A multi-grid correction analysis method and device with physical constraints

Abstract

The application discloses a kind of multiple grid revision analysis method and device with physical constraint, through the original meteorological observation data of the region to be analyzed is converted to coarse grid background field;Select the first observation site on coarse grid, and obtain all second observation sites in its influence radius, based on wind pressure constraint relationship respectively calculate the first potential height observation value between each second observation site and the first observation site, distance and weight coefficient, by fitting distance and weight coefficient, obtain interpolation coefficient, for the interpolation processing of coarse grid background field, obtain the first analysis field of coarse grid, and it is as fine grid background field;By calculating the deviation of meteorological observation data and fine grid background field, the deviation is interpolated to fine grid background field, to revise fine grid background field, and according to the fine grid background field after revision, generate the second analysis field of fine grid, compared with prior art, the technical scheme of the application can improve the accuracy of meteorological data analysis.

Description

Technical Field

[0001] This invention relates to the technical field of meteorological data correction analysis, and in particular to a multigrid correction analysis method and apparatus with physical constraints. Background Technology

[0002] The successive correction method involves subtracting the background field from each observation to obtain the observation increment, analyzing the observation increment to obtain the analysis increment, and then adding the analysis increment to the background field to obtain the final analysis. The analysis increment at each analysis grid point is weighted by a linear combination of the observation increments in its surrounding influence area, with the observation weight inversely proportional to the distance between the observation location and the grid point. In the meteorological field, the Cressman interpolation algorithm is a commonly used successive correction analysis method that interpolates discrete observation station data to regular grid points. The successive correction analysis method has advantages such as low computational resources and high computation speed, and it is still widely used in the objective analysis of meteorological surface elements.

[0003] Existing stepwise correction objective analysis methods based on Cressman interpolation are purely mathematical methods that only consider spatial distance. The interpolation algorithm does not take into account the physical constraints of atmospheric dynamics and thermodynamic processes. Furthermore, existing stepwise correction analysis methods based on Cressman interpolation adopt a univariate analysis approach, calculating each meteorological element one by one. The analysis results cannot satisfy the balance relationship between meteorological elements.

[0004] Because atmospheric physical elements such as wind, temperature, pressure, and humidity have obvious multi-scale characteristics, there are obvious constraints on the large scale and very strong locality on the small scale, it is inappropriate to only consider the large-scale constraints or small-scale locality of meteorological elements without establishing a unified physical model in the interpolation algorithm to simulate the complex multi-scale constraints between meteorological elements. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a multi-grid correction analysis method and apparatus with physical constraints, which enables multi-scale meteorological information analysis by adding wind pressure constraint relationship to the interpolation algorithm, thereby improving the accuracy of meteorological data analysis.

[0006] To address the aforementioned technical problems, this invention provides a multigrid correction analysis method with physical constraints, comprising:

[0007] Obtain the original meteorological observation data of the area to be analyzed, and perform gridding processing on the original meteorological observation data to obtain a coarse grid background field;

[0008] Select the first observation station on the coarse grid, obtain all the second observation stations located within the influence radius of the first observation station, and calculate the first potential height observation value between each second observation station and the first observation station based on the wind pressure constraint relationship, so as to obtain the potential height field of the first observation station within the influence radius.

[0009] Based on the geopotential height field, the distance and weight coefficient between each second observation station and the first observation station are calculated, and the fitting relationship between the distance and the weight coefficient is calculated to obtain the interpolation coefficient;

[0010] Based on the interpolation coefficients, the coarse grid background field is interpolated to obtain the first analysis field of the coarse grid, and the first analysis field is linearly interpolated to obtain the fine grid background field of the fine grid.

[0011] Meteorological observation data of the fine grid is acquired, the deviation between the meteorological observation data and the background field of the fine grid is calculated, the deviation is interpolated to the background field of the fine grid based on the interpolation algorithm to correct the background field of the fine grid, and a second analysis field of the fine grid is generated based on the corrected background field of the fine grid.

[0012] In one possible implementation, the raw meteorological observation data of the area to be analyzed is acquired, and the raw meteorological observation data is gridded to obtain a coarse-grid background field, specifically including:

[0013] Obtain raw meteorological observation data of the area to be analyzed, wherein the raw meteorological observation data includes surface air pressure and 10-meter U and V winds;

[0014] The original meteorological observation data is gridded based on the nearest neighbor method to obtain a coarse grid with a preset resolution. The meteorological observation data of the numerical model is extracted from the coarse grid, and the background field of the coarse grid is obtained based on the meteorological observation data of the numerical model.

[0015] In one possible implementation, the first potential height observation value between each second observation station and the first observation station is calculated based on the wind pressure constraint relationship, thereby obtaining the potential height field of the first observation station within the influence radius, specifically including:

[0016] Set up and generate a formula for calculating geopotential height based on the geostrophic balance formula;

[0017] Obtain the geopotential height observation value of each second observation station, and obtain the u wind field observation value and v wind field observation value corresponding to each second observation station and the first observation station. Input the geopotential height observation value, the u wind field observation value and the v wind field observation value into the geopotential height calculation formula to obtain the first geopotential height observation value between each second observation station and the first observation station.

[0018] By integrating all the first potential height observations, the potential height field of the first observation station within the radius of influence is obtained.

[0019] In one possible implementation, the formula for calculating the potential height is as follows:

[0020]

[0021] in, The observed geopotential height is at the second observation station j. The first potential height observation value of the first observation station i is calculated from the second observation station j. i u j v i v j , respectively, are the u-wind field observation values ​​and v-wind field observation values ​​at the first observation station i and the second observation station j, x i x j y i y j These are the x and y coordinates of the first observation station i and the second observation station j, respectively.

[0022] In one possible implementation, based on the geopotential height field, the distance and weighting coefficient between each second observation station and the first observation station are calculated, and the fitting relationship between the distance and the weighting coefficient is calculated to obtain interpolation coefficients, specifically including:

[0023] Obtain the coordinate data corresponding to each second observation station and the first observation station, and calculate the distance between each second observation station and the first observation station based on the coordinate data;

[0024] Obtain the original geopotential height observation value of the first observation station, and based on the geopotential height field, compare each first geopotential height observation value in the geopotential height field with the original geopotential height observation value to obtain the weighting coefficient between each second observation station and the first observation station.

[0025] The distance and the weight coefficients are fitted to obtain a fitting relationship between the distance and the weight coefficients, and the interpolation coefficients are determined based on the fitting relationship.

[0026] In one possible implementation, the coarse-grid background field is interpolated based on the interpolation coefficients to obtain the first analysis field of the coarse-grid, specifically including:

[0027] Based on the interpolation coefficients, the original Cressman interpolation formula is updated to obtain the updated Cressman interpolation formula.

[0028] Based on the updated Cressman interpolation formula, the coarse grid background field is interpolated to progressively correct the coarse grid background field and obtain the first analysis field of the coarse grid.

[0029] In one possible implementation, the first analysis field is subjected to linear interpolation to obtain a fine-mesh background field, specifically including:

[0030] Based on the linear interpolation formula, the interpolation point coordinates of the first analysis field are calculated. Based on the interpolation point coordinates, a fine-grid background field with a fine mesh is generated. The linear interpolation formula is as follows:

[0031] Y=Y1+(Y2-Y1)×(X-X1) / (X2-X1);

[0032] In the formula, Y and X are the interpolation point coordinates, and Y1, X1, Y2, and X2 are the coordinates of known grid points in the first analysis field.

[0033] The present invention also provides a multi-grid correction analysis device with physical constraints, comprising: a coarse grid background field acquisition module, a potential height field calculation module, an interpolation coefficient acquisition module, a fine grid background field acquisition module, and a fine grid analysis field generation module;

[0034] The coarse grid background field acquisition module is used to acquire the original meteorological observation data of the area to be analyzed, and to perform gridding processing on the original meteorological observation data to obtain the coarse grid background field.

[0035] The geopotential height field calculation module is used to select the first observation station on the coarse grid, obtain all the second observation stations located within the influence radius of the first observation station, calculate the first geopotential height observation value between each second observation station and the first observation station based on the wind pressure constraint relationship, and obtain the geopotential height field of the first observation station within the influence radius.

[0036] The interpolation coefficient acquisition module is used to calculate the distance and weight coefficient between each second observation station and the first observation station based on the geopotential height field, and to calculate the fitting relationship between the distance and the weight coefficient to obtain the interpolation coefficient.

[0037] The fine mesh background field acquisition module is used to perform interpolation processing on the coarse mesh background field according to the interpolation coefficient to obtain the first analysis field of the coarse mesh, and to perform linear interpolation processing on the first analysis field to obtain the fine mesh background field of the fine mesh.

[0038] The fine-grid analysis field generation module is used to acquire meteorological observation data of the fine grid, calculate the deviation between the meteorological observation data and the fine-grid background field, interpolate the deviation to the fine-grid background field based on an interpolation algorithm to correct the fine-grid background field, and generate a second analysis field of the fine grid based on the corrected fine-grid background field.

[0039] In one possible implementation, the coarse-grid background field acquisition module is used to acquire the original meteorological observation data of the area to be analyzed, and to perform gridding processing on the original meteorological observation data to obtain a coarse-grid background field, specifically including:

[0040] Obtain raw meteorological observation data of the area to be analyzed, wherein the raw meteorological observation data includes surface air pressure and 10-meter U and V winds;

[0041] The original meteorological observation data is gridded based on the nearest neighbor method to obtain a coarse grid with a preset resolution. The meteorological observation data of the numerical model is extracted from the coarse grid, and the background field of the coarse grid is obtained based on the meteorological observation data of the numerical model.

[0042] In one possible implementation, the geopotential height field calculation module is used to calculate the first geopotential height observation value between each second observation station and the first observation station based on the wind pressure constraint relationship, thereby obtaining the geopotential height field of the first observation station within the influence radius, specifically including:

[0043] Set up and generate a formula for calculating geopotential height based on the geostrophic balance formula;

[0044] Obtain the geopotential height observation value of each second observation station, and obtain the u wind field observation value and v wind field observation value corresponding to each second observation station and the first observation station. Input the geopotential height observation value, the u wind field observation value and the v wind field observation value into the geopotential height calculation formula to obtain the first geopotential height observation value between each second observation station and the first observation station.

[0045] By integrating all the first potential height observations, the potential height field of the first observation station within the radius of influence is obtained.

[0046] In one possible implementation, the potential height calculation formula in the potential height field calculation module is as follows:

[0047]

[0048] in, The observed geopotential height is at the second observation station j. The first potential height observation value of the first observation station i is calculated from the second observation station j. i u j v i v j , respectively, are the u-wind field observation values ​​and v-wind field observation values ​​at the first observation station i and the second observation station j, x i x j y i y j These are the x and y coordinates of the first observation station i and the second observation station j, respectively.

[0049] In one possible implementation, the interpolation coefficient acquisition module is used to calculate the distance and weight coefficient between each second observation station and the first observation station based on the geopotential height field, and to calculate the fitting relationship between the distance and the weight coefficient to obtain the interpolation coefficients, specifically including:

[0050] Obtain the coordinate data corresponding to each second observation station and the first observation station, and calculate the distance between each second observation station and the first observation station based on the coordinate data;

[0051] Obtain the original geopotential height observation value of the first observation station, and based on the geopotential height field, compare each first geopotential height observation value in the geopotential height field with the original geopotential height observation value to obtain the weighting coefficient between each second observation station and the first observation station.

[0052] The distance and the weight coefficients are fitted to obtain a fitting relationship between the distance and the weight coefficients, and the interpolation coefficients are determined based on the fitting relationship.

[0053] In one possible implementation, the fine-mesh background field acquisition module is used to interpolate the coarse-mesh background field according to the interpolation coefficients to obtain the first analysis field of the coarse-mesh, specifically including:

[0054] Based on the interpolation coefficients, the original Cressman interpolation formula is updated to obtain the updated Cressman interpolation formula.

[0055] Based on the updated Cressman interpolation formula, the coarse grid background field is interpolated to progressively correct the coarse grid background field and obtain the first analysis field of the coarse grid.

[0056] In one possible implementation, the fine mesh background field acquisition module is used to perform linear interpolation on the first analysis field to obtain the fine mesh background field, specifically including:

[0057] Based on the linear interpolation formula, the interpolation point coordinates of the first analysis field are calculated. Based on the interpolation point coordinates, a fine-grid background field with a fine mesh is generated. The linear interpolation formula is as follows:

[0058] Y=Y1+(Y2-Y1)×(X-X1) / (X2-X1);

[0059] In the formula, Y and X are the interpolation point coordinates, and Y1, X1, Y2, and X2 are the coordinates of known grid points in the first analysis field.

[0060] The present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the physically constrained multigrid correction analysis method as described in any of the preceding claims.

[0061] The present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform a multigrid correction analysis method with physical constraints as described in any of the preceding claims.

[0062] This invention provides a method and apparatus for multigrid correction analysis with physical constraints, which has the following advantages compared with the prior art:

[0063] The process involves converting raw meteorological observation data of the area to be analyzed into a coarse-grid background field. A first observation station on the coarse grid is selected, and all second observation stations within its influence radius are obtained. Based on wind pressure constraints, the first potential height observation value, distance, and weighting coefficient between each second observation station and the first observation station are calculated. By fitting the distance and weighting coefficients, interpolation coefficients are obtained. These interpolation coefficients with wind pressure constraints are then used to interpolate the coarse-grid background field, resulting in a first analysis field that satisfies large-scale wind pressure balance. This field is then used as the fine-grid background field. The deviation between the meteorological observation data and the fine-grid background field is calculated, and this deviation is interpolated onto the fine-grid background field using an interpolation algorithm to correct the local small- and medium-scale information within the fine-grid. Based on the corrected fine-grid background field, a second analysis field is generated. Compared to existing technologies, this invention incorporates wind pressure constraints into the interpolation algorithm, enabling multi-scale meteorological information analysis and improving the accuracy of meteorological data analysis. Attached Figure Description

[0064] Figure 1 This is a flowchart illustrating an embodiment of a multigrid correction analysis method with physical constraints provided by the present invention.

[0065] Figure 2 This is a schematic diagram of an embodiment of a multigrid correction analysis device with physical constraints provided by the present invention. Detailed Implementation

[0066] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] Example 1

[0068] See Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of a multigrid correction analysis method with physical constraints provided by the present invention, as shown below. Figure 1 As shown, the method includes steps 101-105, as detailed below:

[0069] Step 101: Obtain the original meteorological observation data of the area to be analyzed, and perform gridding processing on the original meteorological observation data to obtain a coarse grid background field.

[0070] In one embodiment, raw meteorological observation data of the area to be analyzed is obtained, wherein the raw meteorological observation data includes surface air pressure and 10-meter U and V winds; specifically, surface air pressure and 10-meter U and V winds are obtained from automatic weather stations.

[0071] In one embodiment, the original meteorological observation data is gridded based on the nearest neighbor method to obtain a coarse grid with a preset resolution. The coarse grid can be a latitude and longitude grid or a kilometer grid, and the preset resolution of the coarse grid is 10-50 km.

[0072] In one embodiment, meteorological observation data from the numerical model are extracted from the coarse grid, and a coarse grid background field is obtained based on the meteorological observation data from the numerical model. In this way, each grid has an initial value from the numerical model. Among these grid points, those within the influence radius of any observation station are reassigned by the algorithm, while grid points outside the influence radius of any station retain their original initial values. Thus, the background field information obtained comes from the model's initial estimate and the observed values.

[0073] Preferably, a background field for a coarse grid can also be generated by providing a missing measurement value. Specifically, a missing measurement value of -99999.0 is given, and each grid point is set as the missing measurement value as the background field. Among these grid points, the grid points within the influence radius of the observation station will be reassigned by this algorithm, while the grid points not within the influence radius of any observation station will always retain the missing measurement value. In this way, the gridded results are all derived from the observation data.

[0074] In one embodiment, the original meteorological observation data is further processed into a grid to obtain a fine grid with a preset resolution, wherein the preset resolution of the fine grid is 1-5 km.

[0075] Step 102: Select the first observation station on the coarse grid, obtain all the second observation stations located within the influence radius of the first observation station, and calculate the first potential height observation value between each second observation station and the first observation station based on the wind pressure constraint relationship, so as to obtain the potential height field of the first observation station within the influence radius.

[0076] In one embodiment, the first observation station is any observation station on the coarse grid. By obtaining the influence radius of the first observation station, all observation stations located within the influence radius of the first observation station are obtained on the coarse grid, and all of them are set as the second observation station.

[0077] In one embodiment, the selection of the radius of influence has certain human factors and is generally taken as a constant; the principle for selecting the radius of influence is from near to far, and the commonly used radii of influence are 1, 2, 4, 7 and 10.

[0078] In one embodiment, a geostrophic balance formula is set so that the height field and wind field in the coarse grid satisfy the geostrophic balance formula; wherein, the geostrophic balance formula is as follows:

[0079]

[0080]

[0081] In the formula, Let u be the potential height, f be the Coriolis force, and u be the value of the potential height. g and v g This is the geostrophic balance component.

[0082] Converting the above geostrophic balance formula into the total differential form of the coarse-grid height field, we get:

[0083]

[0084] In one embodiment, a geopotential height calculation formula is generated based on the geostrophic balance formula, used to calculate the observed geopotential height values ​​for two different observation stations; specifically, the geopotential height calculation formula is as follows:

[0085]

[0086] in, The observed geopotential height is at the second observation station j. The first potential height observation value of the first observation station i is calculated from the second observation station j. i u j v i v j , respectively, are the u-wind field observation values ​​and v-wind field observation values ​​at the first observation station i and the second observation station j, x i x j y i y j These are the x and y coordinates of the first observation station i and the second observation station j, respectively.

[0087] In one embodiment, the geopotential height observation value of each second observation station is obtained, and the u-wind field observation value and v-wind field observation value corresponding to each second observation station and the first observation station are obtained. The geopotential height observation value, the u-wind field observation value and the v-wind field observation value are input into the geopotential height calculation formula to obtain the first geopotential height observation value between each second observation station and the first observation station, wherein the first geopotential height observation value is the first geopotential height observation value of the first observation station calculated by the second observation station.

[0088] In one embodiment, the geopotential height field of the first observation station within the radius of influence is obtained by integrating all first geopotential height observations.

[0089] Step 103: Based on the geopotential height field, calculate the distance and weight coefficient between each second observation station and the first observation station, and calculate the fitting relationship between the distance and the weight coefficient to obtain the interpolation coefficient.

[0090] In one embodiment, coordinate data corresponding to each second observation station and the first observation station are obtained. Based on the coordinate data, the distance between each second observation station and the first observation station is calculated. Specifically, all N second observation stations within the influence radius R of the first observation station i are selected. The obtained coordinate data corresponding to each second observation station and the first observation station are substituted into the distance calculation formula to calculate the distance between each second observation station and the first observation station. The distance calculation formula is as follows:

[0091]

[0092] In the formula, x i y i The coordinate data of the first observation station, x j y j This is the coordinate data for the second observation station.

[0093] In one embodiment, the original geopotential height observation value of the first observation station is obtained, and based on the geopotential height field, each first geopotential height observation value in the geopotential height field is compared with the original geopotential height observation value to obtain a weighting coefficient between each second observation station and the first observation station. Specifically, a weighting coefficient calculation formula is set, and each first geopotential height observation value in the geopotential height field and the original geopotential height observation value are input into the weighting coefficient calculation formula to obtain the weighting coefficient between each second observation station and the first observation station. The weighting coefficient calculation formula is as follows:

[0094]

[0095] In the formula, This represents the original geopotential height observation value at the first observation station.

[0096] In one embodiment, by performing the above operation on all first observation stations on the coarse grid, all second observation stations within the influence radius of each first observation station are obtained, and the distance and weight coefficient between each first observation station and all its corresponding second observation stations are calculated.

[0097] In one embodiment, the distance and the weight coefficients are fitted to obtain a fitting relationship between the distance and the weight coefficients, and the interpolation coefficients are determined based on the fitting relationship; preferably, this fitting relationship can be expressed in linear or exponential form.

[0098] In this embodiment, a linear form is used to construct the fitting relationship between the distance and the weight coefficients, as shown below:

[0099]

[0100] In the formula, k and c are fitting coefficients, and w is an interpolation coefficient.

[0101] Preferably, the values ​​of the fitting coefficients can be obtained through least squares method, linear regression, or neural network training.

[0102] Step 104: Based on the interpolation coefficients, perform interpolation processing on the coarse grid background field to obtain the first analysis field of the coarse grid, and perform linear interpolation processing on the first analysis field to obtain the fine grid background field of the fine grid.

[0103] In one embodiment, the original Cressman interpolation formula is updated based on the interpolation coefficients to obtain an updated Cressman interpolation formula. Specifically, the interpolation coefficients are used as weighting factors of the original Cressman interpolation formula to update it; wherein the general form of the weighting factors of the Cressman interpolation formula is as follows:

[0104]

[0105] In the formula, R is the radius of influence, and d abi It is the distance from grid point (a, b) to observation point i.

[0106] In one embodiment, since the interpolation coefficients are used as weighting factors in the original Cressman interpolation formula, the updated Cressman interpolation formula includes spatial distance and large-scale geostrophic balance constraints of air pressure and wind field. This avoids the problem in existing interpolation algorithms that cannot establish a unified physical model to simulate the complex multi-scale constraints between meteorological elements, and only consider the large-scale constraints or small-scale locality between meteorological elements. As a result, the analysis results are more in line with the laws of geohydrodynamics.

[0107] In one embodiment, the coarse grid background field is interpolated based on the updated Cressman interpolation formula to gradually correct the coarse grid background field. The corrected background field is then subjected to data normalization processing to obtain the first analysis field of the coarse grid, wherein the first analysis field is the analysis field of surface air pressure and 10-meter U and V winds on the coarse grid.

[0108] Specifically, the Cressman interpolation algorithm involves using the difference between the actual data and the background field to modify and correct the original background field, resulting in a new background field. Then, the difference between the new background field and the actual value is calculated to correct the original background field, continuing this process until the corrected analysis field closely approximates the actual data. The Cressman interpolation formula is shown below:

[0109] ′

[0110] α=α0+Δα ab ;

[0111]

[0112] In the formula, α represents any meteorological element, and α0 is the background value of variable α at grid point (a, b).

[0113] ′

[0114] Field value, the correction value of variable α at grid point (a, b); Δα i W is the difference between the observed value at observation point i and the background field value; abi is the weighting factor, which varies between 0 and 1; K is the number of observation stations within the influence radius R.

[0115] In one embodiment, interpolation point coordinate data of the first analysis field are calculated based on a linear interpolation formula, and a fine-grid background field with a fine mesh is generated based on the interpolation point coordinate data. The linear interpolation formula is as follows:

[0116] Y=Y1+(Y2-Y1)×(X-X1) / (X2-X1);

[0117] In the formula, Y and X are the interpolation point coordinates, and Y1, X1, Y2, and X2 are the coordinates of known grid points in the first analysis field.

[0118] Step 105: Obtain meteorological observation data of the fine grid, calculate the deviation between the meteorological observation data and the background field of the fine grid, interpolate the deviation to the background field of the fine grid based on the interpolation algorithm to correct the background field of the fine grid, and generate the second analysis field of the fine grid based on the corrected background field of the fine grid.

[0119] In one embodiment, the deviation between the meteorological observation data and the fine grid background field is calculated. Specifically, it is determined whether the value of the fine grid background field is a missing value. If so, the meteorological observation data is used as the deviation. If not, the value of the fine grid background field is subtracted from the meteorological observation data to obtain the deviation between the meteorological observation data and the fine grid background field. The grid point data is then corrected based on the deviation.

[0120] Preferably, the meteorological observation data of the fine grid obtained is the observation information on the grid obtained by fitting the relationship, and is data from all observation stations within the influence radius.

[0121] In one embodiment, the Cressman interpolation algorithm with a smaller influence radius is used to interpolate the deviation into the background field of the fine grid, thereby updating the background field of the fine grid. The updated background field of the fine grid is then subjected to data assimilation processing to obtain the second analysis field of the fine grid, wherein the second analysis field is the analysis field of surface air pressure and 10-meter U and V winds on the fine grid.

[0122] Preferably, the traditional Cressman interpolation method is still used in fine grids, and a smaller influence radius is selected to analyze local small and medium scale information.

[0123] In summary, this embodiment provides a physically constrained multigrid correction analysis method. Through a multigrid constrained stepwise correction interpolation algorithm, coarse and fine grids are used for interpolation. On the coarse grid, considering the large-scale characteristics of meteorological elements, wind pressure constraints are added to the interpolation scheme as constraints between different physical quantities. This solves the problem that interpolation algorithms cannot establish a unified physical model to simulate the complex multi-scale constraints between meteorological elements, only considering large-scale constraints or small-scale localities between meteorological elements, making the analysis results more consistent with the laws of geohydrodynamics. The fine grid uses the analysis field on the coarse grid as the background field. Since the large-scale analysis has already been completed on the coarse grid, only the Cressman interpolation scheme with a smaller radius of influence is used for local correction. Thus, the fine grid obtains local analysis information based on the large-scale analysis field, improving the accuracy of meteorological data analysis.

[0124] Example 2

[0125] See Figure 2 , Figure 2 This is a schematic diagram of an embodiment of a multi-grid correction analysis device with physical constraints provided by the present invention, as shown below. Figure 2 As shown, the device includes a coarse-grid background field acquisition module 201, a potential height field calculation module 202, an interpolation coefficient acquisition module 203, a fine-grid background field acquisition module 204, and a fine-grid analysis field generation module 205, as detailed below:

[0126] The coarse grid background field acquisition module 201 is used to acquire the original meteorological observation data of the area to be analyzed, and to perform gridding processing on the original meteorological observation data to obtain a coarse grid background field.

[0127] The geopotential height field calculation module 202 is used to select the first observation station on the coarse grid, obtain all the second observation stations located within the influence radius of the first observation station, calculate the first geopotential height observation value between each second observation station and the first observation station based on the wind pressure constraint relationship, and obtain the geopotential height field of the first observation station within the influence radius.

[0128] The interpolation coefficient acquisition module 203 is used to calculate the distance and weight coefficient between each second observation station and the first observation station based on the geopotential height field, and to calculate the fitting relationship between the distance and the weight coefficient to obtain the interpolation coefficient.

[0129] The fine mesh background field acquisition module 204 is used to perform interpolation processing on the coarse mesh background field according to the interpolation coefficients to obtain the first analysis field of the coarse mesh, and to perform linear interpolation processing on the first analysis field to obtain the fine mesh background field of the fine mesh.

[0130] The fine-grid analysis field generation module 205 is used to acquire meteorological observation data of the fine grid, calculate the deviation between the meteorological observation data and the fine-grid background field, interpolate the deviation to the fine-grid background field based on the interpolation algorithm to correct the fine-grid background field, and generate the second analysis field of the fine grid based on the corrected fine-grid background field.

[0131] In one embodiment, the coarse grid background field acquisition module 201 is used to acquire the original meteorological observation data of the area to be analyzed, and to perform gridding processing on the original meteorological observation data to obtain a coarse grid background field. Specifically, it includes: acquiring the original meteorological observation data of the area to be analyzed, wherein the original meteorological observation data includes surface air pressure and 10-meter U and V winds; performing gridding processing on the original meteorological observation data based on the nearest neighbor method to obtain a coarse grid with a preset resolution; extracting the meteorological observation data of the numerical model from the coarse grid; and obtaining the coarse grid background field based on the meteorological observation data of the numerical model.

[0132] In one embodiment, the geopotential height field calculation module 202 is used to calculate the first geopotential height observation value between each second observation station and the first observation station based on the wind pressure constraint relationship, so as to obtain the geopotential height field of the first observation station within the influence radius. Specifically, it includes: setting and generating a geopotential height calculation formula based on the geostrophic balance formula; obtaining the geopotential height observation value of each second observation station, and obtaining the u-wind field observation value and v-wind field observation value corresponding to each second observation station and the first observation station; inputting the geopotential height observation value, the u-wind field observation value, and the v-wind field observation value into the geopotential height calculation formula to obtain the first geopotential height observation value between each second observation station and the first observation station; and obtaining the geopotential height field of the first observation station within the influence radius by integrating all the first geopotential height observation values.

[0133] In one embodiment, the potential height calculation formula in the potential height field calculation module 202 is as follows:

[0134]

[0135] in, The observed geopotential height is at the second observation station j. The first potential height observation value of the first observation station i is calculated from the second observation station j. i u j v i v j , respectively, are the u-wind field observation values ​​and v-wind field observation values ​​at the first observation station i and the second observation station j, x i x j y i y j These are the x and y coordinates of the first observation station i and the second observation station j, respectively.

[0136] In one embodiment, the interpolation coefficient acquisition module 203 is used to calculate the distance and weight coefficient between each second observation station and the first observation station based on the geopotential height field, and to calculate the fitting relationship between the distance and the weight coefficient to obtain the interpolation coefficient. Specifically, this includes: acquiring the coordinate data corresponding to each second observation station and the first observation station; calculating the distance between each second observation station and the first observation station based on the coordinate data; acquiring the original geopotential height observation value of the first observation station; and comparing each first geopotential height observation value in the geopotential height field with the original geopotential height observation value based on the geopotential height field to obtain the weight coefficient between each second observation station and the first observation station; performing fitting processing on the distance and the weight coefficient to obtain the fitting relationship between the distance and the weight coefficient; and determining the interpolation coefficient based on the fitting relationship.

[0137] In one embodiment, the fine-mesh background field acquisition module 204 is used to perform interpolation processing on the coarse-mesh background field according to the interpolation coefficients to obtain the first analysis field of the coarse-mesh. Specifically, this includes: updating the original Cressman interpolation formula based on the interpolation coefficients to obtain an updated Cressman interpolation formula; and performing interpolation processing on the coarse-mesh background field based on the updated Cressman interpolation formula to gradually correct the coarse-mesh background field and obtain the first analysis field of the coarse-mesh.

[0138] In one embodiment, the fine mesh background field acquisition module 204 is used to perform linear interpolation processing on the first analysis field to obtain the fine mesh background field of the fine mesh. Specifically, it includes: setting and calculating the interpolation point coordinate data of the first analysis field based on the linear interpolation formula, and generating the fine mesh background field of the fine mesh based on the interpolation point coordinate data. The linear interpolation formula is as follows:

[0139] Y=Y1+(Y2-Y1)×(X-X1) / (X2-X1);

[0140] In the formula, Y and X are the interpolation point coordinates, and Y1, X1, Y2, and X2 are the coordinates of known grid points in the first analysis field.

[0141] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0142] It should be noted that the above-described embodiment of the multi-grid correction analysis device with physical constraints is merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules 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 modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0143] Based on the above-described embodiments of the physically constrained multigrid correction analysis method, another embodiment of the present invention provides a physically constrained multigrid correction analysis terminal device. This physically constrained multigrid correction analysis terminal device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the physically constrained multigrid correction analysis method of any embodiment of the present invention.

[0144] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the physically constrained multigrid correction analysis terminal device.

[0145] The physically constrained multigrid correction analysis terminal device can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The physically constrained multigrid correction analysis terminal device may include, but is not limited to, a processor and memory.

[0146] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the physically constrained multigrid correction analysis terminal device, connecting all parts of the device via various interfaces and lines.

[0147] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the physically constrained multigrid correction analysis terminal device by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0148] Based on the above embodiments of the physically constrained multigrid correction analysis method, another embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is running, the device where the storage medium is located controls the execution of the physically constrained multigrid correction analysis method of any embodiment of the present invention.

[0149] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0150] In summary, the present invention provides a multi-grid correction analysis method and apparatus with physical constraints. This method acquires raw meteorological observation data of the area to be analyzed, performs gridding on the raw meteorological observation data to obtain a coarse grid background field. A first observation station on the coarse grid is selected, and all second observation stations located within the influence radius of the first observation station are acquired. Based on wind pressure constraints, the first potential height observation value between each second observation station and the first observation station is calculated, obtaining the potential height field of the first observation station within its influence radius. Based on the potential height field, the distance and weight coefficient between each second observation station and the first observation station are calculated, and the distance and weight coefficient are further calculated. The fitting relationship is used to obtain interpolation coefficients; based on the interpolation coefficients, the coarse grid background field is interpolated to obtain the first analysis field of the coarse grid, and the first analysis field is linearly interpolated to obtain the fine grid background field of the fine grid; meteorological observation data of the fine grid is acquired, the deviation between the meteorological observation data and the fine grid background field is calculated, and the deviation is interpolated onto the fine grid background field based on the interpolation algorithm to correct the fine grid background field; and based on the corrected fine grid background field, the second analysis field of the fine grid is generated; compared with the prior art, the technical solution of the present invention adds wind pressure constraint relationship to the interpolation algorithm, enabling it to have multi-scale meteorological information analysis capability and improving the accuracy of meteorological data analysis.

[0151] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A multigrid correction analysis method with physical constraints, characterized in that, include: Obtain the original meteorological observation data of the area to be analyzed, and perform gridding processing on the original meteorological observation data to obtain a coarse grid background field; Select the first observation station on the coarse grid, obtain all the second observation stations located within the influence radius of the first observation station, and calculate the first potential height observation value between each second observation station and the first observation station based on the wind pressure constraint relationship, so as to obtain the potential height field of the first observation station within the influence radius. Based on the geopotential height field, the distance and weight coefficient between each second observation station and the first observation station are calculated, and the fitting relationship between the distance and the weight coefficient is calculated to obtain the interpolation coefficient; Based on the interpolation coefficients, the coarse grid background field is interpolated to obtain the first analysis field of the coarse grid, and the first analysis field is linearly interpolated to obtain the fine grid background field of the fine grid. Meteorological observation data of the fine grid is acquired, the deviation between the meteorological observation data and the background field of the fine grid is calculated, the deviation is interpolated to the background field of the fine grid based on the interpolation algorithm to correct the background field of the fine grid, and a second analysis field of the fine grid is generated based on the corrected background field of the fine grid.

2. The multigrid correction analysis method with physical constraints as described in claim 1, characterized in that, Obtain the raw meteorological observation data of the area to be analyzed, and perform gridding processing on the raw meteorological observation data to obtain a coarse-grid background field, specifically including: Obtain raw meteorological observation data of the area to be analyzed, wherein the raw meteorological observation data includes surface air pressure and 10-meter U and V winds; The original meteorological observation data is gridded using the nearest neighbor method to obtain a coarse grid with a preset resolution. The meteorological observation data of the numerical model is extracted from the coarse grid, and the background field of the coarse grid is obtained based on the meteorological observation data of the numerical model.

3. The multigrid correction analysis method with physical constraints as described in claim 1, characterized in that, Based on the wind pressure constraint relationship, the first potential height observation value between each second observation station and the first observation station is calculated to obtain the potential height field of the first observation station within the influence radius, specifically including: Set up and generate a formula for calculating geopotential height based on the geostrophic balance formula; Obtain the geopotential height observation value of each second observation station, and obtain the u wind field observation value and v wind field observation value corresponding to each second observation station and the first observation station. Input the geopotential height observation value, the u wind field observation value and the v wind field observation value into the geopotential height calculation formula to obtain the first geopotential height observation value between each second observation station and the first observation station. By integrating all the first potential height observations, the potential height field of the first observation station within the radius of influence is obtained.

4. The multigrid correction analysis method with physical constraints as described in claim 3, characterized in that, The formula for calculating the potential height is as follows: in, For the second observation station The observed value of the geopotential height, To connect the second observation station The first observation station obtained by calculation The first potential height observation value, , , , These are the first observation stations. Second observation station The observed values ​​of the u-wind field and the v-wind field on the surface. ， ， ， These are the first observation stations. Second observation station of , coordinate, It is the Coriolis force.

5. The multigrid correction analysis method with physical constraints as described in claim 1, characterized in that, Based on the geopotential height field, the distance and weighting coefficient between each second observation station and the first observation station are calculated, and the fitting relationship between the distance and the weighting coefficient is calculated to obtain interpolation coefficients, specifically including: Obtain the coordinate data corresponding to each second observation station and the first observation station, and calculate the distance between each second observation station and the first observation station based on the coordinate data; Obtain the original geopotential height observation value of the first observation station, and based on the geopotential height field, compare each first geopotential height observation value in the geopotential height field with the original geopotential height observation value to obtain the weighting coefficient between each second observation station and the first observation station. The distance and the weight coefficients are fitted to obtain a fitting relationship between the distance and the weight coefficients, and the interpolation coefficients are determined based on the fitting relationship.

6. The multigrid correction analysis method with physical constraints as described in claim 1, characterized in that, Based on the interpolation coefficients, the coarse-grid background field is interpolated to obtain the first analysis field of the coarse-grid, specifically including: Based on the interpolation coefficients, the original Cressman interpolation formula is updated to obtain the updated Cressman interpolation formula. Based on the updated Cressman interpolation formula, the coarse grid background field is interpolated to progressively correct the coarse grid background field and obtain the first analysis field of the coarse grid.

7. The multigrid correction analysis method with physical constraints as described in claim 1, characterized in that, Linear interpolation is performed on the first analysis field to obtain the fine-grid background field, specifically including: Based on the linear interpolation formula, the interpolation point coordinates of the first analysis field are calculated. Based on the interpolation point coordinates, a fine-grid background field with a fine mesh is generated. The linear interpolation formula is as follows: ； In the formula, Y and X are the coordinates of the interpolated points. The coordinate data of known grid points in the first analysis field.

8. A multigrid correction analysis device with physical constraints, characterized in that, include: The module includes a coarse mesh background field acquisition module, a potential height field calculation module, an interpolation coefficient acquisition module, a fine mesh background field acquisition module, and a fine mesh analysis field generation module. The coarse grid background field acquisition module is used to acquire the original meteorological observation data of the area to be analyzed, and to perform gridding processing on the original meteorological observation data to obtain the coarse grid background field. The geopotential height field calculation module is used to select the first observation station on the coarse grid, obtain all the second observation stations located within the influence radius of the first observation station, calculate the first geopotential height observation value between each second observation station and the first observation station based on the wind pressure constraint relationship, and obtain the geopotential height field of the first observation station within the influence radius. The interpolation coefficient acquisition module is used to calculate the distance and weight coefficient between each second observation station and the first observation station based on the geopotential height field, and to calculate the fitting relationship between the distance and the weight coefficient to obtain the interpolation coefficient. The fine mesh background field acquisition module is used to perform interpolation processing on the coarse mesh background field according to the interpolation coefficient to obtain the first analysis field of the coarse mesh, and to perform linear interpolation processing on the first analysis field to obtain the fine mesh background field of the fine mesh. The fine-grid analysis field generation module is used to acquire meteorological observation data of the fine grid, calculate the deviation between the meteorological observation data and the fine-grid background field, interpolate the deviation to the fine-grid background field based on an interpolation algorithm to correct the fine-grid background field, and generate a second analysis field of the fine grid based on the corrected fine-grid background field.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the physically constrained multigrid correction analysis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the physically constrained multigrid correction analysis method as described in any one of claims 1 to 7.

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