A method and system for collaborative inversion of turbulence signals of multi-source ground-based remote sensing vertical observation

Abstract

The present application relates to the technical field of digital signal processing, and specifically provides a turbulence signal collaborative inversion method and system for multi-source ground-based remote sensing vertical observation, which comprises: performing collaborative data quality control processing on original observation data of multi-source ground-based remote sensing equipment to obtain multi-source ground-based remote sensing quality control data, calculating a thermal dynamic parameter according to the multi-source ground-based remote sensing quality control data to obtain an atmospheric thermal dynamic parameter, identifying a cloud boundary according to the multi-source ground-based remote sensing quality control data and the atmospheric thermal dynamic parameter to obtain a cloud vertical structure parameter and a boundary layer height, respectively inverting and fusing turbulence in the boundary layer and the free atmosphere according to the multi-source ground-based remote sensing observation data, the atmospheric thermal dynamic parameter and the cloud vertical structure parameter to obtain a fused turbulence dissipation rate, and calculating according to the fused turbulence dissipation rate and the atmospheric thermal dynamic parameter to obtain a turbulence characteristic parameter. The present application improves the stability and precision of turbulence parameter inversion and can obtain the fine structure of atmospheric turbulence.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital signal processing, and in particular to a turbulence signal collaborative inversion method and system for multi-source ground-based remote sensing vertical observation. BACKGROUND

[0002] Atmospheric vertical structure observation is one of the cores of the comprehensive meteorological observation system, which refers to the comprehensive observation of meteorological elements in the vertical atmospheric column from the ground to the boundary layer, the troposphere and even the stratosphere. Turbulence is an important dynamic process in the atmosphere, and water vapor, heat, kinetic energy and aerosols are transported to the free atmosphere (stratosphere and above) by turbulence or convection. Therefore, accurate acquisition of turbulence signals is of great significance to ensure the safety of low-altitude aircraft.

[0003] The direct observation means of turbulence signals mainly include sounding, rocket, aircraft, etc. Although turbulence profile data can be observed or inverted from commercial aircraft, due to the near-horizontal flight trajectory of the aircraft, the observation is limited to the main route, avoiding potential strong turbulence areas that have been reported or predicted, making it difficult for the aircraft to link turbulence signals with background atmospheric conditions. At the same time, due to the high cost of these in-situ observation devices, the difficulty of continuous observation, the great influence of weather conditions, etc., only small-scale or point-scale areas can be covered, making it difficult to give long-time series of turbulence profile signals. In recent years, small unmanned aerial vehicles have gradually been used to fill the gaps in low-altitude observation by aircraft, sounding and ground-based radar, but in the environment of strong turbulence, strong wind shear and weather systems, the flight stability of unmanned aerial vehicles may be affected, which will affect the reliability and continuity of the data.

[0004] The ground-based remote sensing vertical observation system mainly consists of a wind profile radar, a millimeter wave cloud radar, a microwave radiometer, an aerosol lidar, and a GNSS / MET water vapor device, which are combined into a collaborative observation device. Through the mutual complementation of different ground-based observation means, minute-level real-time data of wind, water vapor, cloud, temperature, humidity and aerosol elements below 10 kilometers above the observation site can be quickly obtained, realizing the complementary advantages of various devices and collaborative observation at the same site, and forming a three-dimensional monitoring network of various meteorological elements. Ground-based radar inversion of turbulence signals mainly uses power method, Doppler spectrum width method and variance method. Domestic and foreign devices such as wind profile radar and microwave radiometer have been used to develop collaborative inversion technology of turbulence profile signals above 3 kilometers from the ground in the free atmosphere based on the above methods.

[0005] However, the existing turbulence signal inversion technology has the following disadvantages: first, the existing turbulence inversion method is only applicable to the free atmosphere above 3 kilometers above ground level, and has insufficient inversion capability for turbulence signals in the boundary layer; second, conventional sounding is only observed twice a day, which cannot provide continuous atmospheric thermal parameter profiles, making it difficult to obtain minute-level continuous turbulence signal profile products; third, single device observation has limitations, and lacks multi-source ground-based remote sensing device cooperative observation and data fusion technology; finally, there is a lack of turbulence signal cooperative inversion algorithm for different weather conditions (clear sky-cloudy-rain), which cannot realize all-weather high-precision turbulence parameter inversion.

[0006] Therefore, it is urgent to use multi-source ground-based remote sensing vertical observation data to carry out cooperative inversion of turbulence signals such as atmospheric vertical refractive index gradient ( ), turbulence dissipation rate ( ), vertical vorticity diffusion rate ( ), turbulence inner scale ( ), turbulence outer scale ( ) and the like from the near-surface to the atmospheric height below 10 kilometers, which provides important scientific value for studying the evolution law of the fine structure of atmospheric turbulence signals. SUMMARY

[0007] Based on the above technical problems, the present application provides a multi-source ground-based remote sensing vertical observation turbulence signal cooperative inversion method and system to solve the technical problems that sounding, rockets, aircrafts and the like cannot be continuously observed, and ground-based remote sensing vertical observation devices are only applicable to the free atmosphere above 3 kilometers, and have poor turbulence signal inversion capability in the atmospheric boundary layer.

[0008] The present application provides a multi-source ground-based remote sensing vertical observation turbulence signal cooperative inversion method, which comprises:

[0009] S1: collecting original observation data of multi-source ground-based remote sensing devices, and cooperatively performing data quality control processing on the original observation data to obtain multi-source ground-based remote sensing quality control data;

[0010] S2: calculating thermal dynamic parameters according to the multi-source ground-based remote sensing quality control data to obtain atmospheric thermal dynamic parameters;

[0011] S3: performing cloud boundary identification according to the multi-source ground-based remote sensing quality control data and the atmospheric thermal dynamic parameters to obtain cloud vertical structure parameters and boundary layer height;

[0012] S4: obtaining fusion turbulence dissipation rate below 10 kilometers above ground level according to the multi-source ground-based remote sensing quality control data, the atmospheric thermal dynamic parameters, the cloud vertical structure parameters and the boundary layer height;

[0013] S5: According to the fusion turbulent dissipation rate and the atmospheric thermal dynamic parameter, a parameter calculation of characterizing the turbulent intensity and the turbulent scale is carried out, and a turbulent characteristic parameter below 10 kilometers of the height from the ground is obtained.

[0014] According to the application, a multi-source ground-based remote sensing vertical observation turbulent signal cooperative inversion method is provided.

[0015] According to the application, a multi-source ground-based remote sensing vertical observation turbulent signal cooperative inversion method is provided, and the step S1 includes a quality control step of the original observation data, specifically including:

[0016] S11: The wind profile radar data in the original observation data is subjected to quality control; the millimeter wave cloud radar data in the original observation data is subjected to quality control; the wind measurement laser radar data in the original observation data is subjected to quality control; and the microwave radiometer data in the original observation data is subjected to quality control.

[0017] S12: The original observation data after the quality control is subjected to cooperative data quality control.

[0018] According to the application, a multi-source ground-based remote sensing vertical observation turbulent signal cooperative inversion method is provided, and the atmospheric thermal dynamic parameter in the step S2 includes a potential temperature, a buoyancy frequency, a wind shear and a gradient Richardson number.

[0019] The expression of the potential temperature is:

[0020]

[0021] Wherein, is the potential temperature, is the temperature, is the air pressure at any height, and these parameters can be calculated from the microwave radiometer data after the quality control;

[0022] The expression of the square of the buoyancy frequency is:

[0023]

[0024] Wherein, is the buoyancy frequency, is the gravitational acceleration, is the height from the ground, is the specific heat constant, and these parameters can be calculated from the wind profile radar data and the microwave radiometer data after the quality control;

[0025] The expression of the wind shear is:

[0026] ​

[0027] wherein, is the calculated wind shear, is the zonal wind speed, is the meridional wind speed, is the height above ground, which can be calculated from the wind profile radar data after quality control;

[0028] The expression of the gradient Richardson number is:

[0029]

[0030] wherein, is the gradient Richardson number, is the zonal wind speed, is the meridional wind speed, which can be calculated from the wind profile radar and microwave radiometer data after quality control.

[0031] According to the turbulence signal collaborative inversion method for multi-source ground-based remote sensing vertical observation provided by the application, step S3 further comprises:

[0032] S31: According to the microwave radiometer quality control data in the multi-source ground-based remote sensing observation data, cloud layer recognition is performed through a relative humidity threshold judgment method to obtain cloud layer number and cloud boundary parameters based on the microwave radiometer;

[0033] S32: According to the millimeter wave cloud radar data in the multi-source ground-based remote sensing observation data, cloud layer recognition is performed through a reflectivity factor threshold judgment method to obtain cloud layer number and cloud boundary parameters based on the millimeter wave cloud radar;

[0034] S33: According to the cloud layer number and cloud boundary parameters of the microwave radiometer and the millimeter wave cloud radar, cloud vertical structure parameters are jointly identified through a dynamic threshold method, and cloud bottom height, cloud top height and cloud thickness and other cloud vertical structure parameters are obtained; and the boundary layer height is obtained based on the threshold method according to the gradient Richardson number in the atmospheric thermal dynamic parameters.

[0035] According to the turbulence signal collaborative inversion method for multi-source ground-based remote sensing vertical observation provided by the application, the cloud vertical structure parameters in step S3 include cloud bottom height, cloud top height and cloud thickness.

[0036] According to the turbulence signal collaborative inversion method for multi-source ground-based remote sensing vertical observation provided by the application, step S4 further comprises:

[0037] S41: Turbulence dissipation rate is inversed through multi-source radar data and multiple methods to obtain turbulence dissipation rate of multi-source radar data and multiple methods.

[0038] 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;

[0039] 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.

[0040] 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.

[0041] 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 turbulence characteristic parameters in step S5 include vertical refractive index gradient, vertical eddy current diffusivity, turbulence internal scale, turbulence external scale, etc.

[0042] The expression for the vertical refractive index gradient is:

[0043]

[0044] in, The refractive index gradient is perpendicular to the direction of the gradient. For temperature, For potential temperature, For air pressure, For height, For specific moisture

[0045] The expression for the vertical eddy current diffusivity is:

[0046]

[0047]

[0048] 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;

[0049] The expression for the external scale of turbulence is:

[0050]

[0051] in, is the outer scale of turbulence,

[0052] The expression of the inner scale of turbulence is:

[0053]

[0054] wherein, is the inner scale of turbulence, is the Kolmogorov microscale.

[0055] The application provides a turbulence signal collaborative inversion system for multi-source ground-based remote sensing vertical observation, comprising:

[0056] A quality control module is configured to collect original observation data of the multi-source ground-based remote sensing equipment and perform quality control to obtain multi-source ground-based remote sensing quality control data.

[0057] A first calculation module is configured to calculate a thermodynamic parameter according to the multi-source ground-based remote sensing observation data to obtain an atmospheric thermodynamic parameter.

[0058] An identification module is configured to perform cloud boundary identification according to the multi-source ground-based remote sensing observation data and the atmospheric thermodynamic parameter to obtain a cloud vertical structure parameter and a boundary layer height.

[0059] An inversion module is configured to perform multi-method turbulence dissipation rate inversion according to the atmospheric thermodynamic parameter, the cloud vertical structure parameter and the boundary layer height to obtain a fused turbulence dissipation rate.

[0060] A second calculation module is configured to perform parameter calculation for representing turbulence intensity and turbulence scale according to the fused turbulence dissipation rate and the atmospheric thermodynamic parameter to obtain a turbulence characteristic parameter.

[0061] Compared with the prior art, the application has the beneficial effects that:

[0062] By comprehensively utilizing data of multiple source ground-based remote sensing equipment (wind profile radar, millimeter wave cloud radar, wind lidar and microwave radiometer), accurate atmospheric turbulence related information is obtained, through collaborative processing of multiple source data, non-meteorological radar echo signal is effectively removed, more reliable atmospheric turbulence echo data is extracted, and high quality basic data guarantee is provided for subsequent turbulence parameter inversion; compared with direct observation means such as sounding, rocket and airplane, the method has the advantages of high cost and difficult continuous observation, and the method realizes all-weather and continuous observation by using ground-based remote sensing equipment; the application can accurately distinguish the layered structure of the boundary layer and the free atmosphere by using the cloud vertical structure parameter and the boundary layer height, realizes the collaborative inversion strategy for the boundary layer and the free atmospheric turbulence, and effectively solves the limitation that the traditional method is only applicable to the free atmosphere above 3 kilometers; the atmospheric thermal dynamic parameter obtained by adopting the atmospheric thermal dynamic parameter calculation processing provides necessary thermal dynamic background field information for turbulence signal inversion; through multi-source data fusion and collaborative inversion, the atmospheric boundary layer and the turbulence signal in the free atmosphere are effectively inverted, more comprehensive and accurate turbulence dissipation rate below 10 kilometers from the ground height is obtained, and more accurate vertical refractive index gradient (Cn2), vertical vorticity diffusion rate (Kz), turbulence inner scale (Lz), turbulence outer scale (Lz) and other turbulence characteristic parameters are calculated, and the safety protection ability of the low-altitude aircraft in complex weather conditions is enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0064] Figure 1 A multi-source ground-based remote sensing vertical observation turbulence signal collaborative inversion method flowchart provided by the embodiment of the application;

[0065] Figure 2 A multi-source ground-based remote sensing vertical observation turbulence signal collaborative inversion system structure schematic diagram provided by the embodiment of the application;

[0066] Figure 3 A turbulence signal parameter daily variation characteristic diagram at an observation station provided by the embodiment of the application.

[0067] The drawings show that: 100, quality control module; 200, first calculation module; 300, identification module; 400, inversion module; 500, second calculation module.​​​​ DETAILED DESCRIPTION

[0068] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the accompanying drawings in the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all the embodiments. They should not be understood as limiting the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work belong to the scope of protection of the present application. In the description of the present application, it should be understood that the terms used are only for the purpose of description and should not be understood as indicating or implying relative importance.

[0069] In order to better understand the present application, the research background of the present application will be explained in detail first below.

[0070] Common turbulence signal parameters in the prior art include turbulence dissipation rate (ε), ), turbulence kinetic energy (TKE), ), gradient Richardson number (Ri), ), turbulence inner scale (l), ), turbulence outer scale (L), ), vertical vorticity diffusion rate (ωz), ), atmospheric refractive index structure constant (Cn2) and the like. When a ground-based radar inverts a turbulence signal, the turbulence dissipation rate (ε) is a key parameter for inverting the turbulence signal, and the turbulence inner scale (l), the turbulence outer scale (L), and the vertical vorticity diffusion rate (ωz) can be derived from the turbulence dissipation rate (ε) .

[0071] At present, domestic and foreign have used wind profile radar and microwave radiometer sounding data to carry out research on turbulence profile signal inversion technology. Although these technologies realize the estimation of turbulence parameters to some extent, they are only applicable to the free atmosphere above 3 kilometers, and have poor turbulence signal inversion capability in the atmospheric boundary layer, and the research on the spatial and temporal distribution characteristics of the applicable region turbulence signal is still rare. However, due to the relative lack of high spatial and temporal resolution boundary layer thermodynamic key parameter profile collaborative observation, the turbulence signal inversion data set product is still blank. In addition, the current ground-based remote sensing vertical observation system has realized the fine observation of the vertical profiles of temperature, humidity, wind, water condensate and aerosol and other multiple elements in the atmospheric boundary layer, but the current turbulence signal inversion is still relatively scarce.

[0072] Therefore, based on the observation data of the ground-based remote sensing vertical observation system and the like, the vertical refractive index gradient (Cn2) ), 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.

[0073] 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:

[0074] 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.

[0075] 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.

[0076] 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.

[0077] In this embodiment, step S1, which involves quality control of the raw observation data, specifically includes:

[0078] 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.

[0079] 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.

[0080] Secondly, the millimeter wave cloud radar data quality control. The application constructs a cloud echo feature space classifier based on machine learning, uses the polarization characteristics and spatiotemporal texture features of radar echoes to establish an intelligent discrimination model, implements adaptive Gaussian smoothing processing in the height dimension, introduces Kalman filtering algorithm in the time dimension, realizes multi-scale filtering of reflectivity factors, removes ground clutter, clutter near the cloud boundary, and obtains the cloud echoes after quality control.

[0081] Thirdly, the wind measurement laser radar data quality control. The application proposes a laser radar wind field quality control method combining boundary layer dynamics characteristics and statistical learning method. First, based on the boundary layer dynamics characteristics, the atmospheric vertical structure is divided into the near-surface layer, the mixed boundary layer and the free atmosphere layer, and different wind field correction strategies are used respectively. In the near-surface layer, combined with the gradient tower observation data, the algorithm introduces the turbulence similarity theory, and uses the friction velocity and stability parameters to construct the wind profile correction function; in the mixed boundary layer and atmospheric layer above 200 m, combined with the sounding observation wind speed and wind direction profile data, the smoothing correction method based on Gaussian kernel function is used. Through the establishment of the joint probability distribution model of the wind measurement laser radar and the tower and sounding observation, the Bayesian optimal estimation considering the wind speed probability density characteristics is realized. For different weather conditions, the algorithm automatically identifies the clear sky, cloudy and precipitation scenes by analyzing the depolarization ratio, signal-to-noise ratio and other characteristic parameters, and dynamically adjusts the correction parameters to improve the data quality under complex weather conditions.

[0082] Finally, the microwave radiometer data quality control. In order to solve the quality problems of microwave radiometer brightness temperature data such as "drift" and "jump" caused by environmental, calibration, equipment component performance change, antenna cover aging and other factors in actual observation, the application proposes a microwave radiometer quality control method based on multi-source data fusion and machine learning. First, based on the characteristics of brightness temperature observation values, the methods of logical discrimination, cloud type discrimination, precipitation discrimination, consistency discrimination and climate threshold discrimination are used in turn to preliminarily judge the quality of radiometer observation brightness temperature. Then, a dynamic bias correction model is established by using random forest algorithm, combined with the cloud classification results of cloud radar, liquid water content observation, tower in-situ temperature and humidity profile and sounding temperature and humidity data, an adaptive correction scheme for different weather conditions is constructed; under clear sky conditions, the brightness temperature deviation prediction model is trained by using equipment state parameters and atmospheric environmental characteristics; for cloudy weather, different correction schemes are used according to the difference of cloud type.

[0083] S12: Perform collaborative data quality control on the original observation data after quality control.

[0084] On the basis of the above different radar individual quality control, based on the characteristics of the boundary layer turbulence diurnal variation, the atmospheric thermal dynamic process of clear sky-cloudy-rainfall before, the cloud vertical structure and the cloud generation and extinction process, the atmospheric thermal dynamic vertical structure characteristics, the joint quality control of multi-time and space scale constraint ground-based remote sensing vertical observation data is carried out. On the diurnal variation, the development, flourishing and recession characteristics of the boundary layer and free atmosphere turbulence under different atmospheric stability conditions are considered, the signal-to-noise ratio, wind field and speed spectrum width information of the wind profile radar clear sky detection are introduced, and the height-time weight coefficient matrix is preset ( On the thermal dynamic process of clear sky-cloudy-rainfall before, based on the temperature and humidity profile of the microwave radiometer quality control, the reflectivity factor, cloud vertical structure parameter, cloud vertical structure parameter rate information of the cloud radar quality control are introduced, and the weight coefficient matrix of different cloud type conditions ( And ) are preset respectively; combined with the in-situ observation data of the gradient tower and the sounding profile data, the preset joint quality control matrix ( ) is obtained, wherein Based on the random forest algorithm, a dynamic constraint correction model considering the boundary layer thermal dynamic process is established, the joint quality control matrix weight coefficient is continuously optimized, the data quality control of high-dimensional and time-dimensional from clear sky atmosphere to cloud, rain and other weather processes is realized, and the detection accuracy of turbulence spectrum, wind field, temperature and humidity profile is improved. Through the quality control of the data of the multi-source ground-based remote sensing equipment respectively, the errors and interference in the data of each equipment can be removed, the overall data quality is further improved, and the reliability of the subsequent inversion result is ensured.

[0085] S2: calculating the atmospheric thermal dynamic parameters according to the multi-source ground-based remote sensing observation data to obtain the atmospheric thermal dynamic parameters.

[0086] Specifically, the atmospheric thermal dynamic parameters in the step S2 of the present application include four basic parameters of potential temperature, buoyancy frequency, wind shear and gradient Richardson number, which can accurately describe the thermal dynamic state of the atmosphere, provide key basis for subsequent cloud boundary identification and turbulence inversion, and help to more accurately analyze the atmospheric turbulence characteristics.

[0087] The expression of the potential temperature is:

[0088]

[0089] Wherein, is the potential temperature (unit: K), is the air temperature at any height after the quality control of the microwave radiometer (unit: K), is the air pressure at any height from the ground after the quality control of the microwave radiometer (unit: hPa).

[0090] The buoyancy frequency is calculated from the temperature and humidity profiles after quality control by the microwave radiometer .

[0091] The expression of the buoyancy frequency is:

[0092]

[0093] where, is the buoyancy frequency, is the acceleration of gravity, is the height above the ground, is the mean potential temperature, is the mean temperature, is the specific heat constant. It is important to note that, is a slowly varying parameter of the mean field, so we do not need very high resolution temperature data to determine .

[0094] The vertical wind shear can be calculated from the wind profile data.

[0095] The expression of the wind shear is:

[0096]

[0097] where, is the calculated wind shear, is the horizontal meridional wind speed, is the zonal wind speed, is the height above the ground.

[0098] The expression of the gradient Richardson number is:

[0099]

[0100] where, is the gradient Richardson number, is the zonal wind speed, is the meridional wind speed, which can be calculated from the quality-controlled wind profile radar and microwave radiometer data.

[0101] S3: According to the multi-source ground-based remote sensing observation data and atmospheric thermal dynamic parameters, cloud boundary recognition is performed to obtain atmospheric vertical structure parameters and boundary layer height.

[0102] Wherein, step S3 further comprises:

[0103] S31: According to the microwave radiometer data in the multi-source ground-based remote sensing observation data, cloud layer recognition is performed through a relative humidity threshold judgment method to obtain cloud layer number and cloud boundary parameters based on the microwave radiometer.

[0104] The relative humidity threshold judgment method of the application contains 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 at different altitudes to make judgments, and the specific cloud layer recognition process adopts a bottom-up layer-by-layer discrimination method. When the relative humidity at a certain altitude layer exceeds the corresponding min-RH threshold, it is marked as a possible cloud bottom; when the relative humidity at three consecutive altitude layers all exceeds the inter-RH threshold, the cloud bottom height is determined; when the relative humidity drops below the max-RH threshold and the relative humidity at two consecutive altitude layers is below the threshold, the cloud top height is determined; the cloud vertical structure parameters based on the microwave radiometer are obtained, which include cloud bottom height, cloud top height and cloud thickness and the like.

[0105] S32: According to the millimeter wave cloud radar data in the multi-source ground-based remote sensing observation data, the cloud layer recognition is performed through the reflectivity factor threshold judgment method to obtain the cloud layer number and cloud boundary layer parameters based on the millimeter wave cloud radar.

[0106] Further, in step S32, based on the millimeter wave cloud radar reflectivity factor threshold (min-Z) after quality control, the cloud vertical structure parameters of different layers of clouds are identified by judgment from bottom to top. The cloud layer number and cloud boundary layer parameters based on the millimeter wave cloud radar are obtained.

[0107] S33: The cloud layer number and cloud boundary parameters of the microwave radiometer and the millimeter wave cloud radar are jointly identified by using the space-time matching method and the dynamic threshold method. The cloud bottom height, cloud top height, cloud thickness and other cloud vertical structure parameters are obtained by comprehensively utilizing the microwave radiometer and millimeter wave cloud radar data of two different identification methods to more accurately identify the cloud layer. The boundary layer height is obtained based on the threshold method according to the gradient Richardson number in the atmospheric thermal dynamic parameter.

[0108] S4: According to the multi-source ground-based remote sensing observation data, the atmospheric thermal dynamic parameter, the atmospheric vertical structure parameter and the boundary layer height, the turbulence in the boundary layer and the free atmosphere is respectively inverted and fused to obtain the fused turbulence dissipation rate.

[0109] Wherein, step S4 further comprises:

[0110] S41: The turbulence dissipation rate is inverted by multi-source radar data and multiple methods to obtain the multi-source radar data and multiple method turbulence dissipation rate result, so as to improve the reliability of the result by inverting through multiple methods. The multiple method turbulence dissipation rate in step S41 includes power method turbulence dissipation rate inversion, spectrum broadening method turbulence dissipation rate inversion, vertical velocity method turbulence dissipation rate inversion and Thorpe method turbulence dissipation rate inversion.

[0111] Specifically, the power method is used to retrieve the turbulent dissipation rate. The principle of retrieving the turbulent dissipation rate is that the radar echo signal can reflect the structure of the atmospheric refractive index structure constant, and the turbulent dissipation rate is estimated by using the outer scale of turbulence and the atmospheric refractive index structure constant. Therefore, the wind profile radar, wind lidar and microwave radiometer can be used to retrieve the turbulent dissipation rate, and the calculation formula is as follows:

[0112]

[0113] is the turbulent dissipation rate retrieved by the power method, is the atmospheric refractive index structure constant, is a constant, and the value is 2.8, is the vertical gradient of refractive index, and the power method needs the atmospheric refractive index structure constant profile retrieved by the wind profile radar or wind lidar, and the temperature and humidity profiles detected by the microwave radiometer matched in time and space, so as to calculate and .

[0114] For the spectral broadening method, there are three methods to retrieve the turbulence. The first method of the three methods does not need temperature and humidity profiles, and can be estimated only by the wind profile radar. The retrieval principle is that the factors affecting the spectral width include the atmospheric turbulent motion factor and the non-turbulent factor (such as wind shear, beam width, data processing, uneven distribution of terminal velocity of different diameter precipitation particles, gravity wave, etc.). Since each factor affecting the spectral width is independent of each other, it can be assumed that the measured spectral width is the sum of the spectral widths of each influencing factor, and the calculation formula is as follows:

[0115]

[0116] wherein, is the spectral width measured by the radar, includes the contribution of turbulent broadening ( ) and non-turbulent broadening ( ), and the non-turbulent broadening ( ) mainly includes: beam broadening ( ), shear broadening ( ), data processing caused broadening ( ), instantaneous pollution and residual noise of observed spectral width .

[0117] is the spectral width caused by wind shear and beam width, and the data processing caused spectral width includes many error sources in the data processing process (such as low signal-to-noise ratio, window function, etc.). The data processing caused spectral width is about 4% of the observed spectral width, that is, 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:

[0118]

[0119] 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.

[0120] 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:

[0121]

[0122] 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.

[0123] 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:

[0124]

[0125] 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.

[0126] 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.

[0127] 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:

[0128]

[0129] 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.

[0130] 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.

[0131]

[0132] 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 The method is more sensitive to the error of spectral width introduced by the possible non-turbulent contribution.

[0133] Thorpe method inverts the turbulent dissipation rate. Thorpe method is only applicable to the estimation of turbulent parameters in the free atmosphere, because in the convective mixed boundary layer, the ordering of potential temperature profile produces the same Thorpe displacement as the thickness of the well-mixed region, so this method is not applicable to the inversion of mixed boundary layer turbulent parameters. However, in the case of the free atmosphere, the method can identify the turbulent overturning, whose size is larger than the Thorpe displacement.

[0134] The arithmetic mean square root of Thorpe displacement is the Thorpe length , the expression is:

[0135]

[0136] In data processing, in order to reduce the system error, the resolution of potential temperature profile is interpolated to 10m, which is less than 1.1 times the vertical resolution is discarded, and a 9-point moving average is used to weaken the residual noise, is the overturning height.

[0137] Once the is obtained, the turbulent dissipation rate , the expression is:

[0138]

[0139] is a constant, is the buoyancy frequency, which is calculated from the monotonically increasing potential temperature profile after ordering.

[0140] For millimeter wave cloud radar, the dynamic and thermal disturbance caused by the inversion of cloud turbulence will continuously change the temperature and humidity field distribution in the cloud. This dynamic adjustment mechanism will further regulate the development and evolution process of the cloud system, including the generation, maintenance and dissipation of the cloud in different life stages. According to the power spectrum data of cloud radar echo, the atmospheric vertical motion velocity and cloud microphysical parameters can be inverted, and the turbulent dissipation rate in the cloud can be obtained. The average Doppler velocity variance in 60 seconds is counted, assuming that the turbulent eddy scale observed by the cloud radar is in the inertial subregion of the turbulent spectrum, the spectral width caused by the difference in cloud particle size or natural falling speed is extremely small and can be ignored, and the spectral width is considered to be part of the turbulent energy spectrum, and its calculation formula is as follows:

[0141] ;

[0142] where, 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The specific fusion expression is as follows:

[0147]

[0148] 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.

[0149] S5: Based on the fusion of turbulent dissipation rate and atmospheric thermodynamic parameters, turbulence parameters are calculated to obtain turbulence characteristic parameters.

[0150] 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 ( ).

[0151] The vertical refractive index gradient can be calculated using the potential temperature profile and specific humidity profile obtained from a microwave radiometer.

[0152] The expression for the vertical refractive index gradient is:

[0153]

[0154] in, For the perpendicular refractive index gradient, For temperature, For potential temperature, For air pressure, For height, It is more moist than water.

[0155] 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.

[0156] The expression for the vertical eddy current diffusivity is:

[0157]

[0158]

[0159] 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, .

[0160] 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. The "outer limit" can be regarded as the inertial subrange scale, that is, above this scale, the turbulent scale is affected by the stable buoyancy, and below this scale, the turbulent scale is hardly affected by the stable buoyancy.

[0161] The expression of the turbulent outer scale ( ) is:

[0162]

[0163] The expression of the turbulent inner scale ( ) is:

[0164]

[0165] wherein, is the turbulent inner scale, is the Kolmogorov microscale. The is the Kolmogorov microscale, is the kinematic viscosity ( ), is the atmospheric density, which can be estimated according to the pressure and temperature curves measured by a radiosonde.

[0166] Based on the inversion method of the present application, in order to accurately obtain the atmospheric turbulent signal in the whole process of clear sky-cloud-rainfall, it is necessary to identify the number of cloud layers and the vertical structure parameters of the cloud, combine the boundary layer height inversed by the wind profile radar, the wind measuring laser radar and the microwave radiometer, discriminate the coupling state of the boundary layer and the cloud, and according to the relative position of the boundary layer and the cloud, respectively inverse and calculate the turbulent signal in the cloud and the turbulent signal outside the cloud, realize the vertical detection inversion of the whole process turbulent fine structure from the clear sky atmosphere to the cloud development process, and depict the evolution characteristics of the turbulent intensity and the turbulent scale in the cloud and outside the cloud before the rainfall, so as to provide key support for revealing the diurnal variation of the turbulent signal (such as Figure 3 ), the turbulent-cloud interaction and the turbulent process.

[0167] As shown in Figure 3 , wherein Figure 3 a is the diurnal variation characteristic of the turbulent dissipation rate ( ) in the turbulent signal at the observation station, Figure 3 b is the diurnal variation characteristic of the vertical vorticity diffusion rate ( ) at the observation station, Figure 3 c is the diurnal variation characteristic of the turbulent outer scale ( ) at the observation station, Figure 3 d is the diurnal variation characteristic of the turbulent inner scale ( ) at the observation station.

[0168] As shown in Figure 2As shown, the application also provides a turbulence signal collaborative inversion system for multi-source ground-based remote sensing vertical observation, comprising:

[0169] A quality control module: used for collecting original observation data of multi-source ground-based remote sensing equipment and performing quality control to obtain multi-source ground-based remote sensing quality control data;

[0170] A first calculation module: used for calculating thermodynamic parameters according to the multi-source ground-based remote sensing observation data to obtain atmospheric thermodynamic parameters;

[0171] An identification module: used for identifying cloud boundaries according to the multi-source ground-based remote sensing observation data and the atmospheric thermodynamic parameters to obtain cloud vertical structure parameters and boundary layer height;

[0172] An inversion module: used for performing multi-method turbulence dissipation rate inversion according to the atmospheric thermodynamic parameters and the cloud vertical structure parameters and boundary layer height to obtain a fusion turbulence dissipation rate below a height of 10 kilometers from the ground;

[0173] A second calculation module: used for calculating parameters representing turbulence intensity and turbulence scale according to the fusion turbulence dissipation rate and the atmospheric thermodynamic parameters to obtain turbulence characteristic parameters below a height of 10 kilometers from the ground.

[0174] Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions or the essential part of the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the method described in each embodiment or some part of the embodiment.

[0175] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application, and not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A method for collaborative inversion of turbulent signals of vertical observation of multi-source ground-based remote sensing, characterized in that, The method comprises the following steps: S1: collecting original observation data of multi-source ground-based remote sensing equipment, and performing collaborative data quality control processing on the original observation data to obtain multi-source ground-based remote sensing quality control data; S2: calculating atmospheric thermal dynamic parameters according to the multi-source ground-based remote sensing quality control data to obtain the atmospheric thermal dynamic parameters; S3: performing cloud boundary identification according to the multi-source ground-based remote sensing quality control data and the atmospheric thermal dynamic parameters to obtain cloud vertical structure parameters and boundary layer height; S4: obtaining fusion turbulent dissipation rate below 10 km from the ground according to the multi-source ground-based remote sensing quality control data, the atmospheric thermal dynamic parameters, the cloud vertical structure parameters and the boundary layer height; wherein step S4 comprises: S41: obtaining multi-source radar data and multi-method turbulent dissipation rate by multi-source radar data and multi-method turbulent dissipation rate inversion; S42: performing boundary layer and free atmosphere identification processing on the multi-source radar data and multi-method turbulent dissipation rate according to the cloud vertical structure parameters and the boundary layer height to obtain turbulent dissipation rate in the boundary layer and the free atmosphere; S43: performing weight fusion on the turbulent dissipation rate at each height by a space-time fusion model to obtain fusion turbulent dissipation rate including boundary layer turbulent dissipation rate and free atmosphere turbulent dissipation rate; S5: performing parameter calculation representing turbulent intensity and turbulent scale according to the fusion turbulent dissipation rate and the atmospheric thermal dynamic parameters to obtain turbulent characteristic parameters below 10 km from the ground.

2. The method according to claim 1, wherein, The multi-source ground-based remote sensing equipment in step S1 comprises wind profile radar, millimeter wave cloud radar, wind measurement laser radar and microwave radiometer.

3. The method according to claim 1, wherein, The quality control step of the original observation data in step S1 specifically comprises: S11: performing quality control on wind profile radar data in the original observation data; performing quality control on millimeter wave cloud radar data in the original observation data; performing quality control on wind measurement laser radar data in the original observation data; and performing quality control on microwave radiometer data in the original observation data; S12: performing collaborative data quality control on the quality-controlled original observation data.

4. The method according to claim 1, wherein, The atmospheric thermal dynamic parameters in step S2 comprise potential temperature, buoyancy frequency, wind shear and gradient Richardson number; The expression of the potential temperature is: where, is the temperature, is the temperature, is the air pressure at any height, which is calculated from the quality-controlled microwave radiometer data; The buoyancy frequency The expression in square is: where, is the buoyancy frequency, is the acceleration of gravity, is the height above the ground, is the specific heat constant, which is calculated from the wind profiler and microwave radiometer data after quality control; The expression of the wind shear is: wherein, is the computed wind shear, is the zonal wind speed, is the meridional wind speed, is the height above ground, these parameters being computed from wind profiler data after quality control; The expression of the gradient Richardson number is: where, is the gradient Richardson number, is the zonal wind speed, is the meridional wind speed, which are calculated from the quality-controlled wind profiler and microwave radiometer data.

5. The method according to claim 1, wherein, Step S3 further comprises: S31: performing cloud layer identification based on the microwave radiometer by relative humidity threshold judgment method according to the microwave radiometer quality control data in the multi-source ground-based remote sensing observation data to obtain cloud layer number and cloud boundary parameters based on the microwave radiometer; S32: performing cloud layer identification by reflectivity factor threshold judgment method according to millimeter wave cloud radar data in the multi-source ground-based remote sensing observation data to obtain cloud layer number and cloud boundary parameters based on the millimeter wave cloud radar; S33: performing joint identification of cloud vertical structure parameters of the microwave radiometer and the millimeter wave cloud radar by dynamic threshold method according to the cloud layer number and the cloud boundary parameters of the microwave radiometer and the millimeter wave cloud radar to obtain cloud vertical structure parameters of cloud base height, cloud top height and cloud thickness; and obtaining the boundary layer height based on threshold method according to the gradient Richardson number in the atmospheric thermal dynamic parameters.

6. The method according to claim 1, wherein, The cloud vertical structure parameter in step S3 includes a cloud bottom height, a cloud top height, and a cloud thickness.

7. The method according to claim 1, wherein, The multi-method turbulent dissipation rate in step S41 includes a turbulent dissipation rate based on a power method, a turbulent dissipation rate based on a spectral broadening method, a turbulent dissipation rate based on a vertical velocity method, and a turbulent dissipation rate based on a Thorpe method.

8. The method according to claim 1, wherein, The turbulent characteristic parameter in step S5 includes a vertical refractive index gradient, a vertical vorticity diffusion rate, a turbulent inner scale, and a turbulent outer scale. An expression of the vertical refractive index gradient is: wherein, is the vertical refractive index gradient, is the temperature, is the site temperature, is the air pressure, is the altitude, is the specific humidity; An expression of the vertical vorticity diffusion rate is: wherein is the height above ground is the vertical eddy diffusivity at height is the mixing efficiency, is the buoyancy frequency, is the turbulent dissipation rate, is the gradient Richardson number; An expression of the turbulent outer scale is: wherein is the outer scale of the turbulence; An expression of the turbulent inner scale is: wherein, is the inner scale of turbulence, is the Kolmogorov microscale.

9. A multi-source ground-based remote sensing vertical observation turbulence signal collaborative inversion system, characterized in that, The method comprises the following steps: A quality control module is configured to collect original observation data of multi-source ground-based remote sensing equipment and perform quality control to obtain multi-source ground-based remote sensing quality control data; A first calculation module is configured to calculate a thermodynamic parameter according to the multi-source ground-based remote sensing observation data to obtain an atmospheric thermodynamic parameter; An identification module is configured to perform cloud boundary identification according to the multi-source ground-based remote sensing observation data and the atmospheric thermodynamic parameter to obtain a cloud vertical structure parameter and a boundary layer height; An inversion module is configured to perform multi-method turbulent dissipation rate inversion according to the multi-source ground-based remote sensing quality control data, the atmospheric thermodynamic parameter, and the cloud vertical structure parameter and the boundary layer height to obtain a fusion turbulent dissipation rate below a height of 10 km from the ground, including: Performing turbulent dissipation rate inversion according to multi-source radar data and a multi-method to obtain a multi-source radar data and multi-method turbulent dissipation rate; Performing boundary layer and free atmosphere identification processing on the multi-source radar data and multi-method turbulent dissipation rate according to the cloud vertical structure parameter and the boundary layer height to obtain turbulent dissipation rates in the boundary layer and the free atmosphere; Performing weight fusion on the turbulent dissipation rate at each height through a space-time fusion model to obtain a fusion turbulent dissipation rate including a boundary layer turbulent dissipation rate and a free atmosphere turbulent dissipation rate; A second calculation module is configured to perform calculation of a turbulent intensity and a turbulent scale parameter according to the fusion turbulent dissipation rate and the atmospheric thermodynamic parameter to obtain a turbulent characteristic parameter below a height of 10 km from the ground.

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