SST data fusion method and system, electronic equipment and storage medium

Abstract

The invention provides an SST data fusion method and system, electronic equipment and a storage medium. The method comprises the steps of obtaining sea surface temperature background field data of a to-be-fused grid point and sea surface temperature observation data and sea surface temperature background field data of multi-source satellite remote sensing of each grid point in a preset range of the to-be-fused grid point; determining a least square weight factor corresponding to each grid point in a preset range of the to-be-fused grid points; and according to the sea surface temperature background field data of the to-be-fused grid points, obtaining the to-be-fused and determining sea surface temperature fusion data at the to-be-fused grid point according to the sea surface temperature observation data and the sea surface temperature background field data of the multi-source satellite remote sensing of each grid point in the preset range of the to-be-fused grid point and the least square weight factor corresponding to each grid point in the preset range of the to-be-fused grid point. According to the technical scheme provided by the invention, the precision of SST data fusion is improved.

Description

Technical Field

[0001] The present application relates to the field of data fusion, and in particular to an SST data fusion method, system, electronic device and storage medium. Background Art

[0002] Data assimilation technology uses numerical models as dynamic forcing to determine the optimal estimate of the system state based on the evolution law of the dynamic system itself from observational data with uneven temporal and spatial distribution. In the late 1980s, the field of atmospheric science proposed a new method to restore long-term historical climate records using data assimilation technology in numerical weather forecasting, that is, to use data assimilation systems to integrate observational data of various sources and types with numerical weather forecast products. This formed the initial atmospheric reanalysis data, and then the United States, Europe and Japan successively started global ocean observation data reanalysis programs. Ocean observation data reanalysis is also an objective analysis process that uses advanced numerical models and data assimilation systems to unify and coordinate historical observational data of various types and sources. Ocean reanalysis products are the product of organically combining dynamic numerical models with multi-source observational data using assimilation systems, so they integrate the advantages of numerical simulation and observation: spatiotemporal continuity, relatively high spatiotemporal resolution, and the ability to form continuous data of the deep ocean, which to a certain extent makes up for the scarcity of deep ocean observational data. On the one hand, ocean reanalysis data products can be used as diagnostic data and widely used in ocean dynamics research and exploration of the ocean in climate change. On the other hand, they can provide initial fields for short-term numerical ocean forecasts and climate predictions.

[0003] At present, with the continuous enrichment of ocean observation data and the development of ocean data assimilation technology, there is still a lack of autonomous and controllable high-resolution temperature reanalysis data for offshore areas. Therefore, it is urgent to propose a method to develop high-precision temperature fusion data based on the existing rich ocean observation data for use in marine environmental assessment. Summary of the invention

[0004] The present application provides an SST data fusion method, system, electronic device and storage medium to at least solve the technical problem that the prior art has not developed high-precision temperature fusion data based on abundant ocean observation data, thereby making it impossible to accurately assess the marine environment.

[0005] The first embodiment of the present application proposes a SST data fusion method, the method comprising:

[0006] Acquire sea surface temperature background field data of the grid points to be fused and multi-source satellite remote sensing sea surface temperature observation data and sea surface temperature background field data of each grid point within a preset range of the grid points to be fused;

[0007] Determine the minimum square weight factor corresponding to each grid point within the preset range of the grid points to be fused;

[0008] The sea surface temperature fusion data at the grid point to be fused is determined based on the sea surface temperature background field data of the grid point to be fused, the multi-source satellite remote sensing sea surface temperature observation data and the sea surface temperature background field data of each grid point within a preset range of the grid point to be fused, and the minimum square weight factor corresponding to each grid point within the preset range of the grid point to be fused.

[0009] Preferably, the preset range is a 1°×1° range centered on the grid point to be fused.

[0010] Furthermore, the determining of the minimum square weight factor corresponding to each grid point within the preset range of the grid points to be fused includes:

[0011] Respectively obtain the expected values ​​of the sea surface temperature observation data and the sea surface temperature background field data of the multi-source satellite remote sensing of each grid point within the preset range of the grid point to be fused, and form a background error covariance matrix;

[0012] The minimum square weight factor corresponding to each grid point within the preset range of the grid points to be fused is determined based on the background error covariance matrix and the least squares principle.

[0013] Furthermore, the calculation formula of the sea surface temperature fusion data at the grid point to be fused is as follows:

[0014]

[0015] In the formula, A k is the sea surface temperature fusion data at the grid point k to be fused, B k is the sea surface temperature background field data at the grid point k to be fused, O i is the sea surface temperature observation data of multi-source satellite remote sensing at grid point i, B i is the sea surface temperature background field data at grid point i, W ki is the minimum square weight factor corresponding to the grid point i within the preset range of the grid point k to be fused, and N is the total number of grid points within the preset range.

[0016] The second embodiment of the present application provides an SST data fusion system, including:

[0017] An acquisition module is used to acquire the sea surface temperature background field data of the grid points to be fused and the multi-source satellite remote sensing sea surface temperature observation data and the sea surface temperature background field data of each grid point within a preset range of the grid points to be fused;

[0018] A first determination module is used to determine the minimum square weight factor corresponding to each grid point within a preset range of the grid points to be fused;

[0019] The second determination module is used to determine the sea surface temperature fusion data at the grid point to be fused based on the sea surface temperature background field data of the grid point to be fused, the multi-source satellite remote sensing sea surface temperature observation data and the sea surface temperature background field data of each grid point within a preset range of the grid point to be fused, and the minimum square weight factor corresponding to each grid point within the preset range of the grid point to be fused.

[0020] Preferably, the preset range is a 1°×1° range centered on the grid point to be fused.

[0021] Furthermore, the first determining module includes:

[0022] An acquisition unit is used to respectively acquire the sea surface temperature observation data of multi-source satellite remote sensing and the expected values ​​of the sea surface temperature background field data of each grid point within a preset range of the grid point to be fused, so as to form a background error covariance matrix;

[0023] The determination unit is used to determine the minimum square weight factor corresponding to each grid point within a preset range of the grid points to be fused based on the background error covariance matrix and the least squares principle.

[0024] Furthermore, the calculation formula of the sea surface temperature fusion data at the grid point to be fused is as follows:

[0025]

[0026] In the formula, A k is the sea surface temperature fusion data at the grid point k to be fused, B k is the sea surface temperature background field data at the grid point k to be fused, O i is the sea surface temperature observation data of multi-source satellite remote sensing at grid point i, B i is the sea surface temperature background field data at grid point i, W ki is the minimum square weight factor corresponding to the grid point i within the preset range of the grid point k to be fused, and N is the total number of grid points within the preset range.

[0027] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect is implemented.

[0028] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect of the present application.

[0029] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0030] The present application proposes a method, system, electronic device and storage medium for SST data fusion, the method comprising: obtaining the sea surface temperature background field data of the grid point to be fused and the multi-source satellite remote sensing sea surface temperature observation data and sea surface temperature background field data of each grid point within a preset range of the grid point to be fused; determining the minimum square weight factor corresponding to each grid point within the preset range of the grid point to be fused; determining the sea surface temperature fusion data at the grid point to be fused based on the sea surface temperature background field data of the grid point to be fused, the multi-source satellite remote sensing sea surface temperature observation data and sea surface temperature background field data of each grid point within the preset range of the grid point to be fused, and the minimum square weight factor corresponding to each grid point within the preset range of the grid point to be fused. The technical solution proposed in the present application improves the accuracy of SST data fusion.

[0031] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0033] Figure 1 A flowchart of an SST data fusion method provided according to an embodiment of the present application;

[0034] Figure 2 A structural diagram of an SST data fusion system provided according to an embodiment of the present application;

[0035] Figure 3 It is a structural diagram of a first determination module provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0037] The present application proposes a method, system, electronic device and storage medium for SST data fusion, the method comprising: obtaining the sea surface temperature background field data of the grid point to be fused and the multi-source satellite remote sensing sea surface temperature observation data and sea surface temperature background field data of each grid point within a preset range of the grid point to be fused; determining the minimum square weight factor corresponding to each grid point within the preset range of the grid point to be fused; determining the sea surface temperature fusion data at the grid point to be fused based on the sea surface temperature background field data of the grid point to be fused, the multi-source satellite remote sensing sea surface temperature observation data and sea surface temperature background field data of each grid point within the preset range of the grid point to be fused, and the minimum square weight factor corresponding to each grid point within the preset range of the grid point to be fused. The technical solution proposed in the present application improves the accuracy of SST data fusion.

[0038] The following describes an SST data fusion method, system, electronic device and storage medium according to an embodiment of the present application with reference to the accompanying drawings.

[0039] Embodiment 1

[0040] Figure 1 A flow chart of an SST data fusion method provided according to an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0041] Step 1: Obtain the sea surface temperature background field data of the grid points to be fused and the multi-source satellite remote sensing sea surface temperature observation data and sea surface temperature background field data of each grid point within a preset range of the grid points to be fused.

[0042] It should be noted that SST is the multi-source satellite remote sensing satellite sea surface temperature (SeaSurfaceTemperature, SST);

[0043] The preset range is a 1°×1° range centered on the grid point to be fused.

[0044] The background field is a reanalysis field, that is, a numerical simulation result that assimilates measured data.

[0045] Step 2: Determine the minimum square weight factor corresponding to each grid point within the preset range of the grid points to be fused.

[0046] In the embodiment of the present disclosure, step 2 specifically includes:

[0047] Respectively obtain the expected values ​​of the sea surface temperature observation data and the sea surface temperature background field data of the multi-source satellite remote sensing of each grid point within the preset range of the grid point to be fused, and form a background error covariance matrix;

[0048] The minimum square weight factor corresponding to each grid point within the preset range of the grid points to be fused is determined based on the background error covariance matrix and the least squares principle.

[0049] It should be noted that if there are no points within the preset range of the grid points to be fused, the minimum square weight factor is directly assigned a value of 0.

[0050] It should be noted that the minimum square weight factor satisfies the formula Solving the linear equations can obtain the minimum square weight factor corresponding to each grid point within the preset range of grid point k, where M ij is the background error covariance matrix between grid point j and grid point i, where j represents the background field point and i represents the observation point, <π j π k > is the expected value of the correlation or covariance between grid point j and grid point k, and k represents the analysis field, i.e., the grid point to be fused.

[0051] in, ε i is the ratio of the observation data error at grid point i to the standard deviation of the background field, which can be 0.5.

[0052] In the formula, x i is the longitudinal distance of grid point i, x j is the longitudinal distance of grid point j, y i is the latitudinal distance of grid point i, y j is the latitudinal distance of grid point j, λ x is the meridional correlation scale of the SST data, which can be 151 km, λ y For the latitudinal correlation scale of SST data, it can be taken as 155KM.

[0053] Step 3: Determine the sea surface temperature fusion data at the grid point to be fused based on the sea surface temperature background field data of the grid point to be fused, the multi-source satellite remote sensing sea surface temperature observation data and sea surface temperature background field data of each grid point within the preset range of the grid point to be fused, and the minimum square weight factor corresponding to each grid point within the preset range of the grid point to be fused.

[0054] In the embodiment of the present disclosure, the calculation formula of the sea surface temperature fusion data at the grid point to be fused is as follows:

[0055]

[0056] In the formula, A k is the sea surface temperature fusion data at the grid point k to be fused, B k is the sea surface temperature background field data at the grid point k to be fused, O i is the sea surface temperature observation data of multi-source satellite remote sensing at grid point i, B i is the sea surface temperature background field data at grid point i, Wki is the minimum square weight factor corresponding to the grid point i within the preset range of the grid point k to be fused, and N is the total number of grid points within the preset range.

[0057] In summary, the SST data fusion method proposed in this embodiment improves the accuracy of SST data fusion and provides a guarantee for the accuracy of marine environmental assessment.

[0058] Embodiment 2

[0059] Figure 2 FIG. 1 is a structural diagram of an SST data fusion system provided according to an embodiment of the present application, such as Figure 2 As shown, the system comprises:

[0060] An acquisition module 100 is used to acquire the sea surface temperature background field data of the grid point to be fused and the multi-source satellite remote sensing sea surface temperature observation data and the sea surface temperature background field data of each grid point within a preset range of the grid point to be fused;

[0061] A first determination module 200 is used to determine the minimum square weight factor corresponding to each grid point within a preset range of the grid points to be fused;

[0062] The second determination module 300 is used to determine the sea surface temperature fusion data at the grid point to be fused based on the sea surface temperature background field data of the grid point to be fused, the multi-source satellite remote sensing sea surface temperature observation data and sea surface temperature background field data of each grid point within a preset range of the grid point to be fused, and the minimum square weight factor corresponding to each grid point within the preset range of the grid point to be fused.

[0063] The preset range is a 1°×1° range centered on the grid point to be fused.

[0064] In the embodiments of the present disclosure, Figure 3 As shown, the first determination module 200 includes:

[0065] An acquisition unit 201 is used to respectively acquire the sea surface temperature observation data of multi-source satellite remote sensing and the expected values ​​of the sea surface temperature background field data of each grid point within a preset range of the grid point to be fused, and form a background error covariance matrix;

[0066] The determination unit 202 is used to determine the minimum square weight factor corresponding to each grid point within the preset range of the grid points to be fused based on the background error covariance matrix and the least squares principle.

[0067] Furthermore, the calculation formula of the sea surface temperature fusion data at the grid point to be fused is as follows:

[0068]

[0069] In the formula, A k is the sea surface temperature fusion data at the grid point k to be fused, B k is the sea surface temperature background field data at the grid point k to be fused, O i is the sea surface temperature observation data of multi-source satellite remote sensing at grid point i, B i is the sea surface temperature background field data at grid point i, W ki is the minimum square weight factor corresponding to the grid point i within the preset range of the grid point k to be fused, and N is the total number of grid points within the preset range.

[0070] In summary, the SST data fusion system proposed in this embodiment improves the accuracy of SST data fusion and provides a guarantee for the accuracy of marine environmental assessment.

[0071] Embodiment 3

[0072] In order to implement the above embodiments, the present disclosure further proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in Embodiment 1 is implemented.

[0073] Embodiment 4

[0074] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the first embodiment is implemented.

[0075] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0076] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0077] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A SST data fusion method, It is characterized in that The method comprises: Acquire sea surface temperature background field data of the grid points to be fused and multi-source satellite remote sensing sea surface temperature observation data and sea surface temperature background field data of each grid point within a preset range of the grid points to be fused; Determine the minimum square weight factor corresponding to each grid point within the preset range of the grid points to be fused; The sea surface temperature fusion data at the grid point to be fused is determined based on the sea surface temperature background field data of the grid point to be fused, the multi-source satellite remote sensing sea surface temperature observation data and the sea surface temperature background field data of each grid point within a preset range of the grid point to be fused, and the minimum square weight factor corresponding to each grid point within the preset range of the grid point to be fused.

2. The method according to claim 1, It is characterized in that The preset range is a 1°×1° range centered on the grid point to be fused.

3. The method according to claim 2, It is characterized in that The step of determining the minimum square weight factor corresponding to each grid point within the preset range of the grid points to be fused includes: Respectively obtain the expected values ​​of the sea surface temperature observation data and the sea surface temperature background field data of the multi-source satellite remote sensing of each grid point within the preset range of the grid point to be fused, and form a background error covariance matrix; The minimum square weight factor corresponding to each grid point within the preset range of the grid points to be fused is determined based on the background error covariance matrix and the least squares principle.

4. The method according to claim 3, It is characterized in that The calculation formula of the sea surface temperature fusion data at the grid point to be fused is as follows: In the formula, A k is the sea surface temperature fusion data at the grid point k to be fused, B k is the sea surface temperature background field data at the grid point k to be fused, O i is the sea surface temperature observation data of multi-source satellite remote sensing at grid point i, B i is the sea surface temperature background field data at grid point i, W ki is the minimum square weight factor corresponding to the grid point i within the preset range of the grid point k to be fused, and N is the total number of grid points within the preset range.

5. An SST data fusion system, It is characterized in that The system comprises: An acquisition module is used to acquire the sea surface temperature background field data of the grid points to be fused and the multi-source satellite remote sensing sea surface temperature observation data and the sea surface temperature background field data of each grid point within a preset range of the grid points to be fused; A first determination module is used to determine the minimum square weight factor corresponding to each grid point within a preset range of the grid points to be fused; The second determination module is used to determine the sea surface temperature fusion data at the grid point to be fused based on the sea surface temperature background field data of the grid point to be fused, the multi-source satellite remote sensing sea surface temperature observation data and the sea surface temperature background field data of each grid point within a preset range of the grid point to be fused, and the minimum square weight factor corresponding to each grid point within the preset range of the grid point to be fused.

6. The system according to claim 5, It is characterized in that The preset range is a 1°×1° range centered on the grid point to be fused.

7. The system according to claim 6, It is characterized in that The first determining module includes: An acquisition unit is used to respectively acquire the sea surface temperature observation data of multi-source satellite remote sensing and the expected values ​​of the sea surface temperature background field data of each grid point within a preset range of the grid point to be fused, so as to form a background error covariance matrix; The determination unit is used to determine the minimum square weight factor corresponding to each grid point within a preset range of the grid points to be fused based on the background error covariance matrix and the least squares principle.

8. The system of claim 7, It is characterized in that The calculation formula of the sea surface temperature fusion data at the grid point to be fused is as follows: In the formula, A k is the sea surface temperature fusion data at the grid point k to be fused, B k is the sea surface temperature background field data at the grid point k to be fused, O i is the sea surface temperature observation data of multi-source satellite remote sensing at grid point i, B i is the sea surface temperature background field data at grid point i, W ki is the minimum square weight factor corresponding to the grid point i within the preset range of the grid point k to be fused, and N is the total number of grid points within the preset range.

9. An electronic device, It is characterized in that include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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