct the data set M 2, and input them into the Mie scattering model and the optical parameter library model respectively, and obtain the backscattering coefficient and extinction coefficient of aerosol at different altitude layers z respectively; S4, calculating the atmospheric transmittance and the total backscattering coefficient according to the data obtained in step S3, and inputting the collected data into a lidar echo signal simulator to obtain a lidar simulated echo signal corresponding to the actual lidar observation; S5, processing the obtained laser radar simulated echo signal to obtain a simulated power spectrum signal P M (f) Find the power spectrum signal P from the collected data M (f) Time and space consistent power spectrum signal P S (f) and form two data sets P MA (f) and P SA (f) Calculate the spectral skewness of the two data sets and divide the two data sets into three types of training sets; S6. Input the obtained three types of training sets into the selected laser radar power spectrum correction model for model optimization, input the power spectrum measured by the laser wind radar and the calculated spectral skewness into the optimized model to obtain the corrected laser wind radar power spectrum.

[0006] The steps of S1 include: Collect inversion product profile data of networked microwave radiometers, high-frequency sounding balloon observation profile data, observation data of aerosol size spectrometers placed at different altitudes on multiple observation towers, aerosol type data observed by aerosol mass spectrometers, observation parameters of laser wind radar instruments, and power spectrum data sets of high-elevation angle detection; Based on the collected networked microwave radiometer inversion product profile data and high-frequency sounding balloon observation profile data, the spatial position and observation time of each observation data in the high-frequency sounding observation profile are obtained, and the spatial distance difference and observation time difference between each observation point and all observation points in the microwave radiometer inversion product profile in the adjacent space are calculated respectively. The microwave radiometer profile data that matches the sounding balloon observation profile in time and space is selected based on the minimum product of the spatial distance difference and the observation time difference.

[0007] The steps of S2 include: According to the profiles of the matched sounding balloon observations and microwave radiometer inversion products, the difference in temperature and relative humidity at the same point between the two is calculated with the sounding balloon observation profile as the benchmark; Based on the error multiplication model, correction models between the temperature and relative humidity differences and the corresponding meteorological variables inverted by the microwave radiometer are established respectively. The temperature and relative humidity sequences of multiple spatial matching points are input, and nonlinear fitting is performed based on the correction model. The specific values ​​of the parameters in the correction model are determined according to the fitting results. Based on the correction model with specific parameters, the microwave radiometer inversion product data is taken as input, thereby realizing the correction of the temperature and relative humidity profiles inverted by the microwave radiometer.

[0008] The steps of S3 include: The data set M is composed of the collected laser wind radar instrument observation parameters, the temperature and relative humidity profile data inverted by the calibrated microwave radiometer, the distribution profile of air pressure with height under the standard atmospheric assumption, the air molecule density, refractive index and the proportion of the main gas components. 1 , the dataset M 1 Input into the Rayleigh scattering model, run the Rayleigh scattering model to obtain the molecular backscattering coefficient β at different height layers z m (z), and at the same time, the dataset M 1 Input it into the discrete coordinate radiative transfer model, and run the discrete coordinate radiative transfer model to obtain the molecular extinction coefficient a at different height layers z m (z); At the same time, the collected laser wind radar instrument observation parameters, aerosol particle size spectrometer observation data, aerosol mass spectrometer observation data, calibrated high temporal and spatial resolution temperature and relative humidity profile data, set particle size distribution function and aerosol shape together form the data set M 2 , the dataset M 2 Input into the Mie scattering module and run the Mie scattering model to obtain the backscattering coefficient β of aerosol at different altitudes z a (z), and at the same time, the dataset M 2 Input into the aerosol and cloud particle optical parameter library model to obtain the aerosol extinction coefficient a at different altitudes z a (z).

[0009] The steps of S4 include: According to the molecular and aerosol backscattering coefficients and extinction coefficients at different altitudes z, the atmospheric transmittance T(z) is calculated by the Beer-Lambert law, and according to the sum of the atmospheric and aerosol backscattering systems, the total atmospheric and aerosol backscattering system β(z) = β m (z)+ β a (z); The collected laser wind radar instrument observation parameters and the wind speed and wind direction observed by high-frequency sounding balloons, as well as the elevation and azimuth angles of the spatial position of the wind speed and wind direction observations at that moment relative to the laser radar beam, are input into the laser radar echo signal simulator to obtain a laser radar simulated echo signal corresponding to the real laser radar observation.

[0010] The step of S5 comprises: The laser radar simulated echo signal at a certain spatial position at a certain time is transformed by Fourier to generate the corresponding spectrum signal, and then the spectrum signal is modulo-squared to obtain the simulated power spectrum signal P at the spatial position at that time. M (f); According to the power spectrum signal P M (f) The generated time and spatial position information is used to find the power spectrum signal P that is consistent in time and space from the power spectrum data actually detected by the laser wind radar at high elevation angles. S (f); The time and space positions of all simulated power spectrum signals are matched one by one to form two corresponding data sets P MA (f) and P SA (f); Based on the dataset P SA (f), calculate its spectral skewness S K , according to S K The positive, negative and 0 values ​​of the data set P are divided into three categories: positive deviation, normal distribution and negative deviation. SA (f), the corresponding data set P MA (f) is also divided into three categories, thus forming three types of training sets [P SA1 (f), P MA1 (f)]、[P SA2 (f), P MA2 (f)]、[P SA3 (f), P MA3 (f)].

[0011] The step of S6 comprises: The gradient intensity regression model is selected as the lidar power spectrum correction model. The three types of training sets are input into the lidar power spectrum correction model respectively. Through the segmented tuning strategy, the model loss function is minimized and the correction performance is optimized. The power spectrum measured by the laser wind radar and the calculated spectral skewness are used as the input of the optimized laser radar power spectrum correction model to obtain the corrected laser wind radar power spectrum.

[0012] The inversion product profile data of the networked microwave radiometer includes temperature and humidity profile data with high temporal and spatial resolution; The high-frequency sounding balloon observation profile data includes temperature, relative humidity, air pressure, wind speed and wind direction profile data with high temporal and spatial resolution; The observation data of the aerosol particle size spectrometer includes aerosol particle radius and aerosol concentration data; The observation parameters of the laser wind radar instrument include wavelength, frequency, pulse energy, divergence angle of the transmitter, receiver aperture, effective receiving efficiency and area, field of view angle, observation elevation angle, observation azimuth, temporal and spatial resolution and noise level.

[0013] The spectral skewness S K The calculation formula includes: , in , , are the average Doppler velocity, the Doppler velocities of the left and right endpoints of the signal, S i , P N are the echo signal and noise level respectively, and σ is the spectrum width of the target measurement.

[0014] The invention has the following advantages: a laser wind radar power spectrum correction method based on multi-source observation data, based on high-frequency sounding balloon observation data and networked microwave radiometer inversion products, using the deviation of the two data, designing a correction strategy between the deviation and the microwave radiometer observation quantity, generating high-temporal and spatial resolution and high-quality atmospheric temperature and humidity profiles; based on multi-source vertical observation data, it can reflect the atmospheric state and the microphysical inherent properties of aerosols in real time, thereby facilitating scattering models, radiation transmission models and aerosol cloud particle optical parameter libraries, etc., and can accurately 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 Process intent for calibrating microwave radiometer temperature and humidity profiles; Figure 3 Schematic diagram of the process flow for the generation of molecular backscattering and extinction coefficients for different altitude layers; Figure 4 Schematic diagram of the process of generating aerosol particle backscattering and extinction coefficients for different altitude layers. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided below in conjunction with the drawings is not intended to limit the scope of protection of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application. The present invention is further described below in conjunction with the drawings.

[0017] like Figure 1 As shown, the present invention specifically relates to a laser wind radar power spectrum correction method based on multi-source observation data, which specifically includes the following contents: Step 1: Collect the inversion product profile data of the networked microwave radiometer, including temperature and humidity profile data with high temporal and spatial resolution; collect observation data from high-frequency sounding balloons, including temperature, relative humidity, air pressure, wind speed, wind direction and other profile data with high temporal and spatial resolution; collect observation data from aerosol particle size spectrometers placed at different altitudes on multiple observation towers and aerosol type data observed by aerosol mass spectrometers; collect observation parameters of laser wind radar instruments and power spectrum data sets for high-elevation angle detection.

[0018] Among them, the observation data of the aerosol particle size spectrometer include aerosol particle radius and aerosol concentration data; The observation parameters of the laser wind radar instrument mainly refer to the wavelength of 1550 nm, frequency of 10KHz, pulse energy of 300μJ@1200ns, the divergence angle of the transmitter ≤0.1mrad, the aperture of the receiver 100mm, the effective receiving efficiency and area ≥95% and 7850mm respectively. 2 , field of view angle ≤ 0.1mrad, observation elevation angle, observation azimuth, temporal and spatial resolution 10s×30m, noise level 10mVpp and other data.

[0019] Step 2: Based on the inversion product profile data of the networked microwave radiometer and the high-frequency sounding balloon observation profile data collected in step 1, obtain the spatial position (longitude, latitude and altitude) and observation time of each observation data in the high-frequency sounding balloon observation profile, and calculate the spatial distance difference and observation time difference between each observation point and all observation points in the microwave radiometer inversion product profile in the adjacent space. The microwave radiometer profile data that matches the sounding balloon observation profile in time and space is selected based on the minimum product of the spatial distance difference and the observation time difference.

[0020] The sounding balloon observation profile data refers to the temperature and relative humidity profiles, which are represented by T tk,i , Q tk,i To indicate that there are 30 layers of profile data in the altitude range of 0 to 5 km, the value of i is 1, 2, …, 30.

[0021] The microwave radiometer profile data after time and space matching refers to the temperature, relative humidity, air pressure, wind speed and wind direction profiles, where the temperature and relative humidity profiles are represented by T wb,i , Q wb,i To express.

[0022] The spatial distance difference and observation time difference refer to the spatial point A, whose latitude, longitude, altitude and observation time are lat_a, lon_a, h_a and time_a respectively, and are represented by (lat_a, lon_a, h_a, time_a). The representation of spatial point B (lat_b, lon_b, h_b, time_b) is similar to point A. The distance difference ΔR and observation time difference Δt between the two points are: , Δ t =time_a- time_b.

[0023] Step 3: Based on the matched sounding balloon observations and microwave radiometer inversion product profiles obtained in step 2, the temperature or relative humidity difference between the two at the same point is calculated with the sounding balloon observation profile as the benchmark, which are: and After that, based on the error multiplication model, the correction models between the temperature and relative humidity differences and the corresponding meteorological variables inverted by the microwave radiometer are established. The temperature and relative humidity sequences of multiple spatial matching points are input, and nonlinear fitting is performed based on the correction model. The parameters in the correction model can be determined according to the fitting results. , , , , and Based on the correction model with specified parameters, the microwave radiometer inversion product data is used as input to realize the correction of the temperature and relative humidity profiles inverted by the microwave radiometer.

[0024] The correction model refers to the temperature profile correction model , and the relative humidity profile correction model .

[0025] Step 4: The laser wind radar instrument observation parameters collected in step 1, the temperature and relative humidity profile data inverted by the microwave radiometer after correction in step 3, and the distribution profile of air pressure with height under standard atmospheric conditions, air molecular density, refractive index and the proportion of main gas components are combined to form a data set M. 1 , the dataset M 1 Input them into the Rayleigh scattering model together, and run this model to obtain the molecular backscattering coefficient β at different height layers z. m (z). At the same time, the dataset M 1 Input into the discrete coordinate radiation transfer model, and run the model to obtain the molecular extinction coefficient a at different altitudes z m (z).

[0026] Step 5: The laser wind radar instrument observation parameters collected in step 1, the aerosol particle size spectrometer observation data, the aerosol mass spectrometer observation data, the high temporal and spatial resolution temperature and relative humidity profile data corrected in step 3, and the set particle size distribution function and aerosol shape parameters are combined to form a data set M. 2 , the dataset M 2 Input into the Mie scattering model, and run the model to obtain the aerosol backscattering coefficient β at different altitudes z a (z). At the same time, the dataset M 2 By inputting it into the aerosol and cloud particle optical parameter library model, the extinction coefficient a of the aerosol at different altitudes z can be obtained. a (z).

[0027] Step 6: Based on the molecular and aerosol backscattering coefficients and extinction coefficients at different altitudes z obtained in steps 4 and 5, the Beer-Lambert law can be used to calculate the atmospheric transmittance T(z). Based on the sum of the atmospheric and aerosol backscattering coefficients, the total atmospheric and aerosol backscattering coefficient β (z) = β m (z)+ β a (z). Then, the laser wind radar instrument observation parameters collected in step 1 and the wind speed and wind direction observed by the high-frequency sounding balloon, as well as the elevation angle and azimuth angle of the spatial position of the wind speed and wind direction observation at that moment relative to the laser radar beam, are input into the laser radar echo signal simulator (LidarSim). The simulator is run to obtain the echo signal corresponding to the real laser radar observation.

[0028] Among them, the formula for calculating atmospheric transmittance using the Beer-Lambert law is: , where s represents the propagation path.

[0029] Step 7: The laser radar simulated echo signal at a certain spatial position at a certain time obtained in step 6 is subjected to Fourier transform to generate the corresponding spectrum signal. Then the spectrum signal is modulo and squared to obtain the simulated power spectrum signal P at the spatial position at that time. M (f). Press P M (f) The generated time and spatial position information is used to find the power spectrum signal P that is consistent in time and space from the power spectrum data actually detected by the laser wind radar at high elevation angles collected in step 1. S (f) Afterwards, the time and spatial positions of all simulated power spectrum signals are matched one by one to form two corresponding data sets P MA (f) and P SA (f). Based on the dataset P SA (f), calculate its spectral skewness S K , according to S K The positive, negative and 0 values ​​of the data set P are divided into three types: positive deviation, normal distribution and negative deviation. SA (f), the corresponding P M (f) is also divided into three categories, thus forming three types of training sets [P SA1 (f), P MA1 (f)]、[P SA2 (f), P MA2 (f)]、[P SA3 (f), P MA3 (f)]. The spectral skewness S K The calculation formula is: , in , , are the average Doppler velocity, the Doppler velocities of the left and right ends of the signal, S i , P N are the echo signal and noise level respectively, and σ is the spectrum width of the target measurement.

[0030] Step 8: Select the gradient enhancement regression model as the model for lidar power spectrum correction, input the three types of training sets obtained in step 7 into the model respectively, and achieve the minimum model loss function and the optimal correction performance through the segmented tuning strategy.

[0031] The segmented tuning strategy refers to dynamically adjusting the main parameters of the model first, and then dynamically adjusting other secondary parameters of the model. When a good set of secondary parameters is determined, the main parameters of the model are fine-tuned to finally achieve the setting of the optimal parameters of the model.

[0032] Step 9: According to the laser wind radar power spectrum optimization model optimized in step 8, the power spectrum measured by the laser wind radar and the calculated spectrum skewness parameters are used as the input of the model, and the corrected laser wind radar power spectrum is obtained as the output.

[0033] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used for various other combinations, modifications and improvements, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art do not deviate from the spirit and scope of the present invention, and should be within the scope of protection of the claims attached to the present invention.

Claims

1. A laser wind radar power spectrum correction method based on multi-source observation data, characterized in that: The power spectrum correction method comprises: S1. Obtain the spatial position and observation time of each observation data in the high-frequency sounding balloon observation profile based on the collected data and calculate the spatial distance difference and observation time difference, and select the microwave radiometer profile data that matches the sounding balloon observation profile in time and space; S2. Calculate the temperature and relative humidity difference between the matched sounding balloon observation and the profile of the microwave radiometer inversion product at the same point based on the sounding balloon observation profile, and build a correction model based on the error multiplication model to realize the correction of the temperature and relative humidity profile of the microwave radiometer response; S3, constructing data set M1, and inputting it into Rayleigh scattering model and discrete coordinate radiation transfer model, respectively, to obtain molecular backscattering coefficient and molecular extinction coefficient at different altitude layers z, respectively; constructing data set M2, and inputting it into Mie scattering model and optical parameter library model, respectively, to obtain aerosol backscattering coefficient and extinction coefficient at different altitude layers z, respectively; S4, calculating the atmospheric transmittance and the total backscattering coefficient according to the data obtained in step S3, and inputting the collected data into a lidar echo signal simulator to obtain a lidar simulated echo signal corresponding to the actual lidar observation; S5, processing the obtained laser radar simulated echo signal to obtain a simulated power spectrum signal P M (f) Find the power spectrum signal P from the collected data M (f) Time and space consistent power spectrum signal P S (f) and form two data sets P MA (f) and P SA (f) Calculate the spectral skewness of the two data sets and divide the two data sets into three types of training sets; S6. Input the obtained three types of training sets into the selected laser radar power spectrum correction model for model optimization, input the power spectrum measured by the laser wind radar and the calculated spectral skewness into the optimized model to obtain the corrected laser wind radar power spectrum.

2. The method for correcting the power spectrum of a laser wind radar based on multi-source observation data according to claim 1, characterized in that: The steps of S1 include: Collect inversion product profile data of networked microwave radiometers, high-frequency sounding balloon observation profile data, observation data of aerosol size spectrometers placed at different altitudes on multiple observation towers, aerosol type data observed by aerosol mass spectrometers, observation parameters of laser wind radar instruments, and power spectrum data sets of high-elevation angle detection; Based on the collected networked microwave radiometer inversion product profile data and high-frequency sounding balloon observation profile data, the spatial position and observation time of each observation data in the high-frequency sounding observation profile are obtained, and the spatial distance difference and observation time difference between each observation point and all observation points in the microwave radiometer inversion product profile in the adjacent space are calculated respectively. The microwave radiometer profile data that matches the sounding balloon observation profile in time and space is selected based on the minimum product of the spatial distance difference and the observation time difference.

3. The method for correcting the power spectrum of a laser wind radar based on multi-source observation data according to claim 2 is characterized in that: The steps of S2 include: According to the profiles of the matched sounding balloon observations and microwave radiometer inversion products, the temperature and relative humidity differences at the same point between the sounding balloon observation profiles and the profiles of the microwave radiometer inversion products are calculated with the sounding balloon observation profiles as the benchmark; Based on the error multiplication model, correction models between the temperature and relative humidity differences and the corresponding meteorological variables inverted by the microwave radiometer are established respectively. The temperature and relative humidity sequences of multiple spatial matching points are input, and nonlinear fitting is performed based on the correction model. The specific values ​​of the parameters in the correction model are determined according to the fitting results. Based on the correction model with specific parameters, the microwave radiometer inversion product data is taken as input, thereby realizing the correction of the temperature and relative humidity profiles inverted by the microwave radiometer.

4. The method for correcting the power spectrum of a laser wind radar based on multi-source observation data according to claim 3 is characterized in that: The steps of S3 include: The collected laser wind radar instrument observation parameters, the calibrated microwave radiometer inverted temperature and relative humidity profile data, the distribution profile of air pressure with height under standard atmospheric assumptions, air molecule density, refractive index and the proportion of main gas components are combined to form a data set M1. The data set M1 is input into the Rayleigh scattering model, and the Rayleigh scattering model is run to obtain the molecular backscattering coefficient β at different altitude layers z. m (z), and input the data set M1 into the discrete coordinate radiative transfer model. Running the discrete coordinate radiative transfer model can obtain the molecular extinction coefficient a at different altitudes z. m (z); At the same time, the collected observation parameters of the laser wind radar instrument, the observation data of the aerosol particle size spectrometer, the observation data of the aerosol mass spectrometer, the calibrated high temporal and spatial resolution temperature and relative humidity profile data, the set particle size distribution function and the aerosol shape are combined to form the data set M2. The data set M2 is input into the Mie scattering module, and the Mie scattering model is run to obtain the backscattering coefficient β of the aerosol at different altitudes z. a (z), and input the data set M2 into the aerosol and cloud particle optical parameter library model to obtain the aerosol extinction coefficient a at different altitudes z a (z).

5. The method for correcting the power spectrum of a laser wind radar based on multi-source observation data according to claim 4 is characterized in that: The steps of S4 include: According to the molecular and aerosol backscattering coefficients and extinction coefficients at different altitudes z, the atmospheric transmittance T(z) is calculated by the Beer-Lambert law, and according to the sum of the atmospheric and aerosol backscattering systems, the total atmospheric and aerosol backscattering system β(z) = β m (z)+β a (z); The collected laser wind radar instrument observation parameters and the wind speed and wind direction observed by high-frequency sounding balloons, as well as the elevation and azimuth angles of the spatial position of the wind speed and wind direction observations at that moment relative to the laser radar beam, are input into the laser radar echo signal simulator to obtain a laser radar simulated echo signal corresponding to the real laser radar observation.

6. The method for correcting the power spectrum of a laser wind radar based on multi-source observation data according to claim 5 is characterized in that: The step of S5 comprises: The laser radar simulated echo signal at a certain spatial position at a certain time is transformed by Fourier to generate the corresponding spectrum signal, and then the spectrum signal is modulo-squared to obtain the simulated power spectrum signal P at the spatial position at that time. M (f); According to the power spectrum signal P M (f) The generated time and spatial position information is used to find the power spectrum signal P that is consistent in time and space from the power spectrum data actually detected by the laser wind radar at high elevation angles. S (f); The time and space positions of all simulated power spectrum signals are matched one by one to form two corresponding data sets P MA (f) and P SA (f); Based on the dataset P SA (f), calculate its spectral skewness S K , according to S K The positive, negative and 0 values ​​of the data set P are divided into three categories: positive deviation, normal distribution and negative deviation. SA (f), the corresponding data set P MA (f) is also divided into three categories, thus forming three types of training sets [P SA1 (f), P MA1 (f)]、[P SA2 (f), P MA2 (f)]、[P SA3 (f), P MA3 (f)].

7. The method for correcting the power spectrum of a laser wind radar based on multi-source observation data according to claim 6 is characterized in that: The step of S6 comprises: The gradient intensity regression model is selected as the lidar power spectrum correction model. The three types of training sets are input into the lidar power spectrum correction model respectively. Through the segmented tuning strategy, the model loss function is minimized and the correction performance is optimized. The power spectrum measured by the laser wind radar and the calculated spectral skewness are used as the input of the optimized laser radar power spectrum correction model to obtain the corrected laser wind radar power spectrum.

8. The method for correcting the power spectrum of a laser wind radar based on multi-source observation data according to claim 2, characterized in that: The inversion product profile data of the networked microwave radiometer includes temperature and humidity profile data with high temporal and spatial resolution; The high-frequency sounding balloon observation profile data includes temperature, relative humidity, air pressure, wind speed and wind direction profile data with high temporal and spatial resolution; The observation data of the aerosol particle size spectrometer include aerosol particle radius and aerosol concentration data; The observation parameters of the laser wind radar instrument include wavelength, frequency, pulse energy, divergence angle of the transmitter, receiver aperture, effective receiving efficiency and area, field of view angle, observation elevation angle, observation azimuth, temporal and spatial resolution and noise level.

9. The method for correcting the power spectrum of a laser wind radar based on multi-source observation data according to claim 6, characterized in that: The spectral skewness S K The calculation formula includes: ， in , , are the average Doppler velocity, the Doppler velocities of the left and right endpoints of the signal, S i , P N are the echo signal and noise level respectively, and σ is the spectrum width of the target measurement.

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