A method, system, medium and device for assimilating near-surface observations based on real-time boundary layer vertical structure

Abstract

The application discloses a near-surface observation assimilation method, system, medium and equipment based on a real-time boundary layer vertical structure, belongs to the field of atmospheric science technology, and comprises the following steps: acquiring background error sample data set and real-time boundary layer height; according to the background error sample data set, a background average boundary layer height is obtained through statistics; the background error is vertically layered according to the background average boundary layer height, and the layered background error is obtained; the background field is vertically layered according to the real-time boundary layer height, and the layered background field is obtained; the layered background error is mapped to the layered background field through a scaling transformation, and a new background error is obtained; and the new background error is used to perform near-surface observation assimilation based on an assimilation system through minimization iteration; wherein the background average boundary layer height is defined as the average boundary layer height of all horizontal grid points in each background error sample. The application solves the problem of incoordination of an analysis field caused by inaccurate description of the background error.

Description

Technical Field

[0001] This invention relates to a near-surface observation assimilation method, system, medium, and equipment based on real-time boundary layer vertical structure, belonging to the field of atmospheric science and technology. Background Technology

[0002] With the rapid development of computer technology and the continuous improvement of numerical weather prediction models, numerical weather prediction models have become an important means for major operational forecasting centers worldwide to provide weather forecast results. Data assimilation, as a bridge connecting meteorological observations and numerical weather prediction models, improves the accuracy of the initial field of numerical models by optimally fusing observational data with model outputs, and is a crucial technical means to enhance weather forecast accuracy. Currently, my country has basically established the world's largest integrated meteorological observation system, including a near-surface observation network mainly composed of tens of thousands of minute-level automatic weather stations, which can provide dense observational data within the boundary layer. However, this near-surface observational data containing boundary layer information has not yet been effectively utilized in the current assimilation system.

[0003] Due to the influence of complex boundary layer structures, inconsistencies in near-surface observation assimilation are prone to occur, leading to discontinuities in the analysis field and potentially disrupting the boundary layer structure, thus negatively impacting the assimilation effect. Currently, there are two main solutions to the problem of reasonable assimilation of observations within the boundary layer: First, based on boundary layer structure information, the applicable height of ground observations is estimated by fitting the deviation between the observations and the background field, thereby optimizing the vertical propagation of the analysis increment. However, this method is only applicable to observations from automatic ground stations and is difficult to apply to near-surface radiosonde observations. Second, based on adaptive grid theory, a weighting function is used to redistribute equally spaced grids, concentrating them in high-weight regions, thereby improving the background error structure and optimizing the simulation capability for complex boundary layer structures. However, its applicability is still limited by the fundamental theoretical assumptions. Summary of the Invention

[0004] The purpose of this invention is to provide a near-ground observation assimilation method, system, medium, and device based on the real-time boundary layer vertical structure. By dynamically optimizing the vertical distribution of background error to match the real boundary layer structure, the invention solves the problem of inconsistency in the analysis field caused by inaccurate description of background error.

[0005] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution.

[0006] In a first aspect, the present invention provides a near-ground observation assimilation method based on real-time boundary layer vertical structure, comprising:

[0007] Obtain the background error sample dataset and real-time boundary layer height of the target area based on the forecast lead time difference generated by the numerical weather prediction model;

[0008] Based on the background error sample dataset, the average background boundary layer height is statistically obtained;

[0009] The background error is vertically layered based on the average background boundary layer height to obtain the layered background error.

[0010] The background field generated by the numerical weather prediction model is vertically layered based on the real-time boundary layer height to obtain the layered background field.

[0011] The new background error is obtained by mapping the layered background error to the layered background field through scaling transformation;

[0012] By utilizing the new background error, near-ground observation assimilation is performed through minimization iteration based on the assimilation system;

[0013] The average background boundary layer height is defined as the average boundary layer height of all horizontal grid points in each background error sample.

[0014] Furthermore, the average background boundary layer height is expressed as:

[0015] ;

[0016] In the formula, Indicates the average background boundary layer height. Indicates the number of background error samples. Indicates the first The average boundary layer height of all grid points in a background error sample. The boundary layer height of a grid point in the background error sample is represented by i=1,…,n.

[0017] Furthermore, the background error after layering is expressed as:

[0018] ;

[0019] In the formula, This represents the background error after layering. This represents boundary layer error, used to reflect the propagation characteristics within the boundary layer. This represents free atmosphere error, used to reflect the propagation characteristics within the free atmosphere. The vertical grid levels representing background error. This represents the average background boundary layer height.

[0020] Furthermore, the layered background field includes:

[0021] ;

[0022] In the formula, This represents the layered background field. Indicates the boundary layer. Representing the free atmosphere, This represents the vertical grid hierarchy of the background field. This indicates the real-time boundary layer height.

[0023] Furthermore, by scaling transformation, the layered background error is mapped to the layered background field to obtain a new background error, including:

[0024] When the vertical grid level of the background field is less than or equal to the real-time boundary layer height, the boundary layer vertical grid interval of the layered background field is adjusted to the boundary layer error vertical grid interval of the layered background error according to the interval scaling formula, and a new vertical grid interval of the background field is obtained.

[0025] When the vertical grid level of the background field is less than or equal to the real-time boundary layer height, the free atmosphere vertical grid interval of the layered background field is adjusted to the free atmosphere error vertical grid interval of the layered background error according to the interval scaling formula, and a new vertical grid interval of the background field is obtained.

[0026] The new background error is obtained by interpolating the layered background error into the vertical grid interval of the new background field using an interpolation method.

[0027] Furthermore, the interval scaling formula is expressed as:

[0028] ;

[0029] In the formula, The first one represents the new background field. A vertical grid hierarchy, The starting point of the vertical grid hierarchy represents the boundary layer error of the background error after layering. The vertical grid layer termination point represents the boundary layer error of the background error after layering. This indicates the starting point of the vertical grid hierarchy of the boundary layer of the background field after layering. This represents the vertical grid hierarchy termination point of the boundary layer of the layered background field. The starting point of the vertical grid layer represents the free atmospheric error of the background error after layering. The vertical grid level termination point represents the free atmospheric error of the background error after layering. This represents the starting point of the vertical grid hierarchy for the free atmosphere in the layered background field. This represents the vertical grid layer termination point of the free atmosphere in the layered background field. The first layer represents the background field after layering. A vertical grid hierarchy.

[0030] Furthermore, the new background error is expressed as:

[0031] ;

[0032] In the formula, This represents the new background error. This represents the new boundary layer error, used to reflect the real-time propagation characteristics within the boundary layer. This represents the new free atmosphere error, used to describe the real-time propagation characteristics within the free atmosphere.

[0033] Secondly, the present invention provides a near-surface observation assimilation system based on a real-time boundary layer vertical structure, comprising:

[0034] The data acquisition module is used to acquire the background error sample dataset and real-time boundary layer height of the target area forecast time difference generated based on the numerical weather prediction model;

[0035] The statistical modeling module is used to statistically obtain the average background boundary layer height based on the background error sample dataset.

[0036] The error stratification module is used to vertically stratify the background error based on the average background boundary layer height, so as to obtain the stratified background error.

[0037] The background field layering module is used to vertically layer the background field generated by the numerical weather prediction model based on the real-time boundary layer height, so as to obtain the layered background field.

[0038] The scaling transformation module is used to map the layered background error to the layered background field through scaling transformation, so as to obtain a new background error.

[0039] The assimilation iteration module is used to utilize new background errors and perform near-ground observation assimilation based on the assimilation system through minimization iteration.

[0040] The average background boundary layer height is defined as the average boundary layer height of all horizontal grid points in each background error sample.

[0041] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the near-ground observation assimilation method based on a real-time boundary layer vertical structure as described in the first aspect.

[0042] Fourthly, the present invention provides a computer device, comprising:

[0043] Memory, used to store instructions;

[0044] A processor is configured to execute the instructions, causing the device to perform operations implementing the near-surface observation assimilation method based on a real-time boundary layer vertical structure as described in the first aspect.

[0045] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0046] 1. This invention is based on a dual-layer strategy of background average boundary layer height and real-time boundary layer height, combined with scaling transformation mapping. It effectively solves the problem of discontinuity in the analysis field caused by fixed vertical layering in traditional assimilation, ensures the physical consistency between background error and background field in the vertical structure of the boundary layer, thereby avoiding boundary layer structure destruction and improving the accuracy and reliability of near-ground observation assimilation.

[0047] 2. This invention achieves dynamic adaptive matching of background error and background field on the vertical structure of the boundary layer through a dual-layer strategy of background average boundary layer height and real-time boundary layer height. This effectively solves the problem of discontinuity in the analysis field caused by traditional fixed vertical layering, avoids boundary layer structure destruction, and ensures the physical consistency of the assimilation process.

[0048] 3. Based on the interval scaling formula and interpolation method of the scaling transformation module, this invention accurately maps the layered background error to the vertical grid interval of the layered background field, realizing the spatial scale alignment between the error and the background field in the vertical direction, significantly improving the matching accuracy between the error propagation characteristics in the boundary layer and the free atmosphere error, and reducing the error accumulation in the assimilation process.

[0049] 4. Through the synergistic effect of vertical stratification and scaling transformation, this invention enables the new background error to reflect the real-time propagation characteristics of the boundary layer and free atmosphere. In the process of minimizing the assimilation system, it significantly improves the assimilation accuracy and reliability of near-ground observations, especially showing stronger adaptability in regions with variable boundary layer structures. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a near-ground observation assimilation method based on a real-time boundary layer vertical structure provided in an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram showing the effect comparison of the vertical structure of the boundary layer before and after adjusting the background error, provided in an embodiment of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0053] Example 1

[0054] like Figure 1 As shown in the figure, this embodiment introduces a near-ground observation assimilation method based on real-time boundary layer vertical structure, including:

[0055] Step 1: Obtain the background error sample dataset and real-time boundary layer height of the target area forecast time difference generated by the numerical weather prediction model.

[0056] This embodiment obtains a background error sample dataset and real-time boundary layer height to provide a dynamic data foundation for subsequent vertical stratification, ensuring that background error statistics and background field adjustments are always based on the latest boundary layer state, avoiding assimilation bias caused by data lag, and improving the timeliness and representativeness of the data foundation.

[0057] Step 2: Based on the background error sample dataset, the average background boundary layer height is statistically obtained.

[0058] This embodiment uses the average background boundary layer height obtained from the statistical analysis of the background error sample set to eliminate single-sample error fluctuations by averaging horizontal grid points, forming a more statistically significant vertical stratification benchmark. This makes the background error stratification more consistent with the overall error distribution characteristics and enhances the physical rationality of the stratification strategy.

[0059] Step 3: Vertically divide the background error into layers based on the average boundary layer height of the background to obtain the layered background error.

[0060] This embodiment vertically stratifies background errors based on the average background boundary layer height, achieving a clear separation between boundary layer errors and free atmosphere errors. This allows for a targeted characterization of the vertical propagation characteristics of errors, enhancing the structured representation of error stratification.

[0061] Step 4: Vertically stratify the background field generated by the numerical weather prediction model based on the real-time boundary layer height to obtain the stratified background field.

[0062] This embodiment vertically stratifies the background field based on the real-time boundary layer height, so that the boundary layer and free atmosphere structure of the background field are matched with the current actual atmospheric state in real time, avoiding the misalignment of the "boundary layer-free atmosphere" interface caused by fixed stratification, and ensuring the spatial correspondence between the background field and the observation data.

[0063] Step 5: Map the layered background error to the layered background field through scaling transformation to obtain a new background error.

[0064] This embodiment maps the layered background error to the layered background field through scaling transformation, achieving precise alignment between the error and the background field on the vertical grid scale, eliminating mapping deviations caused by differences in vertical grid spacing, and ensuring that the propagation characteristics of the error in the boundary layer and free atmosphere are accurately preserved.

[0065] Step 6: Utilize the new background error to perform near-ground observation assimilation based on the assimilation system through minimization iteration.

[0066] In this embodiment, the average background boundary layer height is defined as the average boundary layer height of all horizontal grid points in each background error sample.

[0067] This embodiment utilizes a new background error to iterate and minimize the assimilation system, making the fusion of observational data and the background field more consistent with the current vertical structure of the boundary layer. This reduces the "assimilation oscillations" caused by traditional fixed errors, significantly improving the assimilation accuracy and physical consistency of near-surface observations. It ensures that the assimilation of near-surface observations generates a more physically reasonable analytical field within the boundary layer, providing a better initial field for numerical weather prediction models. Therefore, it can also effectively improve the accuracy of numerical weather prediction and provide a potential technical path for the future development of a strongly coupled land-atmosphere assimilation framework.

[0068] Example 2

[0069] Based on the same inventive concept as Embodiment 1, this embodiment describes the implementation steps of a near-surface observation assimilation method based on real-time boundary layer vertical structure, including:

[0070] Step 1: Obtain the background error sample dataset and real-time boundary layer height of the target area forecast time difference generated by the numerical weather prediction model.

[0071] In this embodiment, the real-time boundary layer height can be obtained by directly reading the model diagnostic boundary layer height variable stored in the model background field, or by using the turbulent kinetic energy method or the Richardson number method to diagnose the model simulation results of the model background field, or, if sufficient observations are available, by using the air parcel method, potential temperature gradient method or inversion method based on the vertical profile of meteorological elements obtained by direct observation or remote sensing.

[0072] Step 2: Based on the background error sample dataset, the average background boundary layer height is statistically obtained.

[0073] In this embodiment, the average background boundary layer height is expressed as:

[0074] ;

[0075] In the formula, Indicates the average background boundary layer height. Indicates the number of background error samples. Indicates the first The average boundary layer height of all grid points in a background error sample. The boundary layer height of a grid point in the background error sample is represented by i=1,…,n.

[0076] Step 3: Vertically divide the background error into layers based on the average boundary layer height of the background to obtain the layered background error.

[0077] Since the Earth's atmosphere can be divided into two parts vertically: the boundary layer and the free atmosphere, in this embodiment, the background error after layering is expressed as:

[0078] ;

[0079] In the formula, This represents the background error after layering. This represents boundary layer error, used to reflect the propagation characteristics within the boundary layer. This represents free atmosphere error, used to reflect the propagation characteristics within the free atmosphere. The vertical grid levels representing background error. This represents the average background boundary layer height.

[0080] Step 4: Vertically stratify the background field generated by the numerical weather prediction model based on the real-time boundary layer height to obtain the stratified background field.

[0081] In this embodiment, the layered background field includes:

[0082] ;

[0083] In the formula, This represents the layered background field. Indicates the boundary layer. Representing the free atmosphere, This represents the vertical grid hierarchy of the background field. This indicates the real-time boundary layer height.

[0084] Step 5: Map the layered background error to the layered background field through scaling transformation to obtain a new background error.

[0085] like Figure 2As shown, when the real-time boundary layer height is lower than the background average boundary layer height, the boundary layer error is compressed and mapped to the boundary layer, while the free atmosphere error is stretched and mapped to the free atmosphere. When the real-time boundary layer height is higher than the background average boundary layer height, the boundary layer error is stretched and mapped to the boundary layer, while the free atmosphere error is compressed and mapped to the free atmosphere. Compared to a fixed background error, the new background error, after stretching and changing, exhibits dynamic characteristics and can better match the real-time boundary layer height. The specific mapping process is as follows:

[0086] Step 5.1: Adjust the vertical grid interval of the layered background field to the vertical grid interval of the layered background error according to the interval scaling formula to obtain the new vertical grid interval of the background field.

[0087] When the vertical grid level of the background field is less than or equal to the real-time boundary layer height, the boundary layer vertical grid interval of the layered background field is adjusted to the boundary layer error vertical grid interval of the layered background error according to the interval scaling formula, and a new vertical grid interval of the background field is obtained.

[0088] When the vertical grid level of the background field is less than or equal to the real-time boundary layer height, the free atmospheric vertical grid interval of the layered background field is adjusted to the free atmospheric error vertical grid interval of the layered background error according to the interval scaling formula, thus obtaining a new vertical grid interval of the background field.

[0089] In this embodiment, the interval scaling formula is expressed as:

[0090] ;

[0091] In the formula, The first one represents the new background field. A vertical grid hierarchy, The starting point of the vertical grid hierarchy represents the boundary layer error of the background error after layering. The vertical grid layer termination point represents the boundary layer error of the background error after layering. This indicates the starting point of the vertical grid hierarchy of the boundary layer of the background field after layering. This represents the vertical grid hierarchy termination point of the boundary layer of the layered background field. The starting point of the vertical grid layer represents the free atmospheric error of the background error after layering. The vertical grid level termination point represents the free atmospheric error of the background error after layering. This represents the starting point of the vertical grid hierarchy for the free atmosphere in the layered background field. This represents the vertical grid layer termination point of the free atmosphere in the layered background field. The first layer represents the background field after layering. A vertical grid hierarchy.

[0092] Step 5.2: Interpolate the layered background error to the vertical grid interval of the new background field using an interpolation method to obtain the new background error.

[0093] In this embodiment, the new background error is represented as:

[0094] ;

[0095] In the formula, This represents the new background error. This represents the new boundary layer error, used to reflect the real-time propagation characteristics within the boundary layer. This represents the new free atmosphere error, used to describe the real-time propagation characteristics within the free atmosphere.

[0096] Step 6: Utilize the new background error to perform near-ground observation assimilation based on the assimilation system through minimization iteration.

[0097] In this embodiment, the average background boundary layer height is defined as the average boundary layer height of all horizontal grid points in each background error sample.

[0098] Example 3

[0099] Based on the same inventive concept as other embodiments, this embodiment introduces a near-surface observation assimilation system based on a real-time boundary layer vertical structure, comprising:

[0100] The data acquisition module is used to acquire the background error sample dataset and real-time boundary layer height of the target area forecast time difference generated based on the numerical weather prediction model;

[0101] The statistical modeling module is used to statistically obtain the average background boundary layer height based on the background error sample dataset.

[0102] The error stratification module is used to vertically stratify the background error based on the average background boundary layer height, so as to obtain the stratified background error.

[0103] The background field layering module is used to vertically layer the background field generated by the numerical weather prediction model based on the real-time boundary layer height, so as to obtain the layered background field.

[0104] The scaling transformation module is used to map the layered background error to the layered background field through scaling transformation, so as to obtain a new background error.

[0105] The assimilation iteration module is used to utilize new background errors and perform near-ground observation assimilation based on the assimilation system through minimization iteration.

[0106] The average background boundary layer height is defined as the average boundary layer height of all horizontal grid points in each background error sample.

[0107] The specific functions of each module described above are explained in the relevant content of Embodiment 1 or 2, and will not be repeated here.

[0108] Example 4

[0109] Based on the same inventive concept as other embodiments, this embodiment describes a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the methods of Embodiment 1 or 2 described above.

[0110] Example 5

[0111] Based on the same inventive concept as other embodiments, this embodiment introduces a computer program product, including computer instructions that, when executed by a processor, implement the steps of the methods described in Embodiment 1 or 2 above.

[0112] In summary, the present invention, based on a dual-layer strategy of background average boundary layer height and real-time boundary layer height, combined with scaling transformation mapping, effectively solves the problem of discontinuity in the analysis field caused by fixed vertical layering in traditional assimilation, ensures the physical consistency between background error and background field in the vertical structure of the boundary layer, thereby avoiding boundary layer structure destruction and improving the accuracy and reliability of near-ground observation assimilation.

[0113] This invention achieves dynamic adaptive matching between background error and background field on the vertical structure of the boundary layer through a dual-layer strategy of background average boundary layer height and real-time boundary layer height. This effectively solves the problem of discontinuity in the analysis field caused by traditional fixed vertical layering, avoids boundary layer structure destruction, and ensures the physical consistency of the assimilation process.

[0114] This invention uses an interval scaling formula and interpolation method based on a scaling transformation module to accurately map the layered background error to the vertical grid interval of the layered background field. This achieves spatial scale alignment between the error and the background field in the vertical direction, significantly improving the matching accuracy between the error propagation characteristics within the boundary layer and the free atmosphere error, and reducing error accumulation during the assimilation process.

[0115] This invention, through the synergistic effect of vertical stratification and scaling transformation, enables new background errors to reflect the real-time propagation characteristics of the boundary layer and free atmosphere. During the minimization iteration of the assimilation system, it significantly improves the assimilation accuracy and reliability of near-surface observations, especially showing stronger adaptability in regions with variable boundary layer structures.

[0116] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A near-surface observation assimilation method based on real-time boundary layer vertical structure, characterized in that, include: Obtain the background error sample dataset and real-time boundary layer height of the target area based on the forecast lead time difference generated by the numerical weather prediction model; Based on the background error sample dataset, the average background boundary layer height is statistically obtained; The background error is vertically layered based on the average background boundary layer height to obtain the layered background error. The background field generated by the numerical weather prediction model is vertically layered based on the real-time boundary layer height to obtain the layered background field. The new background error is obtained by mapping the layered background error to the layered background field through scaling transformation; By utilizing the new background error, near-ground observation assimilation is performed through minimization iteration based on the assimilation system; The average background boundary layer height is defined as the average boundary layer height of all horizontal grid points in each background error sample. Spend.

2. The near-surface observation assimilation method based on real-time boundary layer vertical structure according to claim 1, characterized in that, The average background boundary layer height is expressed as: ； In the formula, Indicates the average background boundary layer height. Indicates the number of background error samples. Indicates the first The average boundary layer height of all grid points in a background error sample. The boundary layer height of a grid point in the background error sample is represented by i=1,…,n.

3. The near-surface observation assimilation method based on real-time boundary layer vertical structure according to claim 2, characterized in that, The background error after layering is expressed as: ； In the formula, This represents the background error after layering. This represents boundary layer error, used to reflect the propagation characteristics within the boundary layer. This represents free atmosphere error, used to reflect the propagation characteristics within the free atmosphere. The vertical grid levels representing background error. This represents the average background boundary layer height.

4. The near-surface observation assimilation method based on real-time boundary layer vertical structure according to claim 3, characterized in that, The layered background field includes: ； In the formula, This represents the layered background field. Indicates the boundary layer. Representing the free atmosphere, This represents the vertical grid hierarchy of the background field. This indicates the real-time boundary layer height.

5. The near-surface observation assimilation method based on real-time boundary layer vertical structure according to claim 4, characterized in that, By scaling transformation, the layered background error is mapped to the layered background field to obtain a new background error, including: When the vertical grid level of the background field is less than or equal to the real-time boundary layer height, the boundary layer vertical grid interval of the layered background field is adjusted to the boundary layer error vertical grid interval of the layered background error according to the interval scaling formula, and a new vertical grid interval of the background field is obtained. When the vertical grid level of the background field is less than or equal to the real-time boundary layer height, the free atmosphere vertical grid interval of the layered background field is adjusted to the free atmosphere error vertical grid interval of the layered background error according to the interval scaling formula, so as to obtain a new vertical grid interval of the background field. The new background error is obtained by interpolating the layered background error into the vertical grid interval of the new background field using an interpolation method.

6. The near-surface observation assimilation method based on real-time boundary layer vertical structure according to claim 5, characterized in that, The interval scaling formula is expressed as follows: ； In the formula, The first one represents the new background field. A vertical grid hierarchy, The starting point of the vertical grid hierarchy represents the boundary layer error of the background error after layering. The vertical grid layer termination point represents the boundary layer error of the background error after layering. This indicates the starting point of the vertical grid hierarchy of the boundary layer of the background field after layering. This represents the vertical grid hierarchy termination point of the boundary layer of the layered background field. The starting point of the vertical grid layer represents the free atmospheric error of the background error after layering. The vertical grid level termination point represents the free atmospheric error of the background error after layering. This represents the starting point of the vertical grid hierarchy for the free atmosphere in the layered background field. This represents the vertical grid layer termination point of the free atmosphere in the layered background field. The first layer represents the background field after layering. A vertical grid hierarchy.

7. The near-surface observation assimilation method based on real-time boundary layer vertical structure according to claim 5, characterized in that, The new background error is expressed as: ； In the formula, This represents the new background error. This represents the new boundary layer error, used to reflect the real-time propagation characteristics within the boundary layer. This represents the new free atmosphere error, used to describe the real-time propagation characteristics within the free atmosphere.

8. A near-surface observation assimilation system based on a real-time boundary layer vertical structure, characterized in that, include: The data acquisition module is used to acquire the background error sample dataset and real-time boundary layer height of the target area forecast time difference generated based on the numerical weather prediction model; The statistical modeling module is used to statistically obtain the average background boundary layer height based on the background error sample dataset. The error stratification module is used to vertically stratify the background error based on the average background boundary layer height, so as to obtain the stratified background error. The background field layering module is used to vertically layer the background field generated by the numerical weather prediction model based on the real-time boundary layer height, so as to obtain the layered background field. The scaling transformation module is used to map the layered background error to the layered background field through scaling transformation, so as to obtain a new background error. The assimilation iteration module is used to utilize new background errors and perform near-ground observation assimilation based on the assimilation system through minimization iteration. The average background boundary layer height is defined as the average boundary layer height of all horizontal grid points in each background error sample.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the near-ground observation assimilation method based on a real-time boundary layer vertical structure as described in any one of claims 1-7.

10. A computer device, characterized in that, include: Memory, used to store instructions; A processor is configured to execute the instructions, causing the device to perform operations that implement the near-surface observation assimilation method based on a real-time boundary layer vertical structure as described in any one of claims 1-7.

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