Wind profile fitting method suitable for time sequence prediction of complex mountain wind field

Abstract

The invention discloses a wind profile fitting method suitable for time sequence prediction of a complex mountain wind field, and the method comprises the steps: obtaining topographic data and meteorological data, determining a wind shear coefficient of a target region, and constructing a wind profile fitting model; target area real-time topographic data and target area real-time meteorological data are obtained, the wind profile fitting model receives the target area real-time topographic data and the target area real-time meteorological data, and a wind profile set is generated; and for the target area, dynamically adjusting the wind profile parameters in real time, and generating a wind field time sequence prediction result. According to the method, complex mountain landforms, meteorological factors and space-time change characteristics of a wind field are considered, accurate fitting of wind profiles of different terrain sub-regions is realized on the basis of a terrain and meteorological data set, and wind profile fitting better fits the actual situation; on one hand, terrain and meteorological data are updated in real time, on the other hand, a dynamic self-adaptive updating mechanism is established for wind field time sequence prediction, and calculation input reasonability is guaranteed.

Description

Technical Field

[0001] This application relates to the field of wind field time series prediction technology, and in particular to a wind profile fitting method applicable to wind field time series prediction in complex mountainous areas. Background Technology

[0002] To better utilize wind resources in mountainous areas, refined wind field prediction is needed for complex mountainous regions. Accurate setting of inflow boundary conditions is crucial for refined wind field simulation. Wind fields in complex mountainous areas are influenced by factors such as terrain slope, roughness, and valley orientation, with wind speed patterns exhibiting significant spatiotemporal nonuniformity, especially in areas with dramatic terrain undulations. Traditional wind field simulation methods are mostly based on the assumption of flat terrain, neglecting factors such as flow around mountains, uphill effects, and local circulation, resulting in insufficient terrain adaptability and large prediction errors for wind speeds in complex mountainous areas. Furthermore, traditional methods focus on single-point static fitting, failing to capture the temporal patterns of wind field changes with diurnal temperature variations and seasonal variations, leading to significant differences in prediction accuracy across different times. Therefore, this application is proposed. Summary of the Invention

[0003] This application protects a wind profile fitting method applicable to time series prediction of wind fields in complex mountainous areas, in order to solve the problem of low accuracy in wind field prediction for complex terrain in the prior art.

[0004] A wind profile fitting method suitable for time-series prediction of wind fields in complex mountainous areas includes the following steps: acquiring topographic and meteorological data to construct a topographic-meteorological dataset; determining the wind shear coefficient of the target area based on the topographic-meteorological dataset and constructing a wind profile fitting model; acquiring real-time topographic and meteorological data of the target area, with the wind profile fitting model receiving the real-time topographic and meteorological data of the target area to generate a set of wind profiles; dynamically adjusting the wind profile parameters in real time for the target area; and performing numerical solutions for the computational domain to generate time-series prediction results of the wind field.

[0005] Furthermore, before constructing the topographic and meteorological dataset, the steps also include: identifying outliers in the topographic data, fitting a neighborhood topographic trend surface of the outliers, and fitting a corrected and replaced topographic data outliers, wherein the fitting and correction is... ,in H corr These are correction values ​​for terrain data. H j Elevation of neighboring points ω j is the distance weight, and n is the number of neighboring points.

[0006] Furthermore, before constructing the topographic meteorological dataset, the process also includes: identifying meteorological data outliers, including wind speed data outliers; and performing a weighted correction on the wind speed data outliers based on the topographic representativeness of the measured stations and the consistency of multi-source data. The weighted correction is as follows: ,in, V corr This is the wind speed correction value. ω obs This is the terrain representativeness weighting coefficient. V obs Raw observed wind speed values ​​collected from mountain weather stations and wind measurement towers. ω i For a certain meteorological data source, weighting coefficient V i The wind speed value is a data source for a certain meteorological event. n Let n represent the types of meteorological data sources, where n ≥ 1.

[0007] Further, the step of determining the wind shear coefficient of the target area based on the topographic and meteorological dataset includes: acquiring historical wind measurement data of similar terrain, fitting a power-law wind profile model equation to the historical wind measurement data of similar terrain, and inversely calculating the historical average wind shear coefficient of similar terrain. The power-law wind profile model equation is... , h The height of the wind speed to be fitted within the surface boundary layer. V Let h be the wind speed. V r For reference height h ref The average wind speed at that location α This refers to the wind shear coefficient; it involves obtaining the terrain roughness of similar terrain areas and the target area, and generating an initial range for the wind shear coefficient of the target area based on the historical average wind shear coefficient of similar terrain areas. Specifically: in ，α hist For similar terrain, the historical wind shear coefficient is... α range The initial range of wind shear coefficient for the target area ，k The roughness coefficient is the factor that influences roughness. Z 0,target For the terrain roughness of the target area, Z 0,similar This represents the terrain roughness in similar terrain regions.

[0008] Furthermore, based on the topographic and meteorological dataset, the steps determine the wind shear coefficient of the target area, including acquiring short-term high-frequency wind measurement data of the target area, setting the initial range of the wind shear coefficient of the target area as a model constraint, constructing a power-law wind profile model, and inversely calculating the average wind shear coefficient of the target area.

[0009] Furthermore, a power-law wind profile model is constructed, and the average wind shear coefficient of the target area is calculated inversely, including: correcting the terrain undulation height difference based on the power-law wind profile model, and according to the first correction model. To make corrections, among which... V Let h be the wind speed. H To ensure the reliability of meteorological data, V H For reliable speed, dH The elevation difference between the terrain and the baseline is used to correct for the influence of terrain undulation on the vertical distribution of wind speed; h is the height of the wind speed to be fitted within the surface boundary layer. α 1 represents the average wind shear coefficient of the target area.

[0010] Furthermore, the steps for constructing the wind profile fitting model include: based on topographic and meteorological data, dividing the target topographic region into sub-regions according to its topographic features; identifying the height of abrupt changes in wind speed gradients in different sub-regions to determine the confidence layer height for different meteorological data; and optimizing the first correction model based on the topographic correction coefficient β to generate a second correction model, taking into account the topographic differences in the sub-regions. in, V Let h be the wind speed. H To ensure the reliability of meteorological data, V H For reliable speed, dH The elevation difference between the terrain and the baseline is used to correct for the influence of terrain undulation on the vertical distribution of wind speed; h is the height of the wind speed to be fitted within the surface boundary layer. α 1 represents the average wind shear coefficient of the target area, and β represents the terrain correction coefficient.

[0011] Furthermore, determining the confidence level of different meteorological data includes: for wind speed sequences Calculate the wind speed difference between adjacent heights: ,when At that time, take h i The above stable layer heights are reliable levels based on meteorological data. h i For wind speed sequence height, The threshold for limiting the wind speed difference between adjacent height intervals.

[0012] Furthermore, the sub-region includes at least one of the following: high mountain canyon area, low mountain gentle slope area, and mountain plateau area.

[0013] The beneficial effects of this application are: (1) This application obtains topographic and meteorological datasets, determines the wind shear coefficient of the target area, and constructs a wind profile fitting model, so that the wind profile fitting is more in line with the actual situation; (2) A dynamic adaptive update mechanism is established for the time-series prediction of wind field, and the wind profile parameters are updated in real time to adjust the boundary conditions, ensuring the rationality of the calculation input and further improving the accuracy of the prediction of complex mountain wind field. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating one aspect of the process described in this application. Figure 2 This is a schematic diagram of a wind profile fitting model in a complex terrain according to one aspect of this application; Figure 3 This is a schematic diagram of a wind profile fitting model for different sub-regions in a complex terrain according to one aspect of this application; Figure 4 This is a schematic diagram illustrating the determination of the credibility layer height of meteorological data according to one embodiment of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will be combined with the embodiments of this application. The accompanying drawings clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0018] This application protects a wind profile fitting method applicable to time series prediction of wind fields in complex mountainous areas, in order to solve the problem of low accuracy in wind field prediction for complex terrain in the prior art.

[0019] Complex topographic conditions refer to areas with significant spatial heterogeneity in landform, such as mountains (slope ≥ 15°), canyons (width < 500m), plateaus, and hills. The topographic undulations and roughness variations significantly affect airflow and pollutant diffusion paths.

[0020] Digital Elevation Model (DEM) is a physical ground model that uses limited terrain elevation data to digitally simulate the ground terrain (i.e., digitally represent the surface morphology of the terrain). It is a physical ground model that uses an ordered array of numerical values ​​to represent the ground elevation.

[0021] A wind profile is a curve showing the distribution of wind speed with altitude, and it is mainly used to study the wind speed patterns within the atmospheric boundary layer.

[0022] Confidential velocity refers to the incoming wind speed that is stable and reliable in meteorological data above a certain altitude, representing the airflow speed that is not affected by near-surface disturbances caused by complex terrain.

[0023] Reliable altitude refers to the altitude at which meteorological data tends to be stable, as measured by equipment such as lidar, where airflow is less affected by disturbances such as near-surface friction and topographic undulations in complex terrain. Meteorological data above this altitude can be considered uniform and reliable incoming flow data.

[0024] The wind shear coefficient refers to the rate at which wind speed changes with altitude. Different terrains have different wind shear coefficients due to differences in terrain undulation, roughness, etc., reflecting the degree of influence of terrain on the vertical distribution of wind.

[0025] refer to Figure 1 As shown, this application protects a wind profile fitting method applicable to time series prediction of wind fields in complex mountainous areas, comprising the following steps: S1, acquiring topographic and meteorological data to construct a topographic and meteorological dataset; S2, determining the wind shear coefficient of the target area based on the topographic and meteorological dataset and constructing a wind profile fitting model; S3, acquiring real-time topographic and meteorological data of the target area, and the wind profile fitting model receiving the real-time topographic and meteorological data of the target area to generate a set of wind profiles; S4, dynamically adjusting the wind profile parameters in real time for the target area; S5, performing numerical solutions for the computational domain to generate wind field time series prediction results.

[0026] By adopting the above scheme, this method, based on topographic and meteorological data, makes the wind profile fitting more closely reflect the actual situation. On the one hand, it updates the topographic and meteorological data in real time; on the other hand, it establishes a dynamic adaptive update mechanism for the time-series prediction of the wind field, updating the wind profile parameters and adjusting boundary conditions in real time, ensuring the rationality of the calculation input and further improving the accuracy of wind field prediction in complex mountainous areas. Meteorological data such as wind speed and direction at different heights can be collected using meteorological stations and wind measurement towers. Data such as elevation, slope, and undulation of the terrain can be obtained through satellite digital elevation models (DEMs) or geographic information systems (GIS). The computational domain can be the part of the target area that needs to be solved.

[0027] In another embodiment of this application, before constructing the topographic and meteorological dataset, the process further includes cleaning the topographic and meteorological data. The topographic data is, for example, DEM data, and the meteorological data is, for example, satellite remote sensing meteorological data, numerical forecast data, etc. For example, if the deviation between the data source and the measured data is >15%, it is determined to be erroneous data and removed.

[0028] In another embodiment of this application, before constructing the topographic meteorological dataset, the method further includes: identifying outliers in the topographic data, fitting a neighborhood topographic trend surface of the outliers, and fitting a corrected and replaced topographic data outliers, wherein the fitting and correction is... ,in H corr These are correction values ​​for terrain data. H j Elevation of neighboring points ω j is the distance weight, and n is the number of neighboring points.

[0029] In another embodiment of this application, before constructing the topographic meteorological dataset, the method further includes: identifying meteorological data outliers, including wind speed data outliers; and performing a weighted correction on the wind speed data outliers based on the topographic representativeness of the measured stations and the consistency of multi-source data, wherein the weighted correction is... ,in, V corr This is the wind speed correction value. ω obs This is the terrain representativeness weighting coefficient. V obs Raw observed wind speed values ​​collected from mountain weather stations and wind measurement towers. ω i For a certain meteorological data source, weighting coefficient V i The wind speed value is a data source for a certain meteorological event. n Let n represent the types of meteorological data sources, where n ≥ 1.

[0030] By adopting the above approach, outliers can be corrected, erroneous data can be removed, and missing data can be filled in, thus improving the authenticity and accuracy of the data.

[0031] In another embodiment of this application, the step of determining the wind shear coefficient of the target area based on a topographic and meteorological dataset includes: acquiring historical wind measurement data of similar terrain, fitting a power-law wind profile model equation to the historical wind measurement data of similar terrain, and inversely calculating the historical average wind shear coefficient of similar terrain. The power-law wind profile model equation is... , h The height of the wind speed to be fitted within the surface boundary layer. V Let h be the wind speed. V r For reference height h ref The average wind speed at that location α This refers to the wind shear coefficient; it involves obtaining the terrain roughness of similar terrain areas and the target area, and generating an initial range for the wind shear coefficient of the target area based on the historical average wind shear coefficient of similar terrain areas. Specifically: in ，α hist For similar terrain, the historical wind shear coefficient is... α range The initial range of wind shear coefficient for the target area ，k The roughness coefficient is the factor that influences roughness. Z 0,target For the terrain roughness of the target area, Z 0,similar This represents the terrain roughness in similar terrain regions.

[0032] Using the above scheme, historical wind measurement data for similar terrain can be long-term historical wind measurement data of more than 5 years, including at least 2 height layers. Data can be fitted for each time period, such as monthly / quarterly / yearly. Through the above scheme, the accuracy and authenticity of wind shear coefficient can be improved.

[0033] In another embodiment of this application, the step of determining the wind shear coefficient of the target area based on the topographic and meteorological dataset includes obtaining short-term high-frequency wind measurement data of the target area, setting the initial value range of the wind shear coefficient of the target area as a model constraint, constructing a power-law wind profile model, and inversely calculating the average wind shear coefficient of the target area.

[0034] By adopting the above scheme and using the initial range of the wind shear coefficient in the target area as a model constraint, the accuracy and realism of the wind shear coefficient can be improved.

[0035] In another embodiment of this application, constructing a power-law wind profile model and inversely calculating the average wind shear coefficient of the target area includes: correcting the terrain undulation height difference based on the power-law wind profile model, and calculating the average wind shear coefficient of the target area based on a first correction model. To make corrections, among which... V Let h be the wind speed. H To ensure the reliability of meteorological data, V H For reliable speed, dH The elevation difference between the terrain and the baseline is used to correct for the influence of terrain undulation on the vertical distribution of wind speed; h is the height of the wind speed to be fitted within the surface boundary layer. α 1 represents the average wind shear coefficient of the target area.

[0036] The above scheme is adopted, with reference to Figure 2 The diagram shows a wind profile fitting model for a complex terrain in one aspect of this application. The model is modified to take into account the influence of terrain undulation height differences, which can improve the accuracy and realism of the average wind shear coefficient in the target area.

[0037] In another embodiment of this application, the step of constructing a wind profile fitting model includes: dividing the target terrain region into sub-regions based on topographic and meteorological data and the terrain features of the target region; identifying the height of abrupt changes in wind speed gradients for different sub-regions and determining the confidence layer height for different meteorological data; and optimizing a first correction model based on the terrain correction coefficient β to generate a second correction model, taking into account the terrain differences between the sub-regions. in, V Let h be the wind speed. H To ensure the reliability of meteorological data, V H For reliable speed, dH The elevation difference between the terrain and the baseline is used to correct for the influence of terrain undulation on the vertical distribution of wind speed; h is the height of the wind speed to be fitted within the surface boundary layer. α 1 represents the average wind shear coefficient of the target area, and β represents the terrain correction coefficient.

[0038] The above scheme is adopted, with reference to Figure 3 As shown, a schematic diagram of the wind profile fitting model for different sub-regions in a complex terrain is given. By dividing the target area into different sub-regions, the fitting can be combined with the actual situation of different sub-regions, making the fitting result more accurate and in line with reality. The terrain correction coefficient β can be selected according to the terrain stability of the sub-region. For example, the β value in the canyon area is 1.2-1.5, and the β value in the flat area is 0.8-1.0.

[0039] In another embodiment of this application, determining the confidence layer height of different meteorological data includes: for wind speed sequences Calculate the wind speed difference between adjacent heights: ,when At that time, take h i The above stable layer heights are reliable levels based on meteorological data.h i For wind speed sequence height, The threshold for limiting the wind speed difference between adjacent height intervals.

[0040] If the above solution is adopted, such as Figure 4 The diagram shown illustrates the determination of the credibility layer height for meteorological data, which can make the credibility height of meteorological data more accurate. Wind speed sequences can be obtained by acquiring topographic data and meteorological data.

[0041] In another embodiment of this application, the sub-region includes at least one of a high mountain canyon area, a low mountain gentle slope area, and a mountain plateau area.

[0042] Using the above scheme, we can combine topography and historical wind measurement data to divide the region into sub-regions. The high mountain and canyon area has a slope greater than 30°, a maximum elevation difference of more than 500m, prevailing winds along the valley, and a large wind speed gradient. The low mountain and gentle slope area has a slope of 10° ≤ slope ≤ 30°, a maximum elevation difference of 100m ≤ elevation difference of 500m, and a relatively uniform wind field. The mountain plateau area has a slope of < 10°, a maximum elevation difference of < 100m, a small wind speed gradient, and obvious turbulence characteristics.

[0043] This application proposes a wind profile fitting method suitable for time-series prediction of wind fields in complex mountainous areas. Considering the complex topography, meteorological factors, and spatiotemporal variations of wind fields, this method, compared to traditional wind profile models that are only applicable to flat terrain, is based on a topographic meteorological dataset. By leveraging this data, it achieves accurate wind profile fitting for different topographic sub-regions such as ridges and valleys, making the wind profile fit more realistic. On one hand, it updates topographic and meteorological data in real time; on the other hand, it establishes a dynamic adaptive update mechanism for time-series wind field prediction, updating wind profile parameters and adjusting boundary conditions in real time to ensure the rationality of the computational input and further improve the accuracy of wind field prediction in complex mountainous areas. Meteorological data such as wind speed and direction at different heights can be collected using meteorological stations and anemometer towers. Data on terrain elevation, slope, and undulation can be obtained through satellite digital elevation models (DEMs) or geographic information systems (GIS).

Claims

1. A wind profile fitting method suitable for time-series prediction of wind fields in complex mountainous areas, characterized in that, Including the following steps: Acquire topographic and meteorological data to construct a topographic-meteorological dataset; Based on topographic and meteorological datasets, the wind shear coefficient of the target area is determined, and a wind profile fitting model is constructed. The system acquires real-time terrain data and real-time meteorological data of the target area. The wind profile fitting model receives the real-time terrain data and real-time meteorological data of the target area and generates a set of wind profiles. For the target area, adjust the wind profile parameters dynamically in real time; Numerical solutions are performed on the computational domain to generate time-series wind field prediction results.

2. The wind profile fitting method for time-series prediction of complex mountain wind fields according to claim 1, characterized in that, The steps before constructing the topographic and meteorological dataset also include: Identify outliers in terrain data, fit a neighborhood terrain trend surface to the outliers, and then fit and correct the outliers to replace them. The fitted correction is... ,in H corr These are correction values ​​for terrain data. H j Elevation of neighboring points ω j is the distance weight, and n is the number of neighboring points.

3. The wind profile fitting method for time-series prediction of complex mountain wind fields according to claim 1, characterized in that, Before constructing the topographic and meteorological dataset, the steps also include: identifying meteorological data outliers, including wind speed data outliers; and performing a weighted correction on the wind speed data outliers based on the topographic representativeness of the measured stations and the consistency of multi-source data. The weighted correction is as follows: ,in, V corr This is the wind speed correction value. ω obs This is the terrain representativeness weighting coefficient. V obs Raw observed wind speed values ​​collected from mountain weather stations and wind measurement towers. ω i For a certain meteorological data source, weighting coefficient V i The wind speed value is a data source for a certain meteorological event. n Let n represent the types of meteorological data sources, where n ≥ 1.

4. The wind profile fitting method for time-series prediction of complex mountain wind fields according to claim 1, characterized in that, The steps, based on a topographic and meteorological dataset, determine the wind shear coefficient for the target area, including: acquiring historical wind measurement data of similar terrain; fitting a power-law wind profile model equation to the historical wind measurement data of similar terrain; and inversely calculating the historical average wind shear coefficient of similar terrain. The power-law wind profile model equation is... , h The height of the wind speed to be fitted within the surface boundary layer. V Let h be the wind speed. V r For reference height h ref The average wind speed at that location α This refers to the wind shear coefficient; it involves obtaining the terrain roughness of similar terrain areas and the target area, and generating an initial range for the wind shear coefficient of the target area based on the historical average wind shear coefficient of similar terrain areas. Specifically: in ，α hist For similar terrain, the historical wind shear coefficient is... α range The initial range of wind shear coefficient for the target area ，k The roughness coefficient is the factor that influences roughness. Z 0,target For the terrain roughness of the target area, Z 0,similar This refers to the terrain roughness in areas with similar terrain.

5. The wind profile fitting method for time-series prediction of complex mountain wind fields according to claim 4, characterized in that, The steps are based on topographic and meteorological datasets to determine the wind shear coefficient of the target area, including: acquiring short-term high-frequency wind measurement data of the target area, setting the initial range of the wind shear coefficient of the target area as a model constraint, constructing a power-law wind profile model, and inversely calculating the average wind shear coefficient of the target area.

6. The wind profile fitting method for time-series prediction of wind fields in complex mountainous areas according to claim 5, characterized in that, Constructing a power-law wind profile model and inversely calculating the average wind shear coefficient of the target area includes: Based on the power-law wind profile model, the terrain undulation height difference is corrected according to the first correction model. To make corrections, among which... V Let h be the wind speed. H To ensure the reliability of meteorological data, V H For reliable speed, dH The elevation difference between the terrain and the baseline is used to correct for the influence of terrain undulation on the vertical distribution of wind speed; h is the height of the wind speed to be fitted within the surface boundary layer. α 1 represents the average wind shear coefficient of the target area.

7. The wind profile fitting method for time-series prediction of complex mountain wind fields according to claim 6, characterized in that, The steps for constructing the wind profile fitting model include: based on topographic and meteorological data, dividing the target topographic region into sub-regions according to its topographic features; identifying the height of abrupt changes in wind speed gradients in different sub-regions and determining the confidence layer height for different meteorological data; and optimizing the first correction model based on the topographic correction coefficient β to generate a second correction model, taking into account the topographic differences in the sub-regions. ,in, V Let h be the wind speed. H The height of the credibility layer for meteorological data. V H For reliable speed, dH The elevation difference between the terrain and the baseline is used to correct for the influence of terrain undulation on the vertical distribution of wind speed; h is the height of the wind speed to be fitted within the surface boundary layer. α 1 represents the average wind shear coefficient of the target area, and β represents the terrain correction coefficient.

8. The wind profile fitting method for time-series prediction of complex mountain wind fields according to claim 7, characterized in that, Determining the confidence level of different meteorological data includes: For wind speed sequences Calculate the wind speed difference between adjacent heights ,when At that time, take h i The above stable layer heights are reliable levels based on meteorological data. h i For wind speed sequence height, The threshold for limiting the wind speed difference between adjacent height intervals.

9. The wind profile fitting method for time-series prediction of wind fields in complex mountainous areas according to any one of claims 7 or 8, characterized in that, The sub-regions include at least one of the following: high mountain canyon area, low mountain gentle slope area, and mountain plateau area.

Images