Mesoscale numerical weather prediction method with 3-kilometer resolution for meteorological support of China Southern Power Grid

Abstract

This application provides a 3-kilometer resolution mesoscale numerical weather prediction method for meteorological support in the Southern Power Grid. The method involves acquiring regional observation data of the target area to be predicted; extracting wind direction calculation data and cloud computing data for analysis and assimilation from wind direction observation data and cloud observation data, respectively; converting the wind direction calculation data and cloud computing data into wind direction grid data and cloud grid data, respectively, with reference to historical observation data; analyzing and assimilating the wind direction grid data and cloud grid data to obtain wind direction prediction parameters and cloud prediction parameters; adjusting the environmental parameters in the meteorological forecast equation derived from the three-dimensional static reference atmosphere using the cloud prediction parameters to obtain the adjusted target forecast equation; and substituting the wind direction prediction parameters into the adjusted target forecast equation to obtain the meteorological forecast result for the target area. This method can accurately determine the forecast results for the meteorological conditions within the target area, based on the actual situation of the target area.

Description

Technical Field

[0001] This application relates to the field of meteorological forecasting technology, and in particular to a 3-kilometer resolution mesoscale numerical forecasting method for meteorological support of the Southern Power Grid. Background Technology

[0002] In numerical weather prediction, the horizontal resolution of global models at major international operational centers has reached approximately 10 kilometers, while regional high-resolution models have a resolution of 1–3 kilometers. Regional high-resolution models often employ rapid cycle strategies, incorporating high-frequency, high-spatial-density observations to rapidly forecast and warn of small- and medium-scale weather events.

[0003] Currently, the power grid has not established a dedicated numerical weather forecasting system for the target area. When there is a need for weather forecasting in a specific area, the forecast results obtained are all provided by the meteorological department. The forecast area, core indicators, core technologies, and service targets of these forecast results have not been tailored to the needs of the target area. Therefore, the weather forecast results for the target area are inaccurate. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a 3-kilometer resolution mesoscale numerical forecasting method for meteorological support of China Southern Power Grid, which can more accurately predict meteorological results within the target area based on the actual situation of the target area to be forecasted.

[0005] This application provides a forecasting method for 3-kilometer resolution mesoscale numerical weather prediction for meteorological support in the Southern Power Grid. The forecasting method includes:

[0006] Acquire regional observation data involving the target area to be forecasted; wherein, the regional observation data includes wind direction observation data and cloud observation data; the target area to be forecasted is the area involving the Southern Power Grid that is subject to mesoscale weather events;

[0007] Wind direction calculation data and cloud computing data for analysis and assimilation are extracted from the wind direction observation data and the cloud observation data, respectively.

[0008] Based on historical observation data, the wind direction calculation data and the cloud computing data are converted into wind direction grid data and cloud grid data, respectively; wherein, the grid resolution corresponding to each grid data is 3 kilometers;

[0009] The wind direction grid data and the cloud grid data are analyzed and assimilated to obtain wind direction prediction parameters and cloud prediction parameters, respectively.

[0010] By adjusting the environmental parameters in the meteorological forecast equation derived from the three-dimensional static reference atmosphere using the cloud prediction parameters, the adjusted target forecast equation is obtained.

[0011] Substituting the wind direction prediction parameters into the adjusted target forecast equation, the meteorological forecast results for the target area to be forecasted are obtained.

[0012] In one possible implementation, the environmental parameters include humidity parameters and water vapor parameters; the step of adjusting the environmental parameters in the meteorological forecast equation derived from the three-dimensional static reference atmosphere using the cloud prediction parameters to obtain the adjusted target forecast equation includes:

[0013] The humidity parameter in the target forecast equation is adjusted using the cloud prediction parameters to obtain the first forecast equation;

[0014] The water vapor parameters in the first forecast equation are then adjusted using the cloud prediction parameters to obtain the adjusted target forecast equation.

[0015] In one possible implementation, the wind direction grid data is analyzed and assimilated through the following steps to obtain the wind direction prediction parameters:

[0016] Based on the regional observation data and the background data of the target area to be predicted, the wind direction update parameters of the target area to be predicted are determined;

[0017] The forecast field model is updated using the wind direction update parameters to obtain the updated forecast field model;

[0018] The updated forecast field model is used to analyze and assimilate the wind direction grid data to obtain wind direction prediction parameters.

[0019] In one possible implementation, the cloud grid data is analyzed and assimilated through the following steps to obtain the cloud prediction parameters:

[0020] Read radar reflectivity data;

[0021] Using the radar reflectivity and the cloud grid data, the background field cloud amount is calculated using an empirical formula to determine the initial grid cloud amount corresponding to the cloud grid data and the background field cloud amount of the target area to be predicted.

[0022] Based on the cloud thickness, air temperature, and static stability in the cloud grid data, the cloud type is determined;

[0023] Based on the background cloud cover and the cloud type, determine the cloud water content and cloud ice content of the target area to be forecasted;

[0024] Based on the radar reflectivity observations, cloud water volume, and cloud ice volume, the cloud prediction parameters are determined using the empirical relationship between precipitation particles.

[0025] In one possible implementation, the reference historical observation data is used to convert the wind direction calculation data and the cloud computing data into wind direction grid data and cloud grid data, respectively, including:

[0026] Referring to the presentation format of the historical observation data, the wind direction calculation data and the cloud computing data are subjected to data coordinate transformation and grid interpolation processing respectively to obtain the wind direction grid data and the cloud grid data.

[0027] In one possible implementation, the meteorological forecasting equations derived from a three-dimensional static reference atmosphere are combined through the following steps:

[0028] Obtain the basic forecast equation set under the GRAPES model; wherein the basic forecast equation set includes multiple forecast sub-equations;

[0029] Using the three-dimensional static reference atmosphere, the atmospheric parameters in the basic prediction equations are divided into a static equilibrium part and a deviation part to obtain the divided atmospheric parameters.

[0030] Substituting the divided atmospheric parameters into each forecast sub-equation of the basic forecast equation set yields the over-forecast equation set; wherein the over-forecast equation set includes multiple over-forecast sub-equations;

[0031] Each transitional sub-equation is linearized and separated into linear and nonlinear terms.

[0032] For each transitional sub-equation, which includes both linear and nonlinear terms, the Helmholtz equation for implicit solution is derived using elimination, and the implicitly solved Helmholtz equation is determined as the weather forecast equation.

[0033] In one possible implementation, the regional observation data includes one or more of the following: geopotential height, temperature, wind direction, vorticity, divergence, relative humidity, surface temperature, sea level pressure, and surface pressure of the target area to be predicted.

[0034] This application also provides a forecasting device for 3-kilometer resolution mesoscale numerical weather prediction for meteorological support of the Southern Power Grid, the forecasting device comprising:

[0035] The data acquisition module is used to acquire regional observation data related to the target area to be forecasted; wherein, the regional observation data includes wind direction observation data and cloud observation data; the target area to be forecasted is the area covered by the Southern Power Grid that is subject to mesoscale weather events;

[0036] The data extraction module is used to extract wind direction calculation data and cloud computing data for analysis and assimilation processing from the wind direction observation data and the cloud observation data, respectively.

[0037] The gridding processing module is used to convert the wind direction calculation data and the cloud computing data into wind direction grid data and cloud grid data respectively, with reference to historical observation data; wherein, the grid resolution corresponding to each grid data is 3 kilometers;

[0038] The analysis and assimilation processing module is used to analyze and assimilate the wind direction grid data and the cloud grid data respectively to obtain wind direction prediction parameters and cloud prediction parameters.

[0039] The equation adjustment module is used to adjust the environmental parameters in the meteorological forecast equation derived by combining the cloud prediction parameters with the three-dimensional static reference atmosphere, so as to obtain the adjusted target forecast equation.

[0040] The result forecast module is used to substitute the wind direction prediction parameters into the adjusted target forecast equation to obtain the meteorological forecast result for the target area to be forecasted.

[0041] In one possible implementation, the environmental parameters include humidity parameters and water vapor parameters; when the equation adjustment module adjusts the environmental parameters in the meteorological forecast equation derived from the three-dimensional static reference atmosphere using the cloud prediction parameters to obtain the adjusted target forecast equation, the equation adjustment module is used to:

[0042] The humidity parameter in the target forecast equation is adjusted using the cloud prediction parameters to obtain the first forecast equation;

[0043] The water vapor parameters in the first forecast equation are then adjusted using the cloud prediction parameters to obtain the adjusted target forecast equation.

[0044] In one possible implementation, the analysis and assimilation module is used to analyze and assimilate the wind direction grid data through the following steps to obtain the wind direction prediction parameters:

[0045] Based on the regional observation data and the background data of the target area to be predicted, the wind direction update parameters of the target area to be predicted are determined;

[0046] The forecast field model is updated using the wind direction update parameters to obtain the updated forecast field model;

[0047] The updated forecast field model is used to analyze and assimilate the wind direction grid data to obtain wind direction prediction parameters.

[0048] In one possible implementation, the analysis and assimilation module is used to analyze and assimilate the cloud grid data through the following steps to obtain the cloud prediction parameters:

[0049] Read radar reflectivity data;

[0050] Using the radar reflectivity and the cloud grid data, the background field cloud amount is calculated using an empirical formula to determine the initial grid cloud amount corresponding to the cloud grid data and the background field cloud amount of the target area to be predicted.

[0051] Based on the cloud thickness, air temperature, and static stability in the cloud grid data, the cloud type is determined;

[0052] Based on the background cloud cover and the cloud type, determine the cloud water content and cloud ice content of the target area to be forecasted;

[0053] Based on the radar reflectivity observations, cloud water volume, and cloud ice volume, the cloud prediction parameters are determined using the empirical relationship between precipitation particles.

[0054] In one possible implementation, when the gridding processing module is used to convert the wind direction calculation data and the cloud computing data into wind direction grid data and cloud grid data respectively, with reference to historical observation data, the gridding processing module is used to:

[0055] Referring to the presentation format of the historical observation data, the wind direction calculation data and the cloud computing data are subjected to data coordinate transformation and grid interpolation processing respectively to obtain the wind direction grid data and the cloud grid data.

[0056] In one possible implementation, the forecasting device further includes an equation derivation module, which is used to derive meteorological forecasting equations by combining a three-dimensional static reference atmosphere through the following steps:

[0057] Obtain the basic forecast equation set under the GRAPES model; wherein the basic forecast equation set includes multiple forecast sub-equations;

[0058] Using the three-dimensional static reference atmosphere, the atmospheric parameters in the basic prediction equations are divided into a static equilibrium part and a deviation part to obtain the divided atmospheric parameters.

[0059] Substituting the divided atmospheric parameters into each forecast sub-equation of the basic forecast equation set yields the over-forecast equation set; wherein the over-forecast equation set includes multiple over-forecast sub-equations;

[0060] Each transitional sub-equation is linearized and separated into linear and nonlinear terms.

[0061] For each transitional sub-equation, which includes both linear and nonlinear terms, the Helmholtz equation for implicit solution is derived using elimination, and the implicitly solved Helmholtz equation is determined as the weather forecast equation.

[0062] In one possible implementation, the regional observation data includes one or more of the following: geopotential height, temperature, wind direction, vorticity, divergence, relative humidity, surface temperature, sea level pressure, and surface pressure of the target area to be predicted.

[0063] This application embodiment also provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the prediction method described above are performed.

[0064] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the prediction method described above.

[0065] The method for forecasting 3-kilometer resolution mesoscale numerical weather data for meteorological support of the Southern Power Grid provided in this application embodiment acquires regional observation data involving the target area to be forecasted. The regional observation data includes wind direction observation data and cloud observation data. The target area to be forecasted is an area within the Southern Power Grid that experiences mesoscale weather events. Wind direction calculation data and cloud computing data for analysis and assimilation are extracted from the wind direction observation data and the cloud observation data, respectively. Referring to historical observation data, the wind direction calculation data and the cloud computing data are converted into wind direction grid data and cloud grid data, respectively. Each grid data corresponds to a grid resolution of 3 kilometers. The wind direction grid data and cloud grid data are analyzed and assimilated to obtain wind direction prediction parameters and cloud prediction parameters. The environmental parameters in the meteorological forecast equation derived from a three-dimensional static reference atmosphere are adjusted using the cloud prediction parameters to obtain an adjusted target forecast equation. The wind direction prediction parameters are substituted into the adjusted target forecast equation to obtain the meteorological forecast result for the target area to be forecasted. Here, based on the actual situation of the target area to be forecasted, the forecast results of meteorological results within the target area can be obtained more accurately.

[0066] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0067] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 A flowchart illustrating a 3-kilometer resolution mesoscale numerical forecasting method for meteorological support of China Southern Power Grid, provided as an embodiment of this application;

[0069] Figure 2 This is a schematic diagram illustrating the analysis and assimilation process of cloud grid data provided in an embodiment of this application;

[0070] Figure 3 One of the structural schematic diagrams of a forecasting device for 3-kilometer resolution mesoscale numerical weather prediction for China Southern Power Grid, provided as an embodiment of this application;

[0071] Figure 4 The second schematic diagram of a forecasting device for 3-kilometer resolution mesoscale numerical weather prediction for China Southern Power Grid, provided as an embodiment of this application;

[0072] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0074] Research has revealed that the power grid has not yet established a dedicated numerical weather prediction system for the target area. When there is a need for weather forecasting in a specific area, the forecast results obtained are all provided by the meteorological department. The forecast area, core indicators, core technologies, and service targets of these forecast results have not been tailored to the needs of the target area, which leads to inaccurate weather forecast results for the target area.

[0075] Based on this, this application provides a forecasting method for 3-kilometer resolution mesoscale values ​​for meteorological support of the Southern Power Grid, which can improve the accuracy of meteorological forecast results for the target area.

[0076] Please see Figure 1 , Figure 1 This is a flowchart illustrating a 3-kilometer resolution mesoscale numerical forecasting method for meteorological support of the Southern Power Grid, provided as an embodiment of this application. Figure 1 As shown in the embodiments of this application, the forecasting method includes:

[0077] S101. Obtain regional observation data related to the target area to be predicted; wherein, the regional observation data includes wind direction observation data and cloud observation data.

[0078] S102. Extract wind direction calculation data and cloud computing data for analysis and assimilation processing from the wind direction observation data and the cloud observation data, respectively.

[0079] S103. Referring to historical observation data, the wind direction calculation data and the cloud computing data are converted into wind direction grid data and cloud grid data, respectively.

[0080] S104. Analyze and assimilate the wind direction grid data and the cloud grid data respectively to obtain wind direction prediction parameters and cloud prediction parameters.

[0081] S105. Using the cloud prediction parameters, adjust the environmental parameters in the meteorological forecast equation derived from the three-dimensional static reference atmosphere to obtain the adjusted target forecast equation.

[0082] S106. Substitute the wind direction prediction parameters into the adjusted target forecast equation to obtain the meteorological forecast results for the target area to be forecasted.

[0083] This application provides a forecasting method for 3-kilometer resolution mesoscale numerical weather prediction for meteorological support in the Southern Power Grid. When a target area requires meteorological forecasting, regional observation data of the target area to be forecasted is acquired. This regional observation data includes wind direction observation data and cloud observation data, which influence the meteorological forecast results. Wind direction calculation data and cloud computing data for analysis and assimilation are extracted from the wind direction observation data and cloud observation data, respectively. To refine the scope of the meteorological forecast results, the wind direction calculation data and cloud computing data are gridded based on historical observation data to obtain wind direction grid data and cloud grid data. Based on this, the obtained wind direction grid data and cloud grid data are analyzed and assimilated to obtain wind direction prediction parameters and cloud prediction parameters. The environmental parameters in the meteorological forecasting equation derived from a three-dimensional static reference atmosphere are adjusted using the cloud prediction parameters to obtain an adjusted target forecasting equation. The wind direction prediction parameters are substituted into the target forecasting equation to obtain the meteorological forecast results for the target area to be forecasted. In this way, when there is a need for weather forecasting in the target area, the forecast results of the weather in the target area can be more accurately obtained based on the actual situation in the target area.

[0084] In step S101, when the target area to be forecasted has a meteorological forecasting requirement, regional observation data involving the target area to be forecasted are acquired.

[0085] Here, the target area to be forecasted is the southern region. When making a weather forecast for the target area, the factors that affect the weather in the target area may include wind factors, cloud factors, and terrain factors. Therefore, the regional observation data includes wind direction observation data, cloud observation data, and terrain observation parameters.

[0086] Specifically, the regional observation data may include one or more of the following: geopotential height, temperature, wind direction, vorticity, divergence, relative humidity, surface temperature, sea level pressure, and surface pressure of the target area.

[0087] The target areas to be forecasted may include areas in the southern region where mesoscale weather events occur. The southern region where the Southern Power Grid is located has significant topographic differences and a large range of altitude variations. The slope aspect and gradient of mountainous areas such as the Yunnan-Guizhou Plateau are complex. The urbanization process in the Guangdong-Hong Kong-Macao Greater Bay Area is accelerating, and land use changes are drastic. Therefore, when making weather forecasts for the southern region, it is necessary to combine the actual conditions of the southern region with the weather forecasts.

[0088] In order to make the forecast results more accurate when conducting weather forecasts in southern regions, it is necessary to analyze and assimilate the wind direction and cloud observation data obtained in the subsequent forecasting process. Therefore, it is also necessary to further extract the wind direction calculation data and cloud computing data that need to be analyzed and assimilated.

[0089] In step S102, wind direction calculation data and cloud computing data for analysis and assimilation are extracted from wind direction observation data and cloud observation data, respectively. Specifically, a data interface for a Fortran-developed assimilation system for receiving radial wind observation data from large-scale multi-type radars is established to realize the reception of wind direction observation data. The Fortran-developed assimilation system is used to establish the relationship between radial wind operators and mode variables, complete the addition of observation operators and the tangential and adjoint modes of observation operators, realize the preprocessing of wind direction observation data such as decoding, format processing and information extraction, and extract wind direction calculation data for analysis and assimilation from the wind direction observation data.

[0090] Similarly, for cloud observation data, the radar reflectivity data interface of the cloud analysis system is developed to receive cloud observation data; the radar assimilation application preprocessing system (RAPS) is used to preprocess the cloud observation data, such as decoding, quality control and data extraction, to extract cloud computing data for analysis and assimilation.

[0091] Furthermore, to ensure the accuracy of the forecast area, the wind direction calculation data and cloud computing data need to be gridded to ensure that the weather forecast results have a 3km resolution.

[0092] In step S103, referring to historical observation data, the wind direction calculation data and cloud computing data are transformed from polar coordinates to Cartesian coordinates on the model grid to obtain gridded wind direction grid data and cloud grid data.

[0093] Each grid data point corresponds to a grid resolution of 3 kilometers.

[0094] In one implementation, step S103 includes: referring to the presentation format of the historical observation data, performing data coordinate transformation processing and grid interpolation processing on the wind direction calculation data and the cloud computing data respectively to obtain the wind direction grid data and the cloud grid data.

[0095] In this step, the historical observation data is already gridded. Therefore, when performing gridding processing on the wind direction calculation data and cloud computing data, the presentation format of the historical observation data can be referenced. Data coordinate transformation and gridding interpolation processing are performed on the wind direction calculation data and cloud computing data respectively to complete the conversion of the data from polar coordinates to Cartesian coordinates on the pattern grid, so as to obtain wind direction grid data and cloud grid data.

[0096] In step S104, the wind direction grid data and cloud grid data are analyzed and assimilated to obtain the wind direction prediction parameters and cloud prediction parameters that affect the weather forecast results. In the actual processing, existing analysis and assimilation techniques can be referenced, which will not be elaborated here.

[0097] In one implementation, the wind direction grid data is analyzed and assimilated to obtain the wind direction prediction parameters through the following steps: based on the regional observation data and the background data of the target area to be predicted, the wind direction update parameters of the target area to be predicted are determined; the forecast field model is updated using the wind direction update parameters to obtain the updated forecast field model; the wind direction grid data is analyzed and assimilated using the updated forecast field model to obtain the wind direction prediction parameters.

[0098] In this step, firstly, using regional observation data and background data of the target area to be predicted, wind direction update parameters for adjusting the forecast field model parameters are determined; wherein, the forecast field model is used to analyze and assimilate historical wind direction grid data of the target area to be predicted at historical time points.

[0099] Then, in order to make the forecast field model suitable for analyzing and assimilating the target area to be forecasted at the current time point, the relevant parameters in the forecast field model are updated using wind direction update parameters to obtain the updated forecast field model.

[0100] Finally, the wind direction grid data is input into the updated forecast field model, and the updated forecast field model is used to analyze and assimilate the wind direction grid data to obtain the wind direction prediction parameters.

[0101] Here, the forecast field model is trained using multiple wind direction sample data and the prediction label corresponding to each wind direction sample data. For details, please refer to the training process of existing forecast field models, which will not be repeated here.

[0102] In one implementation, please refer to Figure 2 , Figure 2 This is a schematic diagram illustrating a cloud grid data analysis and assimilation process provided in an embodiment of this application. Figure 2 As shown, the cloud grid data is analyzed and assimilated through the following steps to obtain the cloud prediction parameters:

[0103] S201, Read radar reflectivity data.

[0104] S202. Using the radar reflectivity and the cloud grid data, calculate the background cloud amount using an empirical formula, and determine the initial grid cloud amount corresponding to the cloud grid data and the background cloud amount of the target area to be predicted.

[0105] In this step, based on radar reflectivity and cloud grid data, and combined with existing empirical formulas for determining background cloud amount, the initial grid point cloud amount corresponding to the cloud grid data and the background cloud amount of the target area to be predicted are determined. This step can be determined using any existing empirical formula for calculating cloud amount, and will not be elaborated here.

[0106] S203. Based on the cloud thickness, air temperature, and static stability in the cloud grid data, determine the cloud type.

[0107] In this step, existing methods for determining cloud types can be referenced, and parameters such as cloud thickness, air temperature, and static stability in the cloud grid data can be used to determine the cloud type of the clouds in the target area to be forecasted.

[0108] S204. Based on the background cloud cover and the cloud type, determine the cloud water volume and cloud ice volume of the target area to be predicted.

[0109] In this step, based on determining the background cloud cover and cloud type of the target area to be forecasted, the existing methods for calculating cloud water volume and cloud ice volume are used to determine the cloud water volume and cloud ice volume within the target area to be forecasted, which will not be elaborated here.

[0110] S205. Based on the radar reflectivity observation, cloud water volume, and cloud ice volume, the cloud prediction parameters are determined using the empirical formula for precipitation particles.

[0111] In this step, existing empirical formulas for determining precipitation particles are used to determine cloud prediction parameters for the target area to be predicted, based on radar reflectivity observations, cloud water volume, and cloud ice volume. Alternatively, a pre-trained cloud parameter prediction model can be used to determine the cloud prediction parameters for the target area to be predicted. The cloud parameter prediction model can be any machine learning model.

[0112] The cloud parameter prediction model can be obtained by training a machine learning model using multiple cloud sample data and the parameter labels corresponding to each cloud sample data. For details, please refer to the training process of existing pre-machine learning models, which will not be elaborated here.

[0113] In step S105, the environmental parameters in the meteorological forecast equation, which is derived from the three-dimensional static reference atmosphere and is applicable to the meteorological forecast of the target area, are adjusted using cloud prediction parameters to obtain the adjusted target forecast equation.

[0114] In one embodiment, the environmental parameters include humidity parameters and water vapor parameters; step S105 includes: adjusting the humidity parameter in the target forecast equation using the cloud prediction parameters to obtain a first forecast equation; and then adjusting the water vapor parameter in the first forecast equation using the cloud prediction parameters to obtain an adjusted target forecast equation.

[0115] In this step, the environmental parameters that can be skipped using cloud prediction parameters in the target forecast equation include humidity parameters and water vapor parameters. First, the humidity parameters in the target forecast equation are adjusted using cloud prediction parameters to obtain the first forecast equation. Then, based on the obtained first forecast equation, the water vapor parameters in the first forecast equation are adjusted again using cloud prediction parameters to obtain the adjusted target forecast equation.

[0116] Here, during the adjustment, the humidity parameter and water vapor parameter in the first forecast equation can be adjusted according to the parameter ratio between the cloud forecast parameters and the preset standard forecast parameters; or, the adjustment of the humidity parameter and water vapor parameter in the first forecast equation can be performed through specific calculation formulas (e.g., addition, subtraction, multiplication, division, etc.). The adjustment can be determined according to the actual situation and is not limited here.

[0117] In one implementation, the meteorological forecasting equations derived from a three-dimensional static reference atmosphere are combined through the following steps:

[0118] Step a: Obtain the basic forecast equation set under the GRAPES model; wherein the basic forecast equation set includes multiple forecast sub-equations.

[0119] In this step, at natural altitude, the basic prediction equations are as follows:

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] Among them, G x is the curvature term; a is the Earth's radius. (1) to (5) are the basic prediction equations of the GRAPES model. The definitions of each symbol can be found in the GRAPES model technical manual (Chen Dehui, Xue Jishan, et al., 2008).

[0128] Step b: Using the three-dimensional static reference atmosphere, the atmospheric parameters in the basic prediction equations are divided into a static equilibrium part and a deviation part to obtain the divided atmospheric parameters.

[0129] In this step, a three-dimensional static reference atmosphere can be used to divide the atmospheric parameters of each prediction sub-equation in the basic prediction equation set into a static equilibrium part and a bias part. The divided atmospheric parameters are as follows:

[0130]

[0131]

[0132] here, Π' represents the three-dimensional reference atmosphere that satisfies static equilibrium; Π' and θ' represent the deviations from the reference atmospheric state. The definitions of other symbols can also be found in the GRAPES model technical manual (Chen Dehui, Xue Jishan, et al., 2008), and will not be repeated here.

[0133] Step c: Substitute the divided atmospheric parameters into each forecast sub-equation of the basic forecast equation set to obtain the over-prediction equation set.

[0134] In this step, the divided atmospheric parameters are substituted back into each forecast sub-equation of the basic forecast equation set, and the reference atmosphere is applied to satisfy the static equilibrium relationship: The over-prediction equation set is obtained; wherein the over-prediction equation set includes multiple over-prediction sub-equations.

[0135] Substituting (8) to (9) into (1) to (5) respectively, and applying the static equilibrium relationship of the reference atmosphere, we obtain several transition equations:

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] The definitions of the symbols in the formula can also be found in the GRAPES pattern technical manual (Chen Dehui, Xue Jishan, et al., 2008), and will not be repeated here.

[0142] Step d: Perform linearization separation on each transition equation, dividing each transition equation into a linear term part and a nonlinear term part.

[0143] In this step, for each of the above transitional sub-equations, namely equations (10) to (14), each transitional sub-equation is linearly separated into linear terms and nonlinear terms.

[0144] Specifically, linearizing and separating the equations (10)-(14) yields:

[0145] Equations of motion: u equation (velocity equation of an object relative to a stationary frame of reference):

[0146]

[0147] v equation (relative velocity equation):

[0148]

[0149]

[0150] Continuity equation:

[0151]

[0152] Thermodynamic equation:

[0153]

[0154] Where L represents the linear term and N represents the nonlinear term, the definitions of other symbols can also be found in the GRAPES Model Technical Manual (Chen Dehui, Xue Jishan, et al., 2008), and will not be repeated here.

[0155] Here, through coordinate transformation and linearization based on the reference atmosphere, the linear and nonlinear terms of each transition equation are obtained:

[0156] The linear and nonlinear terms of the u equation are as follows:

[0157]

[0158]

[0159] Equations (20) to (21) are the linear and nonlinear terms of the u equation. Since the three-dimensional reference atmosphere is considered, the horizontal gradient term of the three-dimensional reference atmosphere is added compared with the one-dimensional reference atmosphere, as shown in the underlined parts of equations (20) to (21). The definitions of each symbol in the equation can also be found in the GRAPES model technical manual (Chen Dehui, Xue Jishan, et al., 2008), and will not be repeated here.

[0160] Here, the linear and nonlinear terms of equation v are similar to those of equation u. The linear and nonlinear terms of equation v are as follows:

[0161]

[0162]

[0163] The linear and nonlinear terms of equation w are as follows:

[0164]

[0165]

[0166] The linear and nonlinear terms of the thermodynamic equations are as follows:

[0167]

[0168]

[0169]

[0170]

[0171] Equations (26) to (29) are the linear and nonlinear terms of the thermodynamic equations. Due to the consideration of a three-dimensional reference atmosphere, the equations have added a horizontal advection term of the reference atmosphere compared to the original one-dimensional reference atmosphere scheme, such as the underlined parts in equations (227) and (28). The definitions of the symbols in the equations can also be found in the GRAPES model technical manual (Chen Dehui, Xue Jishan, et al., 2008), and will not be repeated here.

[0172] The linear and nonlinear terms of the continuity equation are as follows:

[0173]

[0174]

[0175] Step e: For each transitional sub-equation including linear and nonlinear terms, derive the Helmholtz equation for implicit solution using elimination, and determine the implicitly solved Helmholtz equation as the weather forecast equation.

[0176] In this step, for each transitional equation that includes both linear and nonlinear terms, the Helmholtz equation for implicit solution can be derived using elimination, resulting in:

[0177]

[0178]

[0179]

[0180]

[0181]

[0182] Equation (32) is used to solve for (Π'). n+1 The Helmholtz equation, u n+1 v n+1 , θ′ n+1 The equations are solved as shown in equations (33) to (36), where D3 is the three-dimensional divergence, and ξ... u1 ξ w1 ξ θ1 Coefficients of atmospheric parameters, ξ u0 ξ θ0 The explicit calculation part follows the coordinates based on the terrain height. The horizontal and vertical jump point grids are used. The vertical direction adopts the Charney-Philip jump layer setting, and the horizontal equidistant longitude-latitude grid adopts the Arakawa-C jump point. Equations (32) to (36) can be discretized and solved. First, (Π') is established. n+1 The Helmholtz equation is obtained, and then the solutions are obtained according to equations (33) to (36). The definitions of the symbols in the equations can also be found in the GRAPES Model Technical Manual (Chen Dehui, Xue Jishan, et al., 2008), and will not be repeated here.

[0183] Among them, the cloud prediction parameters are substituted into the formula derivation process using the elimination method to adjust the atmospheric parameter Π'; the cloud prediction parameters may include the speed of the cloud relative to the stationary object, the relative speed of the cloud, the angular velocity of the cloud, and the angle between the cloud and the horizontal direction, etc., which are used to adjust the formulas (33) to (36).

[0184] In step S106, the wind direction prediction parameters are substituted into the adjusted target forecast equation to obtain the meteorological forecast result for the target area to be forecasted.

[0185] In this step, the wind direction prediction parameters may include the wind speed relative to stationary objects, the wind relative speed, the wind angular velocity, and the angle between the wind and the horizontal direction. The wind direction prediction parameters are substituted into the adjusted target forecast equations (33) to (36) to realize the prediction of meteorological parameters such as speed, relative speed, angular velocity and angle. Then, based on the meteorological parameters, the meteorological forecast results of the target area to be predicted can be obtained by using the existing forecasting methods.

[0186] The method for forecasting 3-kilometer resolution mesoscale numerical weather data for meteorological support of the Southern Power Grid provided in this application embodiment acquires regional observation data involving the target area to be forecasted. The regional observation data includes wind direction observation data and cloud observation data. The target area to be forecasted is an area within the Southern Power Grid that experiences mesoscale weather events. Wind direction calculation data and cloud computing data for analysis and assimilation are extracted from the wind direction observation data and the cloud observation data, respectively. Referring to historical observation data, the wind direction calculation data and the cloud computing data are converted into wind direction grid data and cloud grid data, respectively. Each grid data corresponds to a grid resolution of 3 kilometers. The wind direction grid data and cloud grid data are analyzed and assimilated to obtain wind direction prediction parameters and cloud prediction parameters. The environmental parameters in the meteorological forecast equation derived from a three-dimensional static reference atmosphere are adjusted using the cloud prediction parameters to obtain an adjusted target forecast equation. The wind direction prediction parameters are substituted into the adjusted target forecast equation to obtain the meteorological forecast result for the target area to be forecasted. Here, based on the actual situation of the target area to be forecasted, the forecast results of meteorological results within the target area can be obtained more accurately.

[0187] Please see Figure 3 , Figure 4 , Figure 3 This is one of the structural schematic diagrams of a forecasting device with a 3-kilometer resolution mesoscale value for meteorological support of the Southern Power Grid, provided in an embodiment of this application. Figure 4 This is a second schematic diagram of a forecasting device for 3-kilometer resolution mesoscale numerical weather prediction for the Southern Power Grid, provided as an embodiment of this application. Figure 3 As shown, the forecasting device 300 includes:

[0188] The data acquisition module 310 is used to acquire regional observation data involving the target area to be forecasted; wherein, the regional observation data includes wind direction observation data and cloud observation data; the target area to be forecasted is the area involving the Southern Power Grid that is subject to mesoscale weather events;

[0189] The data extraction module 320 is used to extract wind direction calculation data and cloud computing data for analysis and assimilation processing from the wind direction observation data and the cloud observation data, respectively.

[0190] The grid processing module 330 is used to convert the wind direction calculation data and the cloud computing data into wind direction grid data and cloud grid data respectively, with reference to historical observation data; wherein, each grid data corresponds to a grid resolution of 3 kilometers;

[0191] The analysis and assimilation processing module 340 is used to analyze and assimilate the wind direction grid data and the cloud grid data respectively to obtain wind direction prediction parameters and cloud prediction parameters.

[0192] The equation adjustment module 350 is used to adjust the environmental parameters in the meteorological forecast equation derived by combining the cloud prediction parameters with the three-dimensional static reference atmosphere, so as to obtain the adjusted target forecast equation.

[0193] The result forecast module 360 ​​is used to substitute the wind direction prediction parameters into the adjusted target forecast equation to obtain the meteorological forecast result of the target area to be forecasted.

[0194] Furthermore, the environmental parameters include humidity parameters and water vapor parameters; when the equation adjustment module 350 is used to adjust the environmental parameters in the meteorological forecast equation derived from the three-dimensional static reference atmosphere using the cloud prediction parameters to obtain the adjusted target forecast equation, the equation adjustment module 350 is used to:

[0195] The humidity parameter in the target forecast equation is adjusted using the cloud prediction parameters to obtain the first forecast equation;

[0196] The water vapor parameters in the first forecast equation are then adjusted using the cloud prediction parameters to obtain the adjusted target forecast equation.

[0197] Furthermore, the analysis and assimilation processing module 340 is used to analyze and assimilate the wind direction grid data through the following steps to obtain the wind direction prediction parameters:

[0198] Based on the regional observation data and the background data of the target area to be predicted, the wind direction update parameters of the target area to be predicted are determined;

[0199] The forecast field model is updated using the wind direction update parameters to obtain the updated forecast field model;

[0200] The updated forecast field model is used to analyze and assimilate the wind direction grid data to obtain wind direction prediction parameters.

[0201] Furthermore, the analysis and assimilation processing module 340 is used to analyze and assimilate the cloud grid data through the following steps to obtain the cloud prediction parameters:

[0202] Read radar reflectivity data;

[0203] Using the radar reflectivity and the cloud grid data, the background field cloud amount is calculated using an empirical formula to determine the initial grid cloud amount corresponding to the cloud grid data and the background field cloud amount of the target area to be predicted.

[0204] Based on the cloud thickness, air temperature, and static stability in the cloud grid data, the cloud type is determined;

[0205] Based on the background cloud cover and the cloud type, determine the cloud water content and cloud ice content of the target area to be forecasted;

[0206] Based on the radar reflectivity observations, cloud water volume, and cloud ice volume, the cloud prediction parameters are determined using the empirical relationship between precipitation particles.

[0207] Furthermore, when the gridding processing module 330 is used to convert the wind direction calculation data and the cloud computing data into wind direction grid data and cloud grid data respectively, with reference to historical observation data, the gridding processing module 330 is used to:

[0208] Referring to the presentation format of the historical observation data, the wind direction calculation data and the cloud computing data are subjected to data coordinate transformation and grid interpolation processing respectively to obtain the wind direction grid data and the cloud grid data.

[0209] Furthermore, such as Figure 4 As shown, the forecasting device 300 further includes an equation derivation module 370, which is used to derive meteorological forecasting equations by combining the three-dimensional static reference atmosphere through the following steps:

[0210] Obtain the basic forecast equation set under the GRAPES model; wherein the basic forecast equation set includes multiple forecast sub-equations;

[0211] Using the three-dimensional static reference atmosphere, the atmospheric parameters in the basic prediction equations are divided into a static equilibrium part and a deviation part to obtain the divided atmospheric parameters.

[0212] Substituting the divided atmospheric parameters into each forecast sub-equation of the basic forecast equation set yields the over-forecast equation set; wherein the over-forecast equation set includes multiple over-forecast sub-equations;

[0213] Each transitional sub-equation is linearized and separated into linear and nonlinear terms.

[0214] For each transitional sub-equation, which includes both linear and nonlinear terms, the Helmholtz equation for implicit solution is derived using elimination, and the implicitly solved Helmholtz equation is determined as the weather forecast equation.

[0215] Furthermore, the regional observation data includes one or more of the following for the target area to be predicted: geopotential height, temperature, wind direction, vorticity, divergence, relative humidity, surface temperature, sea level pressure, and surface pressure.

[0216] The forecasting device provided in this application, which provides a 3-kilometer resolution mesoscale numerical forecast for meteorological support of the Southern Power Grid, acquires regional observation data related to the target area to be forecasted. The regional observation data includes wind direction observation data and cloud observation data. The target area to be forecasted is a region within the Southern Power Grid that experiences mesoscale weather events. Wind direction calculation data and cloud computing data for analysis and assimilation are extracted from the wind direction observation data and cloud observation data, respectively. Referring to historical observation data, the wind direction calculation data and cloud computing data are converted into wind direction grid data and cloud grid data, respectively. Each grid data corresponds to a grid resolution of 3 kilometers. The wind direction grid data and cloud grid data are analyzed and assimilated to obtain wind direction prediction parameters and cloud prediction parameters. The environmental parameters in the meteorological forecasting equation derived from a three-dimensional static reference atmosphere are adjusted using the cloud prediction parameters to obtain an adjusted target forecasting equation. The wind direction prediction parameters are substituted into the adjusted target forecasting equation to obtain the meteorological forecasting result for the target area to be forecasted. Here, based on the actual situation of the target area to be forecasted, the forecast results of meteorological results within the target area can be obtained more accurately.

[0217] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.

[0218] The memory 520 stores machine-readable instructions executable by the processor 510. When the electronic device 500 is running, the processor 510 and the memory 520 communicate via the bus 530. When the machine-readable instructions are executed by the processor 510, they can perform the operations described above. Figure 1 The steps of the forecasting method for 3-kilometer resolution mesoscale values ​​for meteorological support of China Southern Power Grid in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0219] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the forecasting method for 3-kilometer resolution mesoscale values ​​for meteorological support of China Southern Power Grid in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0220] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0221] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0222] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0223] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0224] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part 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 is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0225] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A forecasting method for 3-kilometer resolution mesoscale numerical weather for the Southern Grid, characterized in that, The prediction method comprises: acquiring regional observation data related to a target region to be predicted; wherein the regional observation data comprises wind direction observation data and cloud observation data; the target region to be predicted is a region related to the Southern Power Grid and having a mesoscale weather event; extracting wind direction calculation data and cloud calculation data for analysis and assimilation processing from the wind direction observation data and the cloud observation data respectively; converting the wind direction calculation data and the cloud calculation data into wind direction grid data and cloud grid data respectively with reference to historical observation data; wherein the grid resolution corresponding to each grid data is 3 kilometers; performing analysis and assimilation processing on the wind direction grid data and the cloud grid data respectively to obtain wind direction prediction parameters and cloud prediction parameters; adjusting environmental parameters in a meteorological prediction equation derived in combination with a three-dimensional static reference atmosphere by using the cloud prediction parameters to obtain an adjusted target prediction equation; the environmental parameters comprise humidity parameters and water vapor parameters; the adjusting of the environmental parameters in the meteorological prediction equation derived in combination with the three-dimensional static reference atmosphere by using the cloud prediction parameters to obtain the adjusted target prediction equation comprises: adjusting the humidity parameters in the target prediction equation by using the cloud prediction parameters to obtain a first prediction equation; and then adjusting the water vapor parameters in the first prediction equation by using the cloud prediction parameters to obtain the adjusted target prediction equation; substituting the wind direction prediction parameters into the adjusted target prediction equation to obtain a meteorological prediction result of the target region to be predicted; deriving a meteorological prediction equation in combination with a three-dimensional static reference atmosphere by the following steps: acquiring a basic prediction equation set under a GRAPES mode; wherein the basic prediction equation set comprises a plurality of prediction sub-equations; dividing atmospheric parameters in the basic prediction equation set into a static equilibrium part and a deviation part by using the three-dimensional static reference atmosphere to obtain divided atmospheric parameters; substituting the divided atmospheric parameters into each prediction sub-equation of the basic prediction equation set to obtain an over-prediction equation set; wherein the over-prediction equation set comprises a plurality of over-prediction sub-equations; performing linearization separation processing on each over-prediction sub-equation to divide each over-prediction sub-equation into a linear term part and a nonlinear term part; for each over-prediction sub-equation comprising a linear term part and a nonlinear term part, using an elimination method to derive a Helmholtz equation for implicit solution, and determining the Helmholtz equation for implicit solution as the meteorological prediction equation.

2. The prediction method of claim 1, wherein performing analysis and assimilation processing on the wind direction grid data to obtain the wind direction prediction parameters by the following steps: determining wind direction update parameters of the target region to be predicted based on the regional observation data and background data of the target region to be predicted; updating a prediction field model by using the wind direction update parameters to obtain an updated prediction field model; performing analysis and assimilation processing on the wind direction grid data by using the updated prediction field model to obtain wind direction prediction parameters.

3. The prediction method of claim 2, wherein performing analysis and assimilation processing on the cloud grid data to obtain the cloud prediction parameters by the following steps: reading radar reflectivity data; Using the radar reflectivity and the cloud grid data, a background field cloud amount is calculated by using an empirical relationship, an initial grid point cloud amount corresponding to the cloud grid data and a background field cloud amount of the target area to be predicted are determined; Based on the cloud thickness, air temperature and static stability in the cloud grid data, a cloud type is determined; Based on the background field cloud amount and the cloud type, a cloud water amount and a cloud ice amount of the target area to be predicted are determined; According to the radar reflectivity observation, the cloud water amount and the cloud ice amount, the cloud prediction parameter is determined by using a precipitation particle empirical relationship.

4. The prediction method of claim 1, wherein The reference historical observation data is used to convert the wind direction calculation data and the cloud calculation data into wind direction grid data and cloud grid data, including: Referring to the presentation form of the historical observation data, the wind direction calculation data and the cloud calculation data are respectively subjected to data coordinate conversion processing and gridding interpolation processing to obtain the wind direction grid data and the cloud grid data.

5. The prediction method of claim 1, wherein The regional observation data includes one or more of the potential height, temperature, wind direction, vorticity, divergence, relative humidity, surface temperature, sea level pressure and ground pressure of the target area to be predicted.

6. A device for forecasting 3-kilometer resolution mesoscale numerical weather for the Southern Grid, characterized in that, The prediction device includes: A data acquisition module is configured to acquire regional observation data related to a target area to be predicted, wherein the regional observation data includes wind direction observation data and cloud observation data, and the target area to be predicted is a region related to the Southern Power Grid and having a mesoscale weather event; A data extraction module is configured to extract wind direction calculation data and cloud calculation data for analysis and assimilation processing from the wind direction observation data and the cloud observation data, respectively; A gridding processing module is configured to convert the wind direction calculation data and the cloud calculation data into wind direction grid data and cloud grid data by referring to historical observation data, wherein the grid resolution of each grid data is 3 kilometers; An analysis and assimilation processing module is configured to perform analysis and assimilation processing on the wind direction grid data and the cloud grid data to obtain wind direction prediction parameters and cloud prediction parameters; An equation adjustment module is configured to adjust environmental parameters in a meteorological prediction equation derived in combination with a three-dimensional static reference atmosphere by using the cloud prediction parameters to obtain an adjusted target prediction equation; A result prediction module is configured to substitute the wind direction prediction parameters into the adjusted target prediction equation to obtain a meteorological prediction result of the target area to be predicted; The environmental parameters include humidity parameters and water vapor parameters; when the equation adjustment module is configured to adjust environmental parameters in a meteorological prediction equation derived in combination with a three-dimensional static reference atmosphere by using the cloud prediction parameters to obtain an adjusted target prediction equation, the equation adjustment module is configured to: Adjust humidity parameters in the target prediction equation by using the cloud prediction parameters to obtain a first prediction equation; Adjust water vapor parameters in the first prediction equation by using the cloud prediction parameters to obtain the adjusted target prediction equation; The prediction device further includes an equation derivation module, which is configured to derive a meteorological prediction equation in combination with a three-dimensional static reference atmosphere by the following steps: obtaining a basic prediction equation set in a GRAPES model; wherein the basic prediction equation set comprises a plurality of prediction sub-equations; using the three-dimensional hydrostatic reference atmosphere, dividing an atmospheric parameter in the basic prediction equation set into a hydrostatic equilibrium part and a deviation part, to obtain a divided atmospheric parameter; substituting the divided atmospheric parameter into each prediction sub-equation of the basic prediction equation set, to obtain an over-prediction equation set; wherein the over-prediction equation set comprises a plurality of over-prediction sub-equations; respectively performing linearization separation processing on each over-prediction sub-equation, to divide each over-prediction sub-equation into a linear term part and a nonlinear term part; for each over-prediction sub-equation comprising a linear term part and a nonlinear term part, using an elimination method to derive an implicit Helmholtz equation for implicit solving, and determining the implicit Helmholtz equation as the meteorological prediction equation.

7. An electronic device, comprising: comprising: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, the machine readable instructions are executed by the processor to perform the steps of the prediction method as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, the computer readable storage medium stores a computer program, the computer program is executed by the processor to perform the steps of the prediction method as claimed in any one of claims 1 to 5.

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