CN109033181B - Wind field geographic numerical simulation method for complex terrain area - Google Patents

Wind field geographic numerical simulation method for complex terrain area Download PDF

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CN109033181B
CN109033181B CN201810670326.3A CN201810670326A CN109033181B CN 109033181 B CN109033181 B CN 109033181B CN 201810670326 A CN201810670326 A CN 201810670326A CN 109033181 B CN109033181 B CN 109033181B
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CN109033181A (en
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葛莹
王雪松
刘磊
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Hohai University HHU
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Abstract

The invention relates to a wind field geographic value simulation method in a complex terrain area, which directly obtains NetCDF data and DEM data of a research area from the Internet, does not need data of a wind measuring station of the research area, and adopts raster data for interpolation unlike the traditional method of utilizing vector data for interpolation, thereby saving the time of data preprocessing; the calculation method is simple, and the wind speed data of the research area can be acquired in a short time; the nonlinear weight equation based on the inverse distance weight interpolation is established, the distance coefficient and the fluctuation coefficient of the wind field weight function are gradually corrected by using a Cressman objective analysis method and a computer numerical simulation principle, the nonlinear weight optimal approximation equation is solved by an iteration method, and the precision of the wind field numerical simulation in the complex terrain area is improved.

Description

Wind field geographic numerical simulation method for complex terrain area
Technical Field
The invention relates to a wind field geographic value simulation method in a complex terrain area, and belongs to the technical field of wind speed measurement.
Background
The wind energy resource is used as a clean renewable energy source, is unevenly distributed in the whole situation of China, and the size of the wind energy resource in a certain area needs to be determined for facilitating the site selection of a wind power plant.
The conventional wind speed detection usually relates to NCEP, DEM and IDW, the NCEP is meteorological data jointly developed by an American environment forecasting center and an American atmospheric research center, a grid data form is freely provided for the world, and the data comprises wind speed, temperature and the like, and is widely applied to the aspects of meteorological simulation and prediction due to long disclosing time, free and the like; the NECP data has two forms, both of which can be directly read by ArcGIS, the DEM (Digital Elevation model) is a specific Digital model in the field of mapping and geographic information, and is an entity ground model which uses a group of ordered numerical arrays to represent ground Elevation, the entity ground model exists in the form of grid data in an ArcGIS desktop tool, and the introduction of DEM topographic factors can improve the accuracy of wind field geographic numerical simulation in complex terrain areas.
Inverse Distance Weighted Interpolation (IDW) is a Weighted average of the distances between interpolated points and sample points, where samples points closer to an interpolated point are given more weight. The interpolation method based on the reverse distance weight of the terrain is characterized in that an elevation factor is added on the basis of the reverse distance weight interpolation method to form an interpolation weight function containing elevation, and the method improves the accuracy of interpolation.
The traditional forecasting method based on the wind measuring station needs to be observed all year round, certain manpower and material resources are wasted, and certain difficulty is caused in places with complex terrains.
Disclosure of Invention
The invention aims to solve the technical problem of providing a wind field geographic value simulation method for a complex terrain area, which can efficiently and accurately realize wind speed simulation by adopting a brand-new control strategy.
The invention adopts the following technical scheme for solving the technical problems: the invention designs a wind field geographic numerical simulation method for a complex terrain area, which comprises the following steps:
step A, obtaining actual DEM geographic grid data, actual observation wind field geographic grid data and simulated observation wind field geographic grid corresponding to target area, and simulating observation
Each grid in the wind field geogrid grid is empty, and the data of the simulated observation wind field geogrid grid and the data of the actually observed wind field geogrid grid are consistent with each other in resolution, upper left corner coordinates and a central point; then entering the step B;
b, initializing the cycle number n to 1, respectively initializing a distance coefficient a of the wind field weight function and a fluctuation coefficient b of the wind field weight function to preset values, wherein the value of a is equal to that of b, and then entering the step C;
step C, aiming at each grid in the simulated observation wind field geographic grid corresponding to the target area, obtaining the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid dataiWeight value of
Figure BDA0001707992120000021
I represents the total number of grids in the simulated observation wind field geographic grid corresponding to the target area, m represents grids in the field around the preset gridAnd D, then entering the step D;
step D, respectively obtaining the simulated wind speed in each grid in the simulated observation wind field geographic grid corresponding to the target area
Figure BDA0001707992120000022
Then entering step E;
step E, obtaining the following formula:
Figure BDA0001707992120000023
obtaining a measurement parameter RMSE under the current cycle number nnThen entering step F;
f, judging whether the cycle number n is equal to 1, if so, entering the step H; otherwise, entering step G;
step G, judging RMSEnWhether it is not greater than a predetermined metric threshold, and RMSEn-RMSEn-1If the two judgment results are yes at the same time, obtaining the values of a and b under the current cycle, and entering the step I; otherwise, entering step H;
step H, aiming at the values of a and b, respectively increasing the preset change values which are equal to each other, updating the values of a and b, and then returning to the step C;
step I, generating geographic grids with the resolution ratio of actual observed wind field geographic grid data corresponding to a target region and a preset second multiple, using the geographic grids as interpolation simulated wind fields corresponding to the target region, wherein each grid in the interpolation simulated wind fields is empty, the projection coordinates, grid size and grid upper left corner coordinates of the interpolation simulated wind fields are consistent with the actual observed wind field geographic grid data, and the preset second multiple is greater than 1, and then entering step J;
step J. according to the values of a and b obtained, according to
Figure BDA0001707992120000024
Aiming at the interpolation simulation wind field, inverse distance weight interpolation based on the fluctuation degree is carried out to obtain the observation wind field geographical grid model corresponding to the target areaSimulating data, and then entering a step K;
step K, judging whether the resolution of the simulation data of the observed wind field geogrid grid corresponding to the target area meets the preset resolution requirement, if so, obtaining the simulation data of the observed wind field geogrid grid corresponding to the target area meeting the preset resolution requirement, and finishing the simulation method; and otherwise, taking the simulation data of the observed wind field geogrid grid corresponding to the target area as the actual observed wind field geogrid grid data corresponding to the target area, and returning to the step A.
As a preferred technical solution of the present invention, the step a includes the steps of:
a1, obtaining actual observation wind field geographic grid data corresponding to a target area and actual DEM geographic grid data covering the target area, and then entering step A2;
a2, initializing a simulated observation wind field geographic grid corresponding to the target area, wherein each grid in the simulated observation wind field geographic grid is empty, and the simulated observation wind field geographic grid and the actually observed wind field geographic grid are consistent in resolution, upper left corner coordinates and a central point, and then entering the step A3;
a3, initializing a DEM geographic grid corresponding to the target area, extracting the DEM geographic grid from actual DEM geographic grid data covering the target area according to a preset first multiple of the resolution of the actual observation wind field geographic grid data corresponding to the target area, constructing the actual DEM geographic grid data corresponding to the target area, wherein the preset first multiple is more than 1, and then executing the step B;
and aiming at the judgment in the step K, otherwise, taking the observed wind field geogrid grid simulation data corresponding to the target area as the actual observed wind field geogrid grid data corresponding to the target area, and returning to the step A2.
As a preferred technical solution of the present invention, the step C includes performing the following operations:
aiming at each grid in the simulated observation wind field geographic grid corresponding to the target area, the method comprises the following steps:
Figure BDA0001707992120000031
obtaining the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid dataiWeight value of
Figure BDA0001707992120000032
Figure BDA0001707992120000033
I represents the total number of grids in the simulated observation wind field geographic grid corresponding to the target area, and m represents the number of grids in the field around the preset grid;
Figure BDA0001707992120000034
representing the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid data respectivelyiThe distance of (d); dimaxRepresenting the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid data respectivelyiThe maximum distance of the distance;
Figure BDA0001707992120000035
representing the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid dataiThe difference of the waviness is obtained based on actual DEM geographic grid data corresponding to the target area; r isimaxRepresenting the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid dataiMaximum value of difference in waviness; then step D is entered.
As a preferred technical scheme of the invention, the ith simulation observation wind field geographic grid corresponding to the target areaThe grids and m field grids j preset around the corresponding grids in the actual observation wind field geography grid dataiDifference in waviness of
Figure BDA0001707992120000041
The method comprises the following steps:
firstly, obtaining a grid corresponding to the ith grid in a simulated observation wind field geographic grid corresponding to a target area in actual observation wind field geographic grid data, and obtaining preset m field grids j around the gridi；
Then, obtaining a grid p in the actual DEM geographic grid data corresponding to the target area and corresponding to the ith grid in the simulated observation wind field geographic grid, and obtaining grids j in the actual DEM geographic grid data corresponding to the target area and in each field in the actual observation wind field geographic grid dataiA corresponding grid q;
then, acquiring grids through which connecting lines between the grids p and the grids q pass as each path grid from actual DEM geographic grid data corresponding to the target area; extracting DEM data of the grids p, DEM data of each grid q and DEM data of each approach grid based on actual DEM geographic grid data corresponding to the target area;
finally, respectively aiming at each grid q, obtaining the sum of DEM data difference values of each group of two adjacent grids on the connecting line along the connecting line from the grid p to the grid q, and taking the sum as the fluctuation difference between the grid p and the grid q, namely obtaining the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the grid corresponding to the ith grid in the actual observation wind field geographic grid dataiDifference in waviness of
Figure BDA0001707992120000042
As a preferred technical solution of the present invention, the step D includes performing the following operations:
aiming at each grid in the simulated observation wind field geographic grid corresponding to the target area, the method comprises the following steps:
Figure BDA0001707992120000043
obtaining the simulated wind speed in the ith grid in the simulated observation wind field geographic grid corresponding to the target area
Figure BDA0001707992120000044
In the formula (I), the compound is shown in the specification,
Figure BDA0001707992120000045
representing the grids corresponding to the ith grid in the simulated observation wind field geogrid grid and the jth peripheral grids in the actual observation wind field geogrid grid dataiActual wind speed within the individual field grid, then step E.
Compared with the prior art, the wind field geographic numerical simulation method for the complex terrain area has the following technical effects:
according to the wind field geographic numerical simulation method for the complex terrain area, the NetCDF data and the DEM data of the research area are directly obtained from the network, the data of the wind measuring station of the research area is not needed, interpolation is carried out by adopting grid data instead of the traditional method of carrying out interpolation by utilizing vector data, and the time of data preprocessing is saved; the calculation method is simple, and the wind speed data of the research area can be acquired in a short time; the nonlinear weight equation based on the inverse distance weight interpolation is established, the distance coefficient and the fluctuation coefficient of the wind field weight function are gradually corrected by using a Cressman objective analysis method and a computer numerical simulation principle, the nonlinear weight optimal approximation equation is solved by an iteration method, and the precision of the wind field numerical simulation in the complex terrain area is improved.
Drawings
FIG. 1 is a schematic of the target area NetCDF data and DEM data of the present invention;
fig. 2 is a schematic illustration of actual DEM geogrid data corresponding to the target area in step a3 of the present invention;
FIG. 3 is a schematic diagram of the center point of a data geogrid for actually observing a wind field geogrid according to the present invention;
FIG. 4 is a schematic diagram illustrating an example of how the waviness data is obtained in step E of the present invention;
FIG. 5 is a schematic diagram of observation points and interpolation points of wind field geography grid data according to the present invention;
FIG. 6 is a graph of actual observed wind field data;
FIG. 7 is a graph of the results of three cycles using the simulation method of the present invention.
Detailed Description
The following description will explain embodiments of the present invention in further detail with reference to the accompanying drawings.
The invention designs a wind field geographic value simulation method for a complex terrain area, which comprises the following steps in practical application:
step A, obtaining actual DEM geographic grid data, actual observation wind field geographic grid data and a simulated observation wind field geographic grid corresponding to a target area, wherein each grid in the simulated observation wind field geographic grid is empty, and the simulated observation wind field geographic grid and the actual observation wind field geographic grid data are consistent with each other in resolution, upper left corner coordinates and a central point; then step B is entered.
In practical application, the step a specifically includes the following steps:
step A1, obtaining actual observation wind field geographic grid data corresponding to a target area and actual DEM geographic grid data covering the target area, adopting a WGS 84Mercator projection coordinate system in ArcMap, projecting the actual observation wind field geographic grid data (the observation wind field data is NetCDF meteorological data, 2.5-degree × 2.5-degree grid data) and the actual DEM geographic grid data covering the target area (STRM DEM data, the spatial resolution is 90m × 90m), and then entering step A2. as shown in FIG. 1, the method is a schematic diagram of the NetCDF data and the DEM data of the target area.
Step A2, initializing a simulated observation wind field geographic grid corresponding to the target area based on the WGS 84Mercator projection coordinate system, wherein each grid in the simulated observation wind field geographic grid is empty, the simulated observation wind field geographic grid and actual observation wind field geographic grid data are consistent with each other in resolution, upper left corner coordinates and a central point, and then entering step A3.
And A3, initializing a DEM geographic grid corresponding to the target area, extracting from the actual DEM geographic grid data covering the target area according to a preset first multiple of the resolution of the actual observation wind field geographic grid data corresponding to the target area, constructing the actual DEM geographic grid data corresponding to the target area, as shown in FIG. 2, wherein the preset first multiple is greater than 1, and then executing the step B. In practical application, the first multiple is specifically preset to be 3, the step is called DEM preprocessing, and because calculation is inconvenient and calculation time is long when actual DEM geographic grid data covering a target area are used for simulation, the step is executed to carry out DEM preprocessing, and actual DEM geographic grid data corresponding to the target area are constructed.
And B, initializing the number of loop times n to 1, respectively initializing a distance coefficient a of the wind field weight function and a fluctuation coefficient b of the wind field weight function to preset values, wherein the value of a is equal to that of b, and then entering the step C.
Step C, aiming at each grid in the simulated observation wind field geographic grid corresponding to the target area, obtaining the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid dataiWeight value of
Figure BDA0001707992120000061
I represents the total number of grids in the simulated observation wind field geographic grid corresponding to the target area, m represents the number of grids in the field around the preset grid, and then the step D is carried out.
In practical application, the step C specifically includes performing the following operations:
aiming at each grid in the simulated observation wind field geographic grid corresponding to the target area, the method comprises the following steps:
Figure BDA0001707992120000062
obtaining the simulated observation wind corresponding to the target areaPresetting m field grids j around the ith grid in the field grid and the corresponding grid in the wind field grid data respectively relative to the actual observationiWeight value of
Figure BDA0001707992120000063
Figure BDA0001707992120000064
I represents the total number of grids in the simulated observation wind field geographic grid corresponding to the target area, and m represents the number of grids in the field around the preset grid;
Figure BDA0001707992120000065
representing the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid data respectivelyiThe distance of (d); dimaxRepresenting the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid data respectivelyiThe maximum distance of the distance;
Figure BDA0001707992120000066
representing the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid dataiThe difference of the waviness is obtained based on actual DEM geographic grid data corresponding to the target area; r isimaxRepresenting the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid dataiMaximum value of difference in waviness; then step D is entered.
The different geogrids are different in color as shown in fig. 3, where the color represents their value and when ArcMap is read, the value is by default located at the center point of the geogrid. As shown in fig. 3, the actually observed wind field geogrid grid data includes 90 grids in the actually observed wind field geogrid grid data, and there are 90 grids in the simulated observation wind field geogrid grid, but the simulated observation wind field geogrid grid is a blank grid, and the values of all grid points are 0.
Wherein, the ith grid in the simulation observation wind field geogrid grid corresponding to the target area, m field grids j preset around the corresponding grid in the actual observation wind field geogrid grid data respectivelyiDifference in waviness of
Figure BDA0001707992120000071
The method comprises the following steps:
firstly, obtaining a grid corresponding to the ith grid in a simulated observation wind field geographic grid corresponding to a target area in actual observation wind field geographic grid data, and obtaining preset m field grids j around the gridi；
Then, obtaining a grid p in the actual DEM geographic grid data corresponding to the target area and corresponding to the ith grid in the simulated observation wind field geographic grid, and obtaining grids j in the actual DEM geographic grid data corresponding to the target area and in each field in the actual observation wind field geographic grid dataiA corresponding grid q;
then, acquiring grids through which connecting lines between the grids p and the grids q pass as each path grid from actual DEM geographic grid data corresponding to the target area; extracting DEM data of the grids p, DEM data of each grid q and DEM data of each approach grid based on actual DEM geographic grid data corresponding to the target area;
finally, respectively aiming at each grid q, obtaining the sum of DEM data difference values of each group of two adjacent grids on the connecting line along the connecting line from the grid p to the grid q, and taking the sum as the fluctuation difference between the grid p and the grid q, namely obtaining the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the grid corresponding to the ith grid in the actual observation wind field geographic grid dataiDifference in waviness of
Figure BDA0001707992120000072
Based on the simulated observation wind corresponding to the target areaPresetting m field grids j around the ith grid in the field grid and the corresponding grid in the data of the actual observation wind field gridiDifference in waviness of
Figure BDA0001707992120000073
As shown in fig. 4, two points of the DEM geographic grid through which the grid point 1 and the grid point 2 are connected are set as grid point 1 and two points between grid point 1 and grid point 2, and the value of grid point 2 on the DEM geographic grid is m1，m2，m3，m4Then r is1,2＝|m1-m2|+|m2-m3|+|m3-m4|。
Step D, respectively obtaining the simulated wind speed in each grid in the simulated observation wind field geographic grid corresponding to the target area
Figure BDA0001707992120000081
Then step E is entered.
The step D specifically executes the following operations:
aiming at each grid in the simulated observation wind field geographic grid corresponding to the target area, the method comprises the following steps:
Figure BDA0001707992120000082
obtaining the simulated wind speed in the ith grid in the simulated observation wind field geographic grid corresponding to the target area
Figure BDA0001707992120000083
In the formula (I), the compound is shown in the specification,
Figure BDA0001707992120000084
representing the grids corresponding to the ith grid in the simulated observation wind field geogrid grid and the jth peripheral grids in the actual observation wind field geogrid grid dataiActual wind speed within the individual field grid, then step E.
Step E, obtaining the following formula:
Figure BDA0001707992120000085
obtaining a measurement parameter RMSE under the current cycle number nnThen, step F is entered.
F, judging whether the cycle number n is equal to 1, if so, entering the step H; otherwise, go to step G.
Step G, judging RMSEnWhether it is not greater than a predetermined metric threshold, and RMSEn-RMSEn-1Whether the value of (A) is not more than a preset measurement difference threshold value, if the two judgment results are yes simultaneously, obtaining the current cycleaB, and entering step I; otherwise, go to step H.
And H, respectively increasing the preset change values equal to each other for the values of a and b, updating the values of a and b, and then returning to the step C.
Step I, generating geographic grids with the resolution ratio of the actual observation wind field geographic grid data corresponding to the target area and the preset second multiple, using the geographic grids as interpolation simulation wind fields corresponding to the target area, wherein each grid in the interpolation simulation wind fields is empty, the projection coordinates, the grid size and the grid upper left corner coordinates of the interpolation simulation wind fields are consistent with the actual observation wind field geographic grid data, and the preset second multiple is larger than 1, and then entering the step J. In practical application, 3 is specifically applied to preset second multiples, namely, the geographic grids with the resolution being 3 times of the data of the actual observed wind field geographic grids corresponding to the target region are generated, and the number of grids in the plug-and-play simulation wind field is 9 times of the number of grids in the data of the actual observed wind field geographic grids.
As shown in fig. 5, the triangular points are actually observed wind field geographic grid data grid points, also called observation points, the circular points are interpolation simulation wind field grid points, also called interpolation points, and the interpolation points are 9 times of the observation points.
Step J. according to the values of a and b obtained, according to
Figure BDA0001707992120000091
Aiming at the interpolation simulation wind field, the calculation mode of (1) carries out inverse distance weight interpolation based on the fluctuation degree to obtain the corresponding target areaObserving the simulation data of the wind field geogrid grid, and then entering the step K.
Step K, judging whether the resolution of the simulation data of the observed wind field geogrid grid corresponding to the target area meets the preset resolution requirement, if so, obtaining the simulation data of the observed wind field geogrid grid corresponding to the target area meeting the preset resolution requirement, and finishing the simulation method; otherwise, the simulation data of the observed wind field geographic grid corresponding to the target area is used as the actual observed wind field geographic grid data corresponding to the target area, and the step a2 is returned.
For the embodiment, compared with the actual observation wind field data diagram shown in fig. 6, the design simulation method of the invention is applied to the result diagram of three times of circulation, namely, the result diagram of one time, two times and three times of interpolation, and the accuracy is higher and higher along with the increase of the interpolation times as shown in fig. 7.
According to the wind field geographic value simulation method for the complex terrain area designed by the technical scheme, NetCDF data and DEM data of a research area are directly obtained from the Internet, data of a wind measuring station of the research area is not needed, interpolation is carried out by adopting raster data instead of traditional interpolation by utilizing vector data, and the time for data preprocessing is saved; the calculation method is simple, and the wind speed data of the research area can be acquired in a short time; the nonlinear weight equation based on the inverse distance weight interpolation is established, the distance coefficient and the fluctuation coefficient of the wind field weight function are gradually corrected by using a Cressman objective analysis method and a computer numerical simulation principle, the nonlinear weight optimal approximation equation is solved by an iteration method, and the precision of the wind field numerical simulation in the complex terrain area is improved.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.

Claims (5)

1. A wind field geographic numerical simulation method for a complex terrain area is characterized by comprising the following steps:
step A, obtaining actual DEM geographic grid data, actual observation wind field geographic grid data and a simulated observation wind field geographic grid corresponding to a target area, wherein each grid in the simulated observation wind field geographic grid is empty, and the simulated observation wind field geographic grid and the actual observation wind field geographic grid data are consistent with each other in resolution, upper left corner coordinates and a central point; then entering the step B;
b, initializing the cycle number n to 1, respectively initializing a distance coefficient a of the wind field weight function and a fluctuation coefficient b of the wind field weight function to preset values, wherein the value of a is equal to that of b, and then entering the step C;
step C, aiming at each grid in the simulated observation wind field geographic grid corresponding to the target area, obtaining the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid dataiWeight value of
Figure FDA0001707992110000011
I represents the total number of grids in the simulated observation wind field geographic grid corresponding to the target area, m represents the number of grids in the field around the preset grid, and then the step D is carried out;
step D, respectively obtaining the simulated wind speed in each grid in the simulated observation wind field geographic grid corresponding to the target area
Figure FDA0001707992110000012
Then entering step E;
step E, obtaining the following formula:
Figure FDA0001707992110000013
obtaining a measurement parameter RMSE under the current cycle number nnThen entering step F;
f, judging whether the cycle number n is equal to 1, if so, entering the step H; otherwise, entering step G;
step G, judging RMSEnWhether or not greater than a preset measurement thresholdValue, and RMSEn-RMSEn-1If the two judgment results are yes at the same time, obtaining the values of a and b under the current cycle, and entering the step I; otherwise, entering step H;
step H, aiming at the values of a and b, respectively increasing the preset change values which are equal to each other, updating the values of a and b, and then returning to the step C;
step I, generating geographic grids with the resolution ratio of actual observed wind field geographic grid data corresponding to a target region and a preset second multiple, using the geographic grids as interpolation simulated wind fields corresponding to the target region, wherein each grid in the interpolation simulated wind fields is empty, the projection coordinates, grid size and grid upper left corner coordinates of the interpolation simulated wind fields are consistent with the actual observed wind field geographic grid data, and the preset second multiple is greater than 1, and then entering step J;
step J. according to the values of a and b obtained, according to
Figure FDA0001707992110000024
The calculation mode of (1) is that inverse distance weight interpolation based on the fluctuation degree is carried out aiming at the interpolation simulation wind field to obtain observation wind field geogrid grid simulation data corresponding to the target area, and then the step K is carried out;
step K, judging whether the resolution of the simulation data of the observed wind field geogrid grid corresponding to the target area meets the preset resolution requirement, if so, obtaining the simulation data of the observed wind field geogrid grid corresponding to the target area meeting the preset resolution requirement, and finishing the simulation method; and otherwise, taking the simulation data of the observed wind field geogrid grid corresponding to the target area as the actual observed wind field geogrid grid data corresponding to the target area, and returning to the step A.
2. The wind field geographical numerical simulation method of the complex terrain area according to claim 1, wherein the step a comprises the following steps:
a1, obtaining actual observation wind field geographic grid data corresponding to a target area and actual DEM geographic grid data covering the target area, and then entering step A2;
a2, initializing a simulated observation wind field geographic grid corresponding to the target area, wherein each grid in the simulated observation wind field geographic grid is empty, and the simulated observation wind field geographic grid and the actually observed wind field geographic grid are consistent in resolution, upper left corner coordinates and a central point, and then entering the step A3;
a3, initializing a DEM geographic grid corresponding to the target area, extracting the DEM geographic grid from actual DEM geographic grid data covering the target area according to a preset first multiple of the resolution of the actual observation wind field geographic grid data corresponding to the target area, constructing the actual DEM geographic grid data corresponding to the target area, wherein the preset first multiple is more than 1, and then executing the step B; and aiming at the judgment in the step K, otherwise, taking the observed wind field geogrid grid simulation data corresponding to the target area as the actual observed wind field geogrid grid data corresponding to the target area, and returning to the step A2.
3. The wind field geographical numerical simulation method for the complex terrain area according to claim 2, wherein the step C comprises the following operations:
aiming at each grid in the simulated observation wind field geographic grid corresponding to the target area, the method comprises the following steps:
Figure FDA0001707992110000021
obtaining the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid dataiWeight value of
Figure FDA0001707992110000022
I represents the total number of grids in the simulated observation wind field geographic grid corresponding to the target area, and m represents the number of grids in the field around the preset grid;
Figure FDA0001707992110000023
representing the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid data respectivelyiThe distance of (d); dimaxRepresenting the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid data respectivelyiThe maximum distance of the distance;
Figure FDA0001707992110000034
representing the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid dataiThe difference of the waviness is obtained based on actual DEM geographic grid data corresponding to the target area; r isimaxRepresenting the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the corresponding grid in the actual observation wind field geographic grid dataiMaximum value of difference in waviness; then step D is entered.
4. The method according to claim 3, wherein the target region simulates the ith grid in the observed wind field grid, and m field grids j are preset around the ith grid in the observed wind field grid data and the corresponding grid in the actually observed wind field grid dataiDifference in waviness of
Figure FDA0001707992110000031
The method comprises the following steps:
firstly, obtaining a grid corresponding to the ith grid in a simulated observation wind field geographic grid corresponding to a target area in actual observation wind field geographic grid data, and obtaining preset m field grids j around the gridi；
Then obtaining the ith grid phase in the actual DEM geographic grid data corresponding to the target area and the simulated observation wind field geographic gridCorresponding grids p and grids j in each field in actual DEM geographic grid data corresponding to the target area and actual observation wind field geographic grid dataiA corresponding grid q;
then, acquiring grids through which connecting lines between the grids p and the grids q pass as each path grid from actual DEM geographic grid data corresponding to the target area; extracting DEM data of the grids p, DEM data of each grid q and DEM data of each approach grid based on actual DEM geographic grid data corresponding to the target area;
finally, respectively aiming at each grid q, obtaining the sum of DEM data difference values of each group of two adjacent grids on the connecting line along the connecting line from the grid p to the grid q, and taking the sum as the fluctuation difference between the grid p and the grid q, namely obtaining the ith grid in the simulated observation wind field geographic grid corresponding to the target area, and presetting m field grids j around the grid corresponding to the ith grid in the actual observation wind field geographic grid dataiDifference in waviness of
Figure FDA0001707992110000032
5. The complex terrain region wind field geographical value simulation method of claim 3, wherein the step D comprises performing the following operations:
aiming at each grid in the simulated observation wind field geographic grid corresponding to the target area, the method comprises the following steps:
Figure FDA0001707992110000033
obtaining the simulated wind speed in the ith grid in the simulated observation wind field geographic grid corresponding to the target area
Figure FDA0001707992110000041
In the formula (I), the compound is shown in the specification,
Figure FDA0001707992110000042
representing actual viewsGrid corresponding to ith grid in simulated observation wind field geographic grid in wind field geographic grid data and peripheral jth gridiActual wind speed within the individual field grid, then step E.
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