Turbulent flow signal collaborative inversion method and system for multi-source foundation remote sensing vertical observation

Abstract

The invention relates to the technical field of digital signal processing, and particularly provides a turbulent flow signal collaborative inversion method and system for multi-source foundation remote sensing vertical observation, and the method comprises the steps: carrying out the collaborative data quality control processing of original observation data of multi-source foundation remote sensing equipment, and obtaining multi-source foundation remote sensing quality control data; the method comprises the steps of calculating thermodynamic parameters according to multi-source foundation remote sensing quality control data to obtain atmospheric thermodynamic parameters, carrying out cloud boundary identification according to the multi-source foundation remote sensing quality control data and the atmospheric thermodynamic parameters to obtain cloud vertical structure parameters and boundary layer height, and carrying out cloud boundary identification according to multi-source foundation remote sensing observation data, the atmospheric thermodynamic parameters and the cloud vertical structure parameters to obtain cloud vertical structure parameters and boundary layer height. And respectively inverting and fusing turbulence in the boundary layer and the free atmosphere to obtain a fused turbulence dissipation rate, and carrying out calculation according to the fused turbulence dissipation rate and the atmosphere thermal power parameters to obtain turbulence characteristic parameters. The stability and precision of turbulence parameter inversion are improved, and the fine structure of atmospheric turbulence can be obtained.

Description

Technical Field

[0001] This invention relates to the field of digital signal processing technology, and in particular to a method and system for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation. Background Technology

[0002] Atmospheric vertical structure observation is one of the core components of a comprehensive meteorological observation system. It refers to the comprehensive observation of meteorological elements within the vertical atmospheric column from the ground to the boundary layer, troposphere, and even above the stratosphere. Turbulence is an important dynamic process in the atmosphere, through which water vapor, heat, kinetic energy, and aerosols are transported from the boundary layer to the free atmosphere (stratosphere and above) via turbulent or convective motion. Therefore, accurately acquiring turbulence signals is of great significance for ensuring the safety of low-altitude aircraft.

[0003] Direct observation methods for turbulence signals mainly include radiosondes, rockets, and aircraft. Although turbulence profile data can be observed or retrieved from commercial aircraft, the near-horizontal flight paths, limited observations to main routes, and avoidance of reported or predicted areas of strong turbulence make it difficult to correlate turbulence signals with background atmospheric conditions. Furthermore, these in-situ observation devices are costly, difficult to conduct continuous observations, and highly susceptible to weather conditions, limiting their coverage to small or point-scale areas and making it difficult to provide long-term turbulence profile signals. In recent years, small unmanned aerial vehicles (UAVs) have gradually filled the gaps in low-altitude observation using aircraft, radiosondes, and ground-based radar. However, in environments with strong turbulence, strong wind shear, and weather systems, UAV detection may encounter flight stability issues, affecting the reliability and continuity of its data.

[0004] Ground-based remote sensing vertical observation systems mainly consist of five types of equipment: wind profiler radar, millimeter-wave cloud radar, microwave radiometer, aerosol lidar, and GNSS / MET water vapor radar, forming a collaborative observation device. Through the mutual complementarity of different ground-based observation methods, minute-level real-time data on wind, water vapor, clouds, temperature, humidity, and aerosols at altitudes below 10 kilometers above the observation station can be rapidly obtained. This enables complementary advantages and collaborative observation at the same site, forming a three-dimensional monitoring network for various meteorological elements. Ground-based radar inversion of turbulence signals mainly employs three methods: the power method, the Doppler spectral width method, and the variance method. Domestically and internationally, research on collaborative inversion techniques for free atmospheric turbulence profile signals above 3 kilometers above the ground has been conducted using wind profiler radar, microwave radiometers, and other equipment based on these methods.

[0005] However, existing turbulence signal inversion techniques have the following shortcomings: First, existing turbulence inversion methods are only applicable to the free atmosphere above 3 kilometers above the ground, and their ability to invert turbulence signals within the boundary layer is insufficient; second, conventional radiosondes only observe twice a day, which cannot provide continuous atmospheric thermodynamic parameter profiles, making it difficult to obtain minute-level continuous turbulence signal profile products; third, single-device observations have limitations, and there is a lack of collaborative observation and data fusion technology for multi-source ground-based remote sensing equipment; finally, there is a lack of collaborative inversion algorithms for turbulence signals under different weather conditions (clear sky-cloudy-precipitation), making it impossible to achieve high-precision turbulence parameter inversion in all weather conditions.

[0006] Therefore, it is urgent to utilize multi-source ground-based remote sensing vertical observation data to investigate the vertical refractive index gradient from near-ground to the atmosphere below 10 kilometers above ground. ), Turbulent dissipation rate ( ), Vertical eddy diffusion rate ( ), Turbulent internal scale ( ), turbulent external scale ( The collaborative inversion of turbulence signals such as atmospheric turbulence signals provides important scientific value for studying the evolution of the fine structure of atmospheric turbulence signals. Summary of the Invention

[0007] Based on the above-mentioned technical problems, this application provides a method and system for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation, in order to solve the technical problems in the prior art, such as the difficulty of continuous observation by sounding, rockets, and aircraft, and the fact that ground-based remote sensing vertical observation equipment is only suitable for free atmosphere above 3 kilometers and has poor inversion capability for turbulence signals within the atmospheric boundary layer.

[0008] This invention provides a method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observations, comprising: S1: Collect raw observation data from multi-source ground-based remote sensing equipment, and perform collaborative data quality control processing on the raw observation data to obtain multi-source ground-based remote sensing quality control data; S2: Calculate the thermodynamic parameters based on the multi-source ground-based remote sensing quality control data to obtain atmospheric thermodynamic parameters; S3: Based on the multi-source ground-based remote sensing quality control data and the atmospheric thermodynamic parameters, cloud boundary identification is performed to obtain cloud vertical structure parameters and boundary layer height; S4: Based on the multi-source ground-based remote sensing quality control data, the atmospheric thermodynamic parameters, the cloud vertical structure parameters, and the boundary layer height, the fused turbulent dissipation rate below 10 kilometers above the ground is obtained; S5: Based on the fusion turbulence dissipation rate and the atmospheric thermodynamic parameters, calculate the parameters characterizing turbulence intensity and turbulence scale to obtain turbulence characteristic parameters below 10 km above the ground.

[0009] According to the present invention, a method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation is provided. The multi-source ground-based remote sensing equipment mentioned in step S1 includes a wind profiler radar, a millimeter-wave cloud radar, a wind-measuring lidar, and a microwave radiometer.

[0010] According to the present invention, a method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observations is provided. Step S1 includes a quality control step on the raw observation data, specifically including: S11: Perform quality control on wind profiler radar data in the raw observation data; perform quality control on millimeter-wave cloud radar data in the raw observation data; perform quality control on wind lidar data in the raw observation data; perform quality control on microwave radiometer data in the raw observation data. S12: Perform collaborative data quality control on the raw observation data after quality control.

[0011] According to the present invention, a method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation is provided, wherein the atmospheric thermodynamic parameters in step S2 include potential temperature, buoyancy frequency, wind shear, and gradient Richardson number. The expression for the potential temperature is: in, For potential temperature, For temperature, For the air pressure at any altitude, these parameters can be calculated from the quality-controlled microwave radiometer data; The buoyancy frequency ( The expression for square is: in, The frequency of buoyancy. It is the acceleration due to gravity. Height above the ground These parameters, which are specific heat constants, can be calculated from the data of wind profiler radar and microwave radiometer after quality control. The expression for the wind shear is: in, To calculate the obtained wind shear, For meridional wind speed, For zonal wind speed, These parameters, representing the altitude above the ground, can be calculated from the quality-controlled wind profiler radar data. The expression for the gradient Richardson number is: in, For the gradient Richardson number, For zonal wind speed, For meridional wind speed, these parameters can be calculated from quality-controlled wind profiler radar and microwave radiometer data.

[0012] According to the method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation provided by the present invention, step S3 further includes: S31: Based on the microwave radiometer quality control data in the multi-source ground-based remote sensing observation data, cloud layer identification is performed using the relative humidity threshold judgment method to obtain the cloud layer number and cloud boundary parameters based on the microwave radiometer. S32: Based on the millimeter-wave cloud radar data in the multi-source ground-based remote sensing observation data, cloud layer identification is performed using the reflectivity factor threshold judgment method to obtain the number of cloud layers and cloud boundary parameters based on the millimeter-wave cloud radar. S33: Based on the cloud layer number and cloud boundary parameters of the microwave radiometer and millimeter-wave cloud radar, the cloud vertical structure parameters of the microwave radiometer and millimeter-wave cloud radar are jointly identified by the dynamic threshold method to obtain cloud vertical structure parameters such as cloud base height, cloud top height and cloud thickness; based on the gradient Richardson number in the atmospheric thermodynamic parameters, the boundary layer height is obtained based on the threshold method.

[0013] According to the present invention, the cloud vertical structure parameters in step S3 of the collaborative inversion method of multi-source ground-based remote sensing vertical observation of turbulence signals include cloud base height, cloud top height, and cloud thickness.

[0014] According to the method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation provided by the present invention, step S4 further includes: S41: Turbulent dissipation rate inversion is performed using multi-source radar data and multiple methods to obtain the turbulent dissipation rate from multi-source radar data and multiple methods. S42: Based on the cloud vertical structure parameters and boundary layer height, perform boundary layer and free atmosphere identification processing on multi-source radar data and multi-method turbulence dissipation rates to obtain the turbulence dissipation rates within the boundary layer and in the free atmosphere; S43: The turbulent dissipation rate at each altitude is weighted and fused using a spatiotemporal fusion model to obtain a fused turbulent dissipation rate that includes the boundary layer turbulent dissipation rate and the free atmosphere turbulent dissipation rate.

[0015] According to the present invention, a collaborative inversion method for turbulence signals from multi-source ground-based remote sensing vertical observation is provided. The multi-method turbulence dissipation rate in step S41 includes the turbulence dissipation rate based on the power method, the turbulence dissipation rate based on the spectral broadening method, the turbulence dissipation rate based on the vertical velocity method, and the turbulence dissipation rate based on the Thorpe method.

[0016] According to the present invention, a method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation is provided, wherein the turbulence characteristic parameters in step S5 include the vertical refractive index gradient ( ), Turbulent dissipation rate ( ), Vertical eddy diffusion rate ( ), Turbulent internal scale ( ), turbulent external scale ( )wait; The expression for the vertical refractive index gradient is: in, For the perpendicular refractive index gradient, For temperature, For potential temperature, For air pressure, For height, For specific moisture The expression for the vertical eddy current diffusivity is: in, Height above ground Vertical eddy diffusion rate below For mixing efficiency, The frequency of buoyancy. For turbulent dissipation rate, For the gradient Richardson number; The expression for the external scale of turbulence is: in, For the external scale of turbulence; The expression for the internal scale of turbulence is: in, For the internal scale of turbulence, For Kolmogorov microscale.

[0017] This invention provides a collaborative inversion system for turbulence signals from multi-source ground-based remote sensing vertical observations, comprising: Quality control module: Used to collect raw observation data from multi-source ground-based remote sensing equipment and perform quality control to obtain multi-source ground-based remote sensing quality control data; First calculation module: used to calculate the thermodynamic parameters based on the multi-source ground-based remote sensing observation data, and obtain atmospheric thermodynamic parameters; Identification module: used to identify cloud boundaries based on the multi-source ground-based remote sensing observation data and the atmospheric thermodynamic parameters, and obtain cloud vertical structure parameters and boundary layer height; Inversion module: used to perform multi-method turbulent dissipation rate inversion based on the atmospheric thermodynamic parameters, cloud vertical structure parameters and boundary layer height, to obtain the fused turbulent dissipation rate; The second calculation module is used to calculate parameters characterizing turbulence intensity and turbulence scale based on the fused turbulence dissipation rate and the atmospheric thermodynamic parameters, thereby obtaining turbulence characteristic parameters.

[0018] Compared with existing technologies, the beneficial effects of the multi-source ground-based remote sensing vertical observation turbulence signal collaborative inversion method and system provided in this application are: By comprehensively utilizing data from multi-source ground-based remote sensing equipment (wind profiler radar, millimeter-wave cloud radar, wind-measuring lidar, and microwave radiometer), this invention accurately acquires atmospheric turbulence-related information. Through collaborative processing of multi-source data, non-meteorological radar echo signals are effectively removed, extracting more reliable atmospheric turbulence echo data, providing high-quality foundational data for subsequent turbulence parameter inversion. Compared to the high cost and difficulty in continuous observation of direct observation methods such as sounding, rockets, and aircraft, this method utilizes ground-based remote sensing equipment to achieve all-weather, continuous observation. Furthermore, by using cloud vertical structure parameters and boundary layer height, this invention can accurately distinguish the layered structure of the boundary layer and free atmosphere, realizing a collaborative inversion strategy for boundary layer and free atmosphere turbulence, effectively solving the problem that traditional methods are only applicable to 3D clouds. The limitations of free atmosphere above 10 km were addressed; atmospheric thermodynamic parameters obtained through calculation and processing provided necessary thermodynamic background field information for turbulence signal inversion; through multi-source data fusion and collaborative inversion, effective inversion of turbulence signals in the atmospheric boundary layer and free atmosphere was successfully achieved, yielding a more comprehensive and accurate turbulence fusion dissipation rate below 10 km altitude, which was then used to further calculate a more accurate vertical refractive index gradient. ), Vertical eddy diffusion rate ( ), Turbulent internal scale ( ), turbulent external scale ( Turbulence characteristic parameters such as turbulence characteristics enhance the safety assurance capability of low-altitude aircraft under complex weather conditions. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of a method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation, provided in an embodiment of the present invention. Figure 2 A schematic diagram of a multi-source ground-based remote sensing vertical observation turbulence signal collaborative inversion system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the diurnal variation characteristics of turbulence signal parameters at an observation station, as provided in an embodiment of the present invention.

[0021] Figure reference numerals: 100, quality control module; 200, first calculation module; 300, identification module; 400, inversion module; 500, second calculation module. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0023] To better understand this invention, the research background of this invention will be explained in detail below.

[0024] Common turbulence signal parameters in existing technologies include turbulence dissipation rate (TDI). ), Turbulent kinetic energy (TKE), buoyancy frequency ( ), gradient Richardson number ( ), Turbulent internal scale ( ), turbulent external scale ( ), Vertical eddy diffusion rate ( Atmospheric refractive index structure constant ( ) etc. When ground-based radar inverts turbulence signals, the turbulence dissipation rate ( ) is a key parameter for inverting turbulence signals, and the turbulence internal scale ( ), turbulent external scale ( ) and vertical eddy diffusion rate ( ) can be determined by the turbulent dissipation rate ( This is derived from the derivation.

[0025] Currently, both domestic and international researchers have utilized sounding data from wind profiler radars and microwave radiometers to conduct research on turbulence profile signal inversion techniques. While these techniques have achieved some estimation of turbulence parameters, they are only applicable to the free atmosphere above 3 km, exhibiting poor inversion capabilities for turbulence signals within the atmospheric boundary layer. Furthermore, research on the spatiotemporal distribution characteristics of turbulence signals in applicable regions remains scarce. Moreover, the relative lack of high-spatiotemporal resolution collaborative observations of key boundary layer thermodynamic parameters results in a lack of turbulence signal inversion datasets. Additionally, while current ground-based remote sensing vertical observation systems have achieved refined observations of vertical profiles for multiple elements of the atmospheric boundary layer, including temperature, humidity, wind, water condensates, and aerosols, their application in turbulence signal inversion remains limited.

[0026] Therefore, based on observation data from ground-based remote sensing vertical observation systems, etc., vertical refractive index gradient (... ), Turbulent dissipation rate ( ), Vertical eddy diffusion rate ( ), Turbulent internal scale ( ), turbulent external scale ( The joint inversion of turbulence signals such as tidal signals can provide important scientific value for studying the evolution of fine structure in boundary layer turbulence.

[0027] Please see Figure 1 As shown, this invention provides a method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observations, comprising: S1: Collect raw observation data from multi-source ground-based remote sensing equipment and perform collaborative data quality control processing to obtain multi-source ground-based remote sensing quality control data. By comprehensively utilizing data from multi-source ground-based remote sensing equipment, atmospheric turbulence-related information can be obtained more comprehensively and accurately, overcoming the limitations of traditional observation methods and providing more reliable data support for ensuring the safety of low-altitude aircraft.

[0028] The multi-source ground-based remote sensing equipment in step S1 includes wind profiler radar, millimeter-wave cloud radar, wind-measuring lidar, and microwave radiometer.

[0029] In step S1 of this invention, raw data from multi-source ground-based remote sensing equipment is first collected. However, wind profiler radar, millimeter-wave cloud radar, and wind lidar are easily affected by non-meteorological radar echoes such as ground clutter, insects, and signal processing. The near-surface boundary layer has strong non-meteorological radar echo signals, especially in complex underlying surfaces and areas with human activity. Therefore, in order to effectively remove these non-meteorological radar echo signals, this invention performs separate and coordinated data quality control on the vertical observation data of multi-source ground-based remote sensing.

[0030] In this embodiment, step S1, which involves quality control of the raw observation data, specifically includes: S11: Perform quality control on wind profiler radar data in the raw observation data; perform quality control on millimeter-wave cloud radar data in the raw observation data; perform quality control on wind lidar data in the raw observation data; perform quality control on microwave radiometer data in the raw observation data.

[0031] Specifically, the first step is to control the quality of wind profiler radar data. This invention uses wind speed and direction profile data from radiosonde as a standard, selects long-term series power spectrum raw data, and establishes a sample library of non-meteorological radar echo features such as ground clutter, cloud clutter, and precipitation clutter under multi-beam, multi-detection modes and different altitude conditions. An adaptive filtering verification algorithm is introduced to establish an intelligent identification and removal method for non-meteorological radar echoes based on random forests, in order to remove non-meteorological radar echoes and extract atmospheric echoes.

[0032] Secondly, the quality control of millimeter-wave cloud radar data is addressed. This invention constructs a cloud echo feature spatial classifier based on machine learning, establishes an intelligent discrimination model using the polarization characteristics and spatiotemporal texture features of radar echoes, implements adaptive Gaussian smoothing in the height dimension, and introduces a Kalman filter algorithm in the time dimension to achieve multi-scale filtering of the reflectivity factor, removing ground clutter and clutter near cloud boundaries, and obtaining quality-controlled cloud echoes.

[0033] Secondly, regarding the quality control of wind-measuring lidar data, this invention proposes a lidar wind field quality control method that combines boundary layer dynamics characteristics with statistical learning methods. Firstly, based on boundary layer dynamics characteristics, the atmospheric vertical structure is divided into a near-surface layer, a mixed boundary layer, and a free atmosphere, with different wind field correction strategies employed for each. In the near-surface layer, combining gradient tower observation data, the algorithm introduces turbulence similarity theory and constructs a wind profile correction function using friction velocity and stability parameters. In the mixed boundary layer and atmosphere above 200 m, combining radiosonde observations of wind speed and direction profiles, a smoothing correction method based on a Gaussian kernel function is adopted. By establishing a joint probability distribution model of the wind-measuring lidar, tower, and radiosonde observations, Bayesian optimal estimation considering wind speed probability density characteristics is achieved. For different weather conditions, the algorithm automatically identifies clear skies, cloudy conditions, and precipitation scenarios by analyzing characteristic parameters such as depolarization ratio and signal-to-noise ratio, and dynamically adjusts correction parameters to improve data quality under complex weather conditions.

[0034] Finally, the quality control of microwave radiometer data is addressed. To solve the quality problems of "drift" and "jump" in microwave radiometer brightness temperature data caused by environmental factors, calibration, changes in equipment component performance, and radome aging during actual observations, this invention proposes a microwave radiometer quality control method based on multi-source data fusion and machine learning. First, based on the characteristics of brightness temperature observations, methods such as logical discrimination, cloud type discrimination, precipitation discrimination, consistency discrimination, and climate threshold discrimination are used sequentially to make preliminary quality judgments on the radiometer's observed brightness temperature. Then, a dynamic bias correction model is established using the random forest algorithm. Combining cloud classification results from cloud radar, liquid water content observations, in-situ temperature and humidity profiles of the tower, and radiosonde temperature and humidity data, an adaptive correction scheme is constructed for different weather conditions. Under clear sky conditions, a brightness temperature bias prediction model is trained using equipment status parameters and atmospheric environmental characteristics. For cloudy weather, different correction schemes are adopted according to differences in cloud type.

[0035] S12: Perform collaborative data quality control on the raw observation data after quality control.

[0036] Based on the individual quality control of different radars mentioned above, joint quality control of ground-based remote sensing vertical observation data with multi-temporal and spatial scale constraints is carried out, based on the diurnal variation characteristics of boundary layer turbulence, atmospheric thermodynamic processes before clear sky-cloudy-precipitation, cloud vertical structure and cloud formation and dissipation processes, and atmospheric thermodynamic vertical structure characteristics. Regarding diurnal variation, considering the development, intensity, and decay characteristics of boundary layer and free atmospheric turbulence under different atmospheric stability conditions, the signal-to-noise ratio, wind field, and velocity spectral width information from clear sky detection by wind profiler radar are introduced, and a height-time weighting coefficient matrix is ​​preset (…). Regarding the thermodynamic process before clear skies-cloudy-precipitation, based on the temperature and humidity profile controlled by microwave radiometers, reflectivity factors, cloud vertical structure parameters, and cloud vertical structure parameter variability information controlled by cloud radar are introduced, and weight coefficient matrices for different cloud type conditions are preset respectively. Weighting coefficient matrix for different temperatures and humidity levels ( and ); Combining in-situ observation data from the gradient tower and radiosonde profile data, a pre-defined joint quality control matrix is ​​obtained ( ),in Based on the random forest algorithm, a dynamic constraint correction model considering boundary layer thermodynamic processes is established. The weight coefficients of the joint quality control matrix are continuously optimized to achieve data quality control in both the height and time dimensions, from clear-sky atmosphere to weather processes such as clouds and rain, thereby improving the detection accuracy of turbulence spectra, wind fields, and temperature and humidity profiles. By separately controlling the quality of data from multi-source ground-based remote sensing equipment, errors and interference in the data from each equipment can be specifically removed, further improving the overall data quality and ensuring the reliability of subsequent inversion results.

[0037] S2: Calculate the thermodynamic parameters based on multi-source ground-based remote sensing data to obtain atmospheric thermodynamic parameters.

[0038] Specifically, the atmospheric thermodynamic parameters in step S2 of this invention include four basic parameters: potential temperature, buoyancy frequency, wind shear, and gradient Richardson number. These parameters can accurately describe the thermodynamic state of the atmosphere, providing key basis for subsequent cloud boundary identification and turbulence inversion, and helping to analyze atmospheric turbulence characteristics more accurately.

[0039] The expression for potential temperature is: in, Potential temperature (unit: K). The air temperature (in K) at any height after the microwave radiometer has undergone quality control. The air pressure (unit: hPa) at any altitude above the ground after the microwave radiometer has undergone quality control.

[0040] Calculate the buoyancy frequency from the temperature and humidity profile after quality control by microwave radiometer ( ).

[0041] The expression for buoyancy frequency is: in, The frequency of buoyancy. It is the acceleration due to gravity. Height above the ground The average potential temperature, The average temperature. This is the specific heat constant. It should be noted that... It is a slowly varying parameter of the mean field, therefore we do not need very high-resolution temperature data to determine it. .

[0042] Vertical wind shear can be calculated using wind profile data.

[0043] The expression for wind shear is: in, To calculate the obtained wind shear, The horizontal meridional wind speed, For zonal wind speed, This refers to the height above the ground.

[0044] The expression for the gradient Richardson number is: in, For the gradient Richardson number, For zonal wind speed, For meridional wind speed, these parameters can be calculated jointly from quality-controlled wind profiler radar and microwave radiometer data.

[0045] S3: Based on multi-source ground-based remote sensing observation data and atmospheric thermodynamic parameters, cloud boundaries are identified to obtain atmospheric vertical structure parameters and boundary layer height.

[0046] Step S3 further includes: S31: Based on microwave radiometer data from multi-source ground-based remote sensing observation data, cloud layer identification is performed using the relative humidity threshold judgment method to obtain the cloud layer number and cloud boundary parameters based on the microwave radiometer.

[0047] The relative humidity threshold determination method of this invention includes three key threshold parameters: minimum relative humidity threshold min-RH, maximum relative humidity threshold max-RH, and intermediate relative humidity threshold inter-RH. Different thresholds are selected for different altitudes, and the specific cloud identification process adopts a bottom-up, layer-by-layer discrimination method. When the relative humidity of a certain altitude layer exceeds the corresponding min-RH threshold, it is marked as a possible cloud base; when the relative humidity of three consecutive altitude layers exceeds the inter-RH threshold, the cloud base height is determined; when the relative humidity drops below the max-RH threshold and two consecutive altitude layers are below this threshold, the cloud top height is determined; thus, cloud vertical structure parameters based on a microwave radiometer are obtained, including parameters such as cloud base height, cloud top height, and cloud thickness.

[0048] S32: Based on millimeter-wave cloud radar data from multi-source ground-based remote sensing observation data, cloud layer identification is performed using the reflectivity factor threshold judgment method to obtain the number of cloud layers and cloud boundary layer parameters based on millimeter-wave cloud radar.

[0049] Furthermore, in step S32 of this invention, based on the quality-controlled millimeter-wave cloud radar reflectivity factor threshold (min-Z), a bottom-up judgment is made to identify the cloud vertical structure parameters of different cloud layers. This yields the cloud layer number and cloud boundary layer parameters based on the millimeter-wave cloud radar.

[0050] S33: Cloud layer number and cloud boundary parameters from microwave radiometers and millimeter-wave cloud radars are jointly identified using a spatiotemporal matching method and a dynamic thresholding method. By comprehensively utilizing two different identification methods—microwave radiometer data and millimeter-wave cloud radar data—cloud layers can be identified more accurately, yielding cloud vertical structure parameters such as cloud base height, cloud top height, and cloud thickness. Based on the gradient Richardson number in the atmospheric thermodynamic parameters, the boundary layer height is obtained using a thresholding method.

[0051] S4: Based on multi-source ground-based remote sensing data, atmospheric thermodynamic parameters, atmospheric vertical structure parameters, and boundary layer height, the turbulence in the boundary layer and free atmosphere is inverted and fused to obtain the fused turbulence dissipation rate.

[0052] Step S4 further includes: S41: Turbulent dissipation rate inversion is performed using multi-source radar data and multiple methods to obtain multi-source radar data and multi-method turbulent dissipation rate results. Using multiple methods improves the reliability of the results. The multi-method turbulent dissipation rate inversion in step S41 includes power-based turbulent dissipation rate inversion, spectral broadening-based turbulent dissipation rate inversion, vertical velocity-based turbulent dissipation rate inversion, and Thorpe's method turbulent dissipation rate inversion.

[0053] Specifically, the power method for inverting turbulent dissipation rate is based on the principle that radar echo signals can reflect the structure of the atmospheric refractive index structure constant. The turbulent dissipation rate is estimated using the external scale of turbulence and the atmospheric refractive index structure constant. Therefore, wind profiler radar, wind lidar, and microwave radiometers can be used to invert the turbulent dissipation rate. The calculation formula is as follows: The turbulent dissipation rate is obtained by the power method inversion. The atmospheric refractive index structure constant. It is a constant with a value of 2.8. It is the vertical gradient of the refractive index. The power method requires the atmospheric refractive index structure constant profile calculated by wind profiler radar or wind lidar, as well as the temperature and humidity profiles detected by a spatiotemporally matched microwave radiometer, in order to calculate... and .

[0054] There are three methods for spectral broadening to invert turbulence. The first method does not require temperature and humidity profiles and can estimate turbulence solely from wind profile radar. Its inversion principle is that factors affecting velocity spectral width include atmospheric turbulent motion factors and non-turbulent factors (such as wind shear, beamwidth, data processing, uneven distribution of terminal velocities of precipitation particles of different diameters, gravity waves, etc.). Since the factors affecting spectral width are independent of each other, it can be assumed that the measured spectral width is the sum of the spectral widths of all influencing factors. The calculation formula is as follows: in, It is the spectral width measured by radar. Includes turbulent broadening ( ) and non-turbulent broadening ( The contribution of non-turbulent broadening ( This mainly includes: beam broadening ( ), shear broadening ( ), the broadening caused by data processing ( Residual noise from transient contamination and observed spectral width .

[0055] The spectral width caused by wind shear and beamwidth is approximately 4% of the observed spectral width. The spectral width caused by data processing includes many error sources from the data processing process (such as low signal-to-noise ratio, window function, etc.). When using spectral width analysis to analyze atmospheric turbulence, it is necessary to revise the spectral width broadening values ​​caused by wind shear and beamwidth. The formula for calculating the spectral width caused by wind shear and beamwidth is as follows: in, , , , , , , , For radar half-power beamwidth, For horizontal wind speed, For distance resolution, For sampling distance or a certain distance library The horizontal wind vertical shear at that location. The zenith angle of the beam.

[0056] Assuming the turbulence is homogeneous and isotropic, the turbulence dissipation rate is... It can be expressed as a function of the turbulence spectral width, denoted as The expression is: Among them, parameters The Kolmogorov empirical constant for the inertial subregion of the velocity spectrum is 1.6. It is the Gamma function, a double integral that takes values ​​between 0 and π / 2 in spherical coordinates, and is numerically solved at each measured height. The parameters... It is the radius of the pulse volume (beam cross-section). It is half the pulse length. , , It is the pulse width. It's the speed of light. It is the product of the average wind speed and dwell time (average period) within each beam sampling time of the wind profiler radar.

[0057] The second method of spectral broadening requires temperature and humidity profiles, which need to be jointly estimated by wind profiler radar and microwave radiometer. When temperature and humidity profiles are available, the relationship between turbulent dissipation rate and the mean square velocity fluctuation of the medium and the half-power half-spectral width of the received backscattered signal is discussed. This can be expressed by the following calculation formula: in, It is a constant, ranging from 0.45 to 0.50. It is the spectral width measured by radar. It is the buoyancy frequency.

[0058] The third method of spectral broadening utilizes only the wind-measuring lidar to invert the turbulent dissipation rate. According to Kolmogorov's locally homogeneous and isotropic turbulence theory, within the inertial sub-region, the turbulence scale satisfies... Among them, the external scale of turbulence ( ) represents the size of the maximum eddy, while the inner scale ( The vortex is a measure of how eddies are dissipated by viscous forces and eventually converted into heat.

[0059] By acquiring radial velocity data from wind-measuring lidar, and using the spectral broadening method, turbulent dissipation rate can be inverted. The formula is as follows: in, The maximum turbulent eddy length scale within the inertial subregion. The minimum turbulent eddy length scale within the inertial subregion. To broaden the spectral width of the wind-measuring lidar by averaging multiple beams, To broaden the noise, This represents the number of samples corresponding to the maximum turbulent eddy within the inertial subregion. For horizontal wind speed, For the pulse detection time, it needs to meet the following requirements. , For buoyancy length or turbulent external dimensions, It is the internal scale of turbulence.

[0060] Vertical velocity method. Turbulent dissipation rate can be estimated using only wind lidar. Neglecting second-order terms, the turbulent dissipation rate of an isotropic, stationary, and uniform turbulent flow can be estimated by the following formula.

[0061] in, ,in, The antenna beam width is At radial distance The horizontal dimension of the volume being irradiated. It is the radial dimension of the radar range library. The vertical velocity fluctuation is cubic. This method does not require temperature and humidity profiles, but because it uses... Instead This method is more sensitive to errors in spectral width introduced by possible non-turbulent contributions.

[0062] The Thorpe method is used to invert turbulent dissipation rates. However, the Thorpe method is only applicable to estimating turbulence parameters in the free atmosphere because, in the convective mixing boundary layer, the ordering of potential temperature profiles produces a Thorpe displacement equal to the thickness of the well-mixed region. Therefore, this method is not suitable for inverting turbulence parameters in the mixing boundary layer. However, in the case of the free atmosphere, this method can identify turbulent overturns, the magnitude of which is greater than the Thorpe displacement.

[0063] The arithmetic mean square root of the Thorpe displacement is the Thorpe length. The expression is: In data processing, to reduce systematic errors, the resolution of the potential temperature profile is interpolated to 10m, which is less than 1.1 times the vertical resolution. This was discarded, and a 9-point moving average was used to reduce residual noise. This is the flip height.

[0064] Once you have It can be used to estimate turbulent dissipation rate. The expression is: in, It is a constant. The buoyancy frequency is calculated from the monotonically increasing potential temperature profile after sorting.

[0065] For millimeter-wave cloud radar, the dynamic and thermal disturbances caused by turbulence in cloud regions continuously alter the temperature and humidity field distribution within the cloud. This dynamic adjustment mechanism further regulates the development and evolution of the cloud system, including different life stages such as cloud formation, maintenance, and dissipation. Based on the power spectrum data of the cloud radar echo, the atmospheric vertical motion velocity and cloud microphysical parameters can be inferred, yielding the turbulent dissipation rate within the cloud. The average Doppler velocity variance over 60 seconds is statistically analyzed. Assuming that the turbulent eddy scale observed by the cloud radar is in the inertial sub-region of the turbulence spectrum, the spectral width caused by differences in cloud particle size or natural fall velocity is minimal and negligible. The spectral width caused by turbulence... This can be considered as part of the turbulent energy spectrum, and its calculation formula is as follows: in, This refers to the length scale of turbulent eddies within the radar sampling volume, with a sampling time of over 30 seconds, taking into account horizontal winds in the clouds. The scale calculation formula is adjusted to account for the influence of ) , This represents the turbulent eddy length scale of the scatterer within one second of observation time by the cloud radar. Represents radial distance, horizontal wind speed at different height levels ( This is obtained through linear spline interpolation of data from wind profiler radar or wind lidar at nearby time points. It is the cube of the average spectral width.

[0066] S42: Based on the cloud vertical structure parameters and boundary layer height, perform boundary layer and free atmosphere identification processing on multi-source radar data and multi-method turbulence dissipation rates to obtain the turbulence dissipation rates within the boundary layer and in the free atmosphere. This layered processing allows for more accurate differentiation of turbulence characteristics across different atmospheric layers.

[0067] S43: The turbulent dissipation rate at each altitude is weighted and fused using a spatiotemporal fusion model to obtain a fused turbulent dissipation rate that includes the boundary layer turbulent dissipation rate and the free atmosphere turbulent dissipation rate.

[0068] Specifically, in step S43, the present invention constructs in-situ turbulence observation data of the tower based on spatiotemporal matching, compares and verifies the turbulence dissipation rate of the boundary layer and free atmosphere under clear sky and cloud conditions, and establishes a multi-source radar spatiotemporal fusion model of atmospheric turbulence dissipation rate under cloud cover conditions based on weighting function, thereby improving the reliability of boundary layer and free atmosphere turbulence dissipation rate inversion.

[0069] The specific fusion expression is as follows: in,( )and( The reflectivity factors are respectively: In the case of in-situ turbulence index at a specific altitude and time and its position in the first place. The index in the inversion method It is the first The weights of each inversion method, This represents the fused turbulent dissipation rate result. The final output after fusion includes fused turbulent dissipation rate data containing boundary layer turbulent dissipation rate and free atmosphere turbulent dissipation rate, with a time resolution of 6 minutes, a vertical resolution of 60 meters, and an altitude coverage range from the ground to 10 kilometers.

[0070] S5: Based on the fusion of turbulent dissipation rate and atmospheric thermodynamic parameters, turbulence parameters are calculated to obtain turbulence characteristic parameters.

[0071] Among them, the turbulence characteristic parameters in step S5 include the vertical refractive index gradient ( ), Turbulent dissipation rate ( ), Vertical eddy diffusion rate ( ), Turbulent internal scale ( ), turbulent external scale ( ).

[0072] The vertical refractive index gradient can be calculated using the potential temperature profile and specific humidity profile obtained from a microwave radiometer.

[0073] The expression for the vertical refractive index gradient is: in, For the perpendicular refractive index gradient, For temperature, For potential temperature, For air pressure, For height, It is more moist than water.

[0074] The vertical eddy diffusivity is the ratio of kinetic heat flux to the mean potential temperature gradient, determined by the time-averaged product of turbulent fluctuations and concentration fluctuations. Its magnitude directly depends on the turbulent velocity, both of which are important parameters for quantitatively describing turbulent mixing efficiency. The vertical eddy diffusivity is calculated by combining the buoyancy frequency after inverting the turbulent dissipation rate.

[0075] The expression for the vertical eddy current diffusivity is: in, Height above ground Vertical eddy diffusion rate below For mixing efficiency, The frequency of buoyancy. For turbulent dissipation rate, For the gradient Richardson number; For mixing efficiency, its value varies between 0.2 and 1, when hour, .

[0076] For the outer scale of turbulence ( ), turbulent external scale ( ) represents the size of the maximum eddy, while the inner scale ( ( ) is a measure of how eddies are dissipated by viscous forces and ultimately converted into heat. It can be regarded as the "outer limit" of the inertial scale, that is, above this scale, the turbulent scale is affected by steady buoyancy, while below this scale, the turbulent scale is almost unaffected by steady buoyancy.

[0077] Turbulent outer scale ( The expression for ) is: Turbulent intrascale ( The expression for ) is: in, For the internal scale of turbulence, This is the Kolmogorov microscale. Among them... For Kolmogorov microscale, kinematic viscosity ( ), The atmospheric density can be estimated based on the pressure and temperature curves measured by a radiosonde.

[0078] Based on the inversion method of this invention, in order to accurately obtain atmospheric turbulence signals throughout the entire process from clear sky to cloud formation to precipitation, it is necessary to identify the cloud layer number and cloud vertical structure parameters, and combine the boundary layer height obtained by joint inversion using wind profiler radar, wind lidar, and microwave radiometer to determine the boundary layer-cloud coupling state. Furthermore, based on the relative positions of the boundary layer and clouds, the turbulence signals within and outside the clouds are calculated separately, achieving a fine-structure vertical detection and inversion of turbulence throughout the entire process from clear sky to cloud development. This characterizes the evolution of atmospheric turbulence intensity and scale within and outside the clouds before precipitation, revealing the diurnal variation of turbulence signals (such as...). Figure 3 This provides key support for turbulence-cloud interactions and turbulence processes.

[0079] like Figure 3 As shown, where Figure 3 In the turbulent signal, 'a' represents the turbulent dissipation rate ( ). The diurnal variation characteristics at the observation station, Figure 3 In the middle, b represents the vertical eddy diffusion rate ( The diurnal variation characteristics at the observation station, Figure 3 In this context, c represents the external scale of turbulence ( The diurnal variation characteristics at the observation station, Figure 3 In this context, d represents the internal scale of the turbulence ( The diurnal variation characteristics at the observation station.

[0080] like Figure 2 As shown, the present invention also provides a collaborative inversion system for turbulence signals from multi-source ground-based remote sensing vertical observations, comprising: Quality control module: Used to collect raw observation data from multi-source ground-based remote sensing equipment and perform quality control to obtain multi-source ground-based remote sensing quality control data; First calculation module: used to calculate the thermodynamic parameters based on the multi-source ground-based remote sensing observation data, and obtain atmospheric thermodynamic parameters; Identification module: used to identify cloud boundaries based on the multi-source ground-based remote sensing observation data and the atmospheric thermodynamic parameters, and obtain cloud vertical structure parameters and boundary layer height; Inversion module: used to perform multi-method turbulent dissipation rate inversion based on the atmospheric thermodynamic parameters, cloud vertical structure parameters and boundary layer height, to obtain the fused turbulent dissipation rate below 10 km above the ground; The second calculation module is used to calculate parameters characterizing turbulence intensity and turbulence scale based on the fused turbulence dissipation rate and the atmospheric thermodynamic parameters, and to obtain turbulence characteristic parameters below 10 kilometers above the ground.

[0081] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observations, characterized in that, include: S1: Collect raw observation data from multi-source ground-based remote sensing equipment, and perform collaborative data quality control processing on the raw observation data to obtain multi-source ground-based remote sensing quality control data; S2: Calculate the thermodynamic parameters based on the multi-source ground-based remote sensing quality control data to obtain atmospheric thermodynamic parameters; S3: Based on the multi-source ground-based remote sensing quality control data and the atmospheric thermodynamic parameters, cloud boundary identification is performed to obtain cloud vertical structure parameters and boundary layer height; S4: Based on the multi-source ground-based remote sensing quality control data, the atmospheric thermodynamic parameters, the cloud vertical structure parameters, and the boundary layer height, the fused turbulent dissipation rate below 10 kilometers above the ground is obtained; S5: Based on the fusion turbulence dissipation rate and the atmospheric thermodynamic parameters, calculate the parameters characterizing turbulence intensity and turbulence scale to obtain turbulence characteristic parameters below 10 km above the ground.

2. The method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observations according to claim 1, characterized in that, The multi-source ground-based remote sensing equipment mentioned in step S1 includes wind profiler radar, millimeter-wave cloud radar, wind-measuring lidar, and microwave radiometer.

3. The method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observations according to claim 1, characterized in that, The quality control step in step S1 of the raw observation data specifically includes: S11: Perform quality control on wind profiler radar data in the raw observation data; perform quality control on millimeter-wave cloud radar data in the raw observation data; perform quality control on wind lidar data in the raw observation data; perform quality control on microwave radiometer data in the raw observation data. S12: Perform collaborative data quality control on the raw observation data after quality control.

4. The method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation according to claim 1, characterized in that, The atmospheric thermodynamic parameters mentioned in step S2 include potential temperature, buoyancy frequency, wind shear, and gradient Richardson number; The expression for the potential temperature is: in, For potential temperature, For temperature, For air pressure at any altitude, these parameters can be calculated from the quality-controlled microwave radiometer data; The buoyancy frequency ( The expression for the square is: in, The frequency of buoyancy. It is the acceleration due to gravity. Height above the ground These parameters, which are specific heat constants, can be calculated jointly from the wind profiler radar and microwave radiometer data after quality control. The expression for the wind shear is: in, To calculate the obtained wind shear, For meridional wind speed, For zonal wind speed, These parameters, representing the altitude above the ground, can be calculated from the quality-controlled wind profiler radar data. The expression for the gradient Richardson number is: in, For the gradient Richardson number, For zonal wind speed, For meridional wind speed, these parameters can be calculated jointly from quality-controlled wind profiler radar and microwave radiometer data.

5. The method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation according to claim 1, characterized in that, Step S3 further includes: S31: Based on the microwave radiometer quality control data in the multi-source ground-based remote sensing observation data, cloud layer identification is performed using the relative humidity threshold judgment method to obtain the cloud layer number and cloud boundary parameters based on the microwave radiometer. S32: Based on the millimeter-wave cloud radar data in the multi-source ground-based remote sensing observation data, cloud layer identification is performed using the reflectivity factor threshold judgment method to obtain the number of cloud layers and cloud boundary parameters based on the millimeter-wave cloud radar. S33: Based on the cloud layer number and cloud boundary parameters of the microwave radiometer and millimeter-wave cloud radar, the cloud vertical structure parameters of the microwave radiometer and millimeter-wave cloud radar are jointly identified by the dynamic threshold method to obtain the cloud vertical structure parameters of cloud base height, cloud top height and cloud thickness; based on the gradient Richardson number in the atmospheric thermodynamic parameters, the boundary layer height is obtained based on the threshold method.

6. The method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation according to claim 1, characterized in that, The cloud vertical structure parameters mentioned in step S3 include cloud base height, cloud top height, and cloud thickness.

7. The method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation according to claim 1, characterized in that, Step S4 further includes: S41: Turbulent dissipation rate inversion is performed using multi-source radar data and multiple methods to obtain the turbulent dissipation rate from multi-source radar data and multiple methods. S42: Based on the cloud vertical structure parameters and boundary layer height, perform boundary layer and free atmosphere identification processing on multi-source radar data and multi-method turbulence dissipation rates to obtain the turbulence dissipation rates within the boundary layer and in the free atmosphere; S43: The turbulent dissipation rate at each altitude is weighted and fused using a spatiotemporal fusion model to obtain a fused turbulent dissipation rate that includes the boundary layer turbulent dissipation rate and the free atmosphere turbulent dissipation rate.

8. The method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation according to claim 1, characterized in that, The multi-method turbulence dissipation rate in step S41 includes the turbulence dissipation rate based on the power method, the turbulence dissipation rate based on the spectral broadening method, the turbulence dissipation rate based on the vertical velocity method, and the turbulence dissipation rate based on the Thorpe method.

9. The method for collaborative inversion of turbulence signals from multi-source ground-based remote sensing vertical observation according to claim 1, characterized in that, The turbulence characteristic parameters in step S5 include the vertical refractive index gradient ( ), Turbulent dissipation rate ( ), Vertical eddy diffusion rate ( ), Turbulent internal scale ( ), turbulent external scale ( ); The expression for the vertical refractive index gradient is: in, For the perpendicular refractive index gradient, For temperature, For potential temperature, For air pressure, For height, It is more moist than water; The expression for the vertical eddy current diffusivity is: in, Height above ground Vertical eddy diffusion rate below For mixing efficiency, The frequency of buoyancy. For turbulent dissipation rate, For the gradient Richardson number; The expression for the external scale of turbulence is: in, For the external scale of turbulence; The expression for the internal scale of turbulence is: in, For the internal scale of turbulence, For Kolmogorov microscale.

10. A collaborative inversion system for turbulence signals from multi-source ground-based remote sensing vertical observation, characterized in that, include: Quality control module: Used to collect raw observation data from multi-source ground-based remote sensing equipment and perform quality control to obtain multi-source ground-based remote sensing quality control data; First calculation module: used to calculate the thermodynamic parameters based on the multi-source ground-based remote sensing observation data, and obtain atmospheric thermodynamic parameters; Identification module: used to identify cloud boundaries based on the multi-source ground-based remote sensing observation data and the atmospheric thermodynamic parameters, and obtain cloud vertical structure parameters and boundary layer height; Inversion module: used to perform multi-method turbulent dissipation rate inversion based on the atmospheric thermodynamic parameters, cloud vertical structure parameters and boundary layer height, to obtain the fused turbulent dissipation rate below 10 km above the ground; The second calculation module is used to calculate the turbulence intensity and turbulence scale parameters based on the fused turbulence dissipation rate and the atmospheric thermodynamic parameters, so as to obtain the turbulence characteristic parameters below 10 kilometers above the ground.

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