A method for multi-scale classification of clouds and aerosols and computer readable medium

Abstract

The application provides a multi-scale classification method for clouds and aerosols and a computer readable medium. The application is applied to a spaceborne lidar, first calculates an attenuation scattering ratio profile, second calculates bidirectional transmittance of each feature level, corrects signals subjected to attenuation, further divides scales according to a multi-scale classification model, and finally calculates parameters required by classification based on various scale window sizes, so as to realize classification of clouds and aerosols and sub-type classification. It is considered that the feature level in the spaceborne lidar signal can be split, the multi-scale idea is introduced, and the accuracy of classification is improved. The application fully considers spatial correlation in the level, can effectively realize high-precision classification of mixed levels, the classification result is reasonable, and the misclassification phenomenon is less.

Description

Technical Field

[0001] This invention belongs to the field of atmospheric lidar technology, and particularly relates to a multi-scale classification method for clouds and aerosols and a computer-readable medium. Background Technology

[0002] Aerosols and clouds are important components of the atmosphere. Studies have shown that clouds and aerosols play a significant role in local meteorology and global climate change, and are inextricably linked to the hydrological cycle and radiation transfer. Therefore, accurate measurement of the vertical distribution of atmospheric aerosols and clouds, and precise classification of targets in the vertical direction, are crucial for assessing the atmosphere's impact on climate change. Cloud and aerosol classification can provide precise quantitative information on the vertical distribution and types of clouds and aerosols in the atmosphere, which is of great significance for global cloud and aerosol research.

[0003] Commonly used passive detection methods can only acquire optical characteristics of the atmosphere in the horizontal space, and cannot obtain vertical distribution data. Currently, lidar is one of the main tools for three-dimensional cloud and aerosol detection. Ground-based lidar can continuously observe clouds and aerosols over local areas for extended periods, such as the Micropulse LiDAR Network (MPLNET) established by NASA. The high temporal resolution observations of ground-based lidar have played an important role in improving our understanding of the macroscopic physical characteristics of cloud and aerosol layers on hourly and daily scales, but the spatial coverage of its stations is limited. Spaceborne lidar, such as CALIPSO, can achieve global coverage and is an effective means of observing large spatial areas and long-term atmospheric conditions. However, the official classification algorithm of CALIPSO still has some misclassification issues, especially for the aliasing layer of the boundary layer. Therefore, there is an urgent need to develop more robust and reliable algorithms to achieve high-precision hierarchical classification based on spaceborne lidar detection.

[0004] Currently, feature-level classification methods based on spaceborne lidar are mainly divided into: cloud and aerosol classification algorithms, cloud phase classification algorithms, and aerosol subtype identification algorithms.

[0005] The cloud and aerosol classification algorithm distinguishes between cloud layers and aerosol layers based on differences in their physical and optical properties. The latest version of the algorithm constructs a five-dimensional joint density function of aerosol-cloud using the average attenuation backscattering coefficient, total attenuation color ratio, volume deflection ratio, layer center height, and layer latitude. Considering the complexity of the five-dimensional probability density function and the dependence of cloud and aerosol properties across different physical regions, the official latest version algorithm constructs a two-dimensional Gaussian distribution function of the total attenuation color ratio and layer integral attenuation backscattering for each different volume deflection ratio, layer center height, and layer latitude interval, thus achieving cloud and aerosol classification.

[0006] The cloud phase classification algorithm distinguishes cloud features into water clouds, ice clouds randomly oriented towards particles, ice clouds horizontally oriented towards particles, and clouds of unknown phase. This algorithm has undergone multiple iterations, with the latest version providing a comprehensive cloud phase classification mechanism: First, based on the two-dimensional spatial distribution map of the layer integral attenuation backscattering coefficient and volume depolarization ratio of a certain band from a spaceborne lidar, two two-dimensional linear thresholds are constructed to initially classify clouds into water clouds, ice clouds randomly oriented towards particles, and ice clouds horizontally oriented towards particles. Then, a decision tree is set based on statistical thresholds such as layer centroid temperature, satellite perspective, and spatial correlation to further refine the cloud phase classification. Ultimately, clouds are divided into four categories: water clouds, ice clouds randomly oriented towards particles, ice clouds horizontally oriented towards particles, and clouds of unknown phase (i.e., clouds that cannot be reliably classified into the first three types).

[0007] Aerosol subtype identification algorithms classify aerosols into different subcategories based on their physical and chemical properties. The latest version of this algorithm classifies tropospheric and stratospheric aerosols separately. It calculates attributes such as particle volume depolarization ratio, layer integral attenuation backscattering coefficient, layer top (bottom) height, total attenuation color ratio, and layer center temperature at the current feature layer. These attributes are then combined with latitude, month, and surface type information to establish a decision tree based on statistical thresholds, thus classifying aerosols within the troposphere and stratosphere.

[0008] Previous lidar signal feature hierarchical classification algorithms mostly used feature levels as the basic unit, failing to consider the impact of aliasing levels during the classification process, leading to misclassification. Specifically:

[0009] The cloud and aerosol classification algorithm uses the entire feature level as the basic unit, calculates five attributes of the feature level, calculates the probability that the current feature level belongs to a cloud or aerosol based on the probability density function model, and calculates the confidence level. The cloud phase classification algorithm uses the entire feature level as the basic unit, calculates the basic attributes of the current feature level, performs preliminary classification according to the linear thresholds of the constructed layer integral attenuation backscattering coefficient and volume depolarization ratio, and then performs more refined classification based on other attributes of the current feature level. The aerosol subtype classification algorithm also uses the entire feature level as the basic unit, calculates the particle volume depolarization ratio, layer integral attenuation backscattering coefficient, layer top (bottom) height, layer attenuation total color ratio, layer center temperature and other attributes of the current feature level, and realizes the classification of aerosols in the troposphere and stratosphere.

[0010] Previous classification algorithms have certain shortcomings. First, due to absorption and reflection, lidar signals attenuate as they penetrate feature layers. Therefore, it is necessary to calculate the transmittance of each feature layer and correct for attenuation of the signal. However, since classification algorithms use feature layers as the basic classification unit, attenuation correction does not consider the signal attenuation effect within the feature layer; it only corrects the attenuation of the signal below the current feature layer. Second, because layer detection algorithms identify aliased layers as single layers, and only one classification result exists, classification algorithms exhibit significant misclassification for complex aliased layers.

[0011] To more accurately classify the feature layers detected by lidar, this patent proposes a multi-scale classification algorithm. Based on the concept of multi-scale classification, the algorithm no longer uses feature layers as the basic unit. Therefore, during attenuation correction, it is necessary not only to correct the signal below the current feature layer but also to correct the attenuation of the current feature layer itself. The attenuation degree gradually increases from the top to the bottom of the layer, requiring separate correction for each signal point. Secondly, the multi-scale classification algorithm divides the feature layer into different scales according to the signal-to-noise ratio (SNR), calculates the optical and physical properties of the feature layer at different scales, and combines the detection results at different scales with the classification algorithm to obtain the final classification result. The multi-scale classification algorithm considers the spatial correlation within the layers, making high-precision classification of aliased layers possible and effectively compensating for the misclassification of aliased layers in current classification algorithms. Summary of the Invention

[0012] To address the aforementioned technical problems, this invention proposes a multi-scale classification method for clouds and aerosols, as well as a computer-readable medium.

[0013] The technical solution of this invention is a multi-scale classification method for clouds and aerosols, and the specific steps are as follows:

[0014] Step 1: Obtain the horizontal and vertical components of radar signals from multiple altitudes at multiple spaceborne lidar locations. Calculate the radar signal intensity at each altitude for each spaceborne lidar location based on the horizontal and vertical components. Obtain the attenuation backscattering coefficient of atmospheric molecules at each altitude for each spaceborne lidar location. Calculate the attenuation scattering ratio at each altitude for each spaceborne lidar location by obtaining the total attenuation backscattering coefficient and the attenuation backscattering coefficient of atmospheric molecules. Based on the attenuation scattering ratio at each altitude for each spaceborne lidar location, perform feature level detection using a hierarchical detection algorithm to obtain multiple feature levels at each spaceborne lidar location, i.e., multiple sets of signal points with continuous altitudes.

[0015] Step 2: Based on the attenuation scattering ratio of each feature layer altitude position of each spaceborne lidar position described in Step 1, calculate the bidirectional transmittance of each feature layer at each spaceborne lidar position. According to the bidirectional transmittance of each feature layer at each spaceborne lidar position, correct the horizontal and vertical components of each feature layer altitude position and the altitude position below the current feature layer altitude position, respectively, to obtain the corrected horizontal and vertical components of each feature layer altitude position. Calculate the corrected radar signal strength of each feature layer altitude position based on the corrected horizontal and vertical components, i.e., the corrected total attenuation backscattering coefficient.

[0016] Step 3: Calculate the signal-to-noise ratio of the radar signal at each feature level elevation position at each spaceborne lidar location. Divide the elevation position at each feature level of all spaceborne lidar locations into multiple scale windows using a multi-scale model, and record the scale window number.

[0017] Step 4: Combine the multiple scale windows divided at the altitude of each feature level at all spaceborne lidar locations, and calculate the average attenuation backscattering coefficient, volume depolarization ratio, total attenuation color ratio, average altitude, and average latitude for each scale window.

[0018] Step 5: Repeat steps 1-4 multiple times to obtain a dataset of five parameters for each feature level of altitude position at each feature level of each spaceborne lidar location on multiple spaceborne lidar trajectories: average attenuation backscattering coefficient, volume de-polarization ratio, total attenuation color ratio, average altitude, and average latitude. Then, use the CALIPSO official scene classification algorithm to obtain a cloud and aerosol classification model after training.

[0019] Step 6: Real-time acquisition of horizontal and vertical components of the satellite-borne lidar signal received in a certain band, and the signal received in the 1064nm band of the satellite-borne lidar. Calculate the average attenuation backscattering coefficient, volume depolarization ratio, total attenuation color ratio, average altitude, and average latitude for each scale window of the unknown type feature level by executing steps 1-4 sequentially. Input these values ​​into the cloud and aerosol classification model obtained after training in step 5 to obtain the classification results for each scale window of the unknown type feature level. If the classification result of the scale window containing the unknown type feature level is cloud type, use the average attenuation backscattering coefficient, volume depolarization ratio, and average altitude data to perform CALIP... The official SO cloud phase classification method performs detailed classification of cloud phases, yielding cloud phase classification results for water clouds, randomly oriented ice crystals, and horizontally oriented plate-shaped ice crystals. If the classification result of the scale window containing the unknown type feature level is an aerosol type, based on the average attenuation backscattering coefficient, volume depolarization ratio, total attenuation color ratio, and average altitude data, the official CALIPSO aerosol subtype classification method further classifies aerosol subtypes according to aerosol absorption and particle size, resulting in aerosol subtype classification results including dust aerosols, smoke aerosols, marine aerosols, continental aerosols, pollution aerosols, volcanic ash aerosols, and sulfates.

[0020] Preferably, step 1 involves calculating the radar signal strength at each altitude for each spaceborne lidar location, i.e., the total attenuation backscattering coefficient, as follows:

[0021] β′ total,n (k)=β′ ⊥,n (k)+β′ ||,n (k)

[0022]

[0023]

[0024] Where n represents the location number of the spaceborne lidar, n∈[1,N], and N represents the number of different locations of the spaceborne lidar in a complete trajectory; k is the sequence number, representing the k-th signal point. The k-th signal point at different locations of the spaceborne lidar represents the same altitude, k∈[1,K], and K represents the number of signal points obtained by scanning at a location of the spaceborne lidar; β′ total,n (k) represents the attenuation backscattering coefficient at the altitude of the k-th signal point at the n-th position, received by the satellite-borne lidar in a certain band channel; β′ ||,n (k), β′ ⊥,n (k) represent the horizontal and vertical components of the radar signal received by the spaceborne lidar at the altitude of the k-th signal point at the n-th position via a certain band channel; βm,||,n (k), β m,⊥,n (k) represent the horizontal and vertical components of the backscattering coefficient of atmospheric molecules at the altitude of the k-th signal point at the n-th position, respectively; β p,||,n (k), β p,⊥,n (k) represent the horizontal and vertical components of the particle backscattering coefficient at the altitude of the k-th signal point at the n-th position, respectively; The bidirectional transmittance of atmospheric molecules at the altitude of the k-th signal point at the n-th position; The bidirectional transmittance of ozone at the altitude of the k-th signal point at the n-th position; This represents the bidirectional transmittance of a particle at the altitude of the k-th signal point, located at the n-th position.

[0025] Step 1 involves calculating the attenuation backscattering coefficient of atmospheric molecules at each altitude for each spaceborne lidar location, as detailed below:

[0026]

[0027] Where n represents the location number of the spaceborne lidar, n∈[1,N], and N represents the number of different locations of the spaceborne lidar in a complete trajectory; k is the sequence number, representing the k-th signal point. The k-th signal point at different locations of the spaceborne lidar represents the same altitude, k∈[1,K], and K represents the number of signal points obtained by scanning at a location of the spaceborne lidar; β′ air,n (k) represents the attenuation backscattering coefficient of atmospheric molecules at the altitude of the k-th signal point at the n-th position; β m,n (k) represents the backscattering coefficient of atmospheric molecules at the altitude of the k-th signal point at the n-th position; The bidirectional transmittance of atmospheric molecules at the altitude of the k-th signal point at the n-th position; This represents the bidirectional transmittance of ozone at the altitude of the k-th signal point at the n-th location.

[0028] Step 1 involves calculating the attenuation scattering ratio at each altitude for each spaceborne lidar location, as detailed below:

[0029]

[0030] Where n represents the location number of the spaceborne lidar, n∈[1,N], and N represents the number of different locations of the spaceborne lidar in a complete trajectory; k is the sequence number, representing the k-th signal point. The k-th signal point at different locations of the spaceborne lidar represents the same altitude, k∈[1,K], and K represents the number of signal points obtained by scanning at a location of the spaceborne lidar; R′ n (k) represents the attenuation scattering ratio at the altitude of the k-th signal point at the n-th location of the spaceborne lidar; β′ total,n (k) represents the attenuation backscattering coefficient at the altitude of the k-th signal point at the n-th position, received by the satellite-borne lidar in a certain band channel; β′ air,n (k) represents the attenuation backscattering coefficient of atmospheric molecules at the altitude of the k signal point at the nth position.

[0031] Step 1 describes using a hierarchical detection algorithm to detect feature levels at each altitude location of a spaceborne lidar, based on the attenuation and scattering ratio. This results in multiple feature levels at each lidar location, i.e., a set of signal points with continuous altitudes, as detailed below:

[0032] Using existing publicly available hierarchical detection algorithms, the probability that a signal point belongs to clean atmosphere is calculated based on the Bernoulli distribution principle using attenuation scattering ratio data of signal points at multiple adjacent altitudes of multiple adjacent spaceborne lidar locations. If the probability that the set of signal points at multiple adjacent altitudes of multiple adjacent spaceborne lidar locations belongs to clean atmosphere is less than a probability threshold, then the corresponding altitude positions on the spaceborne lidar where these signal points are located are marked as feature levels. Altitude positions that meet the feature levels are selected from multiple altitude positions at each spaceborne lidar location. All feature levels with consecutive altitude positions are sequentially numbered, h = 1, 2, 3, ..., H, and the altitude positions and signal point numbers of the top and bottom of each feature level are recorded. This enables the detection and localization of all feature levels within a complete trajectory of a spaceborne lidar.

[0033] Preferably, step 2, which calculates the bidirectional transmittance at the altitude of each feature layer for each spaceborne lidar location, is as follows:

[0034]

[0035]

[0036]

[0037] B n (k)=β m,n (k)·R′ n (k)

[0038] Where n represents the location number of the spaceborne lidar, n∈[1,N], and N represents the number of different locations of the spaceborne lidar in a complete trajectory; h represents the feature level number detected by the spaceborne lidar at a location, h=1,2,3,…,H, and H represents the number of feature levels detected by the spaceborne lidar at a location. The number of feature levels at different locations of the spaceborne lidar is not necessarily the same; T layer,n,h The unidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar; The bidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar; The estimated value of the unidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar; This represents the estimated bidirectional transmittance of the h-th feature layer at the n-th position of the spaceborne lidar. The mean attenuation scattering ratio of the spaceborne lidar at the h-th feature level below the altitude of the n-th location within the minimum clean atmospheric distance; γ′ n,h The integral attenuation backscattering coefficient of the lidar layer at the h-th feature level of the n-th position represents the lidar layer; S represents the empirical lidar ratio; M represents the integral attenuation backscattering coefficient of the lidar layer at the h-th feature level of the spaceborne lidar. h,t M h,b These represent the top and bottom signal points of the h-th feature layer at the n-th location of the spaceborne lidar, respectively; k is the sequence number, representing the k-th signal point. The k-th signal point at different locations of the spaceborne lidar represents the same altitude, k∈[M]. h,t M h,b ];Z k Z represents the altitude of the k-th signal point; k-1 This represents the altitude of the (k-1)th signal point; Representing the Mth h,t The altitude of each signal point; Representing the Mth h,b The altitude of each signal point; B n (k), B n (k-1) represent the attenuation backscattering coefficients of the spaceborne lidar at a certain wavelength, corrected for the bidirectional transmittance of atmospheric molecules and ozone, at the altitude of the k-th and k-1-th signal points at the n-th position, respectively; B n (M h,t B n (M h,b ) represent the Mth position of the spaceborne lidar at the nth position. h,t M h,bThe attenuation backscattering coefficient of a certain band of the spaceborne lidar at the altitude of each signal point, corrected for the effects of atmospheric molecules and ozone bidirectional transmittance; β m,n (k) represents the backscattering coefficient of atmospheric molecules at the altitude of the k-th signal point at the n-th location; R′ n (k) represents the attenuation scattering ratio of the satellite lidar at the nth position and at the kth signal point at the altitude.

[0039] Step 2 involves calculating the bidirectional transmittance at each elevation position within each feature layer of each satellite-borne lidar location, as detailed below:

[0040]

[0041] Where n represents the location number of the spaceborne lidar, n∈[1,N], and N represents the number of different locations of the spaceborne lidar in a complete trajectory; h represents the feature layer number detected by the spaceborne lidar at a location, h=1,2,3,…,H, and H represents the number of feature layers detected by the spaceborne lidar at a location, the number of feature layers at different locations of the spaceborne lidar is not necessarily the same; k is the sequence number, representing the k-th signal point, the altitude represented by the k-th signal point at different locations of the spaceborne lidar is the same; M h,t M h,b These represent the numbers of the top and bottom signal points of the h-th feature layer at the n-th position of the spaceborne lidar; T layer,n,h (k) represents the unidirectional transmittance of the signal point with index k within the h-th feature layer at the n-th location of the spaceborne lidar, where k∈[M h,t M h,b ]; This represents the bidirectional transmittance of the signal point with index k within the h-th feature layer at position n of the spaceborne lidar, where k ∈ [M]. h,t M h,b ]; T layer,n,h The unidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar; The bidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar;

[0042] Step 2 involves correcting the horizontal and vertical components of each feature level elevation position and each elevation position below the current feature level for each spaceborne lidar location, and calculating the corrected radar signal strength at each elevation position, i.e., the corrected total attenuation backscattering coefficient, as detailed below:

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] Where n represents the location number of the spaceborne lidar, n∈[1,N], and N represents the number of different locations of the spaceborne lidar in a complete trajectory; h represents the feature layer number detected by the spaceborne lidar at a location, h=1,2,3,…,H, and H represents the number of feature layers detected by the spaceborne lidar at a location, the number of feature layers at different locations of the spaceborne lidar is not necessarily the same; k is the sequence number, representing the k-th signal point, the altitude represented by the k-th signal point at different locations of the spaceborne lidar is the same; K represents the number of signal points obtained by scanning at a location of the spaceborne lidar. The total attenuation backscattering coefficient represents the corrected feature hierarchy attenuation effect at the altitude of the signal point at position k at position n, received by a certain band channel of a spaceborne lidar. M represents the horizontal and vertical components of the radar signal received by a certain band channel of the spaceborne lidar at the nth position and the kth signal point, after correction for the attenuation effect of the characteristic hierarchy; h,t M h,b These represent the numbers of the top and bottom signal points of the h-th feature layer at the n-th position of the spaceborne lidar; T layer,n,h (k) represents the unidirectional transmittance of the signal point with index k within the h-th feature layer at the n-th location of the spaceborne lidar, where k∈[M h,t M h,b ]; This represents the bidirectional transmittance of the signal point with index k within the h-th feature layer at position n of the spaceborne lidar, where k ∈ [M]. h,t M h,b ]; T layer,n,h The unidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar; β′ represents the bidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar. ||,n (k), β′ ⊥,n (k) represent the horizontal and vertical components of the radar signal received by the spaceborne lidar at the altitude of the k-th signal point at the n-th position via a certain band channel.

[0049] Preferably, step 3, which calculates the radar signal signal-to-noise ratio at each feature level elevation position of each spaceborne lidar location, is as follows:

[0050]

[0051]

[0052] Where L represents the number of spaceborne lidar locations used to calculate the signal-to-noise ratio (SNR) of each lidar location at each altitude; n and i represent the lidar location numbers, i∈[nL / 2,n+L / 2], n∈[1,N], and N represents the number of different locations of the lidar in a complete trajectory; SNR n (k) represents the signal-to-noise ratio of the radar signal at the altitude of the k-th signal point at the n-th location of the spaceborne lidar; The total attenuation backscattering coefficient, corrected for the influence of characteristic layer attenuation, is the signal received at the k-th signal point at the i-th position via a certain band channel of the spaceborne lidar; μ n (k) represents the mean of the attenuation backscattering coefficients of L spaceborne lidar positions at the altitude of the kth signal point at the nth position, after correcting for the influence of feature hierarchy attenuation.

[0053] Step 3 describes the process of dividing the location into multiple scale windows using a multi-scale model to obtain various scale windows at the elevation position of each feature level for all spaceborne lidar locations, as detailed below:

[0054] Set the number of scale windows to W;

[0055] The size of the scale window is set to 1×1, 2×2, 3×3..., W×W, which is a window composed of the elevation positions of W adjacent signal points on W adjacent spaceborne lidar positions;

[0056] Step 3.1: Use the radar signal-to-noise ratio (SNR) data of each feature level elevation position at each spaceborne lidar location to filter out the SNR that meets the scale determination criteria. n (k)≥T threshold The signal points are identified and marked as scale 1. At the same time, the signal points are assigned scale numbers s = 1, 2, 3, ..., Q1. The signal points that have been marked do not participate in subsequent calculations and scale division.

[0057] Step 3.2: Using the signal-to-noise ratio (SNR) of the radar signals from every two adjacent altitude positions at every two adjacent spaceborne lidar locations in the previous scale, calculate the updated SNR as follows:

[0058]

[0059] Where M represents the total number of unmarked signal points under the new multi-scale window; W represents the size of the new multi-scale window, i.e., W×W; n and i represent the positions of the spaceborne lidar, i∈[n,n+W], n∈[1,N], and N represents the number of different positions of the spaceborne lidar in a complete trajectory; j and k represent the sequence numbers, representing the j-th and k-th signal points, k∈[1,K], and K represents the number of signal points obtained by scanning at a position of the spaceborne lidar, i∈[k,k+W]; SNR i (j) represents the signal-to-noise ratio (SNR) at the altitude of the j-th signal point at the i-th satellite lidar location; update,n (k) represents the radar signal-to-noise ratio at the altitude of the k-th signal point at the n-th satellite lidar position, calculated using the adjacent altitude positions at the neighboring satellite lidar positions.

[0060] Step 3.3: Use the updated signal-to-noise ratio (SNR) data to filter out SNR values ​​that meet the scaling criteria. update,n (k)≥T threshold The signal points are identified and marked with a scale of 2. The signal points are then numbered according to scale s = Q1+1, Q1+2, Q1+3, ..., Q2. The marked signal points do not participate in subsequent calculations or scale division.

[0061] Step 3.4: Repeat steps 3.2-3.3 above to complete the update of signal-to-noise ratio data and scale division. Mark all unmarked signal points as the last scale, complete the scale division of radar signal points at the elevation of each feature level at all spaceborne lidar locations, and obtain multiple scale windows at the elevation of each feature level at all spaceborne lidar locations.

[0062] The specific numbering of the recording scale window in step 3 is as follows:

[0063] s=1,2,3,…,Q1,…,Q2,…,Q3,…,Q W .

[0064] Preferably, step 4, which calculates the average attenuation backscattering coefficient for each scale window, is as follows:

[0065] The window number for each scale is:

[0066] s=1,2,3,…,Q1,…,Q2,…,Q3,…,Q W

[0067]

[0068] Where s represents the number of the scale window that the radar signal points at each altitude location, which are all detected as feature levels at all spaceborne lidar locations, have passed through in a multi-scale division; W s n represents the size of the scale window numbered s; s k represents the location number of the spaceborne lidar where the scale window with the number s is located. s The s represents the signal point index corresponding to the altitude of the scale window with the number s, and i represents the location number of the spaceborne lidar, i∈[n]. s ,n s +W s ]; j represents the sequence number, representing the j-th signal point, j∈[k s ,k s +W s M represents the total number of signal points with scale number s; The attenuation backscattering coefficient, which is corrected for the influence of characteristic layer attenuation, is the signal point at the nth position and the kth position received by a certain band channel of the spaceborne lidar. The average attenuation backscattering coefficient is represented by the scale number s.

[0069] Step 4 involves calculating the volume deflection ratio for each scale window, as detailed below:

[0070]

[0071] Where s represents the number of the scale window that the radar signal points at each altitude location, which are all detected as feature levels at all spaceborne lidar locations, have passed through in a multi-scale division; W s n represents the size of the scale window numbered s. s k represents the location number of the spaceborne lidar where the scale window with the number s is located. s The s represents the signal point index corresponding to the altitude of the scale window with the number s, and i represents the location number of the spaceborne lidar, i∈[n]. s ,n s +W s ]; j represents the sequence number, representing the j-th signal point, j∈[k s ,k s +W s ]; The horizontal and vertical components of the radar signal at the j-th signal point at the i-th position, received by a certain band channel of the spaceborne lidar, are corrected for the attenuation effect of the characteristic hierarchy at altitude; δ s The body deterioration ratio is represented by the scale number s.

[0072] Step 4 involves calculating the total attenuation color ratio for each scale window, as detailed below:

[0073]

[0074]

[0075] Where s represents the number of the scale window that the radar signal points at each altitude location, which are all detected as feature levels at all spaceborne lidar locations, have passed through in a multi-scale division; W s n represents the size of the scale window numbered s; s The number represents the location number of the spaceborne lidar where the scale window with the number 's' is located, 'i' represents the location number of the spaceborne lidar, and 'k' represents the location number of the spaceborne lidar. s The signal point index corresponding to the altitude of the scale window with the number s, i∈[n s ,n s +W s ]; j represents the sequence number, representing the j-th signal point, j∈[k s ,k s +W s ];B i (j) represents the attenuated backscattering coefficient of a certain band channel of the spaceborne lidar at the altitude of the j-th signal point at the i-th position, after correction for the effects of atmospheric molecules and ozone bidirectional transmittance; B-1064 n (k) represents the attenuated backscattering coefficient of the 1064nm band channel of the spaceborne lidar at the altitude of the k-th signal point at the n-th location, after correction for the effects of atmospheric molecule and ozone bidirectional transmittance; β-1064 m,n (k) represents the backscattering coefficient of atmospheric molecules at the altitude of the k-th signal point at the n-th location in the 1064nm band, where m represents atmospheric molecules; β-1064 p,n (k) represents the backscattering coefficient of a particle at the altitude of the k-th signal point at the n-th position in the 1064nm band, where p represents the particle; T-1064 p,n (k) represents the unidirectional transmittance of a particle in the 1064nm band at the altitude of the kth signal point at the nth position, and p represents the particle. χ represents the bidirectional transmittance of a particle in the 1064nm band at an altitude of the kth signal point at the nth position, where p represents the particle; s The total attenuation ratio represents the scale number s.

[0076] Step 4 involves calculating the average altitude for each scale window, as detailed below:

[0077]

[0078] Where s represents the number of the scale window that the radar signal points at each altitude location, which are all detected as feature levels at all spaceborne lidar locations, have passed through in a multi-scale division; W s The size of the scale window with the number s represents n. s k represents the location number of the spaceborne lidar where the scale window with the number s is located. s The s represents the signal point index corresponding to the altitude of the scale window with the number s, and i represents the location number of the spaceborne lidar, i∈[n]. s ,n s +W s ]; j represents the sequence number, representing the j-th signal point, j∈[k s ,k s +W s M represents the total number of signal points with scale number s; Z j Represents the altitude of the j-th signal point; The average altitude represented by the scale number 's'.

[0079] Step 4 involves calculating the average latitude for each scale window, as detailed below:

[0080]

[0081] Where s represents the number of the scale window that the radar signal points at each altitude location, which are all detected as feature levels at all spaceborne lidar locations, have passed through in a multi-scale division; W s The size of the scale window with the number s represents n. s k represents the location number of the spaceborne lidar where the scale window with the number s is located. s The s represents the signal point index corresponding to the altitude of the scale window with the number s, and i represents the location number of the spaceborne lidar, i∈[n]. s ,n s +W s ]; j represents the sequence number, representing the j-th signal point, j∈[k s ,k s +W s ]; M represents the total number of signal points with scale number s, Lat i Lat represents the latitude of the location of the i-th spaceborne lidar; s The average latitude represented by the scale number 's';

[0082] The present invention also provides a computer-readable medium storing a computer program executed by an electronic device, which, when the computer program is run on the electronic device, enables the steps of the power supply method for optimizing the black-start power supply layout.

[0083] This invention has the following advantages: Based on the multi-scale algorithm concept, this algorithm considers that the feature levels in spaceborne lidar signals can be decomposed, no longer using the entire feature level as the classification unit. Therefore, during attenuation correction, it can correct not only the attenuated signal below the current feature level but also the attenuated signal within the current feature level. Finally, it iteratively calculates and corrects all signals attenuated due to the existence of feature levels. Based on signal-to-noise ratio data, the feature levels are divided into multiple scales. The results of this multi-scale division are combined into cloud and aerosol classification and subtype differentiation algorithms. Compared with existing classification algorithms, the multi-scale classification algorithm fully considers the spatial correlation within feature levels, making high-precision classification of aliased levels possible and effectively compensating for the misclassification of aliased levels in current classification algorithms. Attached Figure Description

[0084] Figure 1 : Flowchart of the method according to an embodiment of the present invention;

[0085] Figure 2 : Iterative attenuation correction flowchart of an embodiment of the present invention;

[0086] Figure 3 Comparison diagram of iterative attenuation correction before and after in this embodiment of the invention;

[0087] Figure 4 : A schematic diagram of multi-scale division according to an embodiment of the present invention;

[0088] Figure 5 The result of multi-scale windowing of spaceborne lidar data in this embodiment of the invention;

[0089] Figure 6 The cloud and aerosol classification results based on the multi-scale classification method for clouds and aerosols in this embodiment of the invention are shown in the figure.

[0090] Figure 7 Comparison chart of classification results in embodiments of the present invention. Detailed Implementation

[0091] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0092] In specific implementation, the method proposed in the technical solution of this invention can be automatically executed by those skilled in the art using computer software technology. System devices for implementing the method, such as computer-readable storage media storing the corresponding computer program of the technical solution of this invention and computer equipment including the computer program running the corresponding computer program, should also be within the protection scope of this invention.

[0093] The following is combined Figure 1-7 The technical solution of the method described in this invention is a multi-scale classification method for clouds and aerosols, as detailed below:

[0094] The flowchart of the method of the present invention is as follows Figure 1 As shown.

[0095] Step 1: Obtain the horizontal and vertical components of radar signals from multiple altitudes at multiple spaceborne lidar locations. Calculate the radar signal intensity at each altitude for each spaceborne lidar location based on the horizontal and vertical components. Obtain the attenuation backscattering coefficient of atmospheric molecules at each altitude for each spaceborne lidar location. Calculate the attenuation scattering ratio at each altitude for each spaceborne lidar location by obtaining the total attenuation backscattering coefficient and the atmospheric molecule attenuation backscattering coefficient. Based on the attenuation scattering ratio at each altitude for each spaceborne lidar location, perform feature level detection using a hierarchical detection algorithm to obtain multiple feature levels at each spaceborne lidar location, i.e., multiple sets of signal points with continuous altitudes.

[0096] Step 1 involves calculating the radar signal strength at each altitude for each spaceborne lidar location, i.e., the total attenuation backscattering coefficient, as detailed below:

[0097] β′ total,n (k)=β′ ⊥,n (k)+β′ ||,n (k)

[0098]

[0099]

[0100] Where n represents the location number of the spaceborne lidar, n∈[1,N], N=3936 represents the number of different locations of the spaceborne lidar in a complete trajectory; k is the sequence number, representing the k-th signal point. The k-th signal point at different locations of the spaceborne lidar represents the same altitude, k∈[1,K], K=1291 represents the number of signal points obtained by scanning at one location of the spaceborne lidar; β′ total,n(k) represents the attenuation backscattering coefficient at the altitude of the k-th signal point at the n-th position, received by the satellite-borne lidar in a certain band channel; β′ ||,n (k), β′ ⊥,n (k) represent the horizontal and vertical components of the radar signal received by the spaceborne lidar at the altitude of the k-th signal point at the n-th position via a certain band channel; β m,||,n (k), β m,⊥,n (k) represent the horizontal and vertical components of the backscattering coefficient of atmospheric molecules at the altitude of the k-th signal point at the n-th position, respectively; β p,||,n (k), β p,⊥,n (k) represent the horizontal and vertical components of the particle backscattering coefficient at the altitude of the k-th signal point at the n-th position, respectively; The bidirectional transmittance of atmospheric molecules at the altitude of the k-th signal point at the n-th position; The bidirectional transmittance of ozone at the altitude of the k-th signal point at the n-th position; This represents the bidirectional transmittance of a particle at the altitude of the k-th signal point, located at the n-th position.

[0101] Step 1 involves calculating the attenuation backscattering coefficient of atmospheric molecules at each altitude for each spaceborne lidar location, as detailed below:

[0102]

[0103] Where n represents the location number of the spaceborne lidar, n∈[1,N], N=3936 represents the number of different locations of the spaceborne lidar in a complete trajectory; k is the sequence number, representing the k-th signal point. The k-th signal point at different locations of the spaceborne lidar represents the same altitude, k∈[1,K], K=1291 represents the number of signal points obtained by scanning at one location of the spaceborne lidar; β′ air,n (k) represents the attenuation backscattering coefficient of atmospheric molecules at the altitude of the k-th signal point at the n-th position; β m,n (k) represents the backscattering coefficient of atmospheric molecules at the altitude of the k-th signal point at the n-th position; The bidirectional transmittance of atmospheric molecules at the altitude of the k-th signal point at the n-th position; This represents the bidirectional transmittance of ozone at the altitude of the k-th signal point at the n-th location.

[0104] Step 1 involves calculating the attenuation scattering ratio at each altitude for each spaceborne lidar location, as detailed below:

[0105]

[0106] Where n represents the location number of the spaceborne lidar, n∈[1,N], N=3936 represents the number of different locations of the spaceborne lidar in a complete trajectory; k is the sequence number, representing the k-th signal point. The k-th signal point at different locations of the spaceborne lidar represents the same altitude, k∈[1,K], K=1291 represents the number of signal points obtained by scanning at one location of the spaceborne lidar; R′ n (k) represents the attenuation scattering ratio at the altitude of the k-th signal point at the n-th location of the spaceborne lidar; β′ total,n (k) represents the attenuation backscattering coefficient at the altitude of the k-th signal point at the n-th position, received by the satellite-borne lidar in a certain band channel; β′ air,n (k) represents the attenuation backscattering coefficient of atmospheric molecules at the altitude of the k signal point at the nth position.

[0107] Step 1 describes using a hierarchical detection algorithm to detect feature levels at each altitude location of a spaceborne lidar, based on the attenuation and scattering ratio. This results in multiple feature levels at each lidar location, i.e., a set of signal points with continuous altitudes, as detailed below:

[0108] Using existing publicly available hierarchical detection algorithms, such as a simple multi-scale hierarchical detection algorithm, the probability that a signal point belongs to clean atmosphere is calculated based on the Bernoulli distribution principle using the attenuation scattering ratio data of signal points at multiple adjacent altitudes of multiple adjacent spaceborne lidar locations. If the probability that the set of signal points at multiple adjacent altitudes of multiple adjacent spaceborne lidar locations belongs to clean atmosphere is less than 0.01, then the corresponding altitude position on the spaceborne lidar where these signal points are located is marked as a feature hierarchy. Altitude positions that meet the feature hierarchy criteria are selected from multiple altitude positions at each spaceborne lidar location. All feature hierarchies with consecutive altitude positions are sequentially numbered, h = 1, 2, 3, ..., H, and the altitude positions and signal point numbers of the top and bottom of each feature hierarchy are recorded. This allows for the detection and localization of all feature hierarchies within a complete trajectory of the spaceborne lidar.

[0109] The flowchart of the iterative attenuation correction in step 2 is as follows: Figure 2 As shown.

[0110] The comparison before and after iterative attenuation correction of lidar signals acquired at multiple altitude positions from a single spaceborne lidar location in step 2 is as follows: Figure 3 As shown, Figure 3 (a) represents the lidar signal without iterative attenuation correction. Figure 3 (b) represents the lidar signal that has undergone iterative attenuation correction.

[0111] Step 2: Based on the attenuation scattering ratio of each feature layer altitude position of each spaceborne lidar position described in Step 1, calculate the bidirectional transmittance of each feature layer of each spaceborne lidar position. According to the bidirectional transmittance of each feature layer of each spaceborne lidar position, correct the horizontal and vertical components of each feature layer altitude position and the altitude position below the current feature layer altitude position, respectively, to obtain the corrected horizontal and vertical components of each feature layer altitude position of each spaceborne lidar position. Calculate the corrected radar signal strength of each feature layer altitude position based on the corrected horizontal and vertical components of each feature layer altitude position of each spaceborne lidar position, i.e., the corrected total attenuation backscattering coefficient.

[0112] Step 2 involves calculating the bidirectional transmittance at the altitude of each feature layer for each spaceborne lidar location, as detailed below:

[0113]

[0114]

[0115]

[0116] B n (k)=β m,n (k)·R′ n (k)

[0117] Where n represents the location number of the spaceborne lidar, n∈[1,N], and N=3936 represents the number of different locations of the spaceborne lidar in a complete trajectory; h represents the feature level number detected by the spaceborne lidar at a location, h=1,2,3,…,H, where H represents the number of feature levels detected by the spaceborne lidar at a location, and the number of feature levels at different locations of the spaceborne lidar is not necessarily the same; T layer,n,h The unidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar; The bidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar; The estimated value of the unidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar; This represents the estimated bidirectional transmittance of the h-th feature layer at the n-th position of the spaceborne lidar. The mean attenuation scattering ratio of the spaceborne lidar at the h-th feature level below the altitude of the n-th location within the minimum clean atmospheric distance; γ′n,h The integral attenuation backscattering coefficient of the lidar layer at the h-th feature level of the n-th position represents the lidar layer; S represents the empirical lidar ratio; M represents the integral attenuation backscattering coefficient of the lidar layer at the h-th feature level of the spaceborne lidar. h,t M h,b These represent the top and bottom signal points of the h-th feature layer at the n-th location of the spaceborne lidar, respectively; k is the sequence number, representing the k-th signal point. The k-th signal point at different locations of the spaceborne lidar represents the same altitude, k∈[M]. h,t M h,b ];Z k Z represents the altitude of the k-th signal point; k-1 This represents the altitude of the (k-1)th signal point; Representing the Mth h,t The altitude of each signal point; Representing the Mth h,b The altitude of each signal point; B n (k), B n (k-1) represent the attenuated backscattering coefficients of the spaceborne lidar at a certain wavelength, corrected for the bidirectional transmittance of atmospheric molecules and ozone, at the altitude of the k-th and k-1-th signal points at the n-th position, respectively; B n (M h,t B n (M h,b ) represent the Mth position of the spaceborne lidar at the nth position. h,t M h,b The attenuation backscattering coefficient of a certain band of the spaceborne lidar at the altitude of each signal point, corrected for the effects of atmospheric molecules and ozone bidirectional transmittance; β m,n (k) represents the backscattering coefficient of atmospheric molecules at the altitude of the k-th signal point at the n-th location; R′ n (k) represents the attenuation scattering ratio of the satellite lidar at the nth position and at the kth signal point at the altitude.

[0118] Step 2 involves calculating the bidirectional transmittance at each elevation position within each feature layer of each satellite-borne lidar location, as detailed below:

[0119]

[0120] Where n represents the location number of the spaceborne lidar, n∈[1,N], and N=3936 represents the number of different locations of the spaceborne lidar in a complete trajectory; h represents the feature layer number detected by the spaceborne lidar at a location, h=1,2,3,…,H, where H represents the number of feature layers detected by the spaceborne lidar at a location, and the number of feature layers at different locations of the spaceborne lidar is not necessarily the same; k is the sequence number, representing the k-th signal point, and the altitude represented by the k-th signal point at different locations of the spaceborne lidar is consistent; M h,t M h,b These represent the numbers of the top and bottom signal points of the h-th feature layer at the n-th position of the spaceborne lidar; T layer,n,h (k) represents the unidirectional transmittance of the signal point with index k within the h-th feature layer at the n-th location of the spaceborne lidar, where k∈[M h,t M h,b ]; This represents the bidirectional transmittance of the signal point with index k within the h-th feature layer at position n of the spaceborne lidar, where k ∈ [M]. h,t M h,b ]; T layer,n,h The unidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar; The bidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar;

[0121] Step 2 involves correcting the horizontal and vertical components of each feature level elevation position and each elevation position below the current feature level for each spaceborne lidar location, and calculating the corrected radar signal strength at each elevation position, i.e., the corrected total attenuation backscattering coefficient, as detailed below:

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] Where n represents the location number of the spaceborne lidar, n∈[1,N], and N=3936 represents the number of different locations of the spaceborne lidar in a complete trajectory; h represents the feature layer number detected by the spaceborne lidar at a location, h=1,2,3,…,H, where H represents the number of feature layers detected by the spaceborne lidar at a location, and the number of feature layers at different locations of the spaceborne lidar is not necessarily the same; k is the sequence number, representing the k-th signal point, and the altitude represented by the k-th signal point at different locations of the spaceborne lidar is consistent; K=1291 represents the number of signal points obtained by scanning at a location of the spaceborne lidar. The total attenuation backscattering coefficient represents the corrected feature hierarchy attenuation effect at the altitude of the signal point at position k at position n, received by a certain band channel of a spaceborne lidar. M represents the horizontal and vertical components of the radar signal received by a certain band channel of the spaceborne lidar at the nth position and the kth signal point, after correction for the attenuation effect of the characteristic hierarchy; h,t M h,b These represent the numbers of the top and bottom signal points of the h-th feature layer at the n-th position of the spaceborne lidar; T layer,n,h (k) represents the unidirectional transmittance of the signal point with index k within the h-th feature layer at the n-th location of the spaceborne lidar, where k∈[M h,t M h,b ]; This represents the bidirectional transmittance of the signal point with index k within the h-th feature layer at position n of the spaceborne lidar, where k ∈ [M]. h,t M h,b ]; T layer,n,h The unidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar; β′ represents the bidirectional transmittance of the h-th feature level at the n-th position of the spaceborne lidar. ||,n (k), β′ ⊥,n (k) represent the horizontal and vertical components of the radar signal received by the spaceborne lidar at the altitude of the k-th signal point at the n-th position via a certain band channel.

[0128] The schematic diagram of multi-scale window division based on lidar signals described in step 3 is shown below. Figure 4 As shown. Figure 3From left to right, the table shows the division results of scale 1, scale 2, scale 3 and scale 4 in the multi-scale algorithm. Each table represents the signal detected as a feature level in the LiDAR signal. The black grid line represents the current scale window size, the dark gray table represents the signal points that meet the conditions at the current scale, the light gray table represents the signal points that have not yet been divided into scales, and the white table represents the signal points that have been divided into scales.

[0129] The result of using lidar signals to perform multi-scale window partitioning in step 3 is as follows: Figure 5 As shown, Figure 5 (a) represents the total attenuation backscattering coefficient received by the spaceborne lidar. Figure 5 (b) represents the result of multi-scale window partitioning;

[0130] Step 3: Calculate the signal-to-noise ratio (SNR) of the radar signal at each feature level elevation position for each spaceborne lidar location. Divide the elevation position at each feature level using a multi-scale model to obtain multiple scale windows, and record the scale window numbers.

[0131] Step 3 involves calculating the radar signal-to-noise ratio at each feature level elevation position for each spaceborne lidar location, as detailed below:

[0132]

[0133]

[0134] Where L represents the number of spaceborne lidar locations used to calculate the signal-to-noise ratio (SNR) of each lidar location at each altitude; n and i represent the lidar location numbers, i∈[nL / 2,n+L / 2], n∈[1,N], and N=3936 represents the number of different locations of the lidar in a complete trajectory; SNR n (k) represents the signal-to-noise ratio of the radar signal at the altitude of the k-th signal point at the n-th location of the spaceborne lidar; The total attenuation backscattering coefficient, corrected for the influence of characteristic layer attenuation, is the signal received at the k-th signal point at the i-th position via a certain band channel of the spaceborne lidar; μ n (k) represents the mean of the attenuation backscattering coefficients of L spaceborne lidar positions at the altitude of the kth signal point at the nth position, after correcting for the influence of feature hierarchy attenuation.

[0135] Step 3 describes the process of dividing the location into multiple scale windows using a multi-scale model to obtain various scale windows at the elevation position of each feature level for all spaceborne lidar locations, as detailed below:

[0136] Set the number of scale windows to W;

[0137] The size of the scale window is set to 1×1, 2×2, 3×3..., W×W, which is a window composed of the elevation positions of W adjacent signal points on W adjacent spaceborne lidar positions;

[0138] Step 3.1: Use the radar signal-to-noise ratio (SNR) data of each feature level elevation position at each spaceborne lidar location to filter out the SNR that meets the scale determination criteria. n (k)≥T threshold The signal points are identified and marked as scale 1. At the same time, the signal points are assigned scale numbers s = 1, 2, 3, ..., Q1. The signal points that have been marked do not participate in subsequent calculations and scale division.

[0139] Step 3.2: Using the signal-to-noise ratio (SNR) of the radar signals from every two adjacent altitude positions at every two adjacent spaceborne lidar locations in the previous scale, calculate the updated SNR as follows:

[0140]

[0141] Where M represents the total number of unmarked signal points under the new multi-scale window; W represents the size of the new multi-scale window, i.e., W×W; n and i represent the positions of the spaceborne lidar, i∈[n,n+W], n∈[1,N], N=3936 represents the number of different positions of the spaceborne lidar in a complete trajectory; j and k represent the sequence numbers, representing the j-th and k-th signal points, k∈[1,K], K=1291 represents the number of signal points obtained by scanning at a position of the spaceborne lidar, i∈[k,k+W]; SNR i (j) represents the signal-to-noise ratio (SNR) at the altitude of the j-th signal point at the i-th satellite lidar location; update,n (k) represents the radar signal-to-noise ratio at the altitude of the k-th signal point at the n-th satellite lidar position, calculated using the adjacent altitude positions at the neighboring satellite lidar positions.

[0142] Step 3.3: Use the updated signal-to-noise ratio (SNR) data to filter out SNR values ​​that meet the scaling criteria. update,n (k)≥T threshold The signal points are identified and marked with a scale of 2. The signal points are then numbered according to scale s = Q1+1, Q1+2, Q1+3, ..., Q2. The marked signal points do not participate in subsequent calculations or scale division.

[0143] Step 3.4: Repeat steps 3.2-3.3 above to complete the update of signal-to-noise ratio data and scale division. Mark all unmarked signal points as the last scale, and complete the scale division of radar signal points at the elevation of each feature level at all spaceborne lidar locations, obtaining multiple scale windows at the elevation of each feature level at all spaceborne lidar locations.

[0144] The specific numbering of the recording scale window in step 3 is as follows:

[0145] s=1,2,3,…,Q1,…,Q2,…,Q3,…,Q W .

[0146] Step 4: Combine the multiple scale windows divided at the altitude of each feature level at all spaceborne lidar locations, and calculate the average attenuation backscattering coefficient, volume depolarization ratio, total attenuation color ratio, average altitude, and average latitude for each scale window.

[0147] Step 4 involves calculating the average attenuation backscattering coefficient for each scale window, as detailed below:

[0148] The window number for each scale is:

[0149] s=1,2,3,…,Q1,…,Q2,…,Q3,…,Q W

[0150]

[0151] Where s represents the number of the scale window that the radar signal points at each altitude location, which are all detected as feature levels at all spaceborne lidar locations, have passed through in a multi-scale division; W s n represents the size of the scale window numbered s; s k represents the location number of the spaceborne lidar where the scale window with the number s is located. s The s represents the signal point index corresponding to the altitude of the scale window with the number s, and i represents the location number of the spaceborne lidar, i∈[n]. s ,n s +W s ]; j represents the sequence number, representing the j-th signal point, j∈[k s ,k s +W s M represents the total number of signal points with scale number s; The attenuation backscattering coefficient, which is corrected for the influence of characteristic layer attenuation, is the signal point at the nth position and the kth position received by a certain band channel of the spaceborne lidar. The average attenuation backscattering coefficient is represented by the scale number s.

[0152] Step 4 involves calculating the volume deflection ratio for each scale window, as detailed below:

[0153]

[0154] Where s represents the number of the scale window that the radar signal points at each altitude location, which are all detected as feature levels at all spaceborne lidar locations, have passed through in a multi-scale division; W s n represents the size of the scale window numbered s. s k represents the location number of the spaceborne lidar where the scale window with the number s is located. s The s represents the signal point index corresponding to the altitude of the scale window with the number s, and i represents the location number of the spaceborne lidar, i∈[n]. s ,n s +W s ]; j represents the sequence number, representing the j-th signal point, j∈[k s ,k s +W s ]; The horizontal and vertical components of the radar signal at the j-th signal point at the i-th position, received by a certain band channel of the spaceborne lidar, are corrected for the attenuation effect of the characteristic hierarchy at altitude; δ s The body deterioration ratio is represented by the scale number s.

[0155] Step 4 involves calculating the total attenuation color ratio for each scale window, as detailed below:

[0156]

[0157]

[0158] Where s represents the number of the scale window that the radar signal points at each altitude location, which are all detected as feature levels at all spaceborne lidar locations, have passed through in a multi-scale division; W s n represents the size of the scale window numbered s; s The number represents the location number of the spaceborne lidar where the scale window with the number 's' is located, 'i' represents the location number of the spaceborne lidar, and 'k' represents the location number of the spaceborne lidar. s The signal point index corresponding to the altitude of the scale window with the number s, i∈[n s ,n s +W s ]; j represents the sequence number, representing the j-th signal point, j∈[k s ,k s +W s ];B i(j) represents the attenuated backscattering coefficient of a certain band channel of the spaceborne lidar at the altitude of the j-th signal point at the i-th position, after correction for the bidirectional transmittance of atmospheric molecules and ozone; B_1064 n (k) represents the attenuated backscattering coefficient of the 1064nm band channel of the spaceborne lidar at the altitude of the k-th signal point at the n-th location, after correction for the effects of atmospheric molecules and ozone bidirectional transmittance; β_1064 m,n (k) represents the backscattering coefficient of atmospheric molecules at the altitude of the k-th signal point at the n-th location in the 1064nm band, where m represents atmospheric molecules; β_1064 p,n (k) represents the backscattering coefficient of a particle at the altitude of the k-th signal point at the n-th position in the 1064nm band, where p represents the particle; T_1064 p,n (k) represents the unidirectional transmittance of a particle in the 1064nm band at the altitude of the kth signal point at the nth position, and p represents the particle. χ represents the bidirectional transmittance of a particle in the 1064nm band at an altitude of the kth signal point at the nth position, where p represents the particle; s The total attenuation color ratio represents the scale number s.

[0159] Step 4 involves calculating the average altitude for each scale window, as detailed below:

[0160]

[0161] Where s represents the number of the scale window that the radar signal points at each altitude location, which are all detected as feature levels at all spaceborne lidar locations, have passed through in a multi-scale division; W s The size of the scale window with the number s represents n. s k represents the location number of the spaceborne lidar where the scale window with the number s is located. s The s represents the signal point index corresponding to the altitude of the scale window with the number s, and i represents the location number of the spaceborne lidar, i∈[n]. s ,n s +W s ]; j represents the sequence number, representing the j-th signal point, j∈[k s ,k s +W s M represents the total number of signal points with scale number s; Z j This represents the altitude of the j-th signal point; The average altitude represented by the scale number 's'.

[0162] Step 4 involves calculating the average latitude for each scale window, as detailed below:

[0163]

[0164] Where s represents the number of the scale window that the radar signal points at each altitude location, which are all detected as feature levels at all spaceborne lidar locations, have passed through in a multi-scale division; W s The size of the scale window with the number s represents n. s k represents the location number of the spaceborne lidar where the scale window with the number s is located. s The s represents the signal point index corresponding to the altitude of the scale window with the number s, and i represents the location number of the spaceborne lidar, i∈[n]. s ,n s +W s ]; j represents the sequence number, representing the j-th signal point, j∈[k s ,k s +W s ]; M represents the total number of signal points with scale number s, Lat i The latitude represents the location of the i-th spaceborne lidar. The average latitude represented by the scale number 's';

[0165] Step 5: Repeat steps 1-4 multiple times to obtain a dataset of five parameters for each feature level of altitude position at each feature level of each spaceborne lidar location on multiple spaceborne lidar trajectories: average attenuation backscattering coefficient, volume de-polarization ratio, total attenuation color ratio, average altitude, and average latitude. Then, use the CALIPSO official scene classification algorithm to obtain a cloud and aerosol classification model after training.

[0166] Step 6: Real-time acquisition of horizontal and vertical components of the satellite-borne lidar signal received in a certain band, and the signal received in the 1064nm band of the satellite-borne lidar. Calculate the average attenuation backscattering coefficient, volume depolarization ratio, total attenuation color ratio, average altitude, and average latitude for each scale window of the unknown type feature level by executing steps 1-4 sequentially. Input these values ​​into the cloud and aerosol classification model obtained after training in step 5 to obtain the classification results for each scale window of the unknown type feature level. If the classification result of the scale window containing the unknown type feature level is cloud type, use the average attenuation backscattering coefficient, volume depolarization ratio, and average altitude data to perform CALIP... The official SO cloud phase classification method performs detailed classification of cloud phases, yielding cloud phase classification results for water clouds, randomly oriented ice crystals, and horizontally oriented plate-shaped ice crystals. If the classification result of the scale window containing the unknown type feature level is an aerosol type, based on the average attenuation backscattering coefficient, volume depolarization ratio, total attenuation color ratio, and average altitude data, the official CALIPSO aerosol subtype classification method further classifies aerosol subtypes according to aerosol absorption and particle size, resulting in aerosol subtype classification results including dust aerosols, smoke aerosols, marine aerosols, continental aerosols, pollution aerosols, volcanic ash aerosols, and sulfates.

[0167] Figure 6 : Cloud and aerosol classification results using a multi-scale cloud and aerosol classification method;

[0168] Figure 7 This is a comparison chart of the classification results using the official CALIPSO aerosol subtype discrimination algorithm and the results using multi-scale aerosol subtype classification. Figure 7 (a) indicates the flight trajectory of the spaceborne lidar, which passes over the southern Arabian Peninsula and the surrounding area is mostly desert; Figure 7 (b) represents the classification result of the CALIPSO official aerosol subtype differentiation algorithm, which identifies local aerosols into two major categories: dust aerosols and polluting marine aerosols; Figure 7 (c) represents the classification result of the multi-scale aerosol subtype differentiation algorithm, which identifies aerosols as marine aerosols, polluting marine aerosols, dust aerosols, and polluting dust aerosols. The various types of aerosols are progressively layered from bottom to top, which is consistent with the local aerosol propagation situation.

[0169] A specific embodiment of the present invention also provides a computer-readable medium.

[0170] The computer-readable medium is a server workstation;

[0171] The server workstation stores a computer program executed by the electronic device. When the computer program runs on the electronic device, it causes the electronic device to perform the steps of the multi-scale classification method for clouds and aerosols according to the embodiments of the present invention.

[0172] It should be understood that any parts not described in detail in this specification belong to the prior art.

[0173] It should be understood that the above description of the preferred embodiments is quite detailed, but it should not be considered as a limitation on the scope of protection of this invention. Those skilled in the art, under the guidance of this invention, can make substitutions or modifications without departing from the scope of protection of the claims of this invention, and all such substitutions or modifications fall within the scope of protection of this invention. The scope of protection of this invention should be determined by the appended claims.

Claims

1. A multi-scale classification method for clouds and aerosols, characterized in that, Includes the following steps: Step 1: Calculate the radar signal strength at each altitude of each spaceborne lidar location, calculate the attenuation backscattering coefficient of atmospheric molecules at each altitude of each spaceborne lidar location, calculate the attenuation scattering ratio at each altitude of each spaceborne lidar location, and obtain multiple feature layers at each spaceborne lidar location by detecting feature layers through a hierarchical detection algorithm. Step 2: Calculate the bidirectional transmittance of each feature layer at each satellite lidar location, calculate the bidirectional transmittance of each elevation position within each feature layer at each satellite lidar location, correct the horizontal and vertical components of each elevation position at each feature layer at each satellite lidar location and the elevation position below the current feature layer, obtain the corrected horizontal and vertical components of each elevation position at each satellite lidar location, and calculate the corrected radar signal strength of each elevation position at each satellite lidar location. Step 3: Calculate the signal-to-noise ratio of the radar signal at each feature level elevation position at each spaceborne lidar location. Divide the elevation position at each feature level of all spaceborne lidar locations into multiple scale windows using a multi-scale model, and record the scale window number. Step 4: Combine the multiple scale windows divided at the altitude of each feature level at all spaceborne lidar locations, and calculate the average attenuation backscattering coefficient, volume depolarization ratio, total attenuation color ratio, average altitude, and average latitude for each scale window. Step 5: Repeat steps 1-4 multiple times to obtain a dataset of five parameters for each feature level of altitude position at each feature level of each spaceborne lidar location on multiple spaceborne lidar trajectories: average attenuation backscattering coefficient, volume de-polarization ratio, total attenuation color ratio, average altitude, and average latitude. Then, use the CALIPSO official scene classification algorithm to obtain a cloud and aerosol classification model after training. Step 6: Calculate the average attenuation backscattering coefficient, volume depolarization ratio, total attenuation color ratio, average altitude, and average latitude for each scale window of the unknown type feature level. Combine the cloud and aerosol classification model obtained after training to obtain the classification results for each scale window of the unknown type feature level. Perform further classification based on the classification type corresponding to the classification results to obtain secondary classification results.

2. The multi-scale classification method for clouds and aerosols according to claim 1, characterized in that, Step 1 involves calculating the radar signal strength at each altitude for each spaceborne lidar location, i.e., the total attenuation backscattering coefficient, as detailed below: in, n This represents the location number of the spaceborne lidar. , N This represents the number of different locations where a spaceborne lidar is located along a complete trajectory. k The serial number represents the number of the first digit. k The signal point, the first signal point at different locations of the spaceborne lidar. k The altitude represented by each signal point is consistent. k ∈[1, K ], K This represents the number of signal points obtained by scanning at a location using a spaceborne lidar. This represents the signal received by a certain band channel of the spaceborne lidar at the [missing information]. n The position is in the first k Attenuated backscattering coefficient at the altitude of each signal point; , These represent the signals received by the spaceborne lidar in a specific band channel, located at the [missing information]. n The position is in the first k The horizontal and vertical components of the radar signal at the altitude of each signal point; , They represent the positions at the 1st, 2nd, and 3rd respectively. n The position is in the first k The horizontal and vertical components of the backscattering coefficient of atmospheric molecules at the altitude of each signal point; , They represent the positions at the 1st, 2nd, and 3rd respectively. n The position is in the first k The horizontal and vertical components of the particle backscattering coefficient at the altitude of each signal point; Representative in the first n The position is in the first k Bidirectional transmittance of atmospheric molecules at the altitude of each signal point; Representative in the first n The position is in the first k Ozone bidirectional transmittance at the altitude of each signal point; Representative in the first n The position is in the first k Bidirectional transmittance of particles at the altitude of each signal point; Step 1 involves calculating the attenuation backscattering coefficient of atmospheric molecules at each altitude for each spaceborne lidar location, as detailed below: in, n This represents the location number of the spaceborne lidar. , N This represents the number of different locations where a spaceborne lidar is located along a complete trajectory. k The serial number represents the number of the first digit. k The signal point, the first signal point at different locations of the spaceborne lidar. k The altitude represented by each signal point is consistent. k ∈[1, K ], K This represents the number of signal points obtained by scanning at a location using a spaceborne lidar. Representative in the first n The position is in the first k The attenuation backscattering coefficient of atmospheric molecules at the altitude of each signal point; Representative in the first n The position is in the first k Backscattering coefficient of atmospheric molecules at the altitude of each signal point; Representative in the first n The position is in the first k Bidirectional transmittance of atmospheric molecules at the altitude of each signal point; Representative in the first n The position is in the first k Ozone bidirectional transmittance at the altitude of each signal point; Step 1 involves calculating the attenuation scattering ratio at each altitude for each spaceborne lidar location, as detailed below: in, n This represents the location number of the spaceborne lidar. , N This represents the number of different locations where a spaceborne lidar is located along a complete trajectory. k The serial number represents the number of the first digit. k The signal point, the first signal point at different locations of the spaceborne lidar. k The altitude represented by each signal point is consistent. k ∈[1, K ], K This represents the number of signal points obtained by scanning at a location using a spaceborne lidar. The representative of the spaceborne lidar is at the first n The position is in the first k Attenuation scattering ratio at the altitude of each signal point; This represents the signal received by a certain band channel of the spaceborne lidar at the [missing information]. n The position is in the first k Attenuated backscattering coefficient at the altitude of each signal point; Representative in the first n The position is in the first k The attenuation backscattering coefficient of atmospheric molecules at the altitude of each signal point; Step 1 describes using a hierarchical detection algorithm to detect feature levels, resulting in multiple feature levels at each spaceborne lidar location. These are sets of signal points with continuous elevation positions, as detailed below: Using a hierarchical detection algorithm, the probability that a signal point belongs to clean atmosphere is calculated based on the Bernoulli distribution principle using attenuation scattering ratio data of signal points at multiple adjacent altitudes from multiple adjacent spaceborne lidar locations. If the probability that the set of signal points at multiple adjacent altitudes from multiple adjacent spaceborne lidar locations belongs to clean atmosphere is less than a certain probability threshold, the corresponding altitude positions on the spaceborne lidar where these signal points are located are marked as feature levels. Altitude positions that meet the feature levels are then selected from multiple altitude positions at each spaceborne lidar location, and all feature levels with consecutive altitude positions are sequentially numbered. It records the elevation position and signal point number of the top and bottom of each feature level, thereby realizing the detection and positioning of all feature levels within a complete trajectory of the spaceborne lidar.

3. The multi-scale classification method for clouds and aerosols according to claim 2, characterized in that, Step 2 involves calculating the bidirectional transmittance at the altitude of each feature layer for each spaceborne lidar location, as detailed below: in, n This represents the location number of the spaceborne lidar. , N This represents the number of different locations where a spaceborne lidar is located along a complete trajectory. h This represents the feature level number detected at a location by the spaceborne lidar. , H This represents the number of feature layers detected at a location by a spaceborne lidar. The number of feature layers may vary depending on the location of the spaceborne lidar. The representative of the spaceborne lidar is at the first n The position of the first h Unidirectional transmittance at each feature level; The representative of the spaceborne lidar is at the first n The position of the first h Bidirectional transmittance at each feature level; The representative of the spaceborne lidar is at the first n The position of the first h Estimates of unidirectional transmittance at each feature level; The representative of the spaceborne lidar is at the first n The position of the first h Estimates of bidirectional transmittance at each feature level; The representative of the spaceborne lidar is at the first n The position of the first h The mean attenuation scattering ratio within the minimum clean atmosphere distance below each feature level of altitude; The representative of the spaceborne lidar is at the first n The position of the first h The integral attenuation backscattering coefficient of the lidar layer at each feature level; S Representative experience of lidar; , These represent the positions of the spaceborne lidar at the [number]th [position]. n The position of the first h Numbering of the top and bottom signal points of each feature level; k The serial number represents the number of the first digit. k The signal point, the first signal point at different locations of the spaceborne lidar. k The altitude represented by each signal point is consistent. k ∈[ , ]; Representing the k The altitude of each signal point; Representing the k-1 The altitude of each signal point; Representing the The altitude of each signal point; Representing the The altitude of each signal point; , These represent the positions of the spaceborne lidar at the [number]th [position]. n The position of the first k、 The attenuation backscattering coefficient of a certain band of the spaceborne lidar at the altitude of each signal point is corrected for the influence of the bidirectional transmittance of atmospheric molecules and ozone. , These represent the positions of the spaceborne lidar at the [number]th [position]. n The position of the first , The attenuation backscattering coefficient of a certain band of the spaceborne lidar at the altitude of each signal point is corrected for the influence of the bidirectional transmittance of atmospheric molecules and ozone. Representative in the first n The position is in the first k Backscattering coefficient of atmospheric molecules at the altitude of each signal point; The representative of the spaceborne lidar is at the first n The position is in the first k Attenuation scattering ratio at the altitude of each signal point; Step 2 involves calculating the bidirectional transmittance at each elevation within each feature layer of each spaceborne lidar location, as detailed below: in, n This represents the location number of the spaceborne lidar. , N This represents the number of different locations where a spaceborne lidar is located along a complete trajectory. h This represents the feature level number detected at a location by the spaceborne lidar. , H This represents the number of feature layers detected at a location by a spaceborne lidar. The number of feature layers may vary depending on the location of the spaceborne lidar. k The serial number represents the number of the first digit. k The signal point, the first signal point at different locations of the spaceborne lidar. k The altitude represented by each signal point is consistent; , These represent the positions of the spaceborne lidar at the [number]th [position]. n The position of the first h Numbering of the top and bottom signal points of each feature level; The representative of the spaceborne lidar is at the first n The position of the first h The internal sequence number of each feature level is: k One-way transmittance of the signal point, k ∈[ , ]; The representative of the spaceborne lidar is at the first n The position of the first h The internal sequence number of each feature level is: k The bidirectional transmittance of the signal point. k ∈[ , ]; The representative of the spaceborne lidar is at the first n The position of the first h Unidirectional transmittance at each feature level; The representative of the spaceborne lidar is at the first n The position of the first h Bidirectional transmittance at each feature level; Step 2 involves calculating the corrected radar signal strength at each altitude for each spaceborne lidar location, i.e., the corrected total attenuation backscattering coefficient, as detailed below: in, n This represents the location number of the spaceborne lidar. , N This represents the number of different locations where a spaceborne lidar is located along a complete trajectory. h This represents the feature level number detected at a location by the spaceborne lidar. , H This represents the number of feature layers detected at a location by a spaceborne lidar. The number of feature layers may vary depending on the location of the spaceborne lidar. k The serial number represents the number of the first digit. k The signal point, the first signal point at different locations of the spaceborne lidar. k The altitude represented by each signal point is consistent; K This represents the number of signal points obtained by scanning at a location using a spaceborne lidar. This represents the signal received by a certain band channel of the spaceborne lidar at the [missing information]. n The position is in the first k The total attenuation backscattering coefficient at the altitude of each signal point, after correction of the characteristic layer attenuation effect; , These represent the signals received by the spaceborne lidar in a specific band channel, located at the [missing information]. n The position is in the first k The horizontal and vertical components of the radar signal at the altitude of each signal point were corrected for the influence of characteristic layer attenuation. , These represent the positions of the spaceborne lidar at the [number]th [position]. n The position of the first h Numbering of the top and bottom signal points of each feature level; The representative of the spaceborne lidar is at the first n The position of the first h The internal sequence number of each feature level is: k One-way transmittance of the signal point, k ∈[ , ]; The representative of the spaceborne lidar is at the first n The position of the first h The internal sequence number of each feature level is: k The bidirectional transmittance of the signal point. k ∈[ , ]; The representative of the spaceborne lidar is at the first n The position of the first h Unidirectional transmittance at each feature level; The representative of the spaceborne lidar is at the first n The position of the first h Bidirectional transmittance at each feature level; , These represent the signals received by the spaceborne lidar in a specific band channel, located at the [missing information]. n The position is in the first k The horizontal and vertical components of the radar signal at the altitude of each signal point.

4. The multi-scale classification method for clouds and aerosols according to claim 3, characterized in that, Step 3 involves calculating the radar signal-to-noise ratio at each feature level elevation position for each spaceborne lidar location, as detailed below: in, L This represents the number of spaceborne lidar locations used to calculate the signal-to-noise ratio of the radar signal at each altitude for each spaceborne lidar location. n、i The number representing the location of the spaceborne lidar. i ∈[ , ], , N This represents the number of different locations where a spaceborne lidar is located along a complete trajectory. The representative of the spaceborne lidar is at the first n The position is in the first k The signal-to-noise ratio of radar signals at the altitude of each signal point; This represents the signal received by a certain band channel of the spaceborne lidar at the [missing information]. i The position is in the first k The total attenuation backscattering coefficient at the altitude of each signal point was corrected for the influence of feature hierarchy attenuation. The representative of the spaceborne lidar is at the first n The position is in the first k The altitude of each signal point L The mean value of the attenuation backscattering coefficient of each spaceborne lidar location after correction for the influence of characteristic layer attenuation.

5. The multi-scale classification method for clouds and aerosols according to claim 4, characterized in that, Step 3 describes the process of dividing the location into multiple scale windows using a multi-scale model to obtain various scale windows at the elevation position of each feature level for all spaceborne lidar locations, as detailed below: Set the number of scale windows to W indivual; Set the size of the scale window to 1×1, 2×2, 3×3… W × W That is, adjacent W Adjacent to each spaceborne lidar location W A window composed of the elevation positions of each signal point; Step 3.1: Use the radar signal-to-noise ratio data of each feature level elevation position at each spaceborne lidar location to filter out those that meet the scale determination criteria. The signal points are identified and marked as scale 1, and scale numbers are assigned to the signal points. The marked signal points do not participate in subsequent calculations and scale division; Step 3.2: Using the signal-to-noise ratio (SNR) of the radar signals from every two adjacent altitude positions at every two adjacent spaceborne lidar locations in the previous scale, calculate the updated SNR as follows: in, M This represents the total number of signal points that have not yet been labeled under the new multi-scale window; W This represents the new multi-scale window size, i.e. W × W ; n、i The number representing the location of the spaceborne lidar. i ∈[ n , ], , N This represents the number of different locations where a spaceborne lidar is located along a complete trajectory. j、k Represents the serial number, representing the number. j , k One signal point, k ∈[1, K ], K This represents the number of signal points obtained by scanning at a single location using a spaceborne lidar. i ∈[ k , ]; Representative in the first i The location of the first spaceborne lidar is in the... j Signal-to-noise ratio at the altitude of each signal point; Representatives are in the first n The location of the first spaceborne lidar is in the... k The signal-to-noise ratio of the radar signal at the altitude of each signal point is calculated and updated using the adjacent altitude positions of the adjacent satellite lidar positions; Step 3.3: Use the updated signal-to-noise ratio data to filter out those that meet the scaling criteria. The signal points are identified and marked with a scale of 2. The signal points are then numbered according to their scale. The marked signal points do not participate in subsequent calculations and scale division; Step 3.4: Repeat steps 3.2-3.3 above to complete the update of signal-to-noise ratio data and scale division. Mark all unmarked signal points as the last scale, and complete the scale division of radar signal points at the elevation of each feature level at all spaceborne lidar locations, obtaining multiple scale windows at the elevation of each feature level at all spaceborne lidar locations. The specific numbering of the recording scale window in step 3 is as follows: 。 6. The multi-scale classification method for clouds and aerosols according to claim 5, characterized in that, Step 4 involves calculating the average attenuation backscattering coefficient for each scale window, as detailed below: The window number for each scale is: in, s The radar signal points at each altitude, representing all detections at all spaceborne lidar locations as feature levels, are numbered through a multi-scale division of scale windows. Representative number is s The size of the scale window; Representative number is s The location number of the spaceborne lidar where the scale window is located. Representative number is s The signal point number corresponding to the altitude of the scale window. i The number representing the location of the spaceborne lidar. i ∈[ , ]; j Represents the serial number, representing the number. j One signal point, j ∈[ , ]; M The representative scale number is s The total number of signal points; This represents the signal received by a certain band channel of the spaceborne lidar at the [missing information]. n The position is in the first k The attenuated backscattering coefficient at the altitude of each signal point was corrected for the influence of characteristic layer attenuation. The representative scale number is s The average attenuation backscattering coefficient; Step 4 involves calculating the volume deflection ratio for each scale window, as detailed below: in, s The radar signal points at each altitude, representing all detections at all spaceborne lidar locations as feature levels, are numbered through a multi-scale division of scale windows. Representative number is s The size of the scale window, Representative number is s The location number of the spaceborne lidar where the scale window is located. Representative number is s The signal point number corresponding to the altitude of the scale window. i The number representing the location of the spaceborne lidar. i ∈[ , ]; j Represents the serial number, representing the number. j One signal point, j ∈[ , ]; , This represents the signal received by a certain band channel of the spaceborne lidar at the [missing information]. i The position is in the first j The horizontal and vertical components of the radar signal at the altitude of each signal point were corrected for the influence of characteristic layer attenuation. The representative scale number is s The body's retreat ratio; Step 4 involves calculating the total attenuation color ratio for each scale window, as detailed below: in, s The radar signal points at each altitude, representing all detections at all spaceborne lidar locations as feature levels, are numbered through a multi-scale division of scale windows. Representative number is s The size of the scale window; Representative number is s The location number of the spaceborne lidar where the scale window is located. i The number representing the location of the spaceborne lidar. Representative number is s The signal point number corresponding to the altitude of the scale window. i ∈[ , ]; j Represents the serial number, representing the number. j One signal point, j ∈[ , ]; The representative of the spaceborne lidar is at the first i The position of the first j The attenuation backscattering coefficient of a certain band channel of the spaceborne lidar at the altitude of each signal point is corrected for the influence of the bidirectional transmittance of atmospheric molecules and ozone. The representative of the spaceborne lidar is at the first n The position of the first k The attenuation backscattering coefficient of the 1064 nm band channel of the spaceborne lidar at the altitude of each signal point is corrected for the influence of the bidirectional transmittance of atmospheric molecules and ozone. Representative in the first n The position is in the first k The backscattering coefficient of atmospheric molecules at the 1064 nm band at the altitude of each signal point. m Represents atmospheric molecules; Representative in the first n The position is in the first k The backscattering coefficient of particles in the 1064 nm band at the altitude of each signal point. p Representative particles; Representative in the first n The position is in the first k One-way transmittance of particles in the 1064 nm band at the altitude of each signal point. p Representative particles; Representative in the first n The position is in the first k Bidirectional transmittance of particles in the 1064 nm band at the altitude of each signal point. p Representative particles; The representative scale number is s The total color ratio attenuation; Step 4 involves calculating the average altitude for each scale window, as detailed below: in, s The radar signal points at each altitude, representing all detections at all spaceborne lidar locations as feature levels, are numbered through a multi-scale division of scale windows. Representative number is s The size of the scale window, Representative number is s The location number of the spaceborne lidar where the scale window is located. Representative number is s The signal point number corresponding to the altitude of the scale window. i The number representing the location of the spaceborne lidar. i ∈[ , ]; j Represents the serial number, representing the number. j One signal point, j ∈[ , ]; M The representative scale number is s The total number of signal points; Representing the j The altitude of each signal point; The average altitude represented by the scale number s; Step 4 involves calculating the average latitude for each scale window, as detailed below: in, s The radar signal points at each altitude, representing all detections at all spaceborne lidar locations as feature levels, are numbered through a multi-scale division of scale windows. Representative number is s The size of the scale window, Representative number is s The location number of the spaceborne lidar where the scale window is located. Representative number is s The signal point number corresponding to the altitude of the scale window. i The number representing the location of the spaceborne lidar. i ∈[ , ]; j Represents the serial number, representing the number. j One signal point, j ∈[ , ]; M The representative scale number is s The total number of signal points, Representing the i The latitude of the location of each spaceborne lidar; The average latitude is represented by the scale number 's'.

7. The multi-scale classification method for clouds and aerosols according to claim 6, characterized in that, Step 6 involves calculating the average attenuation backscattering coefficient, volume depolarization ratio, total attenuation color ratio, average altitude, and average latitude for each scale window of the unknown type feature level, as detailed below: The horizontal and vertical components of the satellite lidar signal received in a certain band and the signal received in the 1064nm band of the satellite lidar are acquired in real time. By executing steps 1-4, the average attenuation backscattering coefficient, volume depolarization ratio, total attenuation color ratio, average altitude, and average latitude of each scale window of the unknown type feature level are calculated in sequence.

8. The multi-scale classification method for clouds and aerosols according to claim 7, characterized in that, Step 6 describes combining the cloud and aerosol classification models obtained after training to obtain the classification results for each scale window of the unknown type feature level, as follows: The cloud and aerosol classification model obtained after training is input into the model to obtain the classification results for each scale window of the unknown type feature level. If the classification result of the scale window where the unknown type feature level is located is a cloud type, the cloud phase is further classified in detail according to the average attenuation backscattering coefficient, volume deflection ratio, average altitude data and the CALIPSO official cloud phase classification method, to obtain cloud phase classification results for water clouds, randomly oriented ice crystals and horizontally oriented plate-shaped ice crystals.

9. The multi-scale classification method for clouds and aerosols according to claim 8, characterized in that, Step 6 describes further classification based on the classification type corresponding to the classification result, resulting in a secondary classification result, as detailed below: If the classification result of the scale window where the unknown type feature level is located is a cloud type, the cloud phase is further classified according to the average attenuation backscattering coefficient, volume deflection ratio, and average altitude data using the CALIPSO official cloud phase classification method, resulting in cloud phase classification results for water clouds, randomly oriented ice crystals, and horizontally oriented plate-shaped ice crystals. If the classification result of the scale window where the unknown type feature level is located is an aerosol type, based on the average attenuation backscattering coefficient, volume depolarization ratio, total attenuation color ratio, and average altitude data, the aerosol subtypes are further classified according to aerosol absorption and particle size using the CALIPSO official aerosol subtype classification method. This results in a detailed classification of aerosol subtypes including dust aerosols, smoke aerosols, marine aerosols, continental aerosols, pollution aerosols, volcanic ash aerosols, and sulfates.

10. A computer-readable medium, characterized in that, It stores a computer program executed by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of the method as described in any one of claims 1-9.

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