Laser ceilometer cloud layer inversion method and device, computer equipment and storage medium

Abstract

The cloud layer inversion method of the laser ceilometer comprises the following steps: acquiring original measurement data of the laser ceilometer; dividing the original measurement data into low-altitude data and high-altitude data according to a preset segmentation threshold value; optimizing the low-altitude data according to a Mie-Rayleigh scattering laser radar equation to obtain low-altitude optimized data; comparing the low-altitude optimization data with a preset low-altitude threshold value, and determining the height of a low-altitude cloud layer according to a comparison result; segmenting the high-altitude data to obtain a plurality of data segments, and performing data validity screening on each data segment to extract a high-altitude valid data segment; calculating an effective median value and a data fluctuation quantity of each effective data segment; calculating a dynamic threshold according to all data fluctuation quantities; optimizing the high-altitude data to obtain high-altitude optimized data; and comparing the high-altitude optimization data with a dynamic threshold value, and determining the height of the high-altitude cloud layer according to a comparison result. According to the invention, the accuracy of the inversion result can be effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of laser ceilometers, in particular to a laser ceilometer cloud layer inversion method, device, computer equipment and storage medium. Background Art

[0002] Laser cloud measurement is a commonly used method for measuring cloud height and cloud thickness. The working principle of laser cloud measurement is as follows: the laser emission module emits a periodic laser pulse beam into the atmosphere, and the laser beam interacts with the gas molecules and clouds in the atmosphere to generate a Rayleigh-Mie scattering back echo signal. The signal receiving module receives the back echo signal, and after photoelectric conversion by the photoelectric detector, the original measurement signal is obtained. The original signal is inverted to obtain the cloud height and thickness values.

[0003] The fixed threshold method is currently the most widely used and most stable cloud inversion method for laser ceilometers. It uses a fixed single threshold or uses multiple different fixed thresholds in different height intervals according to different measurement heights, and determines the data with echo signal intensity higher than the threshold as cloud data to obtain cloud height and cloud thickness information. However, the fluctuation of the echo signal of the laser ceilometer mainly changes with the intensity of the sky background noise, and is limited by the working characteristics of the photoelectric detector. When the cloud signal is strong, the signal has significant distortion such as tailing and oscillation. In order to avoid misjudgment of the cloud layer under strong background noise and strong interference, the threshold used is often high, which makes it easy to miss the cloud layer during night measurement and thin cloud layer. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a laser ceilometer cloud inversion method, device, computer equipment and storage medium, which can effectively improve the accuracy of the inversion result.

[0005] In order to achieve the above object, the specific scheme adopted by the present invention is: a laser ceilometer cloud inversion method, comprising the following steps: Obtain the original measurement data of the laser ceilometer; The original measurement data is divided into low-altitude data and high-altitude data according to the preset segmentation threshold, wherein the low-altitude data is used to invert the low-altitude cloud height, and the high-altitude data is used to invert the high-altitude cloud height; Methods for retrieving low-altitude cloud heights include: The aerosol backscattering coefficient is calculated according to the Mie-Rayleigh scattering lidar equation, and the low-altitude data is optimized according to the aerosol backscattering coefficient to obtain the low-altitude optimized data; Compare the low-altitude optimization data with the preset low-altitude threshold, and determine the low-altitude cloud height based on the comparison result; Methods for retrieving high-altitude cloud heights include: The high-altitude data is segmented to obtain multiple data segments, and each data segment is screened for data validity to extract the high-altitude valid data segment; Calculate the effective median and data fluctuation of each effective data segment; Calculate dynamic thresholds based on all data fluctuations; Perform n-order polynomial fitting on all valid medians to generate high-altitude contour lines, and use the high-altitude contour lines to optimize the high-altitude data to obtain high-altitude optimized data; The high-altitude optimized data is compared with the dynamic threshold, and the high-altitude cloud height is determined based on the comparison result.

[0006] As a further optimization of the above-mentioned laser ceilometer cloud inversion method: the method for calculating the aerosol backscattering coefficient includes: For the first low-altitude data point z0 of the low-altitude data, the aerosol backscattering coefficient β is calculated based on the Mie-Rayleigh scattering lidar equation A (z0), where the Mie-Rayleigh scattering lidar equation is: Where K is a constant, W0 is the laser pump energy of the laser ceilometer, O(z) is the geometric overlap factor, and A r is the receiving mirror area of ​​the laser ceilometer, L is the spatial resolution of the laser ceilometer, β A (z) is the aerosol backscattering coefficient, β M (z) is the atmospheric molecular backscattering coefficient, σ A (z) is the aerosol extinction coefficient, and σ A (z) = S A *β A (z),σ M (z) is the extinction coefficient of atmospheric molecules, and σ M (z) = S M *β M (z), S A is the aerosol lidar ratio, S M is the atmospheric molecular laser radar ratio; the aerosol backscattering coefficient corresponding to the low-altitude data point is deduced based on the result ratio of the Mie-Rayleigh scattering laser radar equation corresponding to different low-altitude data points; The calculated aerosol backscattering coefficient is used as the low-altitude optimization data.

[0007] As a further optimization of the above-mentioned laser ceilometer cloud inversion method: a method for recursively deriving the aerosol backscattering coefficient corresponding to the low-altitude data point according to the result ratio of the Mie-Rayleigh scattering laser radar equation corresponding to different low-altitude data points includes: constructing a recursive equation according to the result ratio of the Mie-Rayleigh scattering laser radar equation corresponding to different low-altitude data points: The aerosol backscattering coefficient corresponding to the low-altitude data point is obtained by solving the recursive equation through multiple iterations.

[0008] As a further optimization of the above-mentioned laser ceilometer cloud inversion method: the method of segmenting the high-altitude data segment includes: Calculate the segment length based on the data volume of the high-altitude data; The high-altitude data is evenly segmented according to the calculated segment length to obtain multiple data segments.

[0009] As a further optimization of the above-mentioned laser ceilometer cloud inversion method: the method for screening the validity of the data segment is: Sort the raw measurement data in the data segment according to the echo strength; Delete the first p original measurement data and the last q original measurement data, and the number of remaining original measurement data is r. Calculate the average value of this part of the data. If the average value is within the preset reference range, the data segment is a valid data segment, and this average value is the median value of the data segment.

[0010] As a further optimization of the above-mentioned laser ceilometer cloud inversion method: the method for calculating the data fluctuation amount of the data segment is: The absolute value of the difference between the p+1th data and the p+rth data in the valid data segment is taken as the data fluctuation amount; A cubic polynomial is fitted to all valid medians to generate the high-altitude contour line, which is: data CL =ax 3 +bx 2 +cx+d; Where data CL is the contour function, a, b, c, d are the polynomial fitting coefficients calculated according to the principle of least squares method, x is the series coordinate, the value range is [1, max], and the value interval is 1.

[0011] A laser ceilometer cloud inversion device, used to implement the above-mentioned laser ceilometer cloud inversion method, the device comprising: A data acquisition module, used for acquiring the original measurement data; A data screening module is used to divide the original measurement data into low-altitude data and high-altitude data, and to segment the high-altitude data to obtain multiple data segments; A data processing device for inverting low-altitude cloud layer height and high-altitude cloud layer height; The data storage module is used to store the original measurement data, the low-altitude cloud layer height and the high-altitude cloud layer height.

[0012] Computer equipment, including: Memory for storing computer programs; A processor is used to execute a computer program to implement the above-mentioned laser ceilometer cloud inversion method.

[0013] A storage medium is used to store a computer program, which, when executed, implements the laser ceilometer cloud inversion method as described above.

[0014] Beneficial effects: The present invention can adaptively calibrate the data profile according to the characteristics of the original data obtained by the laser ceilometer measurement, so that the high-altitude data fluctuates around zero as a whole, creating favorable conditions for the selection of the threshold. At the same time, according to the data fluctuation amount, the dynamic threshold is adaptively selected. During the day, when the background noise is strong or when there is strong interference, a larger threshold is used to avoid misjudging the normal fluctuation of the data as cloud layer. At night, when the background noise is weak, a lower threshold is used to avoid missing the cloud layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the method of the present invention;

[0016] Figure 2 It is a diagram of actual measurement results of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1 and 2 The present invention first provides a laser ceilometer cloud inversion method, including S1 to S3.

[0019] S1. Obtain the original measurement data of the laser ceilometer. The original measurement data is recorded as data_raw, which is a sequence with a scale of 1×A, where A is the length of the original sequence.

[0020] S2. Divide the raw measurement data into low-altitude data and high-altitude data according to the preset segmentation threshold, where the low-altitude data is used to invert the low-altitude cloud height, and the high-altitude data is used to invert the high-altitude cloud height. The low-altitude data is recorded as data_low_raw, which is a 1×M sequence, where M is the length of the low-altitude sequence, and each data is recorded as a low-altitude data point; the high-altitude data is recorded as data_high_raw, which is a 1×N sequence, where N is the length of the high-altitude sequence, and each data is recorded as a high-altitude data point, and A=M+N.

[0021] The methods for inverting low-altitude cloud height include S311 to S312.

[0022] S311. Calculate the aerosol backscattering coefficient according to the Mie-Rayleigh scattering laser radar equation, and optimize the low-altitude data according to the aerosol backscattering coefficient to obtain the low-altitude optimized data. Since different temperatures and different pump powers have a great influence on the light output energy of the laser module in the laser ceilometer, and the low-altitude data is highly correlated with the light output energy of the laser module, the low-altitude data is easily affected by the temperature and pump power of the laser module. In S311, after calibrating the low-altitude data according to the temperature and pump power of the laser ceilometer, the validity of the low-altitude data can be improved.

[0023] The method for calculating the aerosol backscatter coefficient is as follows: First, for the first low-altitude data point z0 of the low-altitude data, the aerosol backscatter coefficient β is calculated based on the Mie-Rayleigh scattering lidar equation A (A0), where the Mie-Rayleigh scattering lidar equation is: Where K is a constant, W0 is the laser pump energy of the laser ceilometer, O(z) is the geometric overlap factor, and A r is the receiving mirror area of ​​the laser ceilometer, L is the spatial resolution of the laser ceilometer, β A (z) is the aerosol backscattering coefficient, β M (z) is the atmospheric molecular backscattering coefficient, σ A (z) is the aerosol extinction coefficient, and σ A (z) = S A *β A (z),σ M (z) is the extinction coefficient of atmospheric molecules, and σ M (z) = S M *β M (z), S A is the aerosol lidar ratio, S Mis the atmospheric molecular lidar ratio; secondly, the aerosol backscattering coefficient corresponding to the low-altitude data point is deduced according to the result ratio of the Mie-Rayleigh scattering lidar equation corresponding to different low-altitude data points; then, the calculated aerosol backscattering coefficient is used as the low-altitude optimization data.

[0024] For the first low-altitude data point z0, because the corresponding laser transmission distance is very short, the influence of atmospheric extinction can be ignored. At this time, the only unknown quantity in the Mie-Rayleigh scattering lidar equation is the aerosol backscattering coefficient, from which the aerosol backscattering coefficient β at can be obtained. A (z0).

[0025] Methods for deriving the aerosol backscattering coefficient corresponding to low-altitude data points based on the results of the Mie-Rayleigh scattering lidar equation corresponding to different low-altitude data points include: The recursive equation is constructed based on the results of the Mie-Rayleigh scattering lidar equation corresponding to different low-altitude data points: The aerosol backscattering coefficient corresponding to the low-altitude data point is obtained by solving the recursive equation through multiple iterations.

[0026] S312, compare the low-altitude optimization data with the preset low-altitude threshold, and determine the low-altitude cloud layer height according to the comparison result. Specifically, compare the low-altitude optimization data with the low-altitude threshold. If the low-altitude optimization data is higher than the low-altitude threshold, it means that there is a low-altitude cloud layer, and the low-altitude cloud layer height is determined. The low-altitude threshold, the low-altitude cloud layer height and the corresponding relationship between the two are all prior arts in the field and will not be described in detail here.

[0027] The methods for inverting high-altitude cloud height include S321 to S325.

[0028] S321. Segment the high-altitude data to obtain a plurality of data segments, and perform data validity screening on each data segment to extract valid high-altitude data. The method for segmenting the high-altitude data segments includes: calculating the segment length according to the data volume of the high-altitude data, the segment length is used to control the data volume of the data segment, and avoid the data volume of the data segment being too large or too small, causing subsequent processing difficulties or reducing the accuracy of the results; and evenly segmenting the high-altitude data according to the calculated segment length to obtain a plurality of data segments. For example, in one embodiment of the present invention, the high-altitude data is evenly divided into 10 to 16 data segments, and the data volume of each data segment is 260 to 300, that is, each data segment includes 260 to 300 high-altitude data points.

[0029] After that, the data segment needs to be screened for validity. The method for screening the validity of the data segment is: sort the original measurement data in the data segment according to the echo intensity; delete the first p original measurement data and the last q original measurement data, and the number of remaining original measurement data is r, and the average value of this part of the data is calculated. If the average value is within the preset reference range, the data segment is a valid data segment, and this average value is the median value of the data segment. According to actual needs, p and q can be equal or unequal.

[0030] S322. Calculate the data fluctuation of each data segment based on the high-altitude valid data segment.

[0031] The method for calculating the data fluctuation amount of a data segment is as follows: The method for calculating the data fluctuation amount of a data segment is as follows: the absolute value of the difference between the p+1th data and the p+rth data in the valid data segment is taken as the data fluctuation amount;

[0032] S323, the calculation method of the dynamic threshold is: the data fluctuation amount in the valid data segment is counted, and then the average value of all data fluctuation amounts is calculated, and the dynamic threshold is obtained by multiplying it by a fixed coefficient, and the fixed coefficient is related to the amount of original measurement data discarded in S321, that is, it is related to p and q. The more the amount of data discarded, the larger the fixed coefficient, and vice versa.

[0033] The selection method of the fixed coefficient can be obtained by comparing the cloud measurement data of the laser ceilometer with that of the standard instrument. When the fixed coefficient is too large, the cloud miss rate will increase, and when the fixed coefficient is too low, the cloud misjudgment rate will increase. The sum of the cloud miss rate and misjudgment rate under different fixed coefficients is calculated. The fixed coefficient corresponding to the minimum value is the optimal fixed coefficient.

[0034] S324, performing n-order polynomial fitting on all valid medians to generate high-altitude contour lines, and optimizing the high-altitude data using the high-altitude contour lines to obtain high-altitude optimized data. The specific fitting method is: taking the number of data bits of the middle point of the data segment corresponding to the valid median as the X-coordinate sequence, and taking the valid median as the Y-coordinate sequence; after obtaining the X and Y coordinate sequences, they can be subjected to linear fitting, quadratic polynomial fitting, or multiple polynomial fitting according to the principle of least squares method.

[0035] In one embodiment of the present invention, a cubic polynomial fitting is performed on all valid medians to generate a high-altitude contour line, and the high-altitude contour line is: data CL =ax 3 +bs 2 +cx+d. Where data CLis the contour function, a, b, c, d are polynomial fitting coefficients calculated according to the principle of least squares method, x is the series coordinate, the value range is [1, max], and the value interval is 1. In this embodiment, the value of max is the array length of the high-altitude data.

[0036] S325, compare the high-altitude optimized data with the dynamic threshold, and determine the high-altitude cloud layer height according to the comparison result. Specifically, the part of the high-altitude optimized data that is larger than the dynamic threshold is marked as a pre-selected cloud layer, and then the thickness of the marked pre-selected cloud layer is counted, and the pre-selected cloud layer with a thickness of less than 30m is marked as a false cloud layer and deleted. After removing the false cloud layer, the remaining cloud layer is the high-altitude cloud layer height calculated by the laser ceilometer cloud layer inversion algorithm.

[0037] The present invention further provides a laser ceilometer cloud inversion device for realizing the above-mentioned laser ceilometer cloud inversion method. The device comprises a data acquisition module, a data screening module, a data processing module and a data storage module.

[0038] The data acquisition module is used to acquire original measurement data.

[0039] The data screening module is used to divide the original measurement data into low-altitude data and high-altitude data, and to segment the high-altitude data into multiple data segments.

[0040] A data processing device is used to invert the low-altitude cloud height and the high-altitude cloud height.

[0041] The data storage module is used to store the original measurement data, the low-altitude cloud layer height and the high-altitude cloud layer height.

[0042] The present invention also provides a computer device, including a memory and a processor.

[0043] Memory, used to store computer programs.

[0044] A processor is used to execute a computer program to implement the above-mentioned laser ceilometer cloud inversion method.

[0045] In addition, the present invention further provides a storage medium for storing a computer program, which, when executed, implements the laser ceilometer cloud inversion method as described above.

[0046] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A laser ceilometer cloud inversion method, characterized in that: The steps include: Obtain the original measurement data of the laser ceilometer; The original measurement data is divided into low-altitude data and high-altitude data according to the preset segmentation threshold, wherein the low-altitude data is used to invert the low-altitude cloud height, and the high-altitude data is used to invert the high-altitude cloud height; Methods for retrieving low-altitude cloud heights include: The aerosol backscattering coefficient is calculated according to the Mie-Rayleigh scattering lidar equation, and the low-altitude data is optimized according to the aerosol backscattering coefficient to obtain the low-altitude optimized data; Compare the low-altitude optimization data with the preset low-altitude threshold, and determine the low-altitude cloud height based on the comparison result; Methods for retrieving high-altitude cloud heights include: The high-altitude data is segmented to obtain multiple data segments, and each data segment is screened for data validity to extract the high-altitude valid data segment; Calculate the effective median and data fluctuation of each effective data segment; Calculate dynamic thresholds based on all data fluctuations; Perform n-order polynomial fitting on all valid medians to generate high-altitude contour lines, and use the high-altitude contour lines to optimize the high-altitude data to obtain high-altitude optimized data; The high-altitude optimized data is compared with the dynamic threshold, and the high-altitude cloud height is determined based on the comparison result.

2. The laser ceilometer cloud inversion method according to claim 1, characterized in that: Methods for calculating aerosol backscatter coefficients include: For the first low-altitude data point z0 of the low-altitude data, the aerosol backscattering coefficient β is calculated based on the Mie-Rayleigh scattering lidar equation A (z0), where the Mie-Rayleigh scattering lidar equation is: Where K is a constant, W0 is the laser pump energy of the laser ceilometer, O(z) is the geometric overlap factor, and A r is the receiving mirror area of ​​the laser ceilometer, L is the spatial resolution of the laser ceilometer, β A (z) is the aerosol backscattering coefficient, β M (z) is the atmospheric molecular backscattering coefficient, σ A (z) is the aerosol extinction coefficient, and σ A (z) = S A *β A (z),σ M (z) is the extinction coefficient of atmospheric molecules, and σ M (z) = S M *β M (z), S A is the aerosol lidar ratio, S M is the atmospheric molecular laser radar ratio; the aerosol backscattering coefficient corresponding to the low-altitude data point is deduced based on the result ratio of the Mie-Rayleigh scattering laser radar equation corresponding to different low-altitude data points; The calculated aerosol backscattering coefficient is used as the low-altitude optimization data.

3. The laser ceilometer cloud inversion method according to claim 1, characterized in that: Methods for deriving the aerosol backscattering coefficient corresponding to low-altitude data points based on the results of the Mie-Rayleigh scattering lidar equation corresponding to different low-altitude data points include: The recursive equation is constructed based on the results of the Mie-Rayleigh scattering lidar equation corresponding to different low-altitude data points: The aerosol backscattering coefficient corresponding to the low-altitude data point is obtained by solving the recursive equation through multiple iterations.

4. The laser ceilometer cloud inversion method according to claim 1, characterized in that: Methods for segmenting high-altitude data segments include: Calculate the segment length based on the data volume of the high-altitude data; The high-altitude data is evenly segmented according to the calculated segment length to obtain multiple data segments.

5. The laser ceilometer cloud inversion method according to claim 1, characterized in that: The method for screening the validity of data segments is: Sort the raw measurement data in the data segment according to the echo strength; Delete the first p original measurement data and the last q original measurement data, and the number of remaining original measurement data is r. Calculate the average value of this part of the data. If the average value is within the preset reference range, the data segment is a valid data segment, and this average value is the median value of the data segment.

6. The laser ceilometer cloud inversion method according to claim 1, characterized in that: The method for calculating the data fluctuation of a data segment is: The absolute value of the difference between the p+1th data and the p+rth data in the valid data segment is taken as the data fluctuation amount.

7. The laser ceilometer cloud inversion method according to claim 1, characterized in that: A cubic polynomial is fitted to all valid medians to generate the high-altitude contour line, which is: data CL =ax 3 +bx 2 +cx+d; Where data CL is the contour function, a, b, c, d are the polynomial fitting coefficients calculated according to the principle of least squares method, x is the series coordinate, the value range is [1, max], and the value interval is 1.

8. Laser ceilometer cloud inversion device, characterized in that: Used to implement the laser ceilometer cloud inversion method according to any one of claims 1 to 7, the device comprises: A data acquisition module, used for acquiring the original measurement data; A data screening module is used to divide the original measurement data into low-altitude data and high-altitude data, and to segment the high-altitude data to obtain multiple data segments; A data processing device for inverting low-altitude cloud layer height and high-altitude cloud layer height; The data storage module is used to store the original measurement data, the low-altitude cloud layer height and the high-altitude cloud layer height.

9. Computer device, characterized in that include: Memory for storing computer programs; A processor, configured to execute a computer program to implement the laser ceilometer cloud inversion method as claimed in any one of claims 1 to 7.

10. A storage medium, characterized in that Used to store a computer program, which, when executed, implements the laser ceilometer cloud inversion method as described in any one of claims 1 to 7.

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