Aerosol optical thickness inversion method and device and medium

Abstract

The invention discloses an aerosol optical thickness inversion method and device and a medium. The method comprises the steps that an inversion sample data set is determined according to meteorological station data in an aerosol optical thickness pixel of a satellite load, and the inversion sample data set comprises observation visibility, altitude, water vapor content and aerosol optical thickness; performing normalization processing on the inversion sample data set to obtain a normalized inversion sample data set; training to-be-solved parameters of a pre-constructed target function according to the normalized inversion sample data set by adopting a deep neural network algorithm, and determining the to-be-solved parameters of the target function; and calculating an aerosol optical thickness inversion result of the to-be-predicted meteorological station according to the inversion data of the to-be-predicted meteorological station, the to-be-solved parameters and the objective function.

Description

Technical Field

[0001] The present invention relates to the technical fields of atmospheric pollution monitoring, weather forecasting and environmental science research, and more specifically to an aerosol optical thickness inversion method, device and medium. Background Art

[0002] Aerosol optical depth (AOD) is an important indicator for describing the concentration of aerosols in the atmosphere. The optical properties of aerosols are closely related to factors such as atmospheric visibility, meteorological conditions, altitude, and water vapor content. Existing methods for retrieving AOD are mainly based on satellite observation data or ground-based monitoring networks. However, due to the shortcomings of these observation methods, such as short time series and a small number of ground-based stations, it is difficult to obtain long-term aerosol data with high accuracy, making it difficult to conduct diagnostic analysis of aerosol climate effects. The biggest advantage of retrieving AOD based on visibility data is the high density of stations worldwide, wide coverage, long time series, and relatively simple data acquisition, which is very helpful for studying aerosol characteristics over large areas and long periods of time. Therefore, how to use existing visibility data and other environmental parameters (such as water vapor content, altitude, etc.) to invert AOD has become an important issue in current research. Summary of the Invention

[0003] In view of the deficiencies of the prior art, the present invention provides an aerosol optical thickness inversion method, device and medium.

[0004] According to one aspect of the present invention, a method for inverting aerosol optical depth is provided, comprising:

[0005] Determine the inversion sample data set based on the meteorological station data within the aerosol optical depth pixel of the satellite payload, where the inversion sample data set includes observed visibility, altitude, water vapor content, and aerosol optical depth;

[0006] Normalizing the inversion sample data set to obtain a normalized inversion sample data set;

[0007] A deep neural network algorithm is used to train the parameters of the pre-built objective function based on the normalized inversion sample data set to determine the parameters of the objective function.

[0008] According to the inversion data of the meteorological station to be predicted, the parameters to be determined and the objective function, the aerosol optical depth inversion result of the meteorological station to be predicted is calculated.

[0009] Optionally, based on meteorological station data within aerosol optical depth pixels of a satellite payload, an inversion sample dataset is determined, including:

[0010] Search for meteorological station data within the aerosol optical depth pixel of the satellite payload;

[0011] The meteorological station data of each meteorological station are averaged to determine the inversion sample data set.

[0012] Optionally, the deep neural network algorithm is a multi-layer perceptron network, including multiple input layers, hidden layers and output layers.

[0013] Optionally, the aerosol optical depth inversion result of the meteorological station to be predicted is calculated according to the inversion data of the meteorological station to be predicted, the parameters to be determined, and the objective function, including:

[0014] Calculate the horizontal visibility value of the meteorological station to be predicted based on the observed visibility value in the inversion data;

[0015] The observed visibility value, horizontal visibility value and the parameters to be determined are input into the objective function to calculate the aerosol optical depth inversion result of the meteorological station to be predicted.

[0016] Optionally, the relationship between observed visibility and horizontal visibility is:

[0017]

[0018] Where k4, k5, k6, k7, k8 are constants, which are 3.192, 0.0116, 0.00099, 0.886, and 0.0222 respectively; V is the sea level visibility V Z To observe visibility.

[0019] Optionally, the objective function is expressed as:

[0020]

[0021] Where AOD is aerosol optical depth, k0, k1, k2, k3, k4, k5, k6, k7, k8 are constants, which are 4, 0.55, 12.5, 5.5, 3.192, 0.0116, 0.00099, 0.886, 0.0222 respectively; λ is 0.55 μm; a1, a2, a3, a4, a5, a6, a7, a8, a9 are parameters to be determined, V is sea level visibility; z is altitude; P is water vapor content; V z To observe visibility.

[0022] According to another aspect of the present invention, there is provided an aerosol optical depth inversion device, comprising:

[0023] A determination module is used to determine an inversion sample data set based on meteorological station data within an aerosol optical depth pixel of a satellite payload, wherein the inversion sample data set includes observed visibility, altitude, water vapor content, and aerosol optical depth;

[0024] A processing module is used to perform normalization processing on the inversion sample data set to obtain a normalized inversion sample data set;

[0025] A training module is used to train the parameters of the pre-built objective function based on the normalized inversion sample data set using a deep neural network algorithm to determine the parameters of the objective function;

[0026] The calculation module is used to calculate the aerosol optical thickness inversion result of the meteorological station to be predicted based on the inversion data of the meteorological station to be predicted, the parameters to be determined and the objective function.

[0027] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method according to any one of the above aspects of the present invention.

[0028] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor for reading the executable instructions from the memory and executing the instructions to implement the method described in any one of the above aspects of the present invention.

[0029] Therefore, the present invention provides an aerosol optical depth inversion method based on an objective function and a deep neural network algorithm, which can effectively use data such as visibility, water vapor content, and altitude to predict aerosol optical depth. Compared with traditional physical inversion methods, it has high precision, high efficiency, and strong adaptability, and is suitable for atmospheric environment monitoring and related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0031] Figure 1 1 is a flow chart of an aerosol optical depth inversion method provided by an exemplary embodiment of the present invention;

[0032] Figure 2 1 is a schematic structural diagram of an aerosol optical thickness inversion device provided by an exemplary embodiment of the present invention;

[0033] Figure 3 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0034] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0035] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0036] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0037] It should also be understood that, in the embodiments of the present invention, “a plurality of” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0038] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0039] In addition, the term "and / or" in this invention merely describes an association relationship between related objects, indicating that three possible relationships exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the character " / " in this invention generally indicates that the related objects are in an "or" relationship.

[0040] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced with each other. For the sake of brevity, they will not be described one by one.

[0041] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0042] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0043] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0044] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0045] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate in conjunction with numerous other general-purpose or specialized computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with terminal devices, computer systems, servers, and other electronic devices include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the above.

[0046] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system-executable instructions (such as program modules) executed by a computer system. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked via a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media, including storage devices.

[0047] Exemplary Methods

[0048] Figure 1 FIG. 1 is a flow chart of an aerosol optical thickness inversion method provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the aerosol optical depth inversion method 100 includes the following steps:

[0049] Step 101: determining an inversion sample data set based on meteorological station data within an aerosol optical depth pixel of a satellite payload, wherein the inversion sample data set includes observed visibility, altitude, water vapor content, and aerosol optical depth;

[0050] Step 102, normalizing the inversion sample data set to obtain a normalized inversion sample data set;

[0051] Step 103: using a deep neural network algorithm to train the parameters of the pre-constructed objective function according to the normalized inversion sample data set to determine the parameters of the objective function;

[0052] Step 104 : Calculate the aerosol optical depth inversion result of the meteorological station to be predicted based on the inversion data of the meteorological station to be predicted, the parameters to be determined, and the objective function.

[0053] Specifically, the purpose of the present invention is to provide an aerosol optical depth inversion method based on an objective function and a deep neural network algorithm. This method can accurately solve the nine parameters to be determined in the objective function AOD by training the observed visibility, water vapor content, altitude, and the actual aerosol optical depth values observed by satellite sensors. The aerosol optical depth inversion result is calculated based on the objective function, thereby realizing the inversion of aerosol optical depth. The ground meteorological station or other measuring equipment obtains the observed visibility V z , water vapor content P, and altitude z. The true value of aerosol optical depth T is provided by satellite products or other remote sensing technologies. Based on an objective function and a deep neural network (DNN) algorithm, this paper proposes a new method for inverting aerosol optical depth (AOD). This method can efficiently and accurately invert AOD and is particularly suitable for scenarios where long-term satellite data series are lacking.

[0054] The objective function AOD is a function of the inverted AOD, which is determined by the sea level visibility V, the altitude z and the water vapor content P, as shown below:

[0055] AOD=τ(V,z,P)

[0056]

[0057] Among them, the observed visibility V at altitude z is z Relationship with sea level visibility V:

[0058]

[0059] Where, k0, k1, k2, k3, k4, k5, k6, k7, k8 are constants, which are 4, 0.55, 12.5, 5.5, 3.192, 0.0116, 0.00099, 0.886, 0.0222 respectively; λ is 0.55 microns; a1, a2, a3, a4, a5, a6, a7, a8, a9 are the parameters to be solved. The specific solution process is as follows:

[0060] (1) Based on the aerosol optical depth pixel of the satellite payload, the meteorological station data within the pixel are searched and averaged, including the observed visibility, altitude and water vapor content, to generate a sample data set; all data are normalized to ensure that the scale of all input variables is consistent to eliminate the influence of dimension.

[0061] (2) Constructing a deep neural network model: A multi-layer perceptron (MLP) is used as the deep neural network model. The model structure includes multiple input layers, hidden layers, and output layers. Input layer: The input data includes observed visibility, water vapor content, and altitude features, and each feature corresponds to an input neuron. Hidden layer: Several fully connected layers are used, and each layer uses a nonlinear activation function (such as ReLU or tanh) for transformation. The number of hidden layers and the number of neurons are adjusted according to the experimental results to obtain a better fitting effect. Output layer: Outputs 9 unknown parameters a1, a2, a3, a4, a5, a6, a7, a8, and a9, which are the inversion results of aerosol optical depth.

[0062] (3) Loss Function and Training Process: The mean squared error (MSE) is used as the loss function to measure the error between the model prediction and the true aerosol optical depth. During training, the Adam optimizer is used for parameter optimization, automatically adjusting the weights and biases in the network to minimize the loss function. By continuously optimizing the network structure and adjusting hyperparameters (such as the learning rate and hidden layer size), the model's accuracy and computational efficiency are improved.

[0063] (4) Model application and prediction: Using the trained deep neural network model, new visibility data, water vapor content, altitude and other environmental parameters are input, along with the nine predicted parameters, to calculate the aerosol optical depth. This method can invert aerosol optical depth in real time and is suitable for large-scale monitoring and prediction tasks.

[0064] Therefore, the present invention provides an aerosol optical depth inversion method based on an objective function and a deep neural network algorithm, which can effectively use data such as visibility, water vapor content, and altitude to predict aerosol optical depth. Compared with traditional physical inversion methods, it has high precision, high efficiency, and strong adaptability, and is suitable for atmospheric environment monitoring and related fields.

[0065] Exemplary devices

[0066] Figure 2 FIG. 1 is a schematic diagram of the structure of an aerosol optical thickness inversion device provided by an exemplary embodiment of the present invention. Figure 2 As shown, the apparatus 200 includes:

[0067] A determination module 210 is configured to determine an inversion sample data set based on meteorological station data within an aerosol optical depth pixel of a satellite payload, wherein the inversion sample data set includes observed visibility, altitude, water vapor content, and aerosol optical depth;

[0068] The processing module 220 is used to perform normalization processing on the inversion sample data set to obtain a normalized inversion sample data set;

[0069] The training module 230 is configured to train the parameters of the pre-constructed objective function based on the normalized inversion sample data set using a deep neural network algorithm to determine the parameters of the objective function;

[0070] The calculation module 240 is used to calculate the aerosol optical depth inversion result of the meteorological station to be predicted based on the inversion data of the meteorological station to be predicted, the parameters to be determined, and the objective function.

[0071] Optionally, the determination module 210 includes:

[0072] The search submodule is used to search for meteorological station data within the aerosol optical depth pixel of the satellite payload;

[0073] The determination submodule is used to average the meteorological station data of each meteorological station and determine the inversion sample data set.

[0074] Optionally, the deep neural network algorithm is a multi-layer perceptron network, including multiple input layers, hidden layers and output layers.

[0075] Optionally, the calculation module 240 includes:

[0076] The first calculation submodule is used to calculate the horizontal visibility value of the meteorological station to be predicted based on the observed visibility value in the inversion data;

[0077] The second calculation submodule is used to input the observed visibility value, the horizontal visibility value and the parameters to be calculated into the objective function to calculate the aerosol optical thickness inversion result of the meteorological station to be predicted.

[0078] Optionally, the relationship between observed visibility and horizontal visibility is:

[0079]

[0080] Where k4, k5, k6, k7, k8 are constants, which are 3.192, 0.0116, 0.00099, 0.886, and 0.0222 respectively; V is the sea level visibility V Z To observe visibility.

[0081] Optionally, the objective function is expressed as:

[0082]

[0083] Where AOD is aerosol optical depth, k0, k1, k2, k3, k4, k5, k6, k7, k8 are constants, which are 4, 0.55, 12.5, 5.5, 3.192, 0.0116, 0.00099, 0.886, 0.0222 respectively; λ is 0.55 μm; a1, a2, a3, a4, a5, a6, a7, a8, a9 are parameters to be determined, V is sea level visibility; z is altitude; P is water vapor content; V z To observe visibility.

[0084] Exemplary electronic devices

[0085] Figure 3 This is the structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 3 As shown, the electronic device 30 includes one or more processors 31 and a memory 32 .

[0086] The processor 31 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0087] The memory 32 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may further include: an input device 33 and an output device 34, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0088] In addition, the input device 33 may also include, for example, a keyboard, a mouse, and the like.

[0089] The output device 34 can output various information to the outside. The output device 34 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0090] Of course, to simplify, Figure 3Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0091] Exemplary computer program products and computer-readable storage media

[0092] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to perform the steps of the method according to various embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0093] The computer program product may be written in any combination of one or more programming languages to implement the operations of embodiments of the present invention, including object-oriented programming languages such as Java, C++, and conventional procedural programming languages such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0094] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0095] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0096] The basic principles of the present invention have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in the present invention are merely illustrative and non-limiting, and should not be construed as necessarily possessed by each embodiment of the present invention. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, and are not intended to be limiting. These details do not necessarily limit the present invention to being implemented using these specific details.

[0097] Each embodiment in this specification is described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments are sufficient. For system embodiments, since they largely correspond to method embodiments, their description is relatively simple. For relevant parts, references to the description of the method embodiments are sufficient.

[0098] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "including," "comprising," "having," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0099] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above sequence of steps for the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above, unless otherwise specified. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers recording media that store programs for executing the method according to the present invention.

[0100] It should also be noted that, in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in this field to make or use the present invention. Various modifications to these aspects will be very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but according to the widest scope consistent with the principles disclosed here and novel features.

[0101] The above description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for inverting aerosol optical depth, characterized in that: include: Determining an inversion sample data set based on meteorological station data within an aerosol optical depth pixel of a satellite payload, wherein the inversion sample data set includes observed visibility, altitude, water vapor content, and aerosol optical depth; performing normalization processing on the inversion sample data set to obtain a normalized inversion sample data set; Using a deep neural network algorithm, the parameters of the pre-constructed objective function are trained according to the normalized inversion sample data set to determine the parameters of the objective function; The aerosol optical thickness inversion result of the meteorological station to be predicted is calculated according to the inversion data of the meteorological station to be predicted, the parameters to be determined and the objective function.

2. The method according to claim 1, characterized in that Based on the meteorological station data within the aerosol optical depth pixel of the satellite payload, the inversion sample dataset is determined, including: Searching for meteorological station data within an aerosol optical depth pixel of the satellite payload; The meteorological station data of each meteorological station are averaged to determine the inversion sample data set.

3. The method according to claim 1, characterized in that The deep neural network algorithm is a multi-layer perceptron network, including multiple input layers, hidden layers and output layers.

4. The method according to claim 1, wherein Calculating an aerosol optical depth inversion result of the meteorological station to be predicted according to the inversion data of the meteorological station to be predicted, the parameter to be determined, and the objective function, including: Calculating the horizontal visibility value of the meteorological station to be predicted based on the observed visibility value in the inversion data; The observed visibility value, the horizontal visibility value and the parameter to be determined are input into the objective function to calculate the aerosol optical depth inversion result of the meteorological station to be predicted.

5. The method according to claim 4, characterized in that The relationship between the observed visibility and the horizontal plane visibility is: Where k4, k5, k6, k7, k8 are constants, which are 3.192, 0.0116, 0.00099, 0.886, and 0.0222 respectively; V is the sea level visibility V Z To observe visibility.

6. The method according to claim 4, characterized in that The expression of the objective function is: Where AOD is aerosol optical depth, k0, k1, k2, k3, k4, k5, k6, k7, k8 are constants, which are 4, 0.55, 12.5, 5.5, 3.192, 0.0116, 0.00099, 0.886, 0.0222 respectively; λ is 0.55 μm; a1, a2, a3, a4, a5, a6, a7, a8, a9 are parameters to be determined, V is sea level visibility; z is altitude; P is water vapor content; V z To observe visibility.

7. An aerosol optical thickness inversion device, characterized in that: include: A determination module is used to determine an inversion sample data set based on meteorological station data within an aerosol optical depth pixel of a satellite payload, wherein the inversion sample data set includes observed visibility, altitude, water vapor content, and aerosol optical depth; A processing module, configured to perform normalization processing on the inversion sample data set to obtain a normalized inversion sample data set; A training module is used to train the parameters of the pre-constructed objective function according to the normalized inversion sample data set using a deep neural network algorithm to determine the parameters of the objective function; The calculation module is used to calculate the aerosol optical thickness inversion result of the meteorological station to be predicted based on the inversion data of the meteorological station to be predicted, the parameters to be determined and the objective function.

8. The device according to claim 7, characterized in that Identify modules, including: A search submodule, configured to search for meteorological station data within an aerosol optical depth pixel of the satellite payload; The determination submodule is used to average the meteorological station data of each meteorological station to determine the inversion sample data set.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 6.

10. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of claims 1 to 6.

Images