A cloud base height inversion method

Abstract

The application provides a cloud bottom height inversion method, and belongs to the technical field of cloud bottom height inversion, and comprises the following steps: obtaining a cloud echo signal; segmenting the cloud echo signal, and setting a threshold value for each segment; dividing an inversion interval, and obtaining a forward difference echo signal sequence by differentiating the cloud echo signal in the inversion interval; selecting points from positive to negative and from negative to positive in the sequence to form a difference mutation sequence; selecting a local increasing interval from the difference mutation sequence, and judging whether the maximum slope in the local increasing interval exceeds a noise difference standard deviation threshold value; if yes, the point is determined to be a non-noise wave peak point; for each segment of the cloud echo signal, if the wave bottom, the wave top and the wave peak of the cloud echo signal all exceed the threshold value, and the extinction coefficient is greater than 0, the cloud echo signal is determined to be a final cloud echo signal; and the cloud bottom height is inverted by using the final cloud echo signal. The method can invert the cloud bottom height.

Description

Technical Field

[0001] This invention belongs to the field of cloud base high inversion technology, and specifically relates to a cloud base high inversion method. Background Technology

[0002] Water vapor in the upper atmosphere condenses into tiny water droplets or fine ice crystals due to low temperatures. The heterogeneous mixture formed by these water droplets and ice crystals is macroscopically called a cloud. The microscopic and macroscopic physical properties of clouds are very complex. Meteorology uses cloud base height, an important cloud parameter, to diagnose and analyze clouds, which is of great significance for weather forecasting and climate assessment.

[0003] Existing laser cloud measuring instruments mainly transmit laser pulses periodically into the sky at a certain angle through a transmitter. When the laser pulses reach the high altitude, they are mainly absorbed and scattered by molecules and aerosol particles. A portion of the laser is scattered and returns to the ground. The laser cloud measuring instrument then uses a receiver to collect these returned micro-pulse echo data in real time at high frequency. The cloud echo signals are then processed and analyzed by algorithms on a computer to deduce cloud distribution parameters such as cloud base height and cloud top height.

[0004] Because clouds are much denser than aerosols, when a laser beam passes through clouds and aerosols at high altitudes, it is obstructed by cloud particles, and a large amount of laser light is scattered back. Therefore, the location of the cloud base can be determined by the sudden increase in the laser cloud echo signal or the sudden increase in the atmospheric extinction coefficient.

[0005] There are several existing algorithms for retrieving cloud height, namely the extinction coefficient method, the differential zero-crossing method, the integral method, the threshold method, and the photon count probability method.

[0006] The extinction coefficient method assumes certain conditions regarding the extinction coefficients and backscattering coefficients of molecules and aerosols. Based on these assumptions, the lidar equations are solved to obtain the extinction coefficients of molecules and aerosols. The cloud base height is then determined by the point of increase in the extinction coefficient. The extinction coefficient method is further divided into the Collis method, the Klettt method, and the Fernald method, depending on the different assumptions given.

[0007] The differential zero-crossing method posits that when a laser passes through a cloud boundary, the cloud echo signal is significantly enhanced due to cloud scattering. At this point, the slope of the cloud echo signal changes from negative to positive, and this position can be considered as the cloud base height.

[0008] The threshold method takes into account the significant amplitude difference between cloud echo signals and noisy cloud echo signals in cloud echo signals, and determines the cloud echoes in the cloud echo signals by setting one or more thresholds.

[0009] The integral method eliminates and smooths out noise information by calculating the integral of a specified window width, thereby highlighting cloud information and determining the location of the cloud base.

[0010] The photon count probability method involves sending multiple laser pulses and extracting the counting probability of each point in a single pulse. By combining the single pulse counting probability with points where the probability of multiple pulses is greater than the statistical probability, the cloud base height can be determined.

[0011] However, extinction coefficient methods such as Kleet and Collis rely on some subjective assumptions, introducing inherent systematic errors in the algorithmic inversion process. Due to the uneven distribution of particles in the atmosphere, differential methods generate numerous zero-crossing points, easily misidentifying noise as clouds and leading to false positives. Thresholding methods are difficult to execute; lowering the threshold condition easily results in false positives, while raising it easily results in false negatives. Integral methods reduce spatial resolution, and the selection of the integration window significantly impacts cloud height inversion; currently, window selection is based solely on personal statistical experience. Photon count probability methods require high sensitivity and high gain for photon count analysis to be effective.

[0012] In summary, existing technologies suffer from a high misjudgment rate of cloud echo signals. Summary of the Invention

[0013] To overcome the shortcomings of the existing technology, the present invention provides a cloud base high inversion method.

[0014] To achieve the above objectives, the present invention provides the following technical solution:

[0015] A method for cloud base inversion includes:

[0016] Acquire cloud echo signals;

[0017] The cloud echo signal is segmented according to height, and a threshold is set for each segment of the cloud echo signal. The thresholds include: noise standard deviation threshold, noise difference standard deviation threshold, extinction coefficient threshold, neighborhood peak threshold, neighborhood base threshold, and neighborhood apex threshold.

[0018] Divide the inversion interval of the cloud echo signal, and within the inversion interval, calculate the extinction coefficient of the cloud echo signal;

[0019] Differentiate the cloud echo signal to obtain the forward differential echo signal sequence; select points from positive to negative and from negative to positive in the forward differential echo sequence to form a differential abrupt change sequence; select points from positive to negative in the differential abrupt change sequence one by one, and form a local increasing interval with the previous differential abrupt change point in the sequence; determine whether the maximum slope in the local increasing interval exceeds the noise difference standard deviation threshold; if it does, the point is determined to be a non-noise peak point.

[0020] The non-noise peak points are further screened using the noise standard deviation threshold, the noise difference standard deviation threshold, and the extinction coefficient threshold.

[0021] For each cloud echo signal, if the wave base, wave crest, and wave peak of the cloud echo signal simultaneously exceed the neighboring wave peak threshold, the neighboring wave base threshold, and the neighboring wave crest threshold, and the extinction coefficient is greater than 0, then it is determined to be the final cloud echo signal.

[0022] The cloud base height is inverted using the final cloud echo signal.

[0023] Furthermore, the acquisition of cloud echo signals includes:

[0024] Acquire raw cloud echo signals;

[0025] The original cloud echo signal is defined as the noise signal in the last kilometer of the detection range. The average value of the noise signal is calculated to obtain the background noise. The background noise is then subtracted from the original cloud echo signal to obtain the cloud echo signal after noise removal.

[0026] Furthermore, the division of the cloud echo signal inversion interval includes:

[0027] The signal-to-noise ratio sequence of the cloud echo signal is obtained by the following formula:

[0028]

[0029] In the formula, For signal-to-noise ratio, P n The cloud echo signal after removing noise. P bn This is background noise;

[0030] Set a signal-to-noise ratio (SNR) threshold, select k consecutive subsequences from the SNR sequence whose SNR is greater than the SNR threshold, and set the continuous heights corresponding to these subsequences as the inversion interval of the cloud echo signal.

[0031] Furthermore, setting the noise difference standard deviation threshold includes:

[0032] Calculate the difference between the noise signals, then calculate the average and standard deviation of the noise difference signals. Finally, use the average value of the noise difference signals... and standard deviation Set the threshold for the standard deviation of the noise difference;

[0033] in,

[0034]

[0035]

[0036] In the formula,

[0037] k = s, s+1, ..., end-1, end

[0038] in, Let be the noise differential signal, s and end be the index values ​​of the first and last elements of the noise differential signal, respectively, and k be the index value of any element of the noise signal.

[0039] Furthermore, setting the noise standard deviation threshold includes:

[0040] Using the mean of the noise signal and standard deviation Set a threshold for multiple noise standard deviations;

[0041] in,

[0042]

[0043]

[0044] In the formula, Let be the noise signal, s and end be the index values ​​of the first and last elements of the noise signal, respectively, and k be the index value of any element in the noise signal.

[0045] Furthermore, the segmentation of the cloud echo signal according to altitude includes:

[0046] The cloud echo signal is divided into four segments: below 1500m, 1500m to 6000m, 6000m to 10000m, and above 10000m.

[0047] Furthermore, in the 1500m to 6000m range of the cloud echo signal, 5.5 times, 1.52 times, and 1.52 times the standard deviation of the signal in the neighboring region were selected as the neighboring peak threshold, the neighboring trough threshold, and the neighboring apex threshold, respectively.

[0048] Furthermore, it also includes: performing overlap factor calibration, distance correction and smoothing on the cloud echo signal to obtain the corrected cloud echo signal, and calculating the difference between the corrected cloud echo signal to obtain the echo forward differential signal sequence;

[0049] The overlap factor calibration is as follows:

[0050]

[0051] In the formula, P z The cloud echo signal after overlap factor calibration. P n The cloud echo signal after removing noise. The system overlap factor;

[0052] Distance correction is as follows:

[0053]

[0054] In the formula, P rc 'r' represents the distance-corrected cloud echo signal, and 'r' represents the sequence number.

[0055] Furthermore, the extinction coefficient is calculated using the Fernald method, and its expression is:

[0056]

[0057] Where C is the lidar system constant, and r is the distance. For overlap factor calibration sequences, For background noise, Extinction coefficient, This is the backscattering coefficient.

[0058] The cloud base high inversion method provided by this invention has the following beneficial effects:

[0059] This invention calculates all peaks on the cloud echo signal based on the original cloud echo signal by using the noise difference standard deviation threshold and the extinction coefficient threshold, thereby reducing the false negative rate of cloud height and enhancing the recognition rate of thin clouds.

[0060] By using multiple cloud features to filter cloud echoes based on peak identification, the interference of noise fluctuations and aerosol clusters can be effectively reduced, and the cloud base, peaks and tops can be accurately identified. At the same time, the impact of background weather on detection is reduced, and the stability of cloud detection inversion is enhanced. Attached Figure Description

[0061] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a schematic diagram of a cloud base height inversion method according to an embodiment of the present invention;

[0063] Figure 2 This is a diagram of the original echo sequence of an embodiment of the present invention;

[0064] Figure 3 This is a diagram of the denoised echo sequence according to an embodiment of the present invention;

[0065] Figure 4This is a signal-to-noise ratio sequence diagram according to an embodiment of the present invention;

[0066] Figure 5 This is the distance-corrected sequence according to an embodiment of the present invention;

[0067] Figure 6 This is the extinction coefficient sequence of an embodiment of the present invention;

[0068] Figure 7 This is the overlap factor calibration sequence according to an embodiment of the present invention;

[0069] Figure 8 This is a forward differential signal sequence according to an embodiment of the present invention. Detailed Implementation

[0070] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0071] Example:

[0072] This invention provides a method for cloud base high inversion, specifically as follows: Figure 1 The process includes: acquiring the original cloud echo signal; filtering out noise signals from the original cloud echo signal; removing noise signals from the original cloud echo signal to obtain a denoised cloud echo signal; segmenting the cloud echo signal according to height; setting thresholds for each segment, including: noise standard deviation threshold, noise difference standard deviation threshold, extinction coefficient threshold, neighborhood peak threshold, neighborhood base threshold, and neighborhood apex threshold; dividing the cloud echo signal into inversion intervals; calculating the extinction coefficient of the cloud echo signal within the inversion intervals; calculating the difference of the cloud echo signal to obtain the echo forward difference signal sequence; and selecting points from positive to negative and from negative to positive in the echo forward difference sequence to form a difference... Differential mutation sequence; from the differential mutation sequence, select points one by one from positive to negative, and form a local increasing interval with the previous differential mutation point in the sequence. Determine whether the maximum slope in the local increasing interval exceeds the noise difference standard deviation threshold. If it does, the point is determined to be a non-noise peak. Further filter the peaks using the noise standard deviation threshold, noise difference standard deviation threshold, and extinction coefficient threshold. For each cloud echo signal, if the wave base, wave top, and wave peak of the cloud echo signal simultaneously exceed the neighboring wave peak threshold, neighboring wave base threshold, and neighboring wave top threshold, and the extinction coefficient is greater than 0, then it is determined as the final cloud echo signal. Use the final cloud echo signal to invert the cloud base height.

[0073] The following are specific embodiments of the present invention:

[0074] (1) Remove background noise

[0075] The lidar transmitting system sends laser pulses into the air, and the receiving system collects the backscattered cloud echo signals. These signals are then processed by fiber optic coupling, digital-to-analog conversion, and computer processing to form the original photon cloud echo signals. The lidar cloud echo signals are the signals scattered back by the interaction of the emitted laser pulse with atmospheric molecules and aerosol particles. Compared to the cloud echo signals, these scattered cloud echo signals that interact with atmospheric molecules and aerosol particles are equivalent to noise.

[0076] To reduce the interference of noise on cloud feature identification in cloud echo signals, the first step is to denoise the original cloud echo signal. The original cloud echo signal is denoised as P(r). The last 1 km of the original cloud echo signal within the detection range is defined as noise, and the sequence is denoted as follows: The average value of the selected noise signal is calculated to obtain the background noise. Then, the background noise is subtracted from the original cloud echo signal one by one to obtain the denoised cloud echo signal.

[0077] Formula (1)

[0078] Formula (2)

[0079] (2) Echo correction processing

[0080] To further divide the inversion interval, it is necessary to obtain a signal-to-noise ratio sequence with the same height resolution as the cloud echo signal. The formula for this is as follows:

[0081] Formula (3)

[0082] Considering that the transmitting and receiving systems of the lidar are not coaxial, overlap factor calibration is performed on the signal to eliminate the influence of the equipment structure.

[0083] Formula (4)

[0084] In addition, to eliminate the influence of different altitudes on cloud echo signals, distance correction is performed on the cloud echo signals.

[0085] Formula (5)

[0086] (3) Divide the inversion interval

[0087] Based on statistical experience and meteorological knowledge, a signal-to-noise ratio (SNR) threshold, SNRt, is set. The SNR sequence obtained in the second step is then filtered to identify k consecutive subsequences with an SNR greater than the threshold SNRt, where k is determined by actual conditions. The continuous altitudes corresponding to these subsequences are defined as inversion intervals. Dividing these intervals further reduces the impact of noise and aerosol clusters on cloud height discrimination, thus decreasing the misclassification rate.

[0088] (4) Inversion extinction coefficient

[0089] Compared to the Kleet and Collis methods, the Fernald extinction coefficient is the most commonly used extinction coefficient inversion method for lidar. The lidar equation can be expressed as follows:

[0090] Formula (6)

[0091] Where C is the lidar system constant, and r is the distance. For overlap factor correction sequences, For background noise, Extinction coefficient, This represents the backscattering coefficient. This step involves using the Fernald algorithm to determine the extinction coefficient sequence.

[0092] (5) Segmented threshold setting

[0093] The noise signal from the first step is differentially analyzed to obtain the average and standard deviation of the noise signal and the noise difference signal.

[0094]

[0095]

[0096] k = s, s+1, ..., end-1, end

[0097]

[0098]

[0099] Where s and end are the indices of the first and last elements of the signal, respectively, and k is the index of any element in the signal. The mean of the noise signal is used... and standard deviation Set threshold values ​​of 3, 4, 5, etc., to represent multiples of the standard deviation, while utilizing the mean of the noise difference signal. and standard deviation Set the threshold for the standard deviation of the noise difference.

[0100] During heavy rain and heavy snow, clouds are mostly distributed below 1.5km, while during light rain, moderate rain, light snow, and moderate snow, clouds are distributed below 6-7km.

[0101] To reduce the impact of weather on cloud height retrieval, cloud echo signals were divided into four segments based on conventional meteorological knowledge and observation experience: below 1500m, 1500m~6000m, 6000m~10000m, and above 10000m. Thresholds such as noise difference standard deviation threshold, extinction coefficient threshold, neighborhood peak, neighborhood trough, and neighborhood apex were set for each segment of cloud echo signals.

[0102] (6) Determination of peak signal

[0103] First, the cloud echo signal after overlap factor correction is differentially analyzed to obtain the forward differential signal sequence. Then, points from positive to negative and from negative to positive in the forward differential sequence are selected to form a differential abrupt change sequence. Points from positive to negative are selected one by one from the differential abrupt change sequence, forming a locally increasing interval with the previous differential abrupt change point in the sequence. Then, it is determined whether the maximum slope in this locally increasing interval exceeds the noise difference standard deviation threshold. If it does, then the point is preliminarily determined to be a non-noise peak.

[0104] (7) Cloud echo determination

[0105] For the selected peak points, the thresholds set in step (5) are used for further filtering to reduce false detections of cloud height.

[0106] When a laser beam is emitted into the atmosphere, it is primarily affected by aerosol particles and atmospheric molecules. Clouds scatter laser light more strongly than aerosol clouds, so cloud peaks are initially screened using extinction coefficient thresholds. However, thin clouds scatter laser light relatively weakly than ordinary clouds, making it difficult to distinguish between thin clouds and aerosol clouds using extinction coefficients. Furthermore, laser light attenuation increases with altitude, but the attenuation fluctuations are not very pronounced. Therefore, additional thresholds need to be added to the cloud echo signals at each segment for further screening of thin clouds.

[0107] In the 1500m to 6000m range, signals from the vicinity of the wave crest are selected. The mean and variance of the signals in the vicinity are calculated. Thresholds of 5.5 times, 1.52 times, and 1.52 times the standard deviation are determined as the wave crest, wave trough, and wave apex, respectively. When the wave trough, wave apex, and wave crest simultaneously exceed the vicinity standard deviation thresholds, and the extinction coefficient is simultaneously greater than 0, it is identified as a cloud echo. The multipliers of the standard deviations can be obtained by calculating the slope of a region with a width of three or more wavelengths centered on the wave crest, and then combining this with the relative positions of the wave trough, wave crest, and wave apex to obtain the multipliers at the corresponding positions.

[0108] Similarly, for the 6000m to 10000m range, the standard deviation threshold of the adjacent region is calculated, and clouds are identified by comparing whether the peaks, troughs, and apexes exceed the threshold and the extinction coefficient is greater than 0. For signals above 10000m, due to the greater laser attenuation, the screening conditions need to be reduced, and the presence of clouds is determined solely by comparing whether the peaks and extinction coefficient exceed the filtering threshold.

[0109] This invention first extracts non-noise echoes from wave crests, then further identifies cloud-like echoes, using two rounds of filtering to obtain cloud features such as cloud base height and cloud top height. The first filtering further denoises and preserves thin cloud echo signals, reducing the false positive rate of cloud height retrieval. The second filtering uses meteorological knowledge and statistical experience to set cloud feature thresholds, filtering out cloud echoes based on multiple segments and thresholds. Combined with the previous filtering, this reduces the false positive rate of cloud height retrieval.

[0110] In the step of screening cloud echo signals from a wave crest sequence in this embodiment of the invention, to reduce interference from solar radiation, the local characteristics of the cloud echo signal are fully considered, and signals from regions near the wave crest are selected. The slope, mean, and standard deviation of these regional signals are calculated, and thresholds for wave crests, wave troughs, and wave apexes are set. Wave crests, wave troughs, and wave apexes are distinguished by the amplitude and dispersion of the neighboring signals, thereby determining whether the wave signal is a cloud echo.

[0111] In the segmented threshold setting step, it is recognized that during the process of the laser pulse traveling from the laser emitter to the receiver, the laser is attenuated by interactions with atmospheric molecules and aerosols, resulting in a weaker laser signal at higher altitudes. Therefore, using a single threshold for an echo that continuously weakens with altitude is insufficient to accurately determine the peak value. It is necessary to incorporate meteorological knowledge to divide the cloud echo signal into multiple echo bands and set an echo peak threshold for each band.

[0112] In the peak signal determination step, non-noise peaks are selected by checking whether the double spike points of the original signal and the differential signal exceed the noise standard deviation threshold.

[0113] In the cloud echo determination step, multiple echo thresholds and extinction coefficient thresholds are used for dual screening to determine whether non-noise peaks are cloud peaks, thereby obtaining the cloud base height, cloud top height, and cloud peak.

[0114] In the step of dividing the inversion interval, based on meteorological knowledge and statistical experience in lidar debugging, a signal-to-noise ratio threshold SNRt and an interval width coefficient k are set to divide the inversion interval, further filter out noise and improve computational efficiency.

[0115] The above-described embodiments are merely preferred embodiments of the present invention, and the scope of protection of the present invention is not limited thereto. Any simple changes or equivalent substitutions of the technical solutions that can be obviously obtained by those skilled in the art within the scope of the technology disclosed in the present invention shall fall within the scope of protection of the present invention.

Claims

1. A method for cloud base height inversion, characterized in that, include: Acquire cloud echo signals; The cloud echo signal is segmented according to height, and a threshold is set for each segment of the cloud echo signal. The thresholds include: noise standard deviation threshold, noise difference standard deviation threshold, extinction coefficient threshold, neighborhood peak threshold, neighborhood trough threshold, and neighborhood apex threshold. Divide the inversion interval of the cloud echo signal, and within the inversion interval, calculate the extinction coefficient of the cloud echo signal; Differentiate the cloud echo signal to obtain the forward differential echo signal sequence; select points from positive to negative and from negative to positive in the forward differential echo sequence to form a differential abrupt change sequence; select points from positive to negative in the differential abrupt change sequence one by one, and form a local increasing interval with the previous differential abrupt change point in the sequence; determine whether the maximum slope in the local increasing interval exceeds the noise difference standard deviation threshold; if it does, the point is determined to be a non-noise peak point. Non-noise peaks are filtered using noise standard deviation threshold, noise difference standard deviation threshold, and extinction coefficient threshold. For each cloud echo signal, if the wave base, wave crest, and wave peak of the cloud echo signal simultaneously exceed the neighboring wave peak threshold, the neighboring wave base threshold, and the neighboring wave crest threshold, and the extinction coefficient is greater than 0, then it is determined to be the final cloud echo signal. The cloud base height is inverted using the final cloud echo signal.

2. The cloud base height inversion method according to claim 1, characterized in that, The acquisition of cloud echo signals includes: Acquire raw cloud echo signals; The original cloud echo signal is defined as the noise signal in the last kilometer of the detection range. The average value of the noise signal is calculated to obtain the background noise. The background noise is then subtracted from the original cloud echo signal to obtain the cloud echo signal after noise removal.

3. The cloud base height inversion method according to claim 2, characterized in that, The division of the cloud echo signal inversion interval includes: The signal-to-noise ratio sequence of the cloud echo signal is obtained by the following formula: In the formula, For signal-to-noise ratio, P n The cloud echo signal after removing noise. P bn This is background noise; Set a signal-to-noise ratio (SNR) threshold, select k consecutive subsequences from the SNR sequence whose SNR is greater than the SNR threshold, and set the continuous heights corresponding to these subsequences as the inversion interval of the cloud echo signal.

4. The cloud base height inversion method according to claim 1, characterized in that, Setting the noise difference standard deviation threshold includes: Calculate the difference between the noise signals, then calculate the average and standard deviation of the noise difference signals. Finally, use the average value of the noise difference signals... and standard deviation Set the threshold for the standard deviation of the noise difference; in, In the formula, ,k=s,s+1,…end-1,end in, Let be the noise differential signal, s and end be the index values ​​of the first and last elements of the noise differential signal, respectively, and k be the index value of any element of the noise signal.

5. The cloud base height inversion method according to claim 1, characterized in that, Setting the noise standard deviation threshold includes: Using the mean of the noise signal and standard deviation Set a threshold for multiple noise standard deviations; in, In the formula, Let s be the index of the first and last elements of the noise signal, respectively, and k be the index of any element in the noise signal.

6. The cloud base height inversion method according to claim 1, characterized in that, The segmentation of cloud echo signals according to altitude includes: The cloud echo signal is divided into four segments: below 1500m, 1500m to 6000m, 6000m to 10000m, and above 10000m.

7. The cloud base height inversion method according to claim 6, characterized in that, In the 1500m to 6000m range of cloud echo signals, 5.5 times, 1.52 times, and 1.52 times the standard deviation of the neighboring region signals were selected as the neighboring peak threshold, the neighboring trough threshold, and the neighboring apex threshold, respectively.

8. The cloud base height inversion method according to claim 1, characterized in that, Also includes: The cloud echo signal is calibrated by overlap factor, distance correction and smoothing to obtain the corrected cloud echo signal. The difference between the corrected cloud echo signal and the cloud echo signal is obtained to obtain the echo forward differential signal sequence. The overlap factor calibration is as follows: In the formula, P z The cloud echo signal after overlap factor calibration. P n The cloud echo signal after removing noise. The system overlap factor; Distance correction is as follows: In the formula, P rc 'r' represents the distance-corrected cloud echo signal, and 'r' represents the sequence number.

9. The cloud base height inversion method according to claim 1, characterized in that, The extinction coefficient can be determined using the Fernald method, and its expression is as follows: Where C is the lidar system constant, and r is the distance. For overlap factor calibration sequences, For background noise, Extinction coefficient, This is the backscattering coefficient.

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