A WRF sea temperature noise removal method, system, device and medium

Abstract

The present invention relates to the field of electronic digital data processing technology, and provides a WRF sea temperature noise removal method, system, device, and medium. The method comprises the following steps: obtaining initial field data and public meteorological files required for the WRF model; preprocessing the initial field data to obtain an initial field file; performing initial sea temperature correction on the initial field file to obtain an initial temperature field; using a multi-scale filtering model to perform physical simulation on the initial temperature field to obtain a simulation result; optimizing the multi-scale filtering model based on the comparison between the public meteorological file and the simulation result to obtain an optimized model; and inputting the data to be noise-removed into the optimized model to obtain a noise-removed result. The present invention achieves filtering of sea temperature by designing a multi-scale filtering model to smooth small-scale noise, retain large-scale structure, and avoid blurring important ocean features such as fronts and vortices.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and provides a WRF sea temperature noise removal method, system, device and medium. Background Art

[0002] The WRF model (Weather Research and Forecasting) plays a vital role in weather and climate research. As an advanced numerical weather forecasting model, it simulates the dynamics, physics, and chemistry of the atmosphere. It is used to provide short-term (hours to days) weather forecasts, helping people and relevant departments to issue early warnings and take countermeasures to meteorological disasters such as heavy rain, storms, typhoons, and tornadoes, thereby reducing losses. The WRF model can also be used in climate research. By simulating and predicting long-term climate change, it reveals the dynamics and patterns of change in the climate system, providing a valuable reference for understanding the causes and trends of climate change.

[0003] However, WRF simulation results are significantly affected by initial sea surface temperature noise, particularly for short-term marine weather forecasts. Even small changes in sea surface temperature can significantly impact the model's forecast results and accuracy. In practical research, WRF sometimes needs to be customized for specific applications. For example, some research projects focus on the impact of local sea temperatures on atmospheric conditions. In such cases, filtering the initial sea surface temperature noise may be necessary to meet specific simulation and forecast requirements. Summary of the Invention

[0004] The present invention aims to address at least one of the technical problems existing in the related art. To this end, the present invention provides a WRF sea temperature noise removal method, system, device, and medium for filtering sea temperature to smooth small-scale noise, preserve large-scale structure, and avoid blurring important ocean features such as fronts and eddies.

[0005] The present invention provides a WRF sea temperature noise removal method, comprising:

[0006] S1: Obtain the initial field data and public meteorological files required by the WRF model;

[0007] S2: Preprocessing the initial field data to obtain an initial field file;

[0008] S3: performing initial sea temperature correction on the initial field file to obtain an initial temperature field;

[0009] S4: using a multi-scale filtering model to perform physical simulation on the initial temperature field to obtain a simulation result;

[0010] S5: Based on the comparison between the public meteorological documents and the simulation results, the multi-scale filtering model is optimized to obtain the optimized model;

[0011] S6: Input the data to be noise-removed into the optimized model to obtain the noise-removed result.

[0012] According to a WRF sea temperature noise removal method provided by the present invention, step S2 includes:

[0013] S21: Arrange the collected initial field data to obtain a length of The original initial field data , ,in, For the The original initial field data, is the original initial field data sequence number, ;

[0014] S22: filtering out the original initial field data whose length is less than the length threshold;

[0015] Calculate the Z score of the original initial field data. The Z score calculation formula is:

[0016]

[0017] in, is the Z score, is the average value of the original initial field data, is the standard deviation of the original initial field data,

[0018] Calculate the proportion of the original initial field data whose Z score is greater than the Z score threshold :

[0019]

[0020] in, is the number of original initial field data whose Z score is greater than the Z score threshold;

[0021] like Greater than the Z-score ratio threshold, The corresponding original initial field data is removed. If Less than or equal to the Z score ratio threshold, The corresponding original initial field data is retained.

[0022] Obtaining the filtered original initial field data;

[0023] S23: Filling missing values ​​in the filtered original initial field data using the SAITS model to obtain the initial field file.

[0024] According to a WRF sea temperature noise removal method provided by the present invention, step S3 includes:

[0025] S31: Downsampling the initial field file to obtain a coarse-resolution temperature field:

[0026]

[0027] in, is the coarse resolution temperature field, is the downsampling factor, is the horizontal coordinate of the initial field file, is the ordinate of the initial field file, are the horizontal and vertical coordinates corresponding to the initial field file;

[0028] S32: Perform Gaussian filtering on the coarse-resolution temperature field to obtain a smooth field:

[0029]

[0030]

[0031] in, is a smooth field, is the convolution operation, is the Gaussian filter function, is the horizontal coordinate offset within the Gaussian kernel, is the vertical coordinate offset within the Gaussian kernel, is the standard deviation of the Gaussian kernel;

[0032] S33: Perform sliding average filtering on the smooth field to obtain an initial temperature field:

[0033]

[0034]

[0035] in, is the initial temperature field, is the abscissa of the initial temperature field, is the vertical coordinate of the initial temperature field, is the sliding average function, is the horizontal sliding half width of the sliding average window, The vertical sliding half width of the sliding average window.

[0036] According to a WRF sea temperature noise removal method provided by the present invention, step S4 includes:

[0037] S41: Smoothing the initial temperature field using a multi-scale filtering model to obtain a smooth output;

[0038] S42: Using a multi-scale filtering model on the edge of the smoothed output data to obtain a detail simulation result;

[0039] S43: combining the smoothed output and the detailed simulation result to obtain a simulation result.

[0040] According to a WRF sea temperature noise removal method provided by the present invention, step S41 includes:

[0041] S411: Calculate the gradient structure preservation factor of the initial temperature field:

[0042]

[0043] in, is the gradient structure preservation factor, is the local gradient of the initial temperature field, is a parameter for adjusting gradient sensitivity;

[0044] S412: Calculate spatial weight window:

[0045]

[0046] in, is the spatial weight window, is the local neighborhood horizontal coordinate, is the local neighborhood ordinate, is the local neighborhood window radius control parameter;

[0047] S413: Filter the initial temperature field to obtain a smooth output:

[0048]

[0049] in, For smooth output, is a local neighborhood.

[0050] According to a WRF sea temperature noise removal method provided by the present invention, step S42 includes:

[0051] S421: Interpolate the nearshore area or data edge of the initial temperature field to obtain the interpolated initial temperature field , where the nearshore area is the area in the same grid as the coast, and the data edge is the grid where the local gradient of the initial temperature field is greater than the threshold;

[0052] S422: Calculate the gradient structure preservation factor of the interpolated initial temperature field:

[0053]

[0054] in, is the gradient structure preservation factor;

[0055] S423: Calculating the spatial weight window of the interpolated initial temperature field;

[0056] S424: Filtering is performed based on the interpolated initial temperature field to obtain detailed simulation results:

[0057]

[0058] in, Simulation results for details.

[0059] According to a WRF sea temperature noise removal method provided by the present invention, step S5 includes:

[0060] S51: Calculate the root mean square error between the public meteorological files and the simulation results:

[0061]

[0062] in, is the root mean square error between the public meteorological files and the simulation results, For the Public weather data files, is the data ordinal number, , is the total number of data, for Corresponding simulation results;

[0063] S52: Calculate the average deviation between public meteorological files and simulation results :

[0064] ;

[0065] S53: Optimizing the multi-scale filtering model so that the average deviation and the root mean square error are minimized to obtain an optimized model.

[0066] The present invention also provides a WRF sea temperature noise removal model, comprising:

[0067] Data acquisition module: obtains the initial field data and public meteorological files required by the WRF model;

[0068] Data processing module: pre-processing the initial field data to obtain an initial field file;

[0069] Data correction module: performs initial sea temperature correction on the initial field file to obtain an initial temperature field;

[0070] Initial simulation prediction module: using a multi-scale filtering model to perform physical simulation on the initial temperature field to obtain simulation results;

[0071] Model optimization module: Based on the comparison between public meteorological documents and simulation results, the multi-scale filtering model is optimized to obtain the optimized model;

[0072] Simulation prediction module: input the data to be noise-removed into the optimized model to obtain the noise-removed results.

[0073] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of any one of the above-described methods for removing WRF sea temperature noise are implemented.

[0074] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of any one of the above-mentioned methods for removing WRF sea temperature noise are implemented.

[0075] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0076] This invention provides a method, system, device, and medium for removing WRF sea temperature noise. By designing a multiscale filtering model, it filters sea temperature to smooth small-scale noise, preserve large-scale structure, and avoid blurring important ocean features such as fronts and eddies. The simulation results are then post-processed and analyzed to generate the desired output products. This may include statistical analysis, visualization, and mapping of meteorological elements such as temperature, wind, and precipitation for further research and application.

[0077] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0079] Figure 1 This is a flow chart of a WRF sea temperature noise removal method provided by the present invention.

[0080] Figure 2 3 is a comparison diagram before and after filtering according to an embodiment of the present invention.

[0081] Figure 3 This is a structural block diagram of a WRF sea temperature noise removal device provided by the present invention.

[0082] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention.

[0083] Reference numerals:

[0084] 101. Data acquisition module; 102. Data processing module; 103. Data correction module; 104. Initial simulation prediction module; 105. Model optimization module; 106. Simulation prediction module; 810. Processor; 820. Communication interface; 830. Memory; 840. Communication bus. DETAILED DESCRIPTION

[0085] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0086] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean 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 embodiment of the present invention. 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 can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0087] The following combination Figures 1 to 4 The present invention is described.

[0088] Example

[0089] like Figure 1 As shown, Figure 1 The present invention provides a flow chart of a WRF sea temperature noise removal method, comprising the following steps:

[0090] S1: Obtain the initial field data and public meteorological files required by the WRF model;

[0091] S2: Preprocessing the initial field data to obtain an initial field file;

[0092] S3: performing initial sea temperature correction on the initial field file to obtain an initial temperature field;

[0093] S4: using a multi-scale filtering model to perform physical simulation on the initial temperature field to obtain a simulation result;

[0094] S5: Based on the comparison between the public meteorological documents and the simulation results, the multi-scale filtering model is optimized to obtain the optimized model;

[0095] S6: Input the data to be noise-removed into the optimized model to obtain the noise-removed result.

[0096] Specifically, the initial field data in step S1 includes sea surface temperature data (SST) and other relevant meteorological data.

[0097] Specifically, step S2 includes:

[0098] S21: Arrange the collected initial field data to obtain a length of The original initial field data , ,in, For the The original initial field data, is the original initial field data sequence number, ;

[0099] S22: filtering out the original initial field data whose length is less than the length threshold;

[0100] Calculate the Z score of the original initial field data. The Z score calculation formula is:

[0101]

[0102] in, is the Z score, is the average value of the original initial field data, is the standard deviation of the original initial field data,

[0103] Calculate the proportion of the original initial field data whose Z score is greater than the Z score threshold :

[0104]

[0105] in, is the number of original initial field data whose Z score is greater than the Z score threshold;

[0106] like Greater than the Z-score ratio threshold, The corresponding original initial field data is removed. If Less than or equal to the Z score ratio threshold, The corresponding original initial field data is retained.

[0107] Obtaining the filtered original initial field data;

[0108] S23: Filling missing values ​​in the filtered original initial field data using the SAITS model to obtain the initial field file.

[0109] Step S2 preprocesses the acquired initial field data, including data cleaning, interpolation, and spatial resolution adjustment, to ensure the quality and applicability of the data.

[0110] Specifically, step S3 includes:

[0111] S31: Downsampling the initial field file to obtain a coarse-resolution temperature field:

[0112]

[0113] in, is the coarse resolution temperature field, is the downsampling factor, is the horizontal coordinate number of the coarse resolution temperature field, is the ordinate number of the coarse resolution temperature field, is the horizontal coordinate of the initial field file, is the ordinate of the initial field file, In the embodiment of the present invention, the coarse resolution used is 1°, and the downsampling factor is the ratio of the coarse resolution to the resolution of the initial field file.

[0114] S32: Perform Gaussian filtering on the coarse-resolution temperature field to obtain a smooth field:

[0115]

[0116]

[0117] in, is a smooth field, is the convolution operation, is the Gaussian filter function, is the horizontal coordinate offset within the Gaussian kernel, is the vertical coordinate offset within the Gaussian kernel, is the standard deviation of the Gaussian kernel;

[0118] S33: Perform sliding average filtering on the smooth field to obtain an initial temperature field:

[0119]

[0120]

[0121] in, is the initial temperature field, is the abscissa of the initial temperature field, is the vertical coordinate of the initial temperature field, is the sliding average function, is the horizontal sliding half width of the sliding average window, The vertical sliding half width of the sliding average window.

[0122] Specifically, step S4 includes:

[0123] S41: Smoothing the initial temperature field using a multi-scale filtering model to obtain a smoothed output. Specifically, in step S41, a medium-resolution multi-scale filtering model is used for smoothing, retaining major structures such as vortices and fronts, so that the smoothed result has no actual errors.

[0124] S411: Calculate the gradient structure preservation factor of the initial temperature field:

[0125]

[0126] in, is the gradient structure preservation factor, is the local gradient of the initial temperature field, is a parameter for adjusting gradient sensitivity;

[0127] S412: Calculate spatial weight window:

[0128]

[0129] in, is the spatial weight window, is the local neighborhood horizontal coordinate, is the local neighborhood ordinate, is the local neighborhood window radius control parameter;

[0130] S413: Filter the initial temperature field to obtain a smooth output:

[0131]

[0132] in, For smooth output, is a local neighborhood.

[0133] S42: Using a multi-scale filtering model on the data edge of the smooth output to obtain a detail simulation result.

[0134] S421: Interpolate the nearshore area or data edge of the initial temperature field to obtain the interpolated initial temperature field , where the nearshore area is the area in the same grid as the coast, and the data edge is the grid where the local gradient of the initial temperature field is greater than the threshold;

[0135] S422: Calculate the gradient structure preservation factor of the interpolated initial temperature field:

[0136]

[0137] in, is the gradient structure preservation factor;

[0138] S423: Calculate the spatial weight window of the interpolated initial temperature field:

[0139]

[0140] S424: Filtering is performed based on the interpolated initial temperature field to obtain detailed simulation results:

[0141]

[0142] in, Simulation results for details.

[0143] S43: combining the smooth output and the detail simulation result to obtain a simulation result, wherein if the detail simulation result exists, the detail simulation result shall prevail; if the detail simulation result does not exist, the smooth output result shall prevail.

[0144] Specifically, step S5 includes:

[0145] S51: Calculate the root mean square error between the public meteorological files and the simulation results:

[0146]

[0147] in, is the root mean square error between the public meteorological files and the simulation results, For the Public weather data files, is the data ordinal number, , is the total number of data, for Corresponding simulation results;

[0148] S52: Calculate the average deviation between public meteorological files and simulation results :

[0149] ;

[0150] S53: Optimizing the multi-scale filtering model so that the average deviation and the root mean square error are minimized to obtain an optimized model.

[0151] Specifically, step S6 includes:

[0152] 1. Read NetCDF file list (*.nc);

[0153] 2. Open each file and extract the three variables SST, XLAT_M, and XLONG_M;

[0154] 3. The SST values ​​in some SST areas are subjected to multiple multi-scale weighted structure-preserving filtering using an optimized multi-scale filtering model;

[0155] 4. Write back the NetCDF file and close the file.

[0156] Next, determine the WRF model's operating parameters, including the simulation time range, time step, grid settings, and physical parameter settings. Run the WRF model using the modified initial condition and parameter files. The WRF model simulates atmospheric dynamics and physical processes based on the provided initial conditions and parameters and generates simulation results files.

[0157] Finally, the simulation results are post-processed and analyzed to generate the desired output products. This may include statistical analysis, visualization, and mapping of meteorological elements such as temperature, wind field, and precipitation for further research and application.

[0158] Specifically, such as Figure 2 As shown, Figure 2 (a) is the temperature data of a certain ocean surface before filtering. The data has temperature anomalies, especially the data in the black frame has errors. After filtering using the technical means proposed in this invention, the obtained Figure 2 In (b), data errors are eliminated.

[0159] like Figure 3 As shown, the present invention provides a WRF sea temperature noise removal model, including:

[0160] Data acquisition module 101: obtains the initial field data and public meteorological files required by the WRF model;

[0161] Data processing module 102: pre-processing the initial field data to obtain an initial field file;

[0162] Data correction module 103: performs initial sea temperature correction on the initial field file to obtain an initial temperature field;

[0163] Initial simulation prediction module 104: using a multi-scale filtering model to perform physical simulation on the initial temperature field to obtain simulation results;

[0164] Model optimization module 105: optimizing the multi-scale filtering model to obtain an optimized model based on comparison between public meteorological documents and simulation results;

[0165] Simulation prediction module 106: inputs the data to be noise-removed into the optimized model to obtain the noise-removed result.

[0166] Figure 4 An example of a physical structure diagram of an electronic device is shown below. Figure 4 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute a WRF sea temperature noise removal method, which includes:

[0167] S1: Obtain the initial field data and public meteorological files required by the WRF model;

[0168] S2: Preprocessing the initial field data to obtain an initial field file;

[0169] S3: performing initial sea temperature correction on the initial field file to obtain an initial temperature field;

[0170] S4: using a multi-scale filtering model to perform physical simulation on the initial temperature field to obtain a simulation result;

[0171] S5: Based on the comparison between the public meteorological documents and the simulation results, the multi-scale filtering model is optimized to obtain the optimized model;

[0172] S6: Input the data to be noise-removed into the optimized model to obtain the noise-removed result.

[0173] Furthermore, the logic instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0174] On the other hand, the present invention further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions. When the program instructions are executed by a computer, the computer is capable of performing a WRF sea temperature noise removal method provided by the above methods, the method comprising:

[0175] S1: Obtain the initial field data and public meteorological files required by the WRF model;

[0176] S2: Preprocessing the initial field data to obtain an initial field file;

[0177] S3: performing initial sea temperature correction on the initial field file to obtain an initial temperature field;

[0178] S4: using a multi-scale filtering model to perform physical simulation on the initial temperature field to obtain a simulation result;

[0179] S5: Based on the comparison between the public meteorological documents and the simulation results, the multi-scale filtering model is optimized to obtain the optimized model;

[0180] S6: Input the data to be noise-removed into the optimized model to obtain the noise-removed result.

[0181] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the WRF sea temperature noise removal method provided above, the method comprising:

[0182] S1: Obtain the initial field data and public meteorological files required by the WRF model;

[0183] S2: Preprocessing the initial field data to obtain an initial field file;

[0184] S3: performing initial sea temperature correction on the initial field file to obtain an initial temperature field;

[0185] S4: using a multi-scale filtering model to perform physical simulation on the initial temperature field to obtain a simulation result;

[0186] S5: Based on the comparison between the public meteorological documents and the simulation results, the multi-scale filtering model is optimized to obtain the optimized model;

[0187] S6: Input the data to be noise-removed into the optimized model to obtain the noise-removed result.

[0188] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0189] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0190] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

[0191] It should be noted that the embodiments of the present disclosure can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, such code is provided on a programmable memory or a data carrier such as an optical or electronic signal carrier.

[0192] In addition, although the operations of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that these operations must be performed in this particular order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the steps depicted in the flow chart can change the order of execution. Additionally or alternatively, certain steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps. It should also be noted that the features and functions of two or more devices according to the present disclosure can be embodied in one device. Conversely, the features and functions of a device described above can be further divided into being embodied by multiple devices.

[0193] Although the present disclosure has been described with reference to several specific embodiments, it should be understood that the present disclosure is not limited to the specific embodiments disclosed. The present disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A WRF sea temperature noise removal method, characterized in that: The following steps are involved: S1: Obtain the initial field data and public meteorological files required by the WRF model; S2: Preprocessing the initial field data to obtain an initial field file; S3: performing initial sea temperature correction on the initial field file to obtain an initial temperature field; S4: Using a multi-scale filtering model to perform physical simulation on the initial temperature field to obtain simulation results, including: S41: Smoothing the initial temperature field using a multi-scale filtering model to obtain a smooth output; Step S41 includes: S411: Calculate the gradient structure preservation factor of the initial temperature field: in, is the gradient structure preservation factor, is the local gradient of the initial temperature field, is the abscissa of the initial temperature field, is the ordinate of the initial temperature field, is a parameter for adjusting gradient sensitivity; S412: Calculate spatial weight window: in, is the spatial weight window, is the local neighborhood horizontal coordinate, is the local neighborhood ordinate, is the local neighborhood window radius control parameter; S413: Filter the initial temperature field to obtain a smooth output: in, For smooth output, is the initial temperature field, is a local neighborhood; S42: Using a multi-scale filtering model on the edge of the smoothed output data to obtain a detail simulation result; Step S42 includes: S421: Interpolate the nearshore area or data edge of the initial temperature field to obtain the interpolated initial temperature field , where the nearshore area is the area in the same grid as the coast, and the data edge is the grid where the local gradient of the initial temperature field is greater than the threshold; S422: Calculate the gradient structure preservation factor of the interpolated initial temperature field: in, is the gradient structure preservation factor; S423: Calculating the spatial weight window of the interpolated initial temperature field; S424: Filtering is performed based on the interpolated initial temperature field to obtain detailed simulation results: in, Simulate results for details; S43: combining the smoothed output and the detailed simulation result to obtain a simulation result; S5: Based on the comparison between the public meteorological documents and the simulation results, the multi-scale filtering model is optimized to obtain the optimized model; S6: Input the data to be noise-removed into the optimized model to obtain the noise-removed result.

2. A WRF sea temperature noise removal method according to claim 1, characterized in that: Step S2 includes: S21: Arrange the collected initial field data to obtain a length of The original initial field data , ,in, For the The original initial field data, is the original initial field data sequence number, ; S22: filtering out the original initial field data whose length is less than the length threshold; Calculate the Z score of the original initial field data. The Z score calculation formula is: in, is the Z score, is the average value of the original initial field data, is the standard deviation of the original initial field data, Calculate the proportion of the original initial field data whose Z score is greater than the Z score threshold : in, is the number of original initial field data whose Z score is greater than the Z score threshold; like Greater than the Z-score ratio threshold, The corresponding original initial field data is removed. If Less than or equal to the Z score ratio threshold, The corresponding original initial field data is retained. Obtaining the filtered original initial field data; S23: Filling missing values ​​in the filtered original initial field data using the SAITS model to obtain the initial field file.

3. A WRF sea temperature noise removal method according to claim 1, characterized in that: Step S3 includes: S31: Downsampling the initial field file to obtain a coarse-resolution temperature field: in, is the coarse resolution temperature field, is the downsampling factor, is the horizontal coordinate number of the coarse resolution temperature field, is the ordinate number of the coarse resolution temperature field, is the horizontal coordinate of the initial field file, is the ordinate of the initial field file, are the horizontal and vertical coordinates corresponding to the initial field file; S32: Perform Gaussian filtering on the coarse-resolution temperature field to obtain a smooth field: in, is a smooth field, is the convolution operation, is the Gaussian filter function, is the horizontal coordinate offset within the Gaussian kernel, is the vertical coordinate offset within the Gaussian kernel, is the standard deviation of the Gaussian kernel; S33: Perform sliding average filtering on the smooth field to obtain an initial temperature field: in, is the sliding average function, is the horizontal sliding half width of the sliding average window, The vertical sliding half width of the sliding average window.

4. The WRF sea temperature noise removal method according to claim 1, characterized in that: Step S5 includes: S51: Calculate the root mean square error between the public meteorological files and the simulation results: in, is the root mean square error between the public meteorological files and the simulation results, For the Public weather data files, is the data ordinal number, , is the total number of data, for Corresponding simulation results; S52: Calculate the average deviation between public meteorological files and simulation results : ； S53: Optimizing the multi-scale filtering model so that the average deviation and the root mean square error are minimized to obtain an optimized model.

5. A WRF sea temperature noise removal system, used to execute a WRF sea temperature noise removal method according to any one of claims 1 to 4, characterized in that: include: Data acquisition module: obtains the initial field data and public meteorological files required by the WRF model; Data processing module: pre-processing the initial field data to obtain an initial field file; Data correction module: performs initial sea temperature correction on the initial field file to obtain an initial temperature field; Initial simulation prediction module: using a multi-scale filtering model to perform physical simulation on the initial temperature field to obtain simulation results; Model optimization module: Based on the comparison between public meteorological documents and simulation results, the multi-scale filtering model is optimized to obtain the optimized model; Simulation prediction module: input the data to be noise-removed into the optimized model to obtain the noise-removed results.

6. 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 computer program, the steps of the WRF sea temperature noise removal method according to any one of claims 1 to 4 are implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the WRF sea temperature noise removal method according to any one of claims 1 to 4 are implemented.

Images