An aerosol cross-border transmission evaluation method, device and storage medium
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- LANZHOU UNIV
- Filing Date
- 2026-02-14
- Publication Date
- 2026-06-19
Smart Images
Figure CN122241144A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of atmospheric detection technology, and in particular to a method, apparatus and storage medium for assessing transboundary aerosol transport. Background Technology
[0002] In related technologies, radiosonde is usually used to obtain the height of the atmospheric boundary layer, but its coverage in both space and time is very limited.
[0003] Transboundary aerosol transport refers to the phenomenon of aerosol particles crossing geographical or political boundaries, typically over long distances. After being transported to a local area, exogenous aerosols invade the atmospheric boundary layer from the free atmosphere and mix with existing aerosols, potentially exacerbating regional pollution and even causing harmful weather phenomena such as smog. Therefore, accurately identifying and quantifying atmospheric boundary layer height and transboundary aerosol transport will play a crucial role in pollutant transport and environmental change.
[0004] In related technologies, research on cross-border aerosol transport is mainly carried out through trajectory simulation, satellite remote sensing and numerical simulation, which cannot achieve near real-time accurate capture of cross-border transport, cannot support near real-time application scenarios such as pollution source tracing and atmospheric environmental assessment, and cannot quantify cross-border transport. Summary of the Invention
[0005] In view of the above problems, embodiments of the present invention provide a method, device and storage medium for assessing aerosol transboundary transport, in order to solve the problem that the relevant research on aerosol transboundary transport in the prior art is mainly carried out through trajectory simulation, satellite remote sensing and numerical simulation, which cannot achieve near real-time accurate capture of transboundary transport, cannot support near real-time application scenarios such as pollution source tracing and atmospheric environmental assessment, and cannot quantify transboundary transport.
[0006] In a first aspect, embodiments of the present invention provide a method for assessing transboundary aerosol transport, the method comprising: The atmosphere is divided into multiple aerosol sublayers, and each aerosol sublayer is traversed from bottom to top to determine the ABLH results. Based on the MBLH and ABLH results and the time-height map, a comprehensive assessment is made to determine whether there is cross-boundary aerosol transport. When aerosol cross-boundary transport exists, AOD is used to quantify the aerosol cross-boundary transport situation.
[0007] In one possible implementation, dividing the atmosphere into multiple aerosol sublayers includes: The wavelet coefficient profile is smoothed, and extreme points are extracted from the smoothed wavelet coefficient profile to generate a set of extreme points. Set the background layer according to the background layer settings, and calculate the mean and variance of the first-order gradient profile corresponding to the background layer. Based on the mean and variance, effective extreme points are selected from the set of extreme points; Based on the height limit, the effective extreme points are sorted in ascending order of height; The atmosphere is divided into multiple aerosol sublayers based on the sorted effective extreme points.
[0008] In one possible implementation, before smoothing the wavelet coefficient profile, extracting extreme points from the smoothed wavelet coefficient profile, and generating the extreme point set, the method further includes: The reference profile for accurate inversion is determined based on the average deflection ratio within a preset height range; The first-order gradient of the reference profile is obtained to obtain the first-order gradient profile. The first-order gradient profile is smoothed to obtain a smoothed first-order gradient profile. The wavelet coefficient profile is obtained by performing a wavelet transform on the first-order gradient smooth profile using wavelet basis functions.
[0009] In one possible implementation, the background layer is set under the following conditions: the coefficient of variation is less than the coefficient threshold, and the mean DEP and mean Int of the background layer are less than the mean DEP and mean Int of the lidar blind zone height to the near-surface shallow layer height, respectively, and the background layer height is greater than the initial height upper limit.
[0010] In one possible implementation, the bottom-up traversal of each aerosol sublayer to determine the ABLH result includes: Calculate the mean Int, variance Int, mean DEP, and variance DEP for each aerosol sublayer; Traverse each aerosol sublayer from bottom to top. If the mean values of Int and DEP of adjacent aerosol sublayers are increasing, it is determined that there is a stagnant aerosol layer formed by clouds or residual layers. If the Int variance and DEP variance of the aerosol sublayer are greater than the Int variance and DEP variance of the background layer, and the Int variance and DEP variance of the aerosol sublayer are greater than the Int variance and DEP variance of the adjacent aerosol sublayers, then the top height of the aerosol sublayer is taken as the ABLH result.
[0011] In one possible implementation, after determining the ABLH result, the following is also included: Determine whether the ABLH results are stable; If the ABLH result is determined to be unstable, the height limit is redefined, and the step of sorting the effective extreme points in order of height from low to high is re-executed based on the new height limit until the convergence condition is met. If the ABLH result is determined to be stable, then no iteration is required.
[0012] In one possible implementation, the determination of whether aerosol transboundary transport exists based on MBLH, ABLH results, and time-height maps includes: Calculate the moving average of MBLH within the moving average period; When the moving average is greater than the maximum value of ABLH and the moving average is greater than the overall mean of MBLH, an image recognition algorithm is used to identify the contour boundaries of the time height map within the moving average period; the maximum value of ABLH is the maximum value of the ABLH results within a natural day; If there are multiple contour boundaries between the maximum values of MBLH and ABLH, it is determined that there is aerosol cross-boundary transmission. Based on the pixel where the contour boundary is located, the height data and occurrence time of the external aerosol are output. Otherwise, it is determined that there is no cross-border aerosol transport.
[0013] In one possible implementation, quantifying aerosol transboundary transport via AOD includes: Based on the height data of exogenous aerosols, the column integral of the extinction coefficient is calculated to obtain the exogenous aerosol AOD for each contour boundary; The contribution of exogenous aerosols to atmospheric column aerosols is obtained by calculating the ratio of the mean AOD of exogenous aerosols to that of atmospheric column aerosols.
[0014] Secondly, embodiments of the present invention provide an aerosol transboundary transport assessment device, the device comprising: The determination module is used to divide the atmosphere into multiple aerosol sublayers, traverse each aerosol sublayer from bottom to top, and determine the ABLH result. The judgment module is used to comprehensively determine whether there is aerosol cross-border transport based on MBLH and ABLH results and time-height map; The quantification module is used to quantify the aerosol cross-border transport situation by means of AOD when the judgment module determines that there is aerosol cross-border transport.
[0015] Thirdly, embodiments of the present invention provide a computer-readable storage medium comprising a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the aerosol transboundary transport assessment method as described in the first aspect or any possible implementation thereof.
[0016] The technical solution provided in this invention not only achieves high-precision and stable inversion of ABLH results, but also enables the determination and quantification of aerosol cross-boundary transport based on ABLH results. Through a dual determination logic combining numerical and image data, near real-time accurate capture of key features in aerosol cross-boundary transport scenarios is achieved. Attached Figure Description
[0017] Figure 1 This is a flowchart illustrating a method for evaluating transboundary aerosol transport provided in an embodiment of the present invention.
[0018] Figure 2 This is a flowchart illustrating an ABLH result determination method provided in an embodiment of the present invention.
[0019] Figure 3 This is a schematic diagram of the structure of an aerosol transboundary transport evaluation device provided in an embodiment of the present invention. Detailed Implementation
[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.
[0021] Figure 1 This is a flowchart illustrating a method for assessing transboundary aerosol transport provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes: Step 101: Divide the atmosphere into multiple aerosol sublayers, traverse each aerosol sublayer from bottom to top, and determine the ABLH result.
[0022] Real-time and historical observation data from lidar are fused to form a continuous observation dataset covering a complete natural day. The data includes backscattering intensity (Int) and depolarization ratio (DEP) profiles. The Int and DEP profiles are preprocessed. The preprocessing steps include: smoothing the Int and DEP profiles; and using interpolation algorithms to complete the data for any missing values, ensuring data continuity. Examples of interpolation algorithms include nearest neighbor interpolation and linear interpolation.
[0023] In this embodiment of the invention, the occurrence of an extreme aerosol event is determined based on the pre-processed DEP profile. Specifically, based on the pre-processed DEP profile, the number of valid data points with a deflection ratio greater than the dust threshold within the height range from the lidar blind zone height to a preset height (e.g., 2 km) is counted. The occurrence of an extreme aerosol event (dust event) is determined based on the number of valid data points. If the number of valid data points is greater than the preset number, an extreme aerosol event is determined to have occurred; if the number of valid data points is less than or equal to the preset number, an extreme aerosol event is determined not to have occurred.
[0024] Figure 2 This is a flowchart illustrating an ABLH result determination method provided in an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes: Step 1011: Determine the reference profile for accurate inversion based on the average deflection ratio within the preset height range.
[0025] In this step, it is determined whether the average depolarization ratio within the preset height range is greater than the effective depolarization ratio threshold. If the average depolarization ratio is greater than the effective depolarization ratio threshold, it indicates that the depolarization ratio profile can effectively reflect the aerosol structure. In this case, the DEP profile and Int profile are used as reference profiles for accurate inversion, and corresponding wavelet coefficient profiles are generated based on the DEP profile and Int profile, respectively. If the average depolarization ratio is less than or equal to the effective depolarization ratio threshold, it indicates that the depolarization ratio profile cannot effectively reflect the aerosol structure. In this case, only the Int profile is used as a reference profile for accurate inversion, and corresponding wavelet coefficient profiles are generated based only on the Int profile. For example, the preset height range is from the lidar blind zone height to 2km, and the effective depolarization ratio threshold is 0.06.
[0026] Step 1012: Calculate the first-order gradient of the reference profile to obtain the first-order gradient profile.
[0027] Step 1013: Smooth the first-order gradient profile to obtain a smoothed first-order gradient profile.
[0028] Step 1014: Perform wavelet transform on the first-order gradient smooth profile using wavelet basis functions to obtain the wavelet coefficient profile.
[0029] In this embodiment of the invention, to better reflect the structure of the aerosol sublayer, the Mexican hat wavelet basis function is selected to perform wavelet transform on the first-order gradient smooth profile. In actual calculations, the wavelet basis function can be selected according to actual needs, and this embodiment of the invention does not limit this selection.
[0030] The expression for the Mexican hat wavelet basis function is: Where z represents the height, Let b represent the convolution scale and b represent the wavelet center position. Represents wavelet coefficients, Take a first-order gradient smooth profile. The b-value and the value of b can be either adaptive or fixed parameters. For example, in aerosol climate effect studies, The values of a and b are fixed parameters, a=2 and b=0, to simplify calculations and meet the needs of "standardization and ease of operation" in the scientific research field.
[0031] Step 1015: Smooth the wavelet coefficient profile, extract extreme points from the smoothed wavelet coefficient profile, and generate an extreme point set.
[0032] Step 1016: Set the background layer according to the background layer setting conditions, and calculate the mean and variance of the first-order gradient profile corresponding to the background layer.
[0033] In this step, the background layer is set under the following conditions: the coefficient of variation is less than the coefficient threshold, and the mean DEP and mean Int of the background layer are less than the mean DEP and mean Int of the lidar blind zone height to the near-surface shallow layer height, respectively, and the background layer height is greater than the initial height upper limit H. max The coefficient of variation, calculated as the ratio of the standard deviation to the mean of the original data, characterizes the dispersion of the data. A smaller coefficient of variation indicates less dispersion, while a larger coefficient of variation indicates greater dispersion. A background layer is used to remove the effects of solar background noise and background aerosols.
[0034] The background layer has corresponding Int profiles and DEP profiles. The first-order gradient profiles of the background layer include the Int first-order gradient profile and the DEP first-order gradient profile. Calculate the mean (dInt_BG_mean) and variance (dInt_BG_devia) of the Int first-order gradient profile.
[0035] In this embodiment of the invention, the initial height upper limit H max The calculation method is as follows: The signal-to-noise ratio (SNR) of the echo signal received by the lidar is calculated using the SNR calculation formula, generating an SNR profile. Within the SNR profile, a height range constitutes a SNR window, and the average SNR within that height range is the average SNR value of the window. Starting from the lidar blind zone height, the average SNR value of the window is calculated according to a preset height range. When the average SNR value of the window first exceeds a preset SNR threshold, the middle height of the SNR window is taken as the initial height of the Material Boundary Layer (MBLH), which is also the upper limit of the initial height H in subsequent iterations. max .
[0036] The formula for calculating the signal-to-noise ratio is: in, The signal-to-noise ratio (SNR) of the echo signal received by the lidar is represented by N, which represents the cumulative number of pulses at each time resolution. This indicates the power value caused by solar noise. This represents the intensity of the echo signal received by the lidar. In practical calculations, it can be replaced by an integer value. .
[0037] Step 1017: Based on the mean and variance, select the effective extreme points from the set of extreme points.
[0038] In this embodiment of the invention, in the set of extreme points, if the maximum value is greater than the sum of the mean and variance of the first-order gradient profile of Int, that is, the maximum value > dInt_BG_mean + dInt_BG_devia; or if the minimum value is less than the difference between the mean and variance of the first-order gradient profile of Int, that is, the minimum value < dInt_BG_mean - dInt_BG_devia, then it is determined as a valid extreme point.
[0039] In practical applications, the extreme points extracted from the smoothed wavelet coefficient profile may be similar, leading to overly dense aerosol sublayers. In this case, aerosol sublayers with heights below a sublayer height threshold are merged, ensuring that the height of each aerosol sublayer is greater than or equal to the sublayer height threshold. For example, the sublayer height threshold is 150m.
[0040] Step 1018: Based on the height limit, sort the valid extreme points in order of height from low to high.
[0041] In this step, the height limits include an upper height limit and a lower height limit. The initial lower height limit is the lidar blind zone height, and the initial upper height limit is the initial height of the material boundary layer.
[0042] Step 1019: Divide the atmosphere into multiple aerosol sublayers according to the sorted effective extreme points.
[0043] In this embodiment of the invention, the region between the height of the lidar blind zone and the height corresponding to the first effective extreme value is divided into the first sub-layer, the region between the heights corresponding to any two adjacent effective extreme values is divided into the middle sub-layer, and the region between the height corresponding to the last effective extreme value and the initial height upper limit is divided into the last sub-layer.
[0044] In this embodiment of the invention, the Atmospheric Boundary Layer Height (ABLH) is calculated for each profile in the continuous observation dataset, using a complete natural day as the unit. The calculation process is as follows: Calculate the mean Int, variance Int, mean DEP, and variance DEP for each aerosol sublayer; traverse each aerosol sublayer from bottom to top. If the mean Int and mean DEP of adjacent aerosol sublayers are increasing, it is determined that a stagnant aerosol layer formed by clouds or residual layers exists, and the ABLH in this case is the bottom of the stagnant aerosol layer. If the variance Int and variance DEP of the aerosol sublayer are greater than the variance Int and variance DEP of the background layer, and the variance Int and variance DEP of the aerosol sublayer are greater than the variance Int and variance DEP of the adjacent aerosol sublayers above and below, then the top height of that aerosol sublayer is taken as the ABLH result. Following the above process, ABLH is calculated for each profile in the continuous observation dataset to obtain the ABLH result for each profile.
[0045] After obtaining the ABLH results for each profile, the stability of the ABLH results for each profile is verified. Assuming no drastic changes in the boundary layer within a short period, the stability of the ABLH results is determined. If the ABLH results are determined to be unstable, the height limit (lower or upper limit) is redefined, and the step of sorting the effective extreme points in ascending order of height is repeated based on the new height limit until the convergence condition is met. If the ABLH results are determined to be stable, no iteration is required. The convergence condition is that the difference between the ABLH results of the corresponding profiles in two adjacent iterations is less than a preset difference threshold, and no profile triggers the redetering of the height limit. By performing iterative optimization when the ABLH results are unstable, stable ABLH result inversion is achieved, effectively avoiding drastic jumps in the ABLH results.
[0046] For example, the stability judgment method is as follows: if the difference between the ABLH result and the nearby ABLH mean is less than a preset difference threshold, the ABLH result is determined to be stable; if the difference between the ABLH result and the nearby ABLH mean is greater than or equal to the preset difference threshold, the ABLH result is determined to be unstable.
[0047] Furthermore, ABLH instability is categorized into two cases: ABLH results that are too low and ABLH results that are too high. For example, if the current ABLH result is less than the moving average of ABLH results over a preset time period (e.g., within 30 minutes), the current ABLH result is determined to be too low, and this low ABLH result is used as the new lower limit of the height. If the current ABLH result is greater than or equal to the moving average of ABLH results over a preset time period, the current ABLH result is determined to be too high, and this high ABLH result is used as the new upper limit of the height.
[0048] Step 102: Based on the MBLH and ABLH results and the time-height map, comprehensively determine whether there is aerosol cross-border transport.
[0049] In this embodiment of the invention, the moving average of MBLH is calculated within the moving average period; when the moving average is greater than the maximum value of ABLH and the moving average is greater than the overall mean of MBLH, an image recognition algorithm is used to identify the contour boundary of the time height map within the moving average period; if (the initial height upper limit H) max If multiple contour boundaries exist between the maximum values of MBLH and ABLH, then aerosol cross-boundary transport is determined to exist, and the height data and occurrence time of the exogenous aerosol are output based on the pixels where the contour boundaries are located; otherwise, aerosol cross-boundary transport is determined not to exist. The maximum value of ABLH is the highest value among all ABLH results within a natural day. max , representing the upper limit of the convective boundary layer and the maximum upper limit of the residual layer.
[0050] Step 103: When there is aerosol cross-border transport, quantify the aerosol cross-border transport situation using AOD.
[0051] In this embodiment of the invention, based on the height data of exogenous aerosols, the column integral of the extinction coefficient is calculated to obtain the exogenous aerosol optical depth (AOD) of each contour boundary. The ratio of the mean AOD of exogenous aerosols to the total AOD of the atmospheric column is calculated to obtain the contribution of exogenous aerosols to the atmospheric column aerosols, thus realizing the quantification of aerosol cross-boundary transport.
[0052] The technical solution provided in this invention not only achieves high-precision and stable inversion of ABLH results, but also enables the determination and quantification of aerosol cross-boundary transport based on ABLH results. Through a dual determination logic combining numerical and image data, near real-time accurate capture of key features in aerosol cross-boundary transport scenarios is achieved.
[0053] The aerosol transboundary transport assessment method provided in this invention has broad application potential. Furthermore, it requires no additional hardware deployment and can be directly integrated into existing lidar systems, reducing application costs. For example, in the environmental protection and meteorological fields, it can facilitate joint prevention and control of regional air pollution. In pollution-sensitive areas, existing lidar monitoring stations can be used to determine the existence of transboundary aerosol transport, providing quantitative data such as transport paths and impact ranges for joint prevention and control. Another example is transmitting the aerosol transport assessment and quantitative results to the operational forecasting system of the local meteorological bureau, enabling high-precision short-term nowcasting. Yet another example is monitoring whether aerosols emitted by enterprises cross the boundary layer (e.g., whether smoke height is within the boundary layer, whether pollutants have spread to surrounding areas), providing objective evidence for enterprise compliance and environmental protection department supervision. Finally, since transboundary aerosol transport affects visibility, the assessment and quantitative results can provide additional technical support for flight take-off and landing safety.
[0054] Figure 2 This is a schematic diagram of the structure of an aerosol transboundary transport assessment device provided in an embodiment of the present invention, as shown below. Figure 2 As shown, the device includes: a determination module 11, a first judgment module 12, and a quantization module 13. The determination module 11 is used to divide the atmosphere into multiple aerosol sublayers, traverse each aerosol sublayer from bottom to top, and determine the ABLH result; the first judgment module 12 is used to comprehensively determine whether there is aerosol cross-boundary transport based on the MBLH, ABLH results, and time-altitude map; the quantization module 13 is used to quantify the aerosol cross-boundary transport situation through AOD when the first judgment module 12 determines that there is aerosol cross-boundary transport.
[0055] In this embodiment of the invention, the determining module 11 includes an extraction submodule, a first calculation submodule, a filtering submodule, a sorting submodule, and a partitioning submodule. The extraction submodule is used to smooth the wavelet coefficient profile, extract extreme points from the smoothed wavelet coefficient profile, and generate an extreme point set. The calculation submodule is used to set a background layer according to background layer setting conditions and calculate the mean and variance of the first-order gradient profile corresponding to the background layer. The filtering submodule is used to filter valid extreme points from the extreme point set based on the mean and variance. The sorting submodule is used to sort the valid extreme points in ascending order of altitude, combined with altitude limits. The partitioning submodule is used to divide the atmosphere into multiple aerosol sublayers according to the sorted valid extreme points.
[0056] In this embodiment of the invention, the determining module 11 further includes a first determining submodule, a gradient calculation submodule, a smoothing submodule, and a wavelet transform submodule. The determining submodule is used to determine the reference profile participating in the accurate inversion based on the average debiasing ratio within a preset height range; the gradient calculation submodule is used to calculate the first-order gradient of the reference profile to obtain a first-order gradient profile; the smoothing submodule is used to smooth the first-order gradient profile to obtain a first-order gradient smoothed profile; and the wavelet transform submodule is used to perform a wavelet transform on the first-order gradient smoothed profile using wavelet basis functions to obtain a wavelet coefficient profile.
[0057] In this embodiment of the invention, the background layer is set under the following conditions: the coefficient of variation is less than the coefficient threshold, and the mean DEP and mean Int of the background layer are less than the mean DEP and mean Int of the laser radar blind zone height to the near-surface shallow layer height, respectively, and the background layer height is greater than the initial height upper limit.
[0058] In this embodiment of the invention, the determining module 11 further includes a second calculation submodule, a judgment submodule, and a second determining submodule. The second calculation submodule is used to calculate the mean Int, variance Int, mean DEP, and variance DEP of each aerosol sublayer; the judgment submodule is used to traverse each aerosol sublayer from bottom to top, and if the mean Int and mean DEP of adjacent aerosol sublayers are increasing, it is determined that there is a stagnant aerosol layer formed by clouds or residual layers; the second determining submodule is used to take the top height of the aerosol sublayer as the ABLH result if the variance Int and variance DEP of the aerosol sublayer are greater than the variance Int and variance DEP of the background layer, and the variance Int and variance DEP of the aerosol sublayer are greater than the variance Int and variance DEP of the adjacent aerosol sublayers above and below.
[0059] In this embodiment of the invention, the device further includes a second judgment module and an iteration module. The second judgment module is used to determine whether the ABLH result is stable; if the second judgment module determines that the ABLH result is unstable, it triggers the iteration module to redetermine the height limit, and re-executes the step of sorting the effective extreme points in ascending order of height based on the new height limit until the convergence condition is reached; if the second judgment module determines that the ABLH result is stable, then no iteration is required.
[0060] In this embodiment of the invention, the first judgment module 12 includes a third calculation submodule and an identification submodule. The third calculation submodule is used to calculate the moving average of MBLH within the moving average time period; the identification submodule is used to identify the contour boundaries of the time height map within the moving average time period using an image recognition algorithm when the moving average is greater than the maximum value of ABLH and the moving average is greater than the overall average of MBLH; the maximum value of ABLH is the maximum value of ABLH results within a natural day; if there are multiple contour boundaries between MBLH and the maximum value of ABLH, it is determined that there is aerosol cross-boundary transport, and the height data and occurrence time of the exogenous aerosol are output based on the pixel point where the contour boundary is located; otherwise, it is determined that there is no aerosol cross-boundary transport.
[0061] In this embodiment of the invention, the quantization module 13 includes a fourth calculation submodule and a ratio submodule. The fourth calculation submodule is used to calculate the column integral of the extinction coefficient based on the height data of the exogenous aerosol, and obtain the exogenous aerosol AOD for each contour boundary; the ratio submodule is used to calculate the ratio of the mean value of the exogenous aerosol AOD to the atmospheric column aerosol, and obtain the contribution of the exogenous aerosol to the atmospheric column aerosol.
[0062] The technical solution provided in this invention not only achieves high-precision and stable inversion of ABLH results, but also enables the determination and quantification of aerosol cross-boundary transport based on ABLH results. Through a dual determination logic combining numerical and image data, near real-time accurate capture of key features in aerosol cross-boundary transport scenarios is achieved.
[0063] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is running, it controls the device where the computer-readable storage medium is located to execute the steps of the above-described aerosol transboundary transport assessment method. For a detailed description, please refer to the above-described aerosol transboundary transport assessment method embodiments.
[0064] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A method for aerosol across border transmission evaluation, characterized in that, The method includes: The atmosphere is divided into multiple aerosol sublayers, and each aerosol sublayer is traversed from bottom to top to determine the ABLH results. Based on the MBLH and ABLH results and the time-height map, a comprehensive assessment is made to determine whether there is cross-boundary aerosol transport. When aerosol cross-boundary transport exists, AOD is used to quantify the aerosol cross-boundary transport situation.
2. The method of claim 1, wherein, The division of the atmosphere into multiple aerosol sublayers includes: The wavelet coefficient profile is smoothed, and extreme points are extracted from the smoothed wavelet coefficient profile to generate a set of extreme points. Set the background layer according to the background layer settings, and calculate the mean and variance of the first-order gradient profile corresponding to the background layer. Based on the mean and variance, effective extreme points are selected from the set of extreme points; Based on the height limit, the effective extreme points are sorted in ascending order of height; The atmosphere is divided into multiple aerosol sublayers based on the sorted effective extreme points.
3. The method of claim 2, wherein, Before smoothing the wavelet coefficient profile, extracting extreme points from the smoothed wavelet coefficient profile, and generating the extreme point set, the process also includes: The reference profile for accurate inversion is determined based on the average deflection ratio within a preset height range; The first-order gradient of the reference profile is obtained to obtain the first-order gradient profile. The first-order gradient profile is smoothed to obtain a smoothed first-order gradient profile. The wavelet coefficient profile is obtained by performing a wavelet transform on the first-order gradient smooth profile using wavelet basis functions.
4. The method of claim 2, wherein, The background layer is set under the following conditions: the coefficient of variation is less than the coefficient threshold, and the mean DEP and mean Int of the background layer are less than the mean DEP and mean Int of the lidar blind zone height to the near-surface shallow layer height, respectively, and the background layer height is greater than the initial height upper limit.
5. The method of claim 1, wherein, The process of traversing each aerosol sublayer from bottom to top to determine the ABLH results includes: Calculate the mean Int, variance Int, mean DEP, and variance DEP for each aerosol sublayer; Traverse each aerosol sublayer from bottom to top. If the mean values of Int and DEP of adjacent aerosol sublayers are increasing, it is determined that there is a stagnant aerosol layer formed by clouds or residual layers. If the Int variance and DEP variance of the aerosol sublayer are greater than the Int variance and DEP variance of the background layer, and the Int variance and DEP variance of the aerosol sublayer are greater than the Int variance and DEP variance of the adjacent aerosol sublayers, then the top height of the aerosol sublayer is taken as the ABLH result.
6. The method of claim 1, wherein, After determining the ABLH result, the following also includes: Determine whether the ABLH results are stable; If the ABLH result is determined to be unstable, the height limit is redefined, and the step of sorting the effective extreme points in order of height from low to high is re-executed based on the new height limit until the convergence condition is met. If the ABLH result is determined to be stable, then no iteration is required.
7. The method of claim 1, wherein, The determination of whether cross-boundary aerosol transport exists based on MBLH, ABLH results, and time-altitude maps includes: Calculate the moving average of MBLH within the moving average period; When the moving average is greater than the maximum value of ABLH and the moving average is greater than the overall mean of MBLH, an image recognition algorithm is used to identify the contour boundaries of the time height map within the moving average period; the maximum value of ABLH is the maximum value of the ABLH results within a natural day; If there are multiple contour boundaries between the maximum values of MBLH and ABLH, it is determined that there is aerosol cross-boundary transmission. Based on the pixel where the contour boundary is located, the height data and occurrence time of the external aerosol are output. Otherwise, it is determined that there is no cross-border aerosol transport.
8. The method of claim 1, wherein, The quantification of aerosol transboundary transport via AOD includes: Based on the height data of exogenous aerosols, the column integral of the extinction coefficient is calculated to obtain the exogenous aerosol AOD for each contour boundary; The contribution of exogenous aerosols to atmospheric column aerosols is obtained by calculating the ratio of the mean AOD of exogenous aerosols to that of atmospheric column aerosols.
9. An aerosol across border transmission evaluation apparatus, characterized by, The device includes: The determination module is used to divide the atmosphere into multiple aerosol sublayers, traverse each aerosol sublayer from bottom to top, and determine the ABLH result. The judgment module is used to comprehensively determine whether there is aerosol cross-border transport based on MBLH and ABLH results and time-height map; The quantification module is used to quantify the aerosol cross-border transport situation by means of AOD when the judgment module determines that there is aerosol cross-border transport.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the aerosol transboundary transport assessment method as described in any one of claims 1 to 8.