Direct assimilation method and system for aerosol attenuation backscattering coefficient

Abstract

The invention relates to a direct assimilation method and system for an aerosol attenuation backscattering coefficient, and relates to the technical field of data assimilation of atmospheric aerosol. The method comprises the following steps: based on a laser radar equation, constructing a nonlinear observation operator of a direct assimilation aerosol attenuation backscattering coefficient by adopting a Mie scattering theory; according to a three-dimensional variational assimilation theory and a nonlinear observation operator, constructing an assimilation model for solving an aerosol attenuation backscattering coefficient of an objective function minimum solution; acquiring assimilated laser radar aerosol observation data for preprocessing, and inputting the preprocessed observation data into an assimilation model; and operating the assimilation model, taking the minimum solution obtained by solving as an aerosol increment field, and superposing the aerosol increment field to a background field to obtain an analysis field of the aerosol. According to the method, direct assimilation of the first-level observation data of the laser radar aerosol is realized, more accurate aerosol initial distribution is provided for the mode after assimilation, and a more accurate aerosol analysis and prediction result can be obtained.

Description

Technical Field

[0001] This application relates to the field of atmospheric aerosol data assimilation technology, and in particular to a direct assimilation method and system for aerosol attenuation backscattering coefficient. Background Technology

[0002] Aerosols are tiny solid or liquid particles suspended in the atmosphere, originating from both natural processes (such as volcanic eruptions, dust storms, and sea salt) and anthropogenic activities (such as industrial emissions, traffic exhaust, and biomass burning). Aerosols have a significant impact on climate, the environment, and human health, and are one of the main pollutants causing air quality deterioration.

[0003] Ground-based or spaceborne lidar plays a crucial role in aerosol monitoring, providing data on the vertical profile of aerosols and enabling high-precision, high-resolution acquisition of their vertical distribution, optical properties, and dynamic changes. Lidar acquires aerosol vertical information by emitting laser pulses and receiving backscattered signals. Due to the light intensity attenuation caused by scattering and absorption by aerosols and atmospheric molecules during atmospheric transmission, the attenuated backscattering coefficients of aerosols at various altitude layers are directly inverted from the signals received by the radar. According to the radar equations, the attenuated backscattering coefficients of aerosols do not introduce additional assumptions during the inversion process, exhibiting high accuracy. They can be used as primary data to invert and calculate other secondary data, such as the aerosol extinction coefficient, backscattering coefficient, and aerosol optical depth (AOD) by introducing the lidar ratio (the ratio of backscattering coefficient to extinction coefficient). Therefore, using the attenuated backscattering coefficients of aerosols as primary data is of greater significance for aerosol research.

[0004] Air quality models are crucial tools for aerosol prediction, but the inherent uncertainty in the aerosol initial field leads to significant errors in predictions. Data assimilation aims to improve the model's initial field, providing a more accurate one and thus enhancing the quality of aerosol analysis and prediction. Data assimilation techniques can integrate various observational data, offering advantages not only in incorporating conventional aerosol observations but also in processing aerosol optical properties. However, current research typically assimilates secondary data such as aerosol mass concentration, extinction coefficient, and satellite AOD. While this method is relatively simple, it falls short of the accuracy required for aerosol analysis and prediction compared to the more precise aerosol attenuation backscattering coefficient. Summary of the Invention

[0005] Therefore, it is necessary to provide a direct assimilation method and system for the aerosol attenuation backscattering coefficient to address the above-mentioned technical problems, which can improve the accuracy of aerosol analysis and prediction.

[0006] A direct assimilation method for aerosol attenuation backscattering coefficient, the method comprising: Based on the lidar equation, a nonlinear observation operator for directly assimilating the aerosol attenuation backscattering coefficient is constructed using Mie scattering theory. Based on the three-dimensional variational assimilation theory and nonlinear observation operators, an assimilation model for solving the minimum solution of the objective function and the aerosol attenuation backscattering coefficient is constructed. Acquire assimilated lidar aerosol observation data, preprocess it, and input the preprocessed observation data into the assimilation model; The assimilation model is run, and the minimum solution obtained is used as the aerosol increment field and superimposed on the background field to obtain the analytical field of aerosols.

[0007] In one embodiment, the lidar equation is expressed as: ; in, The returned signal received by the lidar. For lidar constants, It is the distance between the target aerosol and the lidar. Let be the radius of the aerosol particle. It is the backscattering coefficient. It is the extinction coefficient, subscript and Let represent atmospheric molecules and aerosols, respectively. During transmission, the laser light is exponentially attenuated by the combined effects of atmospheric molecules and aerosols. This attenuation process corresponds to the exp function term in the lidar equation, and the right side of the lidar equation is divided by coefficients. The remaining terms represent the aerosol attenuation backscattering coefficient, where the subscript of the aerosol attenuation backscattering coefficient is... The atmospheric molecular term is calculated using an empirical formula, and its value is much smaller than the subscript . The aerosol term, therefore, the aerosol attenuation backscattering coefficient mainly reflects the characteristics of aerosols.

[0008] In one embodiment, the nonlinear observation operator includes two calculation processes: first, calculating the aerosol attenuation backscattering coefficient at each grid point within the simulation region based on the aerosol control variables; then, interpolating the aerosol attenuation backscattering coefficient at the grid points to the actual observation location, comparing it with the observed value, and calculating the observation increment; wherein, the process of calculating the aerosol attenuation backscattering coefficient from the aerosol control variables includes: Calculate and obtain the average wet radius and average complex refractive index of aerosol particles in a certain particle size range; The size parameters of aerosol particles are obtained by using the incident wavelength and average wet radius of lidar. Based on the Mie scattering theory, the size parameters and average complex refractive index of the aerosol particles are input, and the aerosol extinction efficiency and aerosol backscattering efficiency are calculated and output. The aerosol extinction coefficient and aerosol backscattering coefficient are calculated by using the aerosol extinction efficiency, aerosol backscattering efficiency, and aerosol wet particle number concentration. The atmospheric molecular backscattering coefficient and atmospheric molecular extinction coefficient are further calculated based on the incident wavelength and using empirical formulas. The aerosol attenuated backscattering coefficient is then constructed by combining the atmospheric molecular backscattering coefficient, atmospheric molecular extinction coefficient, aerosol backscattering coefficient, and aerosol extinction coefficient.

[0009] In one embodiment, calculating the average wet radius and average complex refractive index of aerosol particles in a certain particle size range includes: Assuming that the aerosol particles and water are internally mixed, the total aerosol volume for a certain particle size range is obtained by summing the volumes. Dividing the total aerosol volume by the dry particle number concentration yields the average volume of the aerosol particles. Further assuming that the aerosol particles are spherical, the average wet radius of the aerosol particles corresponding to a certain particle size range is calculated from the average volume using the spherical volume formula. ; where subscript i Indicates the particle size range number; Based on the complex refractive index of each aerosol particle, the average complex refractive index of the aerosol particles corresponding to a certain particle size range is calculated using the volume-weighted method. .

[0010] In one embodiment, the size parameters of aerosol particles are calculated using the incident wavelength and average wet radius of a lidar. Based on Mie scattering theory, the size parameters and average complex refractive index of the aerosol particles are input, and the aerosol extinction efficiency and aerosol backscattering efficiency are calculated and output, including: Using the incident wavelength of lidar and average wet radius The size parameters of aerosol particles are calculated and expressed as follows: ; Based on Mie scattering theory, the size parameters of aerosol particles are input. and average complex refractive index The aerosol extinction efficiency was calculated using polynomial fitting. and aerosol backscattering efficiency .

[0011] In one embodiment, the aerosol extinction coefficient and aerosol backscattering coefficient are calculated from the aerosol extinction efficiency, aerosol backscattering efficiency, and aerosol wet particle number concentration, including: Extinction efficiency of aerosols Aerosol backscattering efficiency The aerosol extinction coefficient is obtained by calculating the wet particle number concentration of the aerosol. and aerosol backscattering coefficient , respectively represented as: ; ; Among them, subscript i Indicates the particle size range number. This indicates that the aerosol has four particle size ranges. This refers to the wet particle number concentration within a certain particle size range of the aerosol.

[0012] In one embodiment, based on three-dimensional variational assimilation theory and nonlinear observation operators, an assimilation model for solving the aerosol attenuation backscattering coefficient to obtain the minimum solution of the objective function is constructed, including: Based on the three-dimensional variational assimilation theory and the nonlinear observation operator constructed based on Mie scattering theory, an assimilation model for the aerosol attenuation backscattering coefficient is constructed. The purpose of this assimilation model is to find a minimum solution to an objective function determined by measurement error and mode error. The incremental form of this objective function is as follows: ; in, Indicates the optimization objective. It is the increment of the aerosol control variable in the assimilation model. It is the increment of the observation; and These are the background error covariance matrix and the observation error covariance matrix, respectively. This is a nonlinear observation operator for directly assimilating the aerosol attenuation backscattering coefficient, constructed using Mie scattering theory. Its function is to convert the aerosol control variable into an observation and compare it with the actual observation, thereby adjusting the aerosol control variable. T This indicates transpose.

[0013] In one embodiment, the aerosol control variables are designed based on the MOSAIC multi-species, multi-size aerosol scheme in the WRF-Chem air quality model. The total number of aerosol control variables is 20, including the mass concentration of 5 aerosol particles in 4 size ranges. The 5 aerosol particles include three independent species: black carbon, organic carbon, and other unclassified inorganic salts; a combined species consisting of sulfates, nitrates, and ammonium salts; and a combined species consisting of chlorides and sodium salts.

[0014] In one embodiment, assimilated lidar aerosol observation data is acquired, preprocessed, and then input into the assimilation model, including: Quality control is performed on assimilated lidar aerosol observation data, including extreme value control, outlier removal, data dilution, and noise reduction. After quality control, the data format of the observation data is further converted to a format that matches the assimilation model, and the converted observation data is then input into the assimilation model.

[0015] A direct assimilation system for aerosol attenuation backscattering coefficient, the system comprising: The observation operator construction module is used to construct a nonlinear observation operator that directly assimilates the aerosol attenuation backscattering coefficient based on the lidar equation and Mie scattering theory. The assimilation model construction module is used to construct an assimilation model for solving the minimum solution of the objective function by using the three-dimensional variational assimilation theory and the nonlinear observation operator to solve the aerosol attenuation backscattering coefficient. The data processing module is used to acquire assimilated lidar aerosol observation data, preprocess it, and input the preprocessed observation data into the assimilation model. The assimilation analysis module is used to run the assimilation model, and the minimum solution obtained is used as the aerosol increment field and superimposed on the background field to obtain the aerosol analysis field.

[0016] The aforementioned method and system for directly assimilating aerosol attenuation backscattering coefficients, based on lidar equations, for the first time constructs a nonlinear observation operator for directly assimilating aerosol attenuation backscattering coefficients based on Mie scattering theory. This leads to the construction of an assimilation model for the aerosol attenuation backscattering coefficients. The aerosol attenuation backscattering coefficients are directly inverted from the radar received signal and can be used as primary data to invert other secondary data, exhibiting high accuracy. Compared to other methods for assimilating secondary aerosol data, this application reasonably applies aerosol observation data to air quality models, optimizing the model's initial field. After assimilation, it provides the model with a more accurate initial aerosol distribution, resulting in more accurate aerosol analysis and prediction results. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a direct assimilation method for aerosol attenuation backscattering coefficient in one embodiment. Figure 2 This is a schematic diagram of the process for constructing the aerosol attenuation backscattering coefficient in one embodiment; Figure 3 This is a schematic diagram of the background field, the aerosol attenuation backscattering coefficient profile simulated in the analysis field, and the incremental field generated by assimilation in different time simulation regions of one embodiment; wherein... Figure 3 (a) is a profile of aerosol attenuation backscattering coefficients simulated at time-background field; Figure 3 (b) is the aerosol attenuation backscattering coefficient profile of the time-time analysis field simulation; Figure 3 (c) represents the incremental field generated by time-homogenization; Figure 3 (d) is the aerosol attenuation backscattering coefficient profile simulated in the background field at time two; Figure 3 (e) is the aerosol attenuation backscattering coefficient profile of the analysis field at time two; Figure 3 (f) represents the incremental field generated by the assimilation at time t; Figure 4 This is a schematic diagram showing the attenuated backscattering coefficient profiles of the background field and analysis field at three randomly selected points in the simulation area in one embodiment, along with the corresponding observation profiles; wherein, Figure 4 (a) is a schematic diagram of the profile at the first point; Figure 4 (b) is a schematic diagram of the profile at the second point; Figure 4 (c) is a schematic diagram of the profile at the third point; Figure 5 This is a schematic diagram of the background field, the extinction coefficient profile of the analysis field simulation, and the incremental field generated by assimilation for a simulated region at a given time point in one embodiment; wherein, Figure 5 (a) is the extinction coefficient profile of the simulated background field. Figure 5 (b) shows the extinction coefficient profile from the analytical field simulation. Figure 5 (c) represents the incremental field generated by assimilation; Figure 6 This is a schematic diagram of simulated AOD and statistical indicators in the background field and analysis field at two times, time one and time two, in one embodiment; Figure 7 In one embodiment, a schematic diagram of the changes in forecast results and observed values ​​over forecast time for a 24-hour average PM2.5 mass concentration, with time two as the initial time, is shown. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] In one embodiment, such as Figure 1 As shown, a direct assimilation method for aerosol attenuation backscattering coefficient is provided, comprising the following steps: Step S1: Based on the lidar equation, a nonlinear observation operator for directly assimilating the aerosol attenuation backscattering coefficient is constructed using Mie scattering theory.

[0020] The lidar equation is expressed as: ; in, The returned signal received by the lidar. For lidar constants, This refers to the distance between the target aerosol and the lidar; typically, the laser is emitted vertically upwards from the ground. The vertical height from the launch point; Let be the radius of the aerosol particle. It is the backscattering coefficient. It is the extinction coefficient, subscript and Let represent atmospheric molecules and aerosols, respectively. During transmission, the laser light is exponentially attenuated by the combined effects of atmospheric molecules and aerosols. This attenuation process corresponds to the exp function term in the lidar equation, and the right side of the lidar equation is divided by coefficients. The remaining terms represent the aerosol attenuation backscattering coefficient, where the subscript of the aerosol attenuation backscattering coefficient is... The atmospheric molecular term is calculated using an empirical formula, and its value is much smaller than the subscript . The aerosol term, therefore, the aerosol attenuation backscattering coefficient mainly reflects the characteristics of aerosols.

[0021] The nonlinear observation operator comprises two computational processes: first, calculating the aerosol attenuation backscattering coefficient at each grid point within the simulation region based on the aerosol control variables; then, interpolating the aerosol attenuation backscattering coefficients at the grid points to the actual observation locations, comparing them with the observed values, and calculating the observation increment. The process of calculating the aerosol attenuation backscattering coefficients from the aerosol control variables is as follows: Figure 2 As shown, it includes the following steps: To obtain the aerosol control variables, aerosol water content, and aerosol dry particle number concentration, we first assume that the aerosol particles and water are internally mixed. We then sum the volumes to obtain the total aerosol volume for a given particle size range. Dividing this total aerosol volume by the aerosol dry particle number concentration yields the average volume of the aerosol particles. Further assuming the aerosol particles are spherical, we use the spherical volume calculation formula to calculate the average wet radius of the aerosol particles corresponding to a given particle size range from the average volume. ; where subscript i Indicates the particle size range number.

[0022] Then, based on the complex refractive index of each aerosol particle, the average complex refractive index of the aerosol particles corresponding to a certain particle size range is calculated using the volume-weighted method. .

[0023] Then utilize the incident wavelength of the lidar and average wet radius The size parameters of aerosol particles are calculated and expressed as follows: And based on Mie scattering theory, the size parameters of the input aerosol particles... and average complex refractive index The aerosol extinction efficiency is calculated using a computationally efficient polynomial fitting method. and aerosol backscattering efficiency For example, aerosol extinction efficiency. Represented as: ; in, Average wet radius The normalized logarithm, For the first i Chebyshev polynomial, and , Let be the total order of the Chebyshev polynomial; These are the coefficients of the expansion term, and their values ​​depend specifically on the average complex refractive index. and incident wavelength .

[0024] Subsequently, the extinction efficiency of aerosols was determined. Aerosol backscattering efficiency The aerosol extinction coefficient is obtained by calculating the wet particle number concentration of the aerosol. and aerosol backscattering coefficient , respectively represented as: ; ; Among them, subscript iIndicates the particle size range number. This indicates that the aerosol has four particle size ranges. This refers to the wet particle number concentration within a certain particle size range of the aerosol.

[0025] Finally, the atmospheric molecular backscattering coefficient and atmospheric molecular extinction coefficient are calculated based on the incident wavelength and using empirical formulas. The aerosol attenuated backscattering coefficient is then constructed by combining the atmospheric molecular backscattering coefficient, atmospheric molecular extinction coefficient, aerosol backscattering coefficient, and aerosol extinction coefficient.

[0026] Step S2: Based on the three-dimensional variational assimilation theory and the nonlinear observation operator, construct an assimilation model for the aerosol attenuation backscattering coefficient to solve for the minimum solution of the objective function.

[0027] Specifically, based on the three-dimensional variational assimilation theory and the nonlinear observation operator constructed based on Mie scattering theory, an assimilation model for the aerosol attenuation backscattering coefficient is constructed. The purpose of this assimilation model is to solve for a minimum solution of an objective function determined by measurement error and mode error. The incremental form of this objective function is as follows: ; in, Indicates the optimization objective. It is the increment of the aerosol control variable in the assimilation model. It is the increment of the observation. and They are both one-dimensional vectors, but their lengths are different. The length N depends on the number of three-dimensional grid points in the model and the number of aerosol control variables. The length K depends on the number of valid observation data, that is, the number of aerosol attenuation backscattering coefficients input to the assimilation model; and These are the background error covariance matrix and the observation error covariance matrix, respectively. and These are N×N and K×K square matrices, representing the proportions of background values ​​and observed values ​​in the analyzed values; This is a nonlinear observation operator for directly assimilating the aerosol attenuation backscattering coefficient, constructed using Mie scattering theory. Its function is to convert the aerosol control variable into an observation and compare it with the actual observation, thereby adjusting the aerosol control variable. T This indicates transpose.

[0028] Among them, the aerosol control variables were designed based on the MOSAIC multi-species multi-size aerosol scheme in the WRF-Chem air quality model. The total number of aerosol control variables was 20, including the mass concentration of 5 aerosol particles in 4 particle size ranges. The 5 aerosol particles included three independent species: black carbon (BC), organic carbon (EC), and other unclassified inorganic salts (OIN); a combined species composed of sulfate (SO4), nitrate (NO3), and ammonium salt (NH4); and a combined species composed of chloride (CL) and sodium salt (NA).

[0029] Furthermore, to obtain the minimum solution of the objective function, it is also necessary to calculate the gradient of the objective function using the adjoint operator. That is, after completing the programming of the forward calculation process from aerosol control variables to observations, the calculation code of the adjoint program is constructed using the principles of adjoint programming.

[0030] Step S3: Acquire assimilated lidar aerosol observation data, preprocess it, and input the preprocessed observation data into the assimilation model.

[0031] Specifically, in this embodiment, CALIPSO satellite-borne radar data is selected, and aerosol attenuation backscattering coefficients are downloaded as primary data. Due to the high noise level in the data, quality control is first performed, including extreme value control, outlier removal, data dilution, and noise reduction. Then, the data format is processed to match the assimilation model, specifically converting the satellite data from HDF (hierarchical data) format to text format.

[0032] In addition, based on the need for assimilation effect verification, hourly mass concentrations of aerosols from national monitoring stations can be collected, and secondary data from the CALIPSO satellite, such as extinction coefficient, backscattering coefficient, and AOD observation data, can be downloaded.

[0033] Step S4: Run the assimilation model, use the minimum solution obtained as the aerosol increment field and superimpose it on the background field to obtain the aerosol analysis field.

[0034] The core of this application is the construction of an assimilation model, written in Fortran 90 and compiled and run on a Linux server. The model contains five directories: the first is the source directory (source), containing the Fortran 90 source code files; the second is the executable directory (bin), containing the compiled and linked executable file da.exe, and a text-formatted parameter file da_files.in recording the absolute paths of the input and output files; the third is the data directory (data), containing the assimilated observation data files and background error covariance files; the observation data consists of aerosol attenuation backscattering coefficients; the fourth directory contains the aerosol background field data (background); and the fifth is the analysis directory (analysis), containing the incremental aerosol control variable files (netcdf format) generated by the executable file da.exe. The subdirectory dx2wrf contains the executable file dx2wrf.exe, which superimposes the incremental field onto the background field to generate the analysis field, which is then placed in the analysis directory. Running this assimilation model requires the following steps: Step 1: Compile the program. A Makefile was written to automatically compile each source code file. The `make` command was used in the command line to compile, generating the executable file `da.exe` in the `bin` directory. The same method was used to generate the executable file `dx2wrf.exe` in the `analysis` directory.

[0035] Step 2: Modify the parameter file da_files.in. Modify the da_files.in file according to the paths of the input and output files.

[0036] Step 3: Run da.exe. Write a shell script to submit to the background calculation, and generate an incremental field file after the process is complete.

[0037] Step 4: Run dx2wrf.exe to generate the analysis field files. When running dx2wrf.exe, enter three parameters in the command line, in the format . / dx2wrf.exe parameter1 parameter2 parameter3. Parameter1 is the directory for the background field files, parameter2 is the directory for the analysis field files, and parameter3 is the directory for the incremental field files.

[0038] The aforementioned direct assimilation method for aerosol attenuation backscattering coefficients is the first to construct a nonlinear observation operator and assimilation model for directly assimilating aerosol attenuation backscattering coefficients based on Mie scattering theory, achieving direct assimilation of primary aerosol observation data from lidar. The aerosol attenuation backscattering coefficients are directly inverted from the radar received signal and can be used as primary data to invert other secondary data with high accuracy. Compared to other methods for assimilating secondary aerosol data, this application achieves direct assimilation of primary data, effectively integrating radar observation data into air quality models, improving the accuracy of aerosol analysis, and predicting its changes. This has significant application value and can provide scientific suggestions for regional air pollution analysis, early warning, and control.

[0039] In a specific embodiment, an aerosol pollution process occurring within the simulated region at times one and two was used as the subject to verify the beneficial effects of the proposed method in aerosol data assimilation and forecasting. The specific implementation steps include: Step 1: Compile the assimilation model. This includes aerosol control variables, observation operators, forward calculation processes, and adjoint processes. Repeated testing was conducted beforehand to ensure program stability and correct operation. For the two pollution cases simulated at time one and time two, WRF-Chem was set to a double grid with grid resolutions of 27km and 9km respectively, with the innermost region covering the simulation area. The parameters of the assimilation model were set, and then the program was compiled using the Makefile. Successful compilation will generate an executable file.

[0040] Step 2: Prepare assimilation observation data. Collect two CALIPSO satellite observation cases within the simulated area, namely the aerosol attenuation backscattering coefficient profiles obtained from scans at time 1 and time 2, and perform quality control and format conversion on the collected observation data. Accordingly, collect various observation data required to verify the assimilation effect.

[0041] Step 3: Run the assimilation model to generate the incremental field. By inputting the prepared aerosol attenuation backscattering coefficient observation data into the assimilation model, the incremental field is generated after the run is complete.

[0042] Step 4: The incremental field is superimposed on the background field to obtain the analytical field of the aerosol. By performing a cold start on WRF-Chem, two background fields at time one and time two are output. The incremental field generated by the assimilation model is then superimposed on the background field to obtain the analytical field.

[0043] Since no assimilation observation data was available for the background field, to verify the assimilation effect, the simulated aerosol attenuation backscattering coefficient, extinction coefficient, and AOD in the background and analysis fields were directly compared. The comparison results are as follows: Figures 3 to 6 As shown. Figure 3The diagrams show the simulated aerosol attenuation backscattering coefficient profiles in the background field and the analytical field, as well as the incremental field generated by assimilation, at time points one and two. Figure 3 It can be seen that, compared with the observed value of the aerosol attenuation backscattering coefficient, the simulated value of the aerosol attenuation backscattering coefficient of the analysis field is closer to the observed value, indicating that the aerosol analysis field obtained by the assimilation model of this application can effectively simulate and analyze the aerosol attenuation backscattering coefficient. Figure 4 The simulation results show the attenuation backscattering coefficient profiles of the background field and the analysis field at three randomly selected points in the simulation area, along with the corresponding observation profiles. Figure 4 The blue line represents the attenuated backscattering coefficient profile from the background field simulation, the red line represents the attenuated backscattering coefficient profile from the assimilated analysis field simulation, and the green line represents the observed profile. From Figure 4 It is easy to see that the profile of the attenuated backscattering coefficient in the simulated analytical field after assimilation is closer to the observed value, indicating that assimilation has a significant improvement effect. Figure 5 The extinction coefficient profiles of the background field simulation, the analysis field simulation, and the incremental field generated by assimilation are shown for the simulation region at time one. Figure 6 The simulated AOD and statistical indicators in the background field and analysis field at time points one and two are shown respectively. Figure 6 The blue dots represent control experiments, which do not vary any observational data. Figure 6 The red dots represent the predicted backscattering coefficient after aerosol attenuation. The solid line is the 1:1 line, indicating that the simulated value is equal to the observed value. The dashed lines correspond to 1:2 and 2:1 scale lines. CORR, RMSE, and BIAS represent the correlation coefficient, root mean square error, and average deviation between the simulated and observed values, respectively. Figure 5 and Figure 6 It can also be seen that the analytical field obtained by the assimilation model of this application can effectively simulate and analyze the aerosol extinction coefficient and AOD.

[0044] Simultaneously, using both the background and analysis fields as initial fields, 24-hour PM2.5 forecasts were conducted to examine the effectiveness of assimilation in PM2.5 forecasting. Figure 7 The diagram shows the forecast results and observed values ​​of PM2.5 average mass concentration obtained from a 24-hour forecast using time 2 as the initial time, along with the curves showing the changes over the forecast period. Figure 7 The blue line represents the control experiment, where no observational data was incorporated into the initial field of the model. The red line represents the forecast after assimilating the aerosol attenuation backscattering coefficient, which is clearly close to the black curve representing the observed value. As time goes on, the red line and the blue line overlap, and the improvement effect of assimilation weakens until it disappears. Overall, the improvement effect of assimilation on PM2.5 forecast can last for more than 12 hours.

[0045] In one embodiment, a direct assimilation system for aerosol attenuation backscattering coefficients is provided, comprising: The observation operator construction module is used to construct a nonlinear observation operator that directly assimilates the aerosol attenuation backscattering coefficient based on the lidar equation and Mie scattering theory. The assimilation model construction module is used to construct an assimilation model for solving the minimum solution of the objective function by using the three-dimensional variational assimilation theory and the nonlinear observation operator to solve the aerosol attenuation backscattering coefficient. The data processing module is used to acquire assimilated lidar aerosol observation data, preprocess it, and input the preprocessed observation data into the assimilation model. The assimilation analysis module is used to run the assimilation model, and the minimum solution obtained is used as the aerosol increment field and superimposed on the background field to obtain the aerosol analysis field.

[0046] Specific limitations regarding the direct assimilation system for aerosol attenuation backscattering coefficients can be found in the limitations of the direct assimilation method for aerosol attenuation backscattering coefficients described above, and will not be repeated here. Each module in the aforementioned direct assimilation system for aerosol attenuation backscattering coefficients can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independent of the processor in a computer device, or stored in software in the memory of a computer device, so that the processor can call and execute the corresponding operations of each module.

[0047] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

Claims

1. A method for directly assimilating the aerosol attenuation backscattering coefficient, characterized in that, The method includes: Based on the lidar equation, a nonlinear observation operator for directly assimilating the aerosol attenuation backscattering coefficient is constructed using Mie scattering theory. Based on the three-dimensional variational assimilation theory and the nonlinear observation operator, an assimilation model for solving the minimum solution of the objective function and the aerosol attenuation backscattering coefficient is constructed. Acquire assimilated lidar aerosol observation data, preprocess it, and input the preprocessed observation data into the assimilation model; Running the assimilation model, the minimum solution obtained is used as the aerosol increment field and superimposed on the background field to obtain the aerosol analysis field.

2. The direct assimilation method for aerosol attenuation backscattering coefficient according to claim 1, characterized in that, The lidar equation is expressed as: ； in, The returned signal received by the lidar. For lidar constants, It is the distance between the target aerosol and the lidar. Let be the radius of the aerosol particle. It is the backscattering coefficient. It is the extinction coefficient, subscript and Let represent atmospheric molecules and aerosols, respectively. During transmission, the laser light is exponentially attenuated by the combined effects of atmospheric molecules and aerosols. This attenuation process corresponds to the exp function term in the lidar equation, and the right side of the lidar equation is divided by coefficients. The remaining terms represent the aerosol attenuation backscattering coefficient, where the subscript of the aerosol attenuation backscattering coefficient is... The atmospheric molecular term is calculated using an empirical formula, and its value is much smaller than the subscript . The aerosol term, therefore, the aerosol attenuation backscattering coefficient mainly reflects the characteristics of aerosols.

3. The direct assimilation method for aerosol attenuation backscattering coefficient according to claim 2, characterized in that, The nonlinear observation operator comprises two computational processes: first, calculating the aerosol attenuation backscattering coefficient at each grid point within the simulation region based on the aerosol control variables; then, interpolating the aerosol attenuation backscattering coefficient at the grid points to the actual observation locations, comparing it with the observed values, and calculating the observation increment; wherein, the process of calculating the aerosol attenuation backscattering coefficient from the aerosol control variables includes: Calculate and obtain the average wet radius and average complex refractive index of aerosol particles in a certain particle size range; The size parameters of aerosol particles are calculated using the incident wavelength of the lidar and the average wet radius. Based on the Mie scattering theory, the size parameters and average complex refractive index of the aerosol particles are input, and the aerosol extinction efficiency and aerosol backscattering efficiency are calculated and output. The aerosol extinction coefficient and aerosol backscattering coefficient are calculated from the aerosol extinction efficiency, aerosol backscattering efficiency, and aerosol wet particle number concentration. The atmospheric molecule backscattering coefficient and atmospheric molecule extinction coefficient are further calculated based on the incident wavelength and using empirical formulas. The aerosol attenuated backscattering coefficient is then constructed by combining the atmospheric molecule backscattering coefficient, atmospheric molecule extinction coefficient, aerosol backscattering coefficient, and aerosol extinction coefficient.

4. The direct assimilation method for aerosol attenuation backscattering coefficient according to claim 3, characterized in that, Calculate and obtain the average wet radius and average complex refractive index of aerosol particles in a certain particle size range, including: Assuming that the aerosol particles and water are internally mixed, the total aerosol volume for a certain particle size range is obtained by summing the volumes. Dividing this total aerosol volume by the dry particle number concentration yields the average volume of the aerosol particles. Further assuming the aerosol particles are spherical, the average wet radius of the aerosol particles corresponding to a certain particle size range is calculated from the average volume using the spherical volume formula. ; where subscript i Indicates the particle size range number; Based on the complex refractive index of each aerosol particle, the average complex refractive index of the aerosol particles corresponding to a certain particle size range is calculated using the volume-weighted method. .

5. The direct assimilation method for aerosol attenuation backscattering coefficient according to claim 4, characterized in that, The size parameters of aerosol particles are calculated using the incident wavelength of the lidar and the average wet radius. Based on Mie scattering theory, the size parameters and average complex refractive index of the aerosol particles are input, and the aerosol extinction efficiency and aerosol backscattering efficiency are calculated and output, including: Using the incident wavelength of lidar and average wet radius The size parameters of aerosol particles are calculated and expressed as follows: ; Based on Mie scattering theory, the size parameters of aerosol particles are input. and average complex refractive index The aerosol extinction efficiency was calculated using polynomial fitting. and aerosol backscattering efficiency .

6. The direct assimilation method for aerosol attenuation backscattering coefficient according to claim 5, characterized in that, The aerosol extinction coefficient and aerosol backscattering coefficient are calculated from the aerosol extinction efficiency, aerosol backscattering efficiency, and aerosol wet particle number concentration, including: Extinction efficiency of aerosols Aerosol backscattering efficiency The aerosol extinction coefficient is obtained by calculating the wet particle number concentration of the aerosol. and aerosol backscattering coefficient , respectively represented as: ； ； Among them, subscript i Indicates the particle size range number. This indicates that the aerosol has four particle size ranges. This refers to the wet particle number concentration within a certain particle size range of the aerosol.

7. The direct assimilation method for aerosol attenuation backscattering coefficient according to claim 1, characterized in that, Based on the three-dimensional variational assimilation theory and the aforementioned nonlinear observation operator, an assimilation model for solving the aerosol attenuation backscattering coefficient to find the minimum solution of the objective function is constructed, including: Based on the three-dimensional variational assimilation theory and the nonlinear observation operator constructed based on Mie scattering theory, an assimilation model for the aerosol attenuation backscattering coefficient is constructed. The purpose of this assimilation model is to find a minimum solution to an objective function determined by measurement error and mode error. The incremental form of this objective function is as follows: ； in, Indicates the optimization objective. It is the increment of the aerosol control variable in the assimilation model. It is the increment of the observation; and These are the background error covariance matrix and the observation error covariance matrix, respectively. This is a nonlinear observation operator for directly assimilating the aerosol attenuation backscattering coefficient, constructed using Mie scattering theory. Its function is to convert the aerosol control variable into an observation and compare it with the actual observation, thereby adjusting the aerosol control variable. T This indicates transpose.

8. The direct assimilation method for aerosol attenuation backscattering coefficient according to claim 3 or 7, characterized in that, The aerosol control variables are designed based on the MOSAIC multi-species, multi-size aerosol scheme in the WRF-Chem air quality model. There are a total of 20 aerosol control variables, including the mass concentrations of 5 aerosol particles in 4 size ranges. Among them, the 5 aerosol particles include three independent species: black carbon, organic carbon, and other unclassified inorganic salts; a combined species composed of sulfates, nitrates, and ammonium salts; and a combined species composed of chlorides and sodium salts.

9. The direct assimilation method for aerosol attenuation backscattering coefficient according to claim 1, characterized in that, Acquire assimilated lidar aerosol observation data, preprocess it, and input the preprocessed observation data into the assimilation model, including: Quality control is performed on assimilated lidar aerosol observation data, including extreme value control, outlier removal, data dilution, and noise reduction. After quality control, the data format of the observation data is further converted into a format that matches the assimilation model, and the converted observation data is then input into the assimilation model.

10. A direct assimilation system for aerosol attenuation backscattering coefficient, characterized in that, The system includes: The observation operator construction module is used to construct a nonlinear observation operator that directly assimilates the aerosol attenuation backscattering coefficient based on the lidar equation and Mie scattering theory. The assimilation model construction module is used to construct an assimilation model for solving the minimum solution of the objective function by using the three-dimensional variational assimilation theory and the nonlinear observation operator to solve the aerosol attenuation backscattering coefficient. The data processing module is used to acquire assimilated lidar aerosol observation data, preprocess it, and input the preprocessed observation data into the assimilation model. The assimilation analysis module is used to run the assimilation model, and the minimum solution obtained is used as the aerosol increment field and superimposed on the background field to obtain the aerosol analysis field.

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