CN110910963A - Three-dimensional variation assimilation method and system for optical thickness of aerosol and storage medium - Google Patents
Three-dimensional variation assimilation method and system for optical thickness of aerosol and storage medium Download PDFInfo
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Abstract
The invention discloses a three-dimensional variation assimilation method, a system and a storage medium for optical thickness of aerosol, wherein the method corrects an aerosol component initial field of a numerical model by using AOD inversion data (which can be satellite AOD observation data and ground-based AOD observation data) and a three-dimensional variation assimilation method on the premise of comprehensively considering numerical model errors and observation data errors so as to reduce uncertainty of the initial field, improve subsequent aerosol simulation prediction precision, and construct a FastJ/CRTM-AOD assimilation module by calling AOD tangent operators and accompanying operators in a radiation transmission model and coupling with a Fast-J optical module, so that development efficiency of AOD three-dimensional variation assimilation applied to different aerosol schemes is greatly improved, and the method can be widely applied to the technical field of three-dimensional variation assimilation.
Description
Technical Field
The invention relates to the technical field of three-dimensional variation and assimilation, in particular to a three-dimensional variation and assimilation method, a three-dimensional variation and assimilation system and a storage medium for optical thickness of aerosol.
Background
The aerosol is a multiphase mixed Particulate Matter (PM) composed of solid or liquid particles suspended in the atmosphere and a gas carrier, and mainly comprises black carbon, organic carbon, sulfate, nitrate, ammonium salt, ocean, crustal elements and other multiple species. With the rapid development of Chinese economy, atmospheric environmental pollution is becoming more and more serious, and aerosol pollution has become a domestic and international social and scientific research hotspot. In recent years, a large amount of manpower and material resources are put into China to treat and improve the atmospheric environment, although the treatment is improved, important pollution events taking PM2.5 or PM10 as the primary pollutants are frequently generated in winter, the territory of the country affected by the heavy pollution is wide, and the peak concentration is far higher than the international historical level. The observation and forecast of the concentration and distribution of the atmospheric aerosol have considerable importance for understanding and researching the aerosol on regional air quality, human health, atmospheric visibility, climate response and the like.
In recent years, a great deal of research is carried out on simulation and forecast of aerosol components, transmission, distribution and the like through a three-dimensional atmospheric chemical numerical model. However, most of the numerical models are researched based on cleaner areas such as europe and the united states, and the application of the numerical models in heavily polluted areas in China is uncertain. By data assimilation, the analysis control variable in the numerical model is optimized by utilizing the observation data to the maximum extent on the premise of comprehensively considering the errors of the numerical model and the observation data, so that the simulation and prediction precision of the numerical model on the aerosol are improved.
The aerosol observation data for data assimilation mainly comprise ground PM2.5 observation concentration, ground PM10 observation concentration, ground radar aerosol extinction coefficient and satellite AOD inversion data. The observation of the concentration of PM2.5 and PM10 on the ground is basically based on an air quality observation network, and the site arrangement of the system has administrative characteristics, sparse spatial distribution and lack of vertical spatial information. The ground radar is an unconventional observation platform, the spatial distribution is sparser, and the data quality is greatly influenced by meteorological factors. The satellite AOD inversion data has the advantages of wide observation range, long observation history, systematic data quality control, the inclusion of atmospheric vertical space information and the like, and is widely applied to the assimilation research of aerosol data.
In the application of the data assimilation method, there are mainly an optimal interpolation method (OI), an ensemble kalman filter (EnKF), three-dimensional variational assimilation (3DVAR), and four-dimensional variational assimilation (4 DVAR). OI does not consider dynamic constraints, but simply combines the observed data with the initial field of the numerical model. When more observation points are gathered, the solving of the OI becomes an ill-posed problem. Furthermore, OI cannot handle complex non-linear observation operators. The basic principle of EnKF is Bayesian, and is derived from the combination of Kalman filtering theory and Monte Carlo estimation method. To evaluate the error information of the numerical model, EnKF generally needs to perform at least 50 parallel calculations simultaneously. The calculation cost and the storage cost are huge, and the business application is difficult to realize. Moreover, EnKF is not able to handle non-linear observation operators and is difficult to apply to remote radiation type observation data (e.g., AOD observation data).
Variational assimilation (including 3DVAR and 4DVAR) finds the optimal analysis field by minimizing the cost functional defined. In the process of minimizing the cost functional, dynamic constraints such as a rotational balance and a hydrostatic balance can be added. Secondly, the variational system can simultaneously assimilate different types of observation data, and the obtained analysis field is globally optimal. The 4DVAR is an extension of the 3DVAR in the time dimension, the former is an observation operator in the time dimension, and a dynamic model is included to reflect the state of the observation data in the time dimension. But 4DVAR is based on the assumption of a "perfect" numerical model, and the existing numerical model cannot sufficiently reflect the real atmospheric state. Moreover, the cost of constructing and running accurate tangent modules and companion modules for the entire power model is enormous, making expansion and maintenance difficult. Therefore, the three-dimensional variational assimilation method is widely applied to AOD assimilation.
At present, there are two main methods for three-dimensional variational assimilation of AOD. In the first method, an observation operator, a tangent operator and an accompanying operator of the AOD are constructed by using a radiation transmission model CRTM. The method obtains the optical characteristics of the specific aerosol component in a specific optical wave band through a table look-up form. Because different aerosol numerical models are inconsistent in division and definition of aerosol components, the method is difficult to be applied to other aerosol numerical models in an expanded mode. In the second method, a Fast-J optical module is used as an observation operator of the AOD, and corresponding tangent operators and accompanying operators are constructed through TAPENADE software. But the workload and the difficulty are huge, so that the method is not beneficial to expanding and applying to other aerosol numerical models, and is also difficult to apply to data assimilation research in other aspects.
Disclosure of Invention
In order to solve one of the above technical problems, the present invention aims to provide a compact three-dimensional variation assimilation method, system and storage medium for the optical thickness of aerosol, which is highly adaptable and widely applicable.
The first technical scheme adopted by the invention is as follows:
a three-dimensional variation assimilation method for optical thickness of aerosol comprises the following steps:
after a simulation area of the air quality numerical model and an aerosol scheme are set, operating the air quality numerical model to generate an aerosol initial estimation field;
acquiring a dynamic background error covariance matrix for each aerosol component according to the initial aerosol estimation field, and reading an AOD background field of each aerosol component from the initial aerosol estimation field;
acquiring AOD observation data according to the simulation area, and extracting AOD observation information required by data assimilation;
taking a Fast-J optical calculation module as an observation operator of the optical thickness of the aerosol, and acquiring the optical characteristics of each aerosol component;
acquiring an assimilation module by combining optical characteristics and a preset radiation transmission model;
analyzing and optimizing the three-dimensional concentration field of each aerosol component by combining a background error covariance matrix, AOD observation data, an AOD background field and an assimilation module;
and performing simulation prediction on the aerosol after the air quality numerical model is subjected to hot start by taking the optimized three-dimensional concentration field as an initial field of the aerosol.
Further, after the simulation region of the air quality numerical model and the aerosol scheme are set, the step of operating the air quality numerical model to generate an aerosol initial estimation field specifically includes the following steps:
setting a simulation area of the air quality numerical model after the air quality numerical model is built;
acquiring an input file of a simulation area acquisition model, and selecting an aerosol scheme;
and operating an air quality numerical model by combining the input file and the aerosol scheme to generate an aerosol initial estimation field.
Further, the input file includes a source discharge and a meteorological initial boundary field.
Further, the step of acquiring AOD observation data according to the simulation region and extracting AOD observation information required for data assimilation specifically includes the following steps:
after AOD observation data are obtained according to the simulation area, AOD observation information required by data assimilation is extracted from the AOD observation data, and the AOD observation information is generated into a data file in an ASCII format;
and calling an observation data reading module to read the AOD observation information from the data file in the ASCII format.
Further, the aerosol component comprises at least one of a sulfate component, a nitrate component, an ammonium salt component, a sodium salt component, a chloride component, an organic aerosol, a black carbon aerosol, a sea salt component and a sand dust component.
Further, the AOD observation information comprises longitude and latitude, observation time, AOD inversion numerical value and data inversion quality standard of each observation point.
Furthermore, the air quality numerical model adopts a WRF/Chem numerical model.
The second technical scheme adopted by the invention is as follows:
a three-dimensional variation assimilation system for optical aerosol thickness, comprising:
the model operation module is used for operating the air quality numerical model after setting a simulation region of the air quality numerical model and an aerosol scheme so as to generate an aerosol initial estimation field;
the matrix calculation module is used for acquiring a dynamic background error covariance matrix for each aerosol component according to the initial aerosol estimation field and reading an AOD background field of each aerosol component from the initial aerosol estimation field;
the data acquisition module is used for acquiring AOD observation data according to the simulation area and extracting AOD observation information required by data assimilation;
the characteristic acquisition module is used for taking the Fast-J optical calculation module as an observation operator of the optical thickness of the aerosol and acquiring the optical characteristics of each aerosol component;
the module construction module is used for acquiring the assimilation module by combining the optical characteristics and a preset radiation transmission model;
the analysis optimization module is used for analyzing and optimizing the three-dimensional concentration field of each aerosol component by combining the background error covariance matrix, the AOD observation data, the AOD background field and the assimilation module;
and the hot start module is used for carrying out hot start on the air quality numerical model according to the optimized three-dimensional concentration field serving as the initial field of the aerosol and then carrying out simulation prediction on the aerosol.
The third technical scheme adopted by the invention is as follows:
a three-dimensional variation assimilation system for optical aerosol thickness, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method.
The fourth technical scheme adopted by the invention is as follows:
a storage medium having stored therein processor-executable instructions for performing the method as described above when executed by a processor.
The invention has the beneficial effects that: on the premise of comprehensively considering numerical model errors and observation data errors, the method corrects the aerosol component initial field of the numerical model by using AOD inversion data and a three-dimensional variation assimilation method so as to reduce the uncertainty of the initial field and improve the subsequent aerosol simulation prediction precision.
Drawings
FIG. 1 is a flow chart of the steps of a method of the present invention for three-dimensional variation and assimilation of optical thickness of an aerosol;
FIG. 2 is a schematic illustration of a three-dimensional variation assimilation method for optical thickness of aerosols in a specific embodiment;
FIG. 3 is a block diagram showing the structure of a three-dimensional variation and assimilation system for optical thickness of an aerosol according to the present invention.
Detailed Description
As shown in the figure, the present embodiment provides a three-dimensional variation and assimilation method for optical thickness of aerosol, which includes the following steps:
s1, after setting a simulation area of the air quality numerical model and an aerosol scheme, operating the air quality numerical model to generate an aerosol initial estimation field;
s2, acquiring a dynamic background error covariance matrix for each aerosol component according to the initial aerosol estimation field, and reading the AOD background field of each aerosol component from the initial aerosol estimation field;
s3, obtaining AOD observation data according to the simulation area, and extracting AOD observation information required by data assimilation;
s4, taking a Fast-J optical calculation module as an observation operator of the optical thickness of the aerosol, and acquiring the optical characteristics of each aerosol component;
s5, acquiring an assimilation module by combining optical characteristics and a preset radiation transmission model;
s6, analyzing and optimizing the three-dimensional concentration field of each aerosol component by combining a background error covariance matrix, AOD observation data, an AOD background field and an assimilation module;
and S7, performing hot start on the air quality numerical model by taking the optimized three-dimensional concentration field as an initial field of the aerosol, and performing simulation prediction on the aerosol.
In the prior art, the following defects and shortcomings are mainly included: 1. the spatial distribution of the ground observation data is seriously insufficient, and the requirement of aerosol fine prediction is difficult to meet; 2. the OI assimilation method is too simple, cannot process complex nonlinear observation operators, has ill-defined conditions and is not suitable for data assimilation of AOD; 3. the integration assimilation method has huge requirements on calculated amount and storage amount, can not process a nonlinear observation operator, and is difficult to realize the business of assimilation of aerosol data; 4. the four-dimensional variational assimilation needs to construct and operate an accurate tangent module and an accompanying module for the whole aerosol dynamic model, so that the cost and the difficulty are great, and the expansion application and the maintenance are difficult; 5. the existing AOD three-dimensional variation assimilation method has the advantages of small technology, narrow applicability and no capability of wide popularization and application expansion.
Based on the above disadvantages and defects, in the present embodiment, on the premise of comprehensively considering errors of the numerical model and errors of the observation data, the aerosol component initial field of the numerical model is corrected by using the satellite AOD inversion data and the three-dimensional variation assimilation method, so as to reduce uncertainty of the initial field, and thus improve subsequent aerosol simulation prediction accuracy. In addition, the AOD data assimilation method created by the embodiment can be efficiently extended and applied to different aerosol chemical mechanisms, and the extended applicability of AOD assimilation is improved. The air quality numerical model may be implemented by using an existing air quality numerical model, and is not limited in this embodiment.
The observation operator of the AOD adopts a Fast-J optical calculation module, and particularly, the Fast-J optical calculation module can adopt a Fast-J optical calculation module in a WRF/Chem numerical model. The Fast-J optical module has the characteristics of high efficiency, stability and high accuracy in calculation. The optical characteristics of the aerosol are calculated through the Mie theory, and the calculation process belongs to highly complex nonlinearity. If the corresponding tangent and the accompanying operator are constructed for the highly nonlinear observation operator by the conventional method, the difficulty and the workload are huge. In addition, the tangential operators and the accompanying operators have strict specificity, and related data assimilation research and expansion application are difficult to carry out.
By calling the AOD tangent operator and the accompanying operator in the radiation transmission model (CRTM) and coupling the AOD tangent operator and the accompanying operator with the Fast-J optical module, the FastJ/CRTM-AOD assimilation module is constructed, and the development efficiency of the AOD three-dimensional variation and assimilation applied to different aerosol schemes is greatly improved. The module has strong applicability and expanded applicability, and the feasibility of the aerosol multi-model AOD ensemble prediction assimilation is improved. Meanwhile, the module has strong application compatibility for developing increasingly strong satellite networks.
Wherein, the step S1 specifically includes steps S11 to S13:
and S11, setting a simulation area of the air quality numerical model after the air quality numerical model is built.
S12, acquiring an input file of a simulation region acquisition model, and selecting an aerosol scheme; the input file includes source discharges and a meteorological initial boundary field.
And S13, operating an air quality numerical model by combining the input file and the aerosol scheme to generate an aerosol initial estimation field.
Wherein, the step S3 specifically includes steps S31 to S32:
s31, after AOD observation data are obtained according to the simulation area, AOD observation information required by data assimilation is extracted from the AOD observation data, and the AOD observation information is generated into an ASCII format data file; the AOD observation information comprises longitude and latitude, observation time, AOD inversion numerical values and data inversion quality standards of all observation points.
The AOD observation data can be AOD observation data of a satellite or AOD observation data of a foundation (ground), and can be obtained by downloading; based on the AOD observation information, in this embodiment, the longitude and latitude, the observation time, the AOD inversion value, and the data inversion quality standard of each observation point are mainly obtained.
And S32, calling an observation data reading module, and reading the AOD observation information from the data file in the ASCII format.
Further as a preferred embodiment, the aerosol component includes at least one of a sulfate component, a nitrate component, an ammonium salt component, a sodium salt component, a chloride component, an organic aerosol, a black carbon aerosol, a sea salt component, and a dust component. The input file includes source discharges and a meteorological initial boundary field.
Further preferably, the air quality numerical model adopts a WRF/Chem numerical model.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The above method is explained in detail with reference to fig. 2. The order of execution of the steps in the embodiments may be adapted as understood by those skilled in the art.
As shown in fig. 2, the three-dimensional variation and assimilation method for the optical thickness of the aerosol of the present embodiment mainly includes a data map assimilation preprocessing step and an AOD three-dimensional variation and assimilation step.
The data map assimilation preprocessing step specifically comprises the following steps:
step S101: constructing an operating environment for operating a WRF/Chem numerical model, wherein the operating environment comprises a Linux operating system, a compiler, parallel software and a NETCDF function library;
step S102: installing a WRF/Chem model;
step S103: setting a corresponding simulation area according to a specific research area;
step S104: preparing a corresponding numerical model input file including source emission, a meteorological initial boundary field and the like according to the simulation area set in the S103, and selecting an aerosol scheme;
step S105: operating a WRF/Chem model to generate an aerosol initial estimation field;
step S106: constructing a dynamic background error covariance matrix for each aerosol component by using the initial estimation field generated in the step S105 through the national weather center method (NMC);
step S107: preparing corresponding AOD observation data according to the simulation area set in the step S104, extracting required AOD observation information, and writing the AOD observation information into a data file in an ASCII format;
the AOD three-dimensional variation and assimilation step specifically comprises the following steps:
step S201: calling a corresponding observation data reading module according to the AOD observation data type prepared in the step S107;
step S202: calling a corresponding background field reading module according to the aerosol scheme selected in the step S104, and reading the three-dimensional concentration field of each aerosol component;
step S203: taking the background error covariance matrix generated in the step S106, the observation data read in the step S201 and the background field of each aerosol component read in the step S202 as input, calling a FastJ/CRTM-AOD three-dimensional variation and assimilation module, and analyzing and optimizing the three-dimensional concentration field of each aerosol component;
step S204: calling a corresponding aerosol analysis field writing-out module according to the aerosol scheme selected in the step S104, and updating the optimized analysis field of each aerosol component generated in the step S203;
step S205: taking the three-dimensional concentration field of each aerosol component updated in the step S204 as an initial field of the aerosol, performing hot start on the WRF/Chem model, and performing simulation prediction on the aerosol;
step S206: and returning to the step S105 to carry out the cycle simulation or finish the operation.
The three-dimensional variational assimilation algorithm minimizes the error of the state variable (x) in the numerical model, thereby obtaining the optimal solution of x. In other words, the assimilation process of the three-dimensional variation can be regarded as a secondary functional minimization process of the distance between the analysis field of x and the background field and the observation field. In general, the objective functional is defined as a cost function proportional to the square of the distance between the analysis field and the background field, and between the analysis field and the observation field:
wherein x isbA background field of x, i.e. the initial field of the numerical model; y is the observation field; b is the background error covariance and O is the observation error covariance. H (x) is an observation operator, observation information is linked with a control variable of a background field, and for conventional observation, H (x) can be simple horizontal and vertical interpolation, and regular grid simulation data is interpolated to the position of an observation station; however, for AOD inversion data, h (x) is a complex highly nonlinear operator.
In the present embodiment, in order to improve the calculation efficiency and stability, an incremental method may be employed. Introducing increment as solving object, defined as delta x ═ x-xbAnd assuming that the non-linear observation operator H is at xbCan be linearized into H (tangent operator), the target function can be converted into the following equation:
the solution of the analysis field is made dependent on the incremental field δ x (δ x ═ x-x)b) And an update field d (d ═ y-H (x)b) The difference between the observed field and the background field). The solution to the analytical field is converted into a process of minimizing the gradient of J (δ x) to δ x (first partial derivative). The result is x ═ xb+K[y-H(xb)]Wherein K is BHT(HBHT+O)-1. H and HTRespectively, a tangent operator and an accompanying operator corresponding to the observation operator H. Therefore, in three-dimensional variational assimilation, the construction of a tangent operator and an accompanying operator is a precondition for performing optimal solution. The construction difficulty of the tangent operator and the adjoint operator corresponding to the linear observation operator is low, but the difficulty and the workload are huge for the complex highly nonlinear operator.
In summary, the method of the present embodiment has at least the following beneficial effects:
(1) in the embodiment, a dynamic background error covariance matrix is constructed according to specific simulation time, and the numerical model error information characteristics of an actual simulation area and time are considered, so that the accuracy of prediction is improved.
(2) The method and the device use the AOD observation data for assimilation, can use different satellite data, and have the advantages of systematic quality control, reliable quality, high spatial resolution and capability of better meeting the requirements of high-precision assimilation prediction of an air quality numerical model.
(3) The FastJ/CRTM-AOD three-dimensional variation and assimilation module constructed in the embodiment uses a Fast-J optical calculation module in a WRF/Chem model as an observation operator of AOD, so that a background field calculated by the assimilation module is consistent with an initial estimation field of WRF/Chem.
(4) The FastJ/CRTM-AOD three-dimensional variational assimilation module constructed in the embodiment is simple and direct, has strong applicability and expansion applicability, enhances the application prospect of AOD assimilation of an air quality numerical model, and increases the feasibility of AOD ensemble forecasting assimilation of aerosol multiple models.
(5) The embodiment has strong adaptability to the application of satellite AOD observation data to the satellite network which is developed more and more vigorously.
As shown in fig. 3, the present embodiment also provides a three-dimensional variation and assimilation system for optical thickness of aerosol, including:
the model operation module is used for operating the air quality numerical model after setting a simulation region of the air quality numerical model and an aerosol scheme so as to generate an aerosol initial estimation field;
the matrix calculation module is used for acquiring a dynamic background error covariance matrix for each aerosol component according to the initial aerosol estimation field and reading an AOD background field of each aerosol component from the initial aerosol estimation field;
the data acquisition module is used for acquiring AOD observation data according to the simulation area and extracting AOD observation information required by data assimilation;
the characteristic acquisition module is used for taking the Fast-J optical calculation module as an observation operator of the optical thickness of the aerosol and acquiring the optical characteristics of each aerosol component;
the module construction module is used for acquiring the assimilation module by combining the optical characteristics and a preset radiation transmission model;
the analysis optimization module is used for analyzing and optimizing the three-dimensional concentration field of each aerosol component by combining the background error covariance matrix, the AOD observation data, the AOD background field and the assimilation module;
and the hot start module is used for carrying out hot start on the air quality numerical model according to the optimized three-dimensional concentration field serving as the initial field of the aerosol and then carrying out simulation prediction on the aerosol.
The three-dimensional variation and assimilation system for the optical thickness of the aerosol can execute the three-dimensional variation and assimilation method for the optical thickness of the aerosol provided by the method embodiment of the invention, can execute any combination of the implementation steps of the method embodiment, and has corresponding functions and beneficial effects of the method.
This embodiment also provides an aerosol optical thickness's three-dimensional variation assimilation system, includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method.
The three-dimensional variation and assimilation system for the optical thickness of the aerosol can execute the three-dimensional variation and assimilation method for the optical thickness of the aerosol provided by the method embodiment of the invention, can execute any combination of the implementation steps of the method embodiment, and has corresponding functions and beneficial effects of the method.
The present embodiments also provide a storage medium having stored therein processor-executable instructions, which when executed by a processor, are configured to perform the method as described above.
The storage medium of this embodiment can perform the three-dimensional variation and assimilation method for the optical thickness of the aerosol provided by the method embodiments of the present invention, can perform any combination of the implementation steps of the method embodiments, and has corresponding functions and advantages of the method.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A three-dimensional variation and assimilation method for optical thickness of aerosol is characterized by comprising the following steps of:
after a simulation area of the air quality numerical model and an aerosol scheme are set, operating the air quality numerical model to generate an aerosol initial estimation field;
acquiring a dynamic background error covariance matrix for each aerosol component according to the initial aerosol estimation field, and reading an AOD background field of each aerosol component from the initial aerosol estimation field;
acquiring AOD observation data according to the simulation area, and extracting AOD observation information required by data assimilation;
taking a Fast-J optical calculation module as an observation operator of the optical thickness of the aerosol, and acquiring the optical characteristics of each aerosol component;
acquiring an assimilation module by combining optical characteristics and a preset radiation transmission model;
analyzing and optimizing the three-dimensional concentration field of each aerosol component by combining a background error covariance matrix, AOD observation data, an AOD background field and an assimilation module;
and performing simulation prediction on the aerosol after the air quality numerical model is subjected to hot start by taking the optimized three-dimensional concentration field as an initial field of the aerosol.
2. The method according to claim 1, wherein the step of operating the air quality numerical model to generate the initial aerosol estimation field after the setting of the aerosol recipe and the simulation region of the air quality numerical model comprises the following steps:
setting a simulation area of the air quality numerical model after the air quality numerical model is built;
acquiring an input file of a simulation area acquisition model, and selecting an aerosol scheme;
and operating an air quality numerical model by combining the input file and the aerosol scheme to generate an aerosol initial estimation field.
3. The method of claim 2, wherein the input file includes source discharge and meteorological initial boundary fields.
4. The method according to claim 1, wherein the step of obtaining AOD observation data from the simulation region and extracting AOD observation information required for data assimilation comprises the following steps:
after AOD observation data are obtained according to the simulation area, AOD observation information required by data assimilation is extracted from the AOD observation data, and the AOD observation information is generated into a data file in an ASCII format;
and calling an observation data reading module to read the AOD observation information from the data file in the ASCII format.
5. The method of claim 1, wherein the aerosol component comprises at least one of a sulfate component, a nitrate component, an ammonium salt component, a sodium salt component, a chloride component, an organic aerosol, a black carbon aerosol, a sea salt component, and a dust component.
6. The method of claim 1 or 4, wherein the AOD observation information includes longitude and latitude, observation time, AOD inversion value and data inversion quality standard of each observation point.
7. The method of claim 1, wherein the air quality numerical model is a WRF/Chem numerical model.
8. A three-dimensional variation assimilation system for optical aerosol thickness, comprising:
the model operation module is used for operating the air quality numerical model after setting a simulation region of the air quality numerical model and an aerosol scheme so as to generate an aerosol initial estimation field;
the matrix calculation module is used for acquiring a dynamic background error covariance matrix for each aerosol component according to the initial aerosol estimation field and reading an AOD background field of each aerosol component from the initial aerosol estimation field;
the data acquisition module is used for acquiring AOD observation data according to the simulation area and extracting AOD observation information required by data assimilation;
the characteristic acquisition module is used for taking the Fast-J optical calculation module as an observation operator of the optical thickness of the aerosol and acquiring the optical characteristics of each aerosol component;
the module construction module is used for acquiring the assimilation module by combining the optical characteristics and a preset radiation transmission model;
the analysis optimization module is used for analyzing and optimizing the three-dimensional concentration field of each aerosol component by combining the background error covariance matrix, the AOD observation data, the AOD background field and the assimilation module;
and the hot start module is used for carrying out hot start on the air quality numerical model according to the optimized three-dimensional concentration field serving as the initial field of the aerosol and then carrying out simulation prediction on the aerosol.
9. A three-dimensional variation assimilation system for optical aerosol thickness, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement a method of three-dimensional variational assimilation of optical aerosol thickness according to any one of claims 1 to 7.
10. A storage medium having stored therein processor-executable instructions, which when executed by a processor, are configured to perform the method of any one of claims 1-7.
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