A complex mountainous terrain-based grid correction recurrence period wind speed calculation method

Abstract

The application discloses a kind of complex mountainous terrain based on terrain correction grid recurrence period wind speed calculation method belonging to meteorological observation technical field.The method is as follows: the gale sample of meteorological station gale data screening and the gale sample of grid point that has been calculated are collectively used as gale sample;Extract the topographic parameters of grid point and weather station;The gradient observation data of wind tower and laser radar collected in engineering area are used to determine the gale wind profile characteristics;The distribution uniformity of sample is determined by circulating variable grid search and judgment to determine the gale sample corresponding to each grid point;The grid point wind speed is obtained by topographic parameter correction and plane fitting;The grid-point-site gale mapping relationship is used to calculate to each grid point, and the grid recurrence period wind speed distribution of engineering area is obtained.The application can simply and quickly provide the grid recurrence period wind speed distribution of engineering area, and provide basic basis for engineering wind resistance parameter design.

Description

Technical Field

[0001] This invention relates to the field of meteorological observation technology, and in particular to a gridded return period wind speed calculation method based on topographic correction for complex mountainous terrain. Background Technology

[0002] In complex mountainous areas, in addition to the influence of weather systems, the wind field is quite complex due to topographic dynamics and thermal effects. Furthermore, weather stations are often sparse in these areas. For projects spanning large areas (such as power transmission lines and railways), conventional return-time wind speeds calculated based on reference weather station data cannot meet the engineering design requirements.

[0003] In complex mountainous terrain, the influence of topography on strong winds cannot be ignored. Therefore, one solution is to establish on-site observation stations along the project route for extended periods, establishing connections with national meteorological stations to calculate wind resistance parameters. This method is time-consuming, making it unsuitable for projects with short construction periods. If the project requires extensive deployment of observation equipment, the cost becomes prohibitively high. Alternatively, wind field distribution can be obtained through numerical simulation, modeling, and wind tunnel testing. Numerical simulation can simulate the three-dimensional distribution modes and temporal evolution of the wind field to a certain extent. Detailed numerical simulations based on numerous strong wind cases can reveal the variation of wind speeds with topography; however, extensive numerical simulations require significant time and expense. Fluid dynamics modeling and wind tunnel testing can obtain wind fields and other wind parameters under specific conditions, but these methods are more suitable for smaller areas. Large-scale return-time wind speed analysis also requires extensive simulations and experiments, again necessitating considerable time and cost.

[0004] Wind changes with topography have a continuous characteristic, and wind climate characteristics are similar within a limited area. Therefore, by using wind observation data such as meteorological station data, wind measurement towers, and lidar, and by establishing the correlation between local strong winds and topographic parameters, the strong wind mapping relationship between any point and the meteorological station can be obtained. In this way, the return period wind speed at any point can be calculated, which can provide a simple and quick basis for the design of wind resistance parameters for engineering projects. Summary of the Invention

[0005] The purpose of this invention is to propose a gridded return-time wind speed calculation method based on terrain correction for complex mountainous terrain, comprising the following steps:

[0006] Step 1: Combine the wind samples selected from the meteorological station's wind data with the wind samples from the already calculated grid points to form the wind sample;

[0007] Step 2: Extract the altitude of grid points and meteorological stations based on elevation data;

[0008] Step 3: Determine the characteristics of the strong wind profile using gradient observation data from wind measurement towers and lidar collected in the engineering area;

[0009] Step 4: Determine the strong wind sample corresponding to each grid point by iterative variable grid search and judging the uniformity of sample distribution;

[0010] Step 5: Obtain the wind speed at the grid points through terrain parameter correction and plane fitting;

[0011] Step 6: Based on the return period wind speed calculated from the long-term series of the annual maximum wind speed of the meteorological station, the return period wind speed of each grid point is estimated by using the grid-station wind mapping relationship between each grid point and the wind sample.

[0012] Step 7: If the search conditions are not met within the maximum search radius, jump to the next grid point; perform the next round of search for grid points that do not meet the search conditions until all grid points have completed terrain correction and the return period wind speed has been calculated.

[0013] Step 1 specifically includes:

[0014] Step 11: Based on the historical information of meteorological stations and the variation characteristics of the annual average wind speed sequence of meteorological stations, select meteorological stations with fewer relocations, stable annual average wind speed sequences, and high wind speeds.

[0015] Step 12: Based on the daily maximum wind speed and the time of occurrence of the weather station, select several samples of strong wind speeds; once the search range is determined, select the strong wind speeds of the same process from the stations within the same search range as strong wind samples.

[0016] Step 13: In areas with sparse weather stations, the wind speeds at the already calculated grid points are also included as wind samples.

[0017] Step 4 specifically includes:

[0018] Step 41: Set grid points and set the initial search radius for each grid point;

[0019] Step 42: Perform a point search within the initial search radius. If at least 3 points appear within the search radius, further determine the spatial distribution of these points. If they are evenly distributed in different directions, perform terrain correction. If they are unevenly distributed in different directions, gradually increase the search range by the cyclic step size until at least 3 points appear and are evenly distributed in different directions. Stop the search, perform terrain correction, and calculate the return period wind speed.

[0020] The minimum search radius is set to 5km, and the maximum is set to 50km.

[0021] The cycle step size is 5km.

[0022] Step 5 specifically includes:

[0023] Step 51: Obtain the strong wind profile function by fitting the wind profile observed within the search range or on similar terrain;

[0024] Step 52: Using a power function or linear function fitting relationship between the wind samples and elevation differences within the search range, local corrections are performed to adjust the wind profile index and fitting parameters, obtaining the wind speed V at the grid point height. sd ;

[0025] Step 53: Based on the difference ΔV between the wind speed at the wind sample points obtained from the fitted curve and the actual wind speed at the wind sample points, further correct the wind speed at the grid points of the wind sample points to obtain the corrected wind speed V. sh =V sd +ΔV;

[0026] Step 54: Perform planar interpolation on the wind speed samples at the grid point height using the inverse distance weighted interpolation method to obtain the grid point wind speed V. Gj .

[0027] The power function in step 52 is:

[0028] V s =V0(z / z0) α

[0029] Where z and z0 are both altitudes, V s V0 represents the wind speed at height z of the grid point, V0 represents the wind speed at height z0 of the strong wind sample point, and α represents the wind profile index.

[0030] The linear function in step 52 is:

[0031] V s / V0=kz / z0+b

[0032] Where z and z0 are both altitudes, V s Let V be the wind speed at height z of grid point, V0 be the wind speed at height z0 of strong wind sample point, and k and b are linear fitting parameters.

[0033] The formula for calculating the wind speed at the grid points in step 54 is:

[0034]

[0035] Among them, V shi Let i = 1, ..., n be the wind speed at each sample point at the height of the grid point, and d i , where i = 1, ..., n are the inverse distance weighting coefficients.

[0036] The beneficial effects of this invention are as follows:

[0037] 1. This invention establishes a correlation model between strong wind speed and terrain feature parameters, which can obtain the strong wind mapping relationship between any point and meteorological station, thereby calculating the wind speed of the return period of strong wind at any grid point;

[0038] 2. This invention does not require extensive simulations and experiments, and can simply and quickly provide the return-time wind speed distribution of the gridded engineering area. It solves the problem that the wind resistance parameters of some engineering areas are difficult to determine quickly in the existing technology, and provides a basic basis for the design of engineering wind resistance parameters. Attached Figure Description

[0039] Figure 1 A flowchart illustrating the gridded return period wind speed calculation method based on terrain correction for complex mountainous terrain provided in this embodiment of the invention;

[0040] Figure 2 The 100-year return period wind speed distribution in Southwest China with a resolution of 1km is provided for embodiments of the present invention. Detailed Implementation

[0041] This invention proposes a gridded return period wind speed calculation method based on terrain correction for complex mountainous terrain. The invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0042] Figure 1 This is a flowchart illustrating the gridded return-time wind speed calculation method for complex mountainous terrain based on topographic correction, provided in an embodiment of the present invention. Specifically, it includes the following steps:

[0043] Step 1: Taking the southwestern mountainous region as an example, the wind samples selected based on the wind data from the meteorological station and the wind samples from the already calculated grid are used together as the wind samples.

[0044] (1) Based on the evolution information of meteorological stations and the variation characteristics of the annual average wind speed sequence of meteorological stations, meteorological stations with fewer relocations, stable annual average wind speed sequences and larger wind speeds are selected.

[0045] (2) Based on the daily maximum wind speed and the time of occurrence of the weather stations, 10 samples of strong wind speeds were selected. Within the defined search range, the strong wind speeds of each station belonging to the same strong wind process were selected by judging the time of occurrence of the strong wind.

[0046] (3) In areas with sparse meteorological stations, the strong winds at the already calculated grid points are also used as samples.

[0047] Step 2: Extract the altitude of grid points and meteorological stations based on the elevation data.

[0048] Step 3: Based on the observation results of the wind measurement tower and lidar collected in the engineering area, the wind increases almost linearly with height.

[0049] Step 4: Determine the wind sample corresponding to each grid point through a cyclic variable grid search.

[0050] (1) Set up a grid. For each grid, set the search radius L to 5km. The minimum search radius is set to 5km and the maximum is set to 50km. The search radius cycles in increments of 5km.

[0051] (2) Search for points within the initial search radius. If at least 3 points appear within the search radius, further determine the spatial distribution of these points. If they are evenly distributed in different directions, perform terrain correction. If they are unevenly distributed in different directions, gradually increase the search range by the cycle step size until at least 3 points appear and are evenly distributed in different directions. Stop the search, perform terrain correction, and calculate the return period wind speed.

[0052] Step 5: Obtain the wind speed at the grid points through terrain parameter correction and plane fitting.

[0053] (1) The wind profile function obtained by fitting wind profiles observed within the search range or on similar terrain can be represented by a linear function:

[0054] V s / V0=kz / z0+b

[0055] Where z and z0 are both altitudes, V s Let V be the wind speed at height z of grid point, V0 be the wind speed at height z0 of strong wind sample point, and k and b are linear fitting parameters.

[0056] (2) Using the linear function fitting relationship between the strong wind samples and the elevation difference within the search range, local corrections are performed to adjust the wind profile fitting parameters and calculate the wind speed V at the grid point height of the strong wind sample points. sd ;

[0057] (3) Based on the difference ΔV between the wind speed of the wind sample points obtained from the fitted curve and the actual wind speed of the wind sample points, the wind speed of the wind sample points at the grid points is further corrected to obtain the corrected wind speed V. sh =V sd +ΔV;

[0058] (4) Planar interpolation is performed on the wind speed samples at the grid point height using the inverse distance weighted interpolation method to obtain the wind speed at the grid point location.

[0059]

[0060] Among them, V shi Let i = 1, ..., n be the wind speed at each sample point at the height of the grid point, and d i, i = 1, ..., n are the inverse distance weighting coefficients, which are represented by the reciprocal of the square of the distance between each sample point and the grid point.

[0061] Step 6: Based on the return period wind speed calculated from the long-term series of the annual maximum wind speed of the meteorological station, the return period wind speed of each grid point is estimated using the grid-station wind mapping relationship between each grid point and the wind sample.

[0062] Step 7: If step 4(2) is not satisfied within the maximum search radius, then jump to the next grid point. Perform the next round of search for grid points that do not meet the conditions until all grid points have completed terrain correction and the return period wind speed has been calculated.

[0063] The final gridded return-time wind speed distribution of the engineering area is as follows: Figure 2 As shown. This embodiment does not involve complex calculations. The calculation of the ground-based wind speed distribution at a 1km resolution and a 10m height in the southwest region took two weeks, which is more time-saving and labor-saving than numerical simulation and wind tunnel testing methods. However, the accuracy is not as good as the results obtained by numerical simulation and wind tunnel testing.

[0064] This invention eliminates the need for extensive simulations and experiments, and can quickly and easily provide a gridded return-time wind speed distribution for engineering areas. It solves the problem of difficulty in quickly determining wind resistance parameters for some engineering areas in existing technologies, and provides a basic basis for the design of wind resistance parameters for engineering projects.

Claims

1. A gridded return-time wind speed calculation method based on terrain correction for complex mountainous terrain, characterized in that, Includes the following steps: Step 1: Combine the wind samples selected from the meteorological station's wind data with the wind samples from the already calculated grid points to form the wind sample; Step 2: Extract the altitude of grid points and meteorological stations based on elevation data; Step 3: Determine the characteristics of the strong wind profile using gradient observation data from wind measurement towers and lidar collected in the engineering area; Step 4: Determine the strong wind sample corresponding to each grid point by iterative variable grid search and judging the uniformity of sample distribution; Step 5: Obtain the wind speed at the grid points through terrain parameter correction and plane fitting; Step 6: Based on the return period wind speed calculated from the long-term series of the annual maximum wind speed of the meteorological station, the return period wind speed of each grid point is estimated by using the grid point-station wind mapping relationship between each grid point and the wind sample. Step 7: If the search criteria are not met within the maximum search radius, jump to the next grid point; For grid points that do not meet the search criteria, perform the next round of search until all grid points have completed terrain correction and the return period wind speed has been calculated.

2. The method for calculating gridded return period wind speed in complex mountainous terrain based on topographic correction according to claim 1, characterized in that, Step 1 specifically includes: Step 11: Based on the historical information of meteorological stations and the variation characteristics of the annual average wind speed sequence of meteorological stations, select meteorological stations with fewer relocations, stable annual average wind speed sequences, and high wind speeds. Step 12: Based on the daily maximum wind speed and the time of occurrence of the weather station, select several samples of strong wind speeds; once the search range is determined, select the strong wind speeds of the same process from the stations within the same search range as strong wind samples. Step 13: In areas with sparse weather stations, the wind speeds at the already calculated grid points are also included as wind samples.

3. The method for calculating gridded return period wind speed in complex mountainous terrain based on topographic correction according to claim 1, characterized in that, Step 4 specifically includes: Step 41: Set grid points and set the initial search radius for each grid point; Step 42: Perform a point search within the initial search radius. If at least 3 points appear within the search radius, further determine the spatial distribution of these points. If they are evenly distributed in different directions, perform terrain correction. If they are unevenly distributed in different directions, gradually increase the search range by the cyclic step size until at least 3 points appear and are evenly distributed in different directions. Stop the search, perform terrain correction, and calculate the return period wind speed.

4. The method for calculating gridded return period wind speed in complex mountainous terrain based on topographic correction according to claim 3, characterized in that, The minimum search radius is set to 5km, and the maximum is set to 50km.

5. The method for calculating gridded return period wind speed in complex mountainous terrain based on topographic correction according to claim 4, characterized in that, The cycle step size is 5km.

6. The method for calculating gridded return period wind speed in complex mountainous terrain based on topographic correction according to claim 1, characterized in that, Step 5 specifically includes: Step 51: Obtain the strong wind profile function by fitting the wind profile observed within the search range or on similar terrain; Step 52: Using a power function or linear function fitting relationship between the wind samples and elevation differences within the search range, perform local corrections to adjust the wind profile index and fitting parameters, and obtain the wind speed V at the grid point height. sd ; Step 53: Based on the difference ΔV between the wind speed at the wind sample points obtained from the fitted curve and the actual wind speed at the wind sample points, further correct the wind speed at the grid points of the wind sample points to obtain the corrected wind speed V. sh =V sd +ΔV; Step 54: Perform planar interpolation on the wind speed samples at the grid point height using the inverse distance weighted interpolation method to obtain the grid point wind speed V. Gj .

7. The method for calculating gridded return period wind speed in complex mountainous terrain based on topographic correction according to claim 6, characterized in that, The power function in step 52 is: V s =V0(z / z0) α Where z and z0 are both altitudes, V s V0 represents the wind speed at height z of the grid point, V0 represents the wind speed at height z0 of the strong wind sample point, and α represents the wind profile index.

8. The method for calculating gridded return period wind speed in complex mountainous terrain based on topographic correction according to claim 6, characterized in that, The linear function in step 52 is: V s / V0=kz / z0+b Where z and z0 are both altitudes, V s Let V be the wind speed at height z of grid point, V0 be the wind speed at height z0 of strong wind sample point, and k and b are linear fitting parameters.

9. The method for calculating gridded return period wind speed in complex mountainous terrain based on topographic correction according to claim 6, characterized in that, The formula for calculating the wind speed at the grid points in step 54 is as follows: Among them, V shi Let i = 1, ..., n, be the wind speed at each sample point at the height of the grid point, and d i , i = 1, ..., n, are the inverse distance weighting coefficients.

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