CN108414997B - Boundary layer height inversion method based on particle characteristic difference - Google Patents

Boundary layer height inversion method based on particle characteristic difference Download PDF

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CN108414997B
CN108414997B CN201810019102.6A CN201810019102A CN108414997B CN 108414997 B CN108414997 B CN 108414997B CN 201810019102 A CN201810019102 A CN 201810019102A CN 108414997 B CN108414997 B CN 108414997B
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difference
characteristic
height
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马盈盈
刘博铭
龚威
张明
施一帆
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Wuhan University WHU
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/95—Lidar systems specially adapted for specific applications for meteorological use
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4808—Evaluating distance, position or velocity data
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a boundary layer height inversion method based on particle characteristic difference, which is used for obtaining the top of a boundary layer based on the characteristic difference of atmospheric particles. The method does not depend on the vertical concentration profile of the aerosol to search the height of the boundary layer, but solves the particle characteristic difference, and finally determines the height of the boundary layer through the maximum difference value search, thereby avoiding the influence of complex aerosol layers on the search of the height of the boundary layer. The method has the characteristics of simple execution, high accuracy and wide applicability.

Description

Boundary layer height inversion method based on particle characteristic difference
Technical Field
The invention belongs to the technical field of atmospheric detection, and particularly relates to a method for inverting the height of a boundary layer based on particle characteristic difference.
Background
The atmospheric boundary layer, also called a planet boundary layer, is composed of a near-ground layer, a mixed layer and a sandwich layer on the upper part of the mixed layer. Is the lowest atmosphere directly affected by human activity. The atmospheric boundary layer is an important bridge for the exchange of materials and energy between the atmosphere and the ground. In boundary layer research, the height of the atmospheric boundary layer is a very important parameter in an air pollution model and is also an important constituent parameter of an atmospheric turbulence structure. Thus, accurate finding of atmospheric boundary layer height will play an important role in contaminant transport and environmental changes.
Currently, in the world, there are two detection modes, namely active detection mode and passive detection mode. The passive mode is based on the observation data of a ground passive instrument, and the height of the boundary layer is determined according to meteorological elements such as wind speed, temperature and the like. However, the boundary layer atmosphere is constantly changed, which easily causes errors in the passive search result. The active mode is based on the observation data of an active instrument, such as a laser radar, and the boundary layer height is determined at the place where the aerosol concentration is rapidly attenuated according to the aerosol concentration change. Because of the high spatial resolution and continuous and stable working capability of the laser radar, the laser radar has become a main means for detecting the height of the boundary layer. The main algorithm for calculating the boundary layer height by the laser radar at present comprises the following steps: gradient methods, Wavelet Covariance Transform (WCT) methods, maximum variance techniques, and ideal contour fitting methods. The gradient method is greatly affected by noise common to complex backscatter of the atmosphere. Although filtering or averaging the signal may reduce this problem, it may also distort the signal or reduce the time resolution of the lidar data. The WCT method is suitable for handling complex special cases because the operator can select the appropriate basis function and set the appropriate threshold. The ideal profile fitting method developed by Steyn et al (Steyn DG, Baldi M, Hoff RM, the detection of mixed layer depth and absorption zone from the Lidar backscatter profiles. journal of macromolecular and Oceanic Technology 1999; 16:953-9.) is an effective method to describe mixed boundary layers, but is not satisfactory for complex aerosol layers. The conventional algorithm determines the height of the boundary layer according to the vertical concentration change of the aerosol, which means that when the vertical aerosol concentration is thin or the aerosol level is complex, the boundary layer result inverted by the laser radar is distorted.
Disclosure of Invention
The invention aims to provide a method for inverting the height of a boundary layer based on particle characteristic difference, which is used for solving the problem that the height of the boundary layer is not accurately inverted by laser radar data under the weak convection condition.
The invention provides a boundary layer height inversion method based on particle characteristic difference, which obtains the top of a boundary layer based on the characteristic difference of atmospheric particles and comprises the following steps,
step 1, establishing characteristic values by using laser radar data, wherein the characteristic values comprise differences of characteristics of aerosol particles and molecular particles above and below a boundary layer, establishing a characteristic value A sequence by using a backscattering signal, and establishing a characteristic value B sequence by using a color ratio signal, wherein the characteristic value A sequence represents the difference of extinction capacities between adjacent particles, and the characteristic value B sequence represents the difference of particle sizes between the adjacent particles;
step 2, normalizing the characteristic value A sequence and the characteristic value B sequence;
step 3, establishing a difference degree sequence Z, including establishing the difference degree sequence Z by using the normalized characteristic sequence obtained in the step 2, and expressing the characteristic difference between adjacent particles through the difference degree sequence;
step 4, searching the height of the boundary layer, including primarily determining the position of the height of the boundary layer by searching the point of the maximum value in the difference degree sequence according to the difference degree sequence Z;
step 5, filtering error points, and monitoring whether the backscattering signals at the boundary layer height are less than or equal to a threshold value delta by using a threshold value methodthrIf it is less than or equal to ΔthrOutputting the result if the boundary layer height is correct; if it is larger than ΔthrThe explanation is that at the interface of aerosol layers, after the difference degree of the maximum difference point is returned to 0, the height of the boundary layer is searched again until a correct result is output.
And the step 1 is realized by calculating a backscattering signal and a color ratio signal by using the echo signals of the two channels of the dual-wavelength laser radar, subtracting the backscattering signals of adjacent particles to obtain a characteristic value A sequence representing the difference degree of the extinction capacities between the adjacent particles, and subtracting the color ratio signals of the adjacent particles to obtain a characteristic value B sequence representing the difference of the sizes between the adjacent particles.
Furthermore, step 3 is implemented in such a way that,
the difference degree sequence Z is established by utilizing the characteristic sequence after the normalization treatment as follows,
Z(i)＝X2(i)+Y2(i)
where z (i) represents the magnitude of the degree of difference at the ith sampling point, and X (i) and Y (i) represent the magnitudes of the normalized feature values X and Y at the ith sampling point, respectively.
Also, the threshold value ΔthrThe definition is that,
Δthr＝(BSmax+BSmin)/4
wherein, BSminAnd BSmaxAre respectively provided withRepresenting the minimum and maximum values in the BS signal.
According to the method, the accuracy of the foundation laser radar data inversion boundary layer height is improved by utilizing the characteristic changes of the particle extinction capacity and the size, and the problem that the laser radar cannot provide a high-accuracy inversion result under a weak convection condition can be effectively solved. In the practical process, the method utilizes the data of the dual-wavelength laser radar system, inverts the height of the boundary layer based on the particle characteristic difference, and greatly improves the inversion accuracy and the application range of the height of the boundary layer.
The invention has the following advantages and positive effects:
1) the application range of the laser radar in inverting the boundary layer height is greatly enlarged, and the boundary layer height can be effectively inverted under the condition of weak convection;
2) the method can directly utilize the original data of the laser radar to carry out inversion without preprocessing the original data, thereby reducing inversion errors.
The method can be widely applied to the related industries such as environmental protection, weather forecast and the like.
Drawings
FIG. 1 is a flow chart of inversion of boundary layer height based on particle property differences according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an embodiment of the present invention, in which fig. 2(a) is a schematic diagram of a sequence of feature values a, fig. 2(b) is a schematic diagram of a sequence of feature values b, fig. 2(c) is a schematic diagram of a sequence of degrees of difference z, and fig. 2(d) is a result of boundary layer search.
Detailed Description
In order to facilitate the understanding and practice of the present invention for those of ordinary skill in the art, the present invention will be described in further detail with reference to the accompanying drawings and examples, it being understood that the examples described herein are for purposes of illustration and explanation only and are not intended to be limiting.
In order to overcome the limitation of the prior art, the invention provides a maximum difference search algorithm for obtaining the top of the boundary layer based on the characteristic difference of atmospheric particles, the top of the boundary layer is found by using the difference of particle size and extinction capacity instead of the vertical distribution based on aerosol concentration, and the method can effectively avoid the influence of aerosol vertical concentration change on the boundary layer height inversion, so that the laser radar can accurately invert the boundary layer height.
Referring to fig. 1, in the embodiment of the present invention, the height of the boundary layer is calculated based on data collected at 11 nights of 7 days of 12 months of 2015 in a certain area under a non-cloud condition by using a dual-wavelength laser radar, and the specific calculation steps are as follows.
Step 1, establishing a characteristic value by using laser radar data. The laser radar backscattering signal represents the extinction capability of the atmospheric particles, and the laser radar color ratio signal represents the size of the atmospheric particles. Therefore, by using the dual-wavelength laser radar, a characteristic value A sequence can be constructed by using the backscattering signal based on the difference of the characteristics of aerosol particles and molecular particles above and below the boundary layer; and constructing a characteristic value B sequence by using the color ratio signals. The characteristic value A sequence represents the difference of extinction capacities between adjacent particles, and the characteristic value B sequence represents the difference of particle size between adjacent particles.
Embodiments construct eigenvalues representing the optical and physical properties of atmospheric particles from lidar signals. Establishing a characteristic value A sequence representing the difference of the extinction capacities of the particles by using an echo signal of a 532nm channel; the signal ratio of the 532nm to 355nm channels was used to establish a sequence of eigenvalues B representing the particle scale size differences. The method comprises the following specific steps:
and 1.1, calculating by using a laser radar equation to obtain echo signals of all channels.
Figure GDA0002970067590000031
Wherein r represents the vertical height, C represents the system constant of the receiving channel, P (r) represents the signal intensity collected by the receiving channelnbRepresenting the background noise of the receive channel. exp denotes the exponential operation. Beta is aαAnd betamDenotes the backscattering coefficient, alpha, of atmospheric aerosols and atmospheric molecules, respectivelyαAnd alphamThe extinction coefficients, which represent atmospheric aerosol and atmospheric molecules respectively, are functions of r. Respectively counting based on the laser radar equationThe corresponding echo signal intensity P of the 355nm channel and the 532nm channel can be obtained as the receiving channel355And P532。
Step 1.2 then calculates the Backscatter Signal (BS) and the colour ratio signal (CR) using the echo signals of the two channels.
Figure GDA0002970067590000041
k denotes a channel constant ratio, BS denotes a magnitude of an extinction capability of the particle in a vertical direction, and CR denotes a magnitude of a dimension of the particle in the vertical direction. After obtaining signals representing particle characteristics at different heights, the BS and CR signals are used to establish eigenvalues a and B representing differences in particle characteristics.
Figure GDA0002970067590000042
Wherein i represents the ith sampling point in the vertical direction of the laser radar system. A (i) and B (i) respectively represent the sizes of characteristic values A and B at the ith sampling point, CR (i) and BS (i) respectively represent the sizes of a color ratio value and a backscattering value at the ith sampling point, and CR (i-1) and BS (i-1) respectively represent the sizes of a color ratio value and a backscattering value at the ith sampling point. And subtracting the BS and the CR of the adjacent particles to obtain a characteristic value A sequence representing the difference degree of the extinction capacities between the adjacent particles and a characteristic value B sequence representing the difference of the scale sizes between the adjacent particles. As shown in fig. 2(a) and 2(B), fig. 2(a) shows a schematic diagram of a characteristic value a sequence, fig. 2(B) shows a schematic diagram of a characteristic value B sequence, a star icon shows the height of the aerosol layer bottom in the vertical direction, and a circle icon shows the height of the aerosol layer top in the vertical direction. It can be seen that there are significant differences between aerosol levels due to the different aerosol concentrations. The peak of the signature sequence is a point with a large difference.
And 2, normalizing the characteristic sequence. Signature sequences derived from lidar signals. Due to the large difference in magnitude, it cannot be used directly to invert the boundary layer. Therefore, the feature sequence needs to be normalized. And carrying out normalization processing on the characteristic value sequence by using a Max-Min normalization method to obtain a normalized characteristic sequence.
In the examples, the eigenvalues a and B obtained in step 1.2 were optimized. Since BS and CR belong to two different classes of signals, their signal strength orders of magnitude are different. Therefore, the eigenvalue a needs to be normalized to X and the eigenvalue B needs to be normalized to Y by the maximum-minimum normalization method.
Figure GDA0002970067590000043
Wherein A isminAnd AmaxRespectively representing the maximum and minimum values in a sequence of characteristic values A, BminAnd BmaxRespectively, the maximum value and the minimum value in the feature value B sequence, and X (i) and Y (i) respectively represent the sizes of the normalized feature values X and Y at the ith sampling point.
The compound is obtained according to the formula. The step is mainly to reduce the influence caused by the difference of the magnitude order between the two characteristic values, and the influence may cause the error of the subsequent search result. The normalized eigenvalues are orders of magnitude uniform, and the contribution of this eigenvalue is considered equal.
And 3, establishing a difference degree sequence. And establishing a difference degree sequence Z by using the characteristic sequence after the normalization treatment. The disparity sequence represents the characteristic difference between adjacent particles. This difference is manifested in particle extinction capability and particle size.
In the examples, the sequence of degrees of difference Z is established. And establishing a difference degree sequence Z by using the characteristic sequence after the normalization treatment. The sequence of degree of difference is defined as:
Z(i)＝X2(i)+Y2(i) (5)
the sequence of degrees of difference Z represents the particle difference between adjacent particles. Where z (i) represents the magnitude of the degree of difference at the ith sample point. This difference is due to the extinction capabilities of adjacent particles in combination with the size of the dimensions of the particles. The larger the difference in characteristics between adjacent particles, the larger the value of the sequence of degrees of difference. As shown in fig. 2(c), the red curve represents the disparity sequence Z, and the orange circle represents the point where the disparity is the greatest, and is also the boundary layer height point.
And 4, searching the height of the boundary layer. After the difference degree sequence Z is obtained, according to the atmosphere distribution rule of the boundary layer, most of the atmospheric molecules are on the atmospheric boundary layer, and most of the aerosol particles are below the atmospheric boundary layer. There is a large difference between aerosol particles and molecules, so the boundary layer height can be found where the particle difference is largest:
BLHpoint＝Find(Zmax) (6)
BLHpointrepresenting points representing the height of the boundary layer, ZmaxThe point representing the maximum value in the sequence of degrees of dissimilarity Z is represented, and Find (.) represents the search algorithm. The position of the boundary layer height can be preliminarily determined by searching the point of the maximum value in the sequence of the difference degrees.
And 5, filtering to obtain the product without difference. Due to the complexity of the aerosol layer, there may also be a large degree of difference between the different layers. When the difference is too large, the boundary of the aerosol layers is misjudged as the height of the boundary layer. Therefore, error point monitoring is required. Monitoring whether the backscattering signal at the boundary layer height is less than or equal to delta by using a threshold methodthr. If less than or equal to ΔthrThe result is output for the correct boundary layer height. If the fruit is greater than deltathrThe explanation is that the boundary of the aerosol layer is set to 0, and then the height of the boundary layer is searched again until the correct result is output.
In the embodiment, error point screening is performed by using a threshold value method. Preferably, the threshold value ΔthrIs defined as:
Δthr＝(BSmax+BSmin)/4 (7)
wherein, BSminAnd BSmaxRespectively representing the minimum and maximum values in the BS signal. Because the extinction signal of the aerosol layer is generally larger, whether the backscattering signal at the boundary layer height is less than or equal to delta is detected by using a threshold methodthr. If less than or equal toΔthrThe result is output for the correct boundary layer height. If the fruit is greater than deltathrThe explanation is that at the interface of aerosol layers, the difference value of the maximum difference point is set to 0 (i.e. the currently searched Z is ordered)max0), returning to step 4 to search the height of the boundary layer again until a correct result is output. The boundary layer seek result of the proposed method is shown in fig. 2(d), with a boundary layer height of 987 m.
In specific implementation, the invention can adopt a computer software technology to realize an automatic operation process.
It should be understood that the above description of the preferred embodiments is given for clearness of understanding and no unnecessary limitations are to be understood therefrom, and all changes and modifications may be made by those skilled in the art without departing from the scope of the invention as defined by the appended claims.

Claims (4)

1. A boundary layer height inversion method based on particle characteristic difference is characterized in that: the top of the boundary layer is obtained based on the characteristic difference of the atmospheric particles, and the implementation mode comprises the following steps,
step 1, establishing characteristic values by using laser radar data, wherein the characteristic values comprise differences of characteristics of aerosol particles and molecular particles above and below a boundary layer, establishing a characteristic value A sequence by using a backscattering signal, and establishing a characteristic value B sequence by using a color ratio signal, wherein the characteristic value A sequence represents the difference of extinction capacities between adjacent particles, and the characteristic value B sequence represents the difference of particle sizes between the adjacent particles;
step 2, normalizing the characteristic value A sequence and the characteristic value B sequence;
step 3, establishing a difference degree sequence Z, including establishing the difference degree sequence Z by using the normalized characteristic sequence obtained in the step 2, and expressing the characteristic difference between adjacent particles through the difference degree sequence;
step 4, searching the height of the boundary layer, including primarily determining the position of the height of the boundary layer by searching the point of the maximum value in the difference degree sequence according to the difference degree sequence Z;
step 5, filtering error points, and monitoring whether the backscattering signals at the boundary layer height are less than or equal to a threshold value delta by using a threshold value methodthrIf it is less than or equal to ΔthrOutputting the result if the boundary layer height is correct; if it is larger than ΔthrThe explanation is that at the interface of aerosol layers, after the difference degree of the maximum difference point is returned to 0, the height of the boundary layer is searched again until a correct result is output.
2. The method of claim 1 for boundary layer height inversion based on differences in particle properties, wherein: the implementation mode of the step 1 is that echo signals of two channels of the dual-wavelength laser radar are used for calculating backscattering signals and color ratio signals, the backscattering signals of adjacent particles are subtracted to obtain a characteristic value A sequence representing the difference degree of the extinction capacities between the adjacent particles, and the color ratio signals of the adjacent particles are subtracted to obtain a characteristic value B sequence representing the difference of the sizes between the adjacent particles.
3. The method for boundary layer height inversion based on particle property differences according to claim 1 or 2, wherein: the step 3 is realized in a way that,
the difference degree sequence Z is established by utilizing the characteristic sequence after the normalization treatment as follows,
Z(i)＝X2(i)+Y2(i)
where z (i) represents the magnitude of the degree of difference at the ith sampling point, and X (i) and Y (i) represent the magnitudes of the normalized feature values X and Y at the ith sampling point, respectively.
4. The method for boundary layer height inversion based on particle property differences according to claim 1 or 2, wherein: threshold value deltathrThe definition is that,
Δthr＝(BSmax+BSmin)/4
wherein, BSminAnd BSmaxRespectively representing the minimum and maximum values in the BS signal, which is the backscatter signal.
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