CN112526547B - Atmospheric boundary layer classification method and device based on wind-measuring laser radar - Google Patents
Atmospheric boundary layer classification method and device based on wind-measuring laser radar Download PDFInfo
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
The invention discloses an atmospheric boundary layer classification method and device based on a wind-measuring laser radar. The invention can realize the identification and classification of cloud layers, precipitation, turbulence and wind shear in the atmosphere boundary layer by utilizing the power spectrum of the wind lidar and the inversion parameters thereof. The basic principle is that information such as carrier-to-noise ratio, spectrum width, symmetry of power spectrum and the like is obtained according to original power information of the laser radar. And identifying the cloud base and the cloud height by utilizing the gradient of the signal intensity profile. Then, rainfall is identified according to the symmetry of the spectrum width and the power spectrum, and the wind speed and the rain speed are separated by utilizing double peak fitting. Finally, turbulence and wind shear are distinguished by their dissipation rate and their intensity distribution characteristics. The method is applied to guaranteeing aviation safety, is beneficial to improving a climate model, a weather forecast model and an atmospheric pollution diffusion model, and has the advantages of high flexibility, high space-time resolution and high accuracy.
Description
Technical Field
The invention relates to the field of wind lidar and atmosphere detection, in particular to an atmosphere boundary layer classification method and device based on the wind lidar.
Background
Atmospheric boundary layers play an important role in many areas, including air pollution and diffusion of pollutants, agricultural meteorology, hydrology, aerometeorology, mesoscale meteorology, weather forecast, and gas. The precise measurement and classification of high spatial and temporal resolution of atmospheric boundary layer weather is critical not only for engineering applications, but also for scientific research. For example, wind shear causes changes in aircraft airspeed and, in turn, changes in lift causing accidents; the severe turbulence causes the aircraft to lose control and deviate from a predetermined flight path; frozen precipitation may reduce the aerodynamics of the aircraft. To understand the microscopic physical processes of the atmospheric boundary layer, knowledge of the atmospheric transport and mixing conditions is required, including wind profile, turbulence, wind shear, precipitation, and clouds.
Compared with the traditional atmospheric boundary layer equipment, the wind lidar has the advantages of multiple scanning modes, high space-time resolution and high precision. Lidar still has some problems in atmospheric detection. For example: the cloud top and the cloud bottom are identified by simply utilizing the maximum value and the minimum value of the signal gradient, so that the cloud top identification result is lower, and the cloud bottom identification result is lower; under rainfall conditions, errors are generated in wind speed inversion due to interference of rain spectrums. Rainfall can be judged through the increase of the spectrum width at present, but wind shear, turbulence and multiple scattering of cloud layers can all lead to the increase of the spectrum width. Identifying noise interference to rainfall; wind shear and turbulence both manifest as changes in wind speed, but the mechanisms of impact on aviation safety and atmospheric transport are different, so it is of great importance to identify and distinguish wind shear from turbulence.
Disclosure of Invention
In order to solve the above problems, an object of the present invention is to provide a method and an apparatus for classifying an atmospheric boundary layer based on a lidar. Based on using only one coherent wind lidar, real-time identification and classification of the key components of the atmospheric boundary layer are realized according to the data measured by the wind lidar in real time. Has important significance for pollution diffusion modeling, atmospheric weather mode, aviation safety and the like.
The invention is realized in the following way: an atmospheric boundary layer classification method based on laser radar comprises the following steps:
acquiring original echo data of a laser radar;
performing fast Fourier transform on the original echo data to obtain a power spectrum; analyzing the power spectrum to obtain signal intensity information, spectrum width information and spectrum shape symmetry information corresponding to the power spectrum;
calculating gradient information of the normalized signal intensity profile after distance correction according to the signal intensity information, and identifying cloud layers according to the gradient information;
judging whether the spectrum width information is larger than a preset spectrum width threshold value or not;
if yes, identifying rainfall according to the spectrum width information and the spectrum shape symmetry information;
if not, wind speed is calculated according to the power spectrum, wind shear strength and turbulence strength are calculated, and turbulence and wind shear are identified according to the wind shear strength and the turbulence strength.
Further, the identifying cloud layer according to the gradient information includes:
acquiring a gradient maximum value point and a gradient minimum value point according to the gradient information;
finding out candidate cloud top according to signal gradient maximum value pointFinding out candidate cloud bottom according to gradient minimum value points>
Pairing adjacent cloud bottoms and cloud tops, and searching the position and intensity of a signal peak between each pair of cloud bottoms and cloud tops; further screening out candidate cloud tops according to the intensity of the signal peak valueAnd candidate cloud base->
Candidate cloud top screened by upper nearestCloud signal minimum g of (2) m As a new cloud top locationCandidate cloud bottom which is screened by the nearest lower part>Cloud signal minimum signal g of (2) m As a new cloud base position
In the candidate clouds, if the cloud top of the lower layer and the cloud bottom of the upper layer are less than a preset cloud thickness threshold from bottom to top, combining the two layers of clouds, wherein the cloud top of the upper layer is the cloud top of the new cloudThe cloud bottom of the lower cloud is the cloud bottom of the new cloud +.>
Further, the identifying rainfall according to the spectral width information and the spectral shape symmetry information includes:
if the spectrum width is larger than the preset spectrum width threshold value, utilizing the symmetry information of the spectrum shape to identify whether rainfall exists,
the calculation method of the symmetry of the spectrum shape is as follows:
1) And (3) performing cubic spline interpolation on the power spectrum to obtain a smooth power spectrum curve and a normalized power spectrum f (x).
2) Finding the signal intensity maximum position x m ，x m The signal information S on the left side of the strongest signal position is calculated by taking n points from front and back 1 And right side information S 2 ，
Wherein k has a value of 0,1, 2 or 3;
when k=0, S 1 And S is 2 Representing the signal areas on the left and right sides respectively;
when k=1, S 1 And S is 2 Respectively representing first step distances of the strongest positions of the signals on the left side and the right side relative to the signals;
when k=2, S 1 And S is 2 Representing the second step distance of the strongest signal relative to the signal on the left and right sides;
when k=3, S 1 And S is 2 Representing the third-order distance of the strongest signal relative positions of the signals on the left side and the right side respectively;
3) Calculate max (S) 1 ,S 2 )/min(S 1 ,S 2 ) When max (S 1 ,S 2 )/min(S 1 ,S 2 ) When the power spectrum is larger than a preset second threshold value, indicating that the power spectrum has a plurality of component structures, and identifying the power spectrum as a rainfall spectrum;
and (3) performing bimodal fitting, separating an aerosol signal and a rainfall signal, and respectively obtaining the wind speed and the rain speed according to the aerosol signal and the rainfall signal.
Further, the identifying turbulence and wind shear based on the wind shear strength and turbulence intensity; comprising the following steps:
if the turbulence intensity is larger than a preset turbulence threshold value, identifying the turbulence;
if the wind shear strength is greater than a preset wind shear threshold and is continuously distributed in time, identifying the wind shear;
turbulence is identified if the shear strength of the wind shear is not greater than a preset wind shear threshold and appears arbitrary in time.
Further, the wind shear strength Sh is:
wherein V is i Is a component of the wind profile, including 1 or more components.
Further, parameters such as signal intensity and gradient thereof, power spectrum width and power spectrum symmetry thereof, turbulence intensity and wind shear intensity are selected for atmospheric boundary layer classification.
Further, the identification is performed in the order of cloud cover, rainfall, turbulence and wind shear.
Correspondingly, the invention also provides an atmospheric boundary layer classification device based on the wind-measuring laser radar, which comprises:
the acquisition module is used for acquiring original echo data of the laser radar;
the power spectrum analysis module is used for carrying out fast Fourier transform on the original echo data so as to obtain a power spectrum; analyzing the power spectrum to obtain signal intensity information, spectrum width information and power spectrum symmetry information corresponding to the power spectrum;
the cloud layer identification module is used for calculating gradient information of the normalized signal intensity profile after the distance correction according to the signal intensity information and identifying cloud layers according to the gradient information;
the rainfall identification module is used for identifying rainfall according to the spectrum width information and the power spectrum symmetry information when the spectrum width information is larger than a preset spectrum width threshold value;
and the turbulence and wind shear identification module is used for calculating the wind speed according to the power spectrum, calculating the wind shear strength and the turbulence intensity when the spectrum width information is not more than a preset spectrum width threshold value, and identifying the turbulence and the wind shear according to the wind shear strength and the turbulence intensity.
In summary, the invention provides a method and a device for classifying an atmospheric boundary layer based on a wind-measuring laser radar, which are characterized in that firstly, original echo data of the laser radar is obtained, the original echo data is subjected to fast Fourier transform to obtain a power spectrum, and the power spectrum is analyzed to obtain signal intensity information, spectrum width information and spectrum shape symmetry information corresponding to the power spectrum; and then calculating gradient information of the normalized signal intensity profile after the distance correction according to the signal intensity information, and identifying cloud layers according to the gradient information. Finally, judging whether the spectrum width information is larger than a preset spectrum width threshold value or not; if yes, identifying rainfall according to the spectrum width information and the spectrum shape symmetry information; if not, wind speed is calculated according to the power spectrum, wind shear strength and turbulence intensity are calculated, and turbulence and wind shear are identified according to the wind shear strength and the turbulence intensity. According to the invention, on the basis of using only one coherent wind lidar, real-time identification and classification of key components of an atmospheric boundary layer including cloud, rainfall, turbulence, wind shear and the like can be realized only according to the data measured by the wind lidar in real time. The method is applied to guaranteeing aviation safety, is beneficial to improving a climate model, a weather forecast model and an atmospheric pollution diffusion model, and has the advantages of high flexibility, high space-time resolution and high accuracy. According to the cloud layer identification method, the fine structures in the cloud layer are accurately distinguished through multiple screening of the candidate cloud bottom and the cloud top and accurate judgment of the adjacent cloud layers, so that the cloud layer identification precision and accuracy are remarkably improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for classifying atmospheric boundary layers based on a wind lidar according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for classifying atmospheric boundary layers based on a wind lidar according to an embodiment of the present invention;
FIG. 3 is a flow chart of a cloud layer identification method based on a signal of a wind lidar according to an embodiment of the present invention;
FIG. 4 is a flowchart of a method for identifying cloud layers of a signal based on a wind lidar according to an embodiment of the present invention;
FIG. 5 is a diagram of a cloud layer identification case according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a rainfall identification case according to an embodiment of the present invention;
fig. 7 is a block diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
Examples:
the invention discloses a wind-measuring laser radar-based atmospheric boundary layer classification method.
Fig. 1 and fig. 2 are flowcharts of a method for classifying an atmospheric boundary layer based on a wind-sensing lidar according to an embodiment of the present invention, and according to the embodiment shown in fig. 1 and fig. 2, the method of the present invention is implemented as follows:
s1, acquiring original echo data of a laser radar.
According to the invention, the accurate classification of the atmospheric boundary layer cloud, rain, wind shear and turbulence can be realized by only acquiring the original echo signal data of a single wind lidar.
S2, performing fast Fourier transform on the original echo data to obtain a power spectrum; and analyzing the power spectrum to obtain signal intensity information, spectrum width information and spectrum shape symmetry information corresponding to the power spectrum.
The radar original echo original data is subjected to fast Fourier transformation to obtain a power spectrum, and then the signal intensity, the spectrum width and the power spectrum symmetry of the power spectrum are obtained.
The signal strength calculating method can select carrier-to-noise ratio, signal-to-noise ratio and the like. The carrier-to-noise ratio is defined as the signal area S s Area S of noise n Is a ratio of (2). The signal-to-noise ratio is defined as the signal peak S m Mean value with noiseIs a ratio of (2).
The spectrum width is defined as the second-order center distance of the power spectrum, and the larger the spectrum width is, the less concentrated the representative speed is.
The signal intensity profile may be obtained by laser radar vertical detection, or by cone scanning.
And S3, calculating gradient information of the normalized signal intensity profile after the distance correction according to the signal intensity information, and identifying cloud layers according to the gradient information.
When the key components of the atmospheric boundary layer are identified, the cloud layer is identified according to the gradient of the normalized signal intensity profile after the distance correction.
The basic principle of the cloud layer identification method is as follows: radar signals are sensitive to aerosol concentration and cloud cover, signals are suddenly increased when laser encounters a cloud bottom, and signals leaving the cloud top are suddenly reduced.
Referring to fig. 3 to 4, the method for identifying cloud layer according to the present invention is as follows:
(1) And (3) carrying out distance correction on the carrier-to-noise ratio profile:
g＝g 0 z 2
z represents the height of the profile, and the signal is further normalized such that the value of the signal strength lies in the interval 0, 1.
(2) The gradient of the signal intensity profile is calculated.
The gradient of the signal intensity profile is selectable by a variety of gradient algorithms, preferably least squares, wavelet covariance, etc.
The least square method is used for solving the gradient:
the least squares fit distance is 2n+1 distance gates, g (z) represents the signal strength profile
The gradient of the carrier-to-noise ratio is calculated by a wavelet transformation method:
wherein the wavelet basis function is selectable
Wherein z is t And z b Respectively boundary layer reversalThe upper and lower boundaries of the calculation range are calculated, a is the wavelet scaling factor, and b is the wavelet basis function center position.
(3) And identifying cloud layers according to the gradient information. The method comprises the following steps:
s31, acquiring a gradient maximum value point and a gradient minimum value point according to the gradient information.
S32, finding out candidate cloud top according to the maximum value point of the signal gradientFinding candidate cloud base according to gradient minimum value point
S33, pairing adjacent cloud bottoms and cloud tops, and searching the position and intensity of a signal peak value between each pair of cloud bottoms and cloud tops; further screening out candidate cloud tops according to the intensity of the signal peak valueAnd candidate cloud base->
Specifically, adjacent cloud floors and cloud tops are paired, and the peak position and the intensity of a signal are found between each pair of the cloud floors and the cloud tops.
Further screening out candidate cloud tops according to the intensity of the signal peak valueAnd candidate cloud base->Comprising the following steps: screening out that the peak signal intensity between cloud bottom and cloud top is larger than a preset threshold value to form a new candidate cloud top +.>And (4) cloud bottom->
S34, selecting the candidate cloud top closest to the upper partCloud signal minimum g of (2) m Is the new cloud top position +.>Candidate cloud bottom which is screened by the nearest lower part>Cloud signal minimum signal g of (2) m Is the new cloud base position +.>
Specifically, the cloud signal minimum value g closest to the candidate cloud top above m As a new cloud top locationWith signal g nearest to candidate cloud base below m Is used as a new cloud base->
Wherein the cloud signal minimum g m The minimum signal value can be selected, and other reasonable minimum cloud layer signal values can be selected according to the actual local aerosol distribution characteristics.
S35, in the candidate clouds, if the cloud top of the lower layer and the cloud bottom of the upper layer are less than a preset cloud thickness threshold from bottom to top, combining the two layers of clouds, wherein the cloud top of the upper layer is the cloud top of the new cloudThe cloud bottom of the lower cloud is the cloud bottom of the new cloud
Further, in the candidate clouds screened in step S34, if the cloud top of the lower layer is smaller than the cloud bottom of the upper layer by Yu Yunhou threshold from bottom to top, combining the two layers of clouds, wherein the cloud top of the upper layer is the new cloud topThe cloud bottom of the layer cloud is the cloud bottom of the new cloud +.>
Through the steps, the identification of all cloud layers is finally realized.
The cloud identification method of the present invention is explained below with a specific measurement data, as shown in fig. 5. In fig. 5, (a) is a typical signal strength profile in dB, (b) is a distance corrected normalized signal profile, and (c) is a gradient corresponding to the profile in (b). (c) Middle circle and square labels distinguish maxima representing signal gradientsAnd minimum->When the minimum value g of the cloud layer signal m Taking 0.2, the regular triangle and the inverted triangle in (b) represent the cloud top height which is finally identified according to the above steps, respectively>And cloud base height->
S4, judging whether the spectrum width information is larger than a preset spectrum width threshold value or not; if yes, go to step S5, if no, go to step S6.
S5, identifying rainfall according to the spectrum width information and the spectrum shape symmetry information.
Specifically, the basic principle of the invention for identifying rainfall is as follows: when rainfall occurs, the power spectrum has both aerosol and rain signals, resulting in an increase in spectral width. When the aerosol signal and the rain signal have similar intensities, the power spectrum shows a bimodal structure; when one signal is covered by the other signal, the power is in an asymmetric single-peak structure, for example, in a small rain condition, the raindrop signal is weaker and is easy to be covered by the aerosol signal; and when the aerosol is washed by rainwater, the aerosol signal is easily covered by the raindrop signal.
At present, related documents use the spectrum width to identify rainfall, and obvious defects exist, because extreme weather conditions such as wind shear, turbulence and the like can also cause obvious increase of the spectrum width. In order to improve the rainfall recognition rate, when the spectrum width is larger than a threshold value, the invention performs power spectrum component detection by using the power spectrum symmetry of the power spectrum:
the power spectrum component checking method comprises the following steps:
1) And (3) performing cubic spline interpolation on the power spectrum to obtain a smooth power spectrum curve and a normalized power spectrum f (x).
2) Finding the signal intensity maximum position x m ，x m The signal information S on the left side of the strongest signal position is calculated by taking n points from front and back 1 And right side information S 2 ，
Wherein k has a value of 0,1, 2 or 3;
when k=0, S 1 And S is 2 Representing the signal areas on the left and right sides respectively;
when k=1, S 1 And S is 2 Respectively representing first step distances of the strongest positions of the signals on the left side and the right side relative to the signals;
when k=2, S 1 And S is 2 Representing the second step distance of the strongest signal relative to the signal on the left and right sides;
when k=3, S 1 And S is 2 Representing the third-order distance of the strongest signal relative positions of the signals on the left side and the right side respectively;
3) Calculate max (S) 1 ，S 2 )/min(S 1 ，S 2 ) When max (S 1 ，S 2 )/min(S 1 ，S 2 ) And when the power spectrum is larger than a preset second threshold value, indicating that a plurality of components exist in the power spectrum, and identifying the power spectrum as a rainfall spectrum.
And performing double-peak fitting on the identified rainfall spectrum, separating an aerosol signal and a rainfall signal, and respectively obtaining the wind speed and the rain speed according to the aerosol signal and the rainfall signal.
The bimodal fit may employ a double gaussian fit:
wherein I and f, σ are signal strength, doppler frequency and spectral width, respectively. Subscripts a and r represent aerosol and raindrops, respectively.
The rainfall identification method of the present invention is explained below with a specific measurement data, the circles in fig. 6 represent the original power spectrum under typical rainfall conditions, and when k=0, the ratio of the areas on both sides of the strongest signal position is 3. Illustrating the asymmetry of the power spectrum, there are a variety of signal components. The aerosol spectra and raindrop spectra were separated using a bimodal fit as shown in figure 6.
S6, calculating wind speed according to the power spectrum, calculating wind shear strength and turbulence intensity, and identifying turbulence and wind shear according to the wind shear strength and the turbulence intensity.
Preferably, said identifying turbulence and wind shear from said wind shear strength and turbulence intensity; comprising the following steps:
if the turbulence intensity is larger than a preset turbulence threshold value, identifying the turbulence;
if the wind shear strength is greater than a preset wind shear threshold and is continuously distributed in time, identifying the wind shear;
turbulence is identified if the shear strength of the wind shear is not greater than a preset wind shear threshold and appears arbitrary in time.
Specifically, after the possibility of rainfall and cloud cover is eliminated, doppler velocity is obtained by unimodal fitting. The turbulence intensity and the wind shear intensity are calculated respectively, and the turbulence and the wind shear are identified by combining the turbulence intensity and the wind shear intensity.
The basic principle of the invention for identifying turbulence and wind shear is as follows: the turbulence has larger turbulence intensity, and the wind speed change has arbitrary property; and wind shear appears as a steady change in wind speed.
There are various measures of turbulence intensity, such as turbulence kinetic energy dissipation ratio, autocorrelation covariance, radial wind speed variance, etc. When the turbulence intensity is greater than the threshold, the weather condition at that time is classified as turbulent. However, calculating turbulence intensity requires a higher carrier-to-noise ratio, and low signal-to-noise signals introduce significant errors. The probability of inversion of the turbulence intensity is therefore smaller than the wind speed. Thus, only by the turbulence intensity, it is identified that turbulence conditions that would miss some low carrier-to-noise ratio. To solve this problem, the present invention introduces wind shear strength Sh to increase the recognition rate of turbulence:
V i is a component of the wind profile. When the wind shear strength is greater than a threshold, it is believed that wind shear or turbulence may be present. Wind shear is filtered using a time window function. Stabilizing wind trimming strength (continuous in time), classified as wind shear. Unstable wind shear strength (discontinuous in time), classifying turbulence. Typical weather condition classification within the atmosphere boundary layer is accomplished so far.
In one embodiment, parameters such as signal strength and its gradient, power spectrum width and its power spectrum symmetry, turbulence intensity and wind shear intensity are selected for atmospheric boundary layer classification.
Preferably, the identification is performed in the order of cloud, rainfall, turbulence and wind shear.
Correspondingly, the invention also provides an atmospheric boundary layer classification device based on the wind-measuring laser radar, which comprises:
the acquisition module is used for acquiring original echo data of the laser radar;
the power spectrum analysis module is used for carrying out fast Fourier transform on the original echo data so as to obtain a power spectrum; analyzing the power spectrum to obtain signal intensity information, spectrum width information and power spectrum symmetry information corresponding to the power spectrum;
the cloud layer identification module is used for calculating gradient information of the normalized signal intensity profile after the distance correction according to the signal intensity information and identifying cloud layers according to the gradient information;
the rainfall identification module is used for identifying rainfall according to the spectrum width information and the power spectrum symmetry information when the spectrum width information is larger than a preset spectrum width threshold value;
and the turbulence and wind shear identification module is used for calculating the wind speed according to the power spectrum, calculating the wind shear strength and the turbulence intensity when the spectrum width information is not more than a preset spectrum width threshold value, and identifying the turbulence and the wind shear according to the wind shear strength and the turbulence intensity.
In summary, the invention provides a method and a device for classifying an atmospheric boundary layer based on a wind-measuring laser radar, which are characterized in that firstly, original echo data of the laser radar is obtained, the original echo data is subjected to fast Fourier transform to obtain a power spectrum, and the power spectrum is analyzed to obtain signal intensity information, spectrum width information and spectrum shape symmetry information corresponding to the power spectrum; and then calculating gradient information of the normalized signal intensity profile after the distance correction according to the signal intensity information, and identifying cloud layers according to the gradient information. Finally, judging whether the spectrum width information is larger than a preset spectrum width threshold value or not; if yes, identifying rainfall according to the spectrum width information and the spectrum shape symmetry information; if not, wind speed is calculated according to the power spectrum, wind shear strength and turbulence intensity are calculated, and turbulence and wind shear are identified according to the wind shear strength and the turbulence intensity. According to the invention, on the basis of using only one coherent wind lidar, real-time identification and classification of key components of an atmospheric boundary layer including cloud, rainfall, turbulence, wind shear and the like can be realized only according to the data measured by the wind lidar in real time. The method is applied to guaranteeing aviation safety, is beneficial to improving a climate model, a weather forecast model and an atmospheric pollution diffusion model, and has the advantages of high flexibility, high space-time resolution and high accuracy. According to the cloud layer identification method, the fine structures in the cloud layer are accurately distinguished through multiple screening of the candidate cloud bottom and the cloud top and accurate judgment of the adjacent cloud layers, so that the cloud layer identification precision and accuracy are remarkably improved.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (7)
1. An atmospheric boundary layer classification method based on a wind-measuring laser radar is characterized by comprising the following steps:
acquiring original echo data of a laser radar;
performing fast Fourier transform on the original echo data to obtain a power spectrum; analyzing the power spectrum to obtain signal intensity information, spectrum width information and spectrum shape symmetry information corresponding to the power spectrum;
calculating gradient information of the normalized signal intensity profile after distance correction according to the signal intensity information, and identifying cloud layers according to the gradient information;
judging whether the spectrum width information is larger than a preset spectrum width threshold value or not;
if yes, identifying rainfall according to the spectrum width information and the spectrum shape symmetry information;
if not, wind speed is calculated according to the power spectrum, wind shear strength and turbulence strength are calculated, and turbulence and wind shear are identified according to the wind shear strength and the turbulence strength.
2. The method of claim 1, wherein the identifying cloud layer from the gradient information comprises:
acquiring a gradient maximum value point and a gradient minimum value point according to the gradient information;
finding out candidate cloud top according to signal gradient maximum value pointFinding out candidate cloud bottom according to gradient minimum value points>
Pairing adjacent cloud bottoms and cloud tops, and searching the position and intensity of a signal peak between each pair of cloud bottoms and cloud tops; further screening out candidate cloud tops according to the intensity of the signal peak valueAnd candidate cloud base->
Candidate cloud top screened by upper nearestCloud signal minimum g of (2) m Is the new cloud top position +.>Candidate cloud bottom which is screened by the nearest lower part>Cloud signal minimum signal g of (2) m Is the new cloud base position +.>
In the candidate clouds, if the cloud top of the lower layer and the cloud bottom of the upper layer are less than a preset cloud thickness threshold from bottom to top, combining the two layers of clouds, wherein the cloud top of the upper layer is the cloud top of the new cloudThe cloud bottom of the lower cloud is the cloud bottom of the new cloud +.>
3. The method of claim 1, wherein said identifying rainfall from said spectral width information and spectral shape symmetry information comprises:
if the spectrum width is larger than a preset spectrum width threshold value, identifying whether rainfall exists or not by utilizing the symmetry information of the spectrum shape;
the calculation method of the symmetry of the spectrum shape is as follows:
performing cubic spline interpolation on the power spectrum to obtain a smooth power spectrum curve and a normalized power spectrum f (x);
finding the signal intensity maximum position x m ，x m The signal information S on the left side of the strongest signal position is calculated by taking n points from front and back 1 And right side information S 2 ；
Wherein k has a value of 0,1, 2 or 3;
when k=0, S 1 And S is 2 Representing the signal areas on the left and right sides respectively;
when k=1, S 1 And S is 2 Respectively representing first step distances of the strongest positions of the signals on the left side and the right side relative to the signals;
when k=2, S 1 And S is 2 Representing the second step distance of the strongest signal relative to the signal on the left and right sides;
when k=3, S 1 And S is 2 Representing the third-order distance of the strongest signal relative positions of the signals on the left side and the right side respectively;
calculate max (S) 1 ，S 2 )/min(S 1 ，S 2 ) When max (S 1 ，S 2 )/min(S 1 ，S 2 ) When the rainfall spectrum is larger than a preset second threshold value, identifying the rainfall spectrum;
and (3) performing bimodal fitting, separating an aerosol signal and a rainfall signal, and respectively obtaining the wind speed and the rain speed according to the aerosol signal and the rainfall signal.
4. The method of claim 1, wherein the identifying turbulence and wind shear is based on the wind shear strength and turbulence intensity; comprising the following steps:
if the turbulence intensity is larger than a preset turbulence threshold value, identifying the turbulence;
if the wind shear strength is greater than a preset wind shear threshold and is continuously distributed in time, identifying the wind shear;
turbulence is identified if the shear strength of the wind shear is not greater than a preset wind shear threshold and appears arbitrary in time.
5. The method of claim 1, wherein the atmospheric boundary layer classification is performed using parameters such as signal strength and its gradient, power spectrum width and its power spectrum symmetry, turbulence intensity, and wind shear intensity.
6. The method of claim 1, wherein the identifying is performed in the order of cloud, rainfall, turbulence, and wind shear.
7. Atmospheric boundary layer sorter based on wind-finding laser radar, characterized by comprising:
the acquisition module is used for acquiring original echo data of the laser radar;
the power spectrum analysis module is used for carrying out fast Fourier transform on the original echo data so as to obtain a power spectrum; analyzing the power spectrum to obtain signal intensity information, spectrum width information and power spectrum symmetry information corresponding to the power spectrum;
the cloud layer identification module is used for calculating gradient information of the normalized signal intensity profile after the distance correction according to the signal intensity information and identifying cloud layers according to the gradient information;
the rainfall identification module is used for identifying rainfall according to the spectrum width information and the power spectrum symmetry information when the spectrum width information is larger than a preset spectrum width threshold value;
and the turbulence and wind shear identification module is used for calculating the wind speed according to the power spectrum, calculating the wind shear strength and the turbulence intensity when the spectrum width information is not more than a preset spectrum width threshold value, and identifying the turbulence and the wind shear according to the wind shear strength and the turbulence intensity.
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