Method for quickly speculating high-altitude wind speed based on earth surface observation

Abstract

The invention discloses a method for quickly speculating high-altitude wind speed based on earth surface observation, which belongs to the technical field of wind resource assessment and comprises the following steps: acquiring earth surface high-frequency ultrasonic data and iron tower wind speed data, extracting earth surface wind speed, friction speed and atmospheric stability, and determining earth surface roughness, boundary layer height and earth turning wind; a wind profile calculation model is selected according to different change conditions of the high-altitude wind speed under the atmospheric stability condition, and wind speed vertical distribution data is calculated by combining the surface roughness, the boundary layer height and the geostrophic wind. According to the method, the problem of insufficient precision in high-altitude wind speed calculation of a traditional method is solved, the change of the wind speed along with the height under different atmospheric stability conditions is accurately reflected, the applicability and reliability of the wind profile model are remarkably improved, and efficient and low-cost technical support is provided for wind power plant site selection, wind turbine design and wind power prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of wind resource assessment, and in particular to a method for rapidly estimating high-altitude wind speed based on surface observation. Background Art

[0002] Wind resource assessment is the foundation of wind farm planning, design, and operation, and its accuracy directly impacts the power generation efficiency and economic benefits of wind farms. Accurate estimation of high-altitude wind speed is crucial for wind turbine design, wind farm site selection, and wind power forecasting.

[0003] Currently, numerical models (such as WRF and CFD) are one of the main means of wind resource assessment, but these methods usually require a lot of computing resources and time, making it difficult to meet the needs of rapid assessment. In addition, the height of wind towers built in the early days is generally low, far lower than the hub height of modern wind turbines, and it is impossible to directly obtain wind speed information within the utilization range of the blades. Therefore, it is necessary to use inference methods to make up for this shortcoming. However, traditional inference methods are mostly based on simple logarithmic wind profile models or exponential models. These models are only applicable to the lower atmosphere and neutral stability conditions, and do not fully consider the impact of atmospheric stability on the vertical distribution of wind speed. This leads to large deviations in the high-altitude wind speed inference, which makes it difficult to meet the accuracy requirements under the trend of large-scale wind turbines. Summary of the Invention

[0004] To solve the above problems, the present invention provides a method for quickly estimating high-altitude wind speed based on surface observations. It adopts multi-source data fusion and dynamic model selection mechanism, and comprehensively considers surface friction velocity, atmospheric stability, surface roughness, boundary layer height and geostrophic wind parameters. It can accurately characterize the vertical distribution law of wind speed under different stability conditions, significantly improve the accuracy of high-altitude wind speed estimation and model applicability, and provide high-precision, low-cost real-time technical support for wind farm site selection and wind power forecasting.

[0005] The above objectives can be achieved through the following solutions:

[0006] A method for rapidly estimating high-altitude wind speed based on surface observations comprises acquiring surface high-frequency ultrasonic data and tower wind speed data, wherein the surface high-frequency ultrasonic data includes three-dimensional wind speed and temperature; performing quality control and analysis on the surface high-frequency ultrasonic data to extract a set of key parameters, wherein the set of key parameters includes surface wind speed, surface friction velocity, and Monin-Obukhov length; calculating surface roughness based on the tower wind speed data, the surface friction velocity, and the Monin-Obukhov length; observing whether there is microwave radiometer data and whether the microwave radiometer is effective. The data may cover the boundary layer height; if so, the boundary layer height is calculated based on the microwave radiometer data; if not, the boundary layer height is calculated based on the surface friction velocity and the Monin Obukhov length; meteorological element information above the boundary layer height is obtained according to the preset reanalysis data, the dimensionless coefficient in the geostrophic resistance law is derived, and the geostrophic wind is calculated; a wind profile calculation model is selected according to the key parameter set; and vertical wind speed distribution data is calculated based on the selected wind profile calculation model, the surface roughness, the boundary layer height, and the geostrophic wind.

[0007] Optionally, the obtaining of surface high-frequency ultrasonic data and tower wind speed data includes: setting an ultrasonic anemometer and a tower wind speed measuring device at the same preset location; setting the ultrasonic anemometer at a preset height on the ground and performing horizontal calibration; using the ultrasonic anemometer to obtain surface high-frequency ultrasonic data, and using the tower wind speed measuring device to obtain tower wind speed data; wherein, one layer of the ultrasonic anemometer and the tower wind speed measuring device are at the same height, and the average wind speed deviation within a preset time window does not exceed a preset deviation threshold.

[0008] Optionally, the quality control of the surface high-frequency ultrasonic data includes: performing physical rationality judgment on the three-dimensional wind speed and temperature, and eliminating abnormal data; performing amplitude resolution test, rigid value test, high-order statistics test and stationarity test on the surface high-frequency ultrasonic data, and eliminating invalid data; performing coordinate rotation on the three-dimensional wind speed to eliminate measurement errors.

[0009] Optionally, the extracted key parameter set includes: analyzing the surface high-frequency ultrasonic data after quality control to obtain the pulsation component and temperature pulsation in the coordinate axis direction corresponding to the three-dimensional wind speed; calculating the surface friction velocity based on the pulsation component; and calculating the Monin Obukhov length using the pulsation component, the temperature pulsation, and the surface friction velocity.

[0010] Optionally, the calculation of the surface roughness includes: obtaining multiple candidate roughnesses within a preset range based on a preset step size; obtaining the actual observed wind speed of a preset number of tower floors according to the tower wind speed data; obtaining the theoretical wind speed of the corresponding number of floors using the surface friction speed and the Monin Obukhov length; calculating the relative error between the actual observed wind speed and the theoretical wind speed of the corresponding number of floors; summing the relative errors of the preset number of floors of each candidate roughness to obtain the relative error sum of the corresponding candidate roughness; comparing the relative error sums of different candidate roughnesses, selecting the corresponding candidate roughness according to the comparison result, and obtaining the surface roughness.

[0011] Optionally, the calculation of the boundary layer height based on the surface friction speed and the Monin Obukhov length includes: determining whether the Monin Obukhov length is greater than a preset first threshold and less than or equal to a preset second threshold; if not, calculating a first height based on the surface friction speed, a preset first proportional coefficient, and a preset Coriolis parameter; if so, calculating a second height based on the surface friction speed, a preset second proportional coefficient, the Coriolis parameter, and a preset stability correction function; wherein the boundary layer height includes the first height and the second height, and the absolute value of the second threshold is greater than the absolute value of the first threshold.

[0012] Optionally, the calculating of the geostrophic wind includes: obtaining meteorological element information above the boundary layer height according to preset reanalysis data, and calculating an initial wind speed; calculating a dimensionless coefficient based on the initial wind speed; and calculating a final geostrophic wind using the surface roughness, the surface friction speed, and the dimensionless coefficient.

[0013] Optionally, the selecting of the wind profile calculation model according to the key parameter set includes: when the absolute value of the Monin Obukhov length is greater than the second threshold, selecting a preset logarithmic wind profile model or a preset DH wind profile model according to the surface wind speed; when the Monin Obukhov length is less than the first threshold and the absolute value of the Monin Obukhov length is less than or equal to the second threshold, selecting the preset Gryning model or the logarithmic wind profile model; when the Monin Obukhov length is greater than the first threshold and the absolute value of the Monin Obukhov length is less than or equal to the second threshold, selecting the Gryning model or the preset LOG-Ekman two-layer model; wherein the wind profile calculation model includes the logarithmic wind profile model, the DH wind profile model, the Gryning model and the LOG-Ekman two-layer model.

[0014] Optionally, selecting the Gryning model to calculate the vertical distribution data of wind speed includes: adjusting parameters of the Gryning model to calculate the vertical distribution data of wind speed according to the Monin-Obukhov length and the first threshold and the second threshold.

[0015] Optionally, the LOG-Ekman two-layer model calculates the vertical distribution data of wind speed, including: obtaining the estimated height and the height of the top of the near-ground layer; and selecting the corresponding wind profile expression of the LOG-Ekman two-layer model to calculate the vertical distribution data of wind speed according to the size of the estimated height and the height of the top of the near-ground layer.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] 1. The present invention provides a new idea and method for estimating high-altitude wind speed within the scale of wind turbines. By using a high-altitude wind speed estimation method based on surface observation data, it comprehensively considers key physical quantities such as atmospheric stability, roughness, boundary layer height and geostrophic wind, and combines the advantages of different methods. It effectively solves the problem of insufficient accuracy of traditional methods in estimating high-altitude wind speed, and realizes fast, real-time and high-precision wind speed estimation.

[0018] 2. The present invention can adapt to different atmospheric stability conditions, such as stable, neutral and unstable conditions, and has wide universality.

[0019] 3. The present invention has low requirements for instruments and only requires conventional surface ultrasonic instruments and tower wind speed observation equipment to achieve high-precision calculations, which significantly reduces the implementation cost and technical threshold and is suitable for real-time application scenarios.

[0020] 4. The present invention further improves the applicability and accuracy of the inference method by integrating the optimization framework of multiple models, providing efficient and reliable technical support for wind farm site selection, wind turbine design and wind power prediction.

[0021] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 The present invention is a flowchart of a method for rapidly estimating high-altitude wind speed based on surface observations according to an embodiment of the present invention.

[0024] Figure 2 is a frequency distribution diagram of ground surface stability according to an embodiment of the present invention.

[0025] Figure 3 1 is an average wind profile diagram of different stability conditions according to an embodiment of the present invention.

[0026] Figure 4 This is a comparison diagram of the boundary layer height deduced by the microwave radiometer and the boundary layer height deduced by surface observation in an embodiment of the present invention.

[0027] Figure 5 3 is a comparison chart of high-altitude wind speed estimation under neutral conditions of different models in an embodiment of the present invention.

[0028] Figure 6 3 is a comparison chart of high-altitude wind speed estimation under strong wind conditions under different models of an embodiment of the present invention.

[0029] Figure 7 3. This is a comparison diagram of high-altitude wind speed estimation under unstable conditions of different models in an embodiment of the present invention.

[0030] Figure 8 3 is a comparison chart of high-altitude wind speed estimation under stable conditions of different models in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0032] Reference Figure 1 One embodiment of the present invention proposes a method for quickly estimating high-altitude wind speed based on surface observations. It adopts multi-source data fusion and a dynamic model selection mechanism, and comprehensively considers surface friction velocity, atmospheric stability (Monin Obukhov length), surface roughness, boundary layer height and geostrophic wind parameters. It can accurately characterize the vertical distribution law of wind speed under different stability conditions, significantly improve the accuracy of high-altitude wind speed estimation and model applicability, and provide high-precision, low-cost real-time technical support for wind farm site selection and wind power forecasting.

[0033] The method of this embodiment specifically includes:

[0034] Acquiring surface high-frequency ultrasonic data and tower wind speed data, wherein the surface high-frequency ultrasonic data includes three-dimensional wind speed and temperature;

[0035] Performing quality control and analysis on the surface high-frequency ultrasonic data to extract a set of key parameters, wherein the set of key parameters includes surface wind speed, surface friction speed, and Monin-Obukhov length;

[0036] Calculating the surface roughness based on the tower wind speed data, the surface friction velocity and the Monin Obukhov length;

[0037] Observe whether there is microwave radiometer data and whether the effective data of the microwave radiometer can cover the boundary layer height; if so, calculate the boundary layer height based on the microwave radiometer data; if not, calculate the boundary layer height based on the surface friction velocity and the Monin Obukhov length;

[0038] The meteorological element information above the boundary layer height is obtained based on the preset reanalysis data, the dimensionless coefficient in the geostrophic drag law is derived, and the geostrophic wind is calculated;

[0039] Selecting a wind profiler estimation model according to the key parameter set;

[0040] Based on the selected wind profile calculation model, the surface roughness, the boundary layer height, and the geostrophic wind, vertical wind speed distribution data is calculated.

[0041] Specifically, based on surface high-frequency ultrasonic data and tower wind speed data, key parameters such as surface wind speed, friction velocity, and Monin-Obukhov length are extracted. Combined with physical quantities such as surface roughness, boundary layer height, and geostrophic wind, wind profile models suitable for different atmospheric stability conditions, such as stable, neutral, and unstable, are constructed. Through multi-source data fusion technology, ground observation data and reanalysis data (such as ERA5) are combined to dynamically select optimal wind profile models, such as the logarithmic wind profile model, the DH wind profile model, the Gryning model, and the LOG-Ekman two-layer model, to calculate the vertical distribution of wind speed from the surface to a height of 300 meters. This method requires only conventional surface ultrasonic anemometers and tower wind speed observation equipment to achieve high-precision calculations, providing efficient and reliable technical support for wind resource assessment.

[0042] Optionally, acquiring surface high-frequency ultrasonic data and tower wind speed data includes:

[0043] The ultrasonic anemometer and the tower wind speed measuring device are set up at the same preset location;

[0044] The ultrasonic anemometer is placed at a preset height on the ground and horizontally calibrated;

[0045] Using the ultrasonic anemometer to obtain surface high-frequency ultrasonic data, and using the tower wind speed measuring device to obtain tower wind speed data;

[0046] The ultrasonic anemometer and one of the layers of the tower wind speed measuring device are at the same height, and the average wind speed deviation within a preset time window does not exceed a preset deviation threshold.

[0047] Specifically, the data acquisition device includes an ultrasonic anemometer and a tower wind speed measurement device. The ultrasonic anemometer is installed on the ground at a height of 4 meters and requires calibration to ensure the instrument is installed horizontally. The ultrasonic data includes three-dimensional wind speed and temperature, with a time resolution of 10Hz or above. The tower height must be at least 30 meters. The tower and the ultrasonic anemometer are installed in the same location. The half-hour average wind speed at the same height of the ultrasonic anemometer and the tower should be compared, and the deviation between the two should not exceed a preset deviation threshold, such as 10%, to ensure the accuracy of the observation data.

[0048] Optionally, the quality control of the surface high-frequency ultrasonic data includes:

[0049] Performing physical rationality judgment on the three-dimensional wind speed and temperature and eliminating abnormal data;

[0050] Performing amplitude resolution test, rigidity test, high-order statistics test and stationarity test on the surface high-frequency ultrasonic data to eliminate invalid data;

[0051] Specifically, the ultrasonic data quality control targets four variables, namely, the horizontal wind speed in the x-axis direction, the horizontal wind speed in the y-axis direction, the vertical wind speed in the z-axis direction, and the temperature. The analysis time in the method of the present invention is 30 minutes. First, the rationality of the data of each physical quantity is judged. For the horizontal wind speed: eliminate the data with a horizontal wind speed greater than 30m / s in the x-axis direction and a horizontal wind speed greater than 30m / s in the y-axis direction; eliminate the data with a vertical wind speed greater than 5m / s in the z-axis direction; eliminate the data with a temperature less than -30°C and a temperature greater than 50°C. And eliminate the wild points beyond the five-fold variance threshold. Secondly, discard the data that fails the amplitude resolution test, the rigid value test, the high-order statistics test, and the stationarity test.

[0052] The coordinates of the three-dimensional wind speed are rotated to eliminate measurement errors.

[0053] Specifically, the three-dimensional wind speed is rotated for the first time, rotating the x-axis of the coordinate system to the dominant direction of the horizontal wind speed so that the average speed along the y-axis is 0. The coordinate system is established according to the right-hand screw rule, and the instantaneous wind speed obtained by turbulence observation is multiplied by the rotation matrix during rotation to obtain the three-dimensional wind speed in the new coordinate system. For the horizontal wind speed u1 along the x-axis after the first rotation, the horizontal wind speed v1 along the y-axis after the first rotation, and the vertical wind speed w1 along the z-axis after the first rotation, we have:

[0054]

[0055] Where u0 is the horizontal wind speed in the x-axis direction of the original three-dimensional wind speed, v0 is the horizontal wind speed in the y-axis direction of the original three-dimensional wind speed, and w0 is the vertical wind speed in the z-axis direction of the original three-dimensional wind speed. is the average horizontal wind speed in the x-axis direction of the original three-dimensional wind speed, is the average horizontal wind speed in the y-axis direction of the original three-dimensional wind speed, and satisfies the average horizontal wind speed in the y-axis direction of the rotated three-dimensional wind speed

[0056] Then, to eliminate the measurement error caused by the uneven installation, the average vertical speed is set to 0 by a second coordinate rotation. The u1, v1, and w1 obtained by the first coordinate rotation are multiplied by the rotation matrix to obtain the new three-dimensional wind speed. For the horizontal wind speed u2 in the x-axis direction after the second rotation, and the horizontal wind speed v2 in the y-axis direction after the second rotation, the following conditions are met: The vertical wind speed w2 in the z direction after the second rotation satisfies have:

[0057]

[0058] Where, is the average horizontal wind speed in the x-axis direction after the first rotation, is the average vertical wind speed in the z-axis direction after the first rotation, and satisfies the average horizontal wind speed in the y-axis direction after the second rotation. The average vertical wind speed in the z-axis direction after the second rotation is satisfied After performing a secondary coordinate rotation on the three-dimensional wind speed, a quadratic function was fitted with a 10-min time period to remove the trend, and then the turbulence statistics were calculated.

[0059] Optionally, the extracting a key parameter set includes:

[0060] Analyzing the surface high-frequency ultrasonic data after quality control to obtain the pulsation component of the three-dimensional wind speed corresponding to the coordinate axis direction and the temperature pulsation;

[0061] Calculating the surface friction velocity according to the pulsation component;

[0062] Specifically, the surface friction velocity u is obtained by observing the turbulence of the surface ultrasonic anemometer. * ,have:

[0063]

[0064] Where u' is the pulsating component of the three-dimensional wind speed in the x-axis direction after the second rotation, v' is the pulsating component of the three-dimensional wind speed in the y-axis direction after the second rotation, and w' is the pulsating component of the three-dimensional wind speed in the z-axis direction after the second rotation. is the covariance of the x-axis and z-axis pulsation components, that is, the Reynolds stress component, is the covariance of the y-axis and z-axis pulsation components, and the Reynolds stress component reflects the turbulent momentum transmission efficiency.

[0065] The Monin Obukhov length is calculated using the pulsation component, the temperature pulsation, and the surface friction velocity.

[0066] Specifically, the Monin Obukhov length is a characteristic quantity obtained by integrating ground momentum and heat information, which represents the stability of the atmosphere. For the Monin Obukhov length L, we have:

[0067]

[0068] Where κ is the von Karman constant, which is 0.4, and g is the acceleration due to gravity, which is 9.8 m / s. 2 , T is the temperature in the surface high-frequency ultrasonic data, T' is the pulsating component of the temperature in the surface high-frequency ultrasonic data, is the covariance between the z-axis pulsation component and the temperature pulsation component, reflecting the surface heat flux.

[0069] Among them, most of the previous methods for estimating high-altitude wind speed are based on the assumption that the ground surface is neutral, and a major drawback is the lack of consideration of stability. Figure 2 As shown, the neutral condition accounts for a small proportion, and a large proportion is stable and unstable. Figure 3 As shown in Table 1, the wind speed increases with height under different stability conditions. The method of the present invention determines the atmospheric stability based on the Monin Obukhov length range, as shown in Table 1.

[0070] Table 1 Classification of atmospheric stability

[0071] Stability L range / m Extremely stable 0<L≤50 Moderately stable 50<L≤200 Weak stability 200<L≤500 neutral |L|>500 Weak instability -500≤L<-200 Moderately unstable -200≤L<-50 Extremely unstable -50≤L<0

[0072] Optionally, calculating the surface roughness includes:

[0073] Based on a preset step size, multiple candidate roughnesses are obtained within a preset range;

[0074] Obtaining the actual observed wind speed of a preset number of floors of the tower according to the tower wind speed data;

[0075] The theoretical wind speed of the corresponding layer is calculated using the surface friction velocity and Monin Obukhov length;

[0076] Calculate the relative error between the actual observed wind speed and the theoretical wind speed at the corresponding floor;

[0077] Summing the relative errors of the preset number of layers of each candidate roughness to obtain the relative error sum of the corresponding candidate roughness;

[0078] The relative errors and sizes of different candidate roughnesses are compared, and the corresponding candidate roughness is selected according to the comparison result to obtain the surface roughness.

[0079] Specifically, the logarithmic wind profile within a 50m height range closely matches the actual wind profile. Therefore, the present method selects the wind speeds of the lowest five floors of the meteorological tower, along with the derived surface friction velocity and Monin-Obukhov length, to determine the roughness corresponding to each 30-minute average period by ensuring that the logarithmic wind profile has the minimum error sum. This strategy, combined with stability correction, ensures the accuracy of the roughness under varying stability conditions.

[0080] For example, the preset step size is set to 0.0001m, and the preset range is set to 0.0001 to 2.5m, thereby obtaining 25,000 candidate roughnesses. The actual observed wind speed and the theoretical wind speed are obtained layer by layer. Using the candidate roughnesses, the relative error between the actual observed wind speed and the theoretical wind speed is calculated layer by layer. For the nth candidate roughness h n The relative error Re between the actual observed wind speed and the theoretical wind speed of the i-th layer i (h n ),have:

[0081]

[0082] Where abs is the absolute value function, is the nth candidate roughness h n The theoretical wind speed of the i-th layer, is the actual observed wind speed at the i-th layer. The theoretical wind speed is calculated using the logarithmic wind profile model, i.e., the logarithmic wind profile wind speed calculation method. For the n-th candidate roughness h n Theoretical wind speed at layer i have:

[0083]

[0084] Where h obs is the observation height, is the correction function describing the stability effect in the logarithmic wind profile model. For neutral conditions, that is, when the absolute value of the Monin Obukhov length is greater than 500m, we have:

[0085]

[0086] For stable conditions, that is, when the Monin Obukhov length is greater than 0 and less than or equal to 500, we have:

[0087]

[0088] In the formula, a1, b1, c1, d1 are constant coefficients, and after experimental calibration, a1=1, c1=5、d1=0.35,exp is the exponential function. Under unstable conditions, that is, when the Monin Obukhov length is greater than or equal to -500 and less than 0, we have:

[0089]

[0090] Where, If wind speed data from a wind tower is missing, select the roughness from Table 2 based on the surface type.

[0091] Table 2 Different surface roughness

[0092]

[0093]

[0094] The relative errors of the preset number of layers of each candidate roughness are summed, and the preset number of layers is 5. For the nth candidate roughness h n The 5-layer relative error and Te(h n ),have:

[0095]

[0096] Where, Re i (h n ) is the nth candidate roughness h n The relative error between the actual observed wind speed and the theoretical wind speed of the i-th layer, N is the preset number of layers, here it is 5. Compare the sizes of the candidate roughness, and select the candidate with the smallest relative error as the surface roughness h z .

[0097] Optionally, calculating the boundary layer height based on the surface friction velocity and the Monin-Obukhov length includes:

[0098] Determine whether the Monin Obukhov length is greater than a preset first threshold and less than or equal to a preset second threshold;

[0099] If not, calculating a first height based on the surface friction speed, a preset first proportionality coefficient, and a preset Coriolis parameter;

[0100] If yes, calculating a second height according to the surface friction speed, a preset second proportional coefficient, the Coriolis parameter, and a preset stability correction function;

[0101] The boundary layer height includes the first height and the second height, and the absolute value of the second threshold is greater than the absolute value of the first threshold.

[0102] Specifically, if microwave radiometer data is available and its valid data can cover the boundary layer height, the boundary layer height is estimated based on the temperature and humidity profile data of the microwave radiometer. If microwave radiometer data is not available, the boundary layer height is calculated based on the surface friction velocity and the Monin-Obukhov length.

[0103] Specifically, according to Table 1, the first threshold value can be determined to be 0 and the second threshold value to be 500. Under neutral and unstable conditions, that is, when the Monin Obukhov length is less than 0 or greater than 500, the ratio of the friction velocity to the Coriolis parameter is used to calculate the first height as the boundary layer height. For the first height H1, there is:

[0104]

[0105] Where C1 is the first proportional coefficient, f is the Coriolis parameter, f = 2*Ω*sinΦ; where Ω is the angular velocity of the Earth's rotation, which is 7.29×10 -5 rad / s, where Φ is the latitude. The scaling factor is C1 = 0.1. However, in the DH wind profiler model, the required boundary layer height is higher than in other models, so C1 = 0.4 is more suitable for wind speed simulation. Under stable conditions, that is, when the Monin Obukhov length is greater than 0 and less than or equal to 500, the second height is calculated as the boundary layer height. For the second height H2, we have:

[0106]

[0107] Where C2 is the second proportional coefficient, F(μ) is the stability correction function, F(μ)=(1+0.011μ+0.022μ 2 ) -1 / 4 ,in, according to Figure 4 Compared with the result of the stable boundary layer height obtained by the microwave radiometer, the second proportional coefficient C2=0.22 is recommended.

[0108] Optionally, calculating the geostrophic wind includes:

[0109] The meteorological element information above the boundary layer height is obtained based on the preset reanalysis data, and the initial wind speed is calculated;

[0110] Specifically, geostrophic wind, that is, the wind speed at the boundary layer height, is also an important reference quantity. In order to obtain the geostrophic wind, the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis data is used. The data used are the potential on different isobaric surfaces, with a time resolution of 1h, a horizontal spatial resolution of 0.25° (about 31km), and a vertical resolution of 25hPa. The potential height of the isobaric surface can be calculated by dividing the potential by the earth's gravitational acceleration. The isobaric surface data with the potential height closest to the boundary layer height (plus the altitude) is used as the data at the top of the boundary layer. According to the definition of geostrophic wind, the initial wind speed can be obtained based on the horizontal gradient of air pressure. For the initial wind speed G0, there is:

[0111]

[0112] Where p is the air pressure, represents the latitudinal pressure gradient, Represents the meridional pressure gradient.

[0113] Calculating a dimensionless coefficient based on the initial wind speed;

[0114] Specifically, the dimensionless coefficient can be calculated based on the initial wind speed. There are two dimensionless coefficients, namely coefficient A and coefficient B. For coefficient A, we have:

[0115]

[0116] Where α is the angle between the surface wind and the geostrophic wind. For the coefficient B, we have:

[0117]

[0118] Using the above method, we can obtain the values ​​of coefficients A and B for each time step. The values ​​of coefficients A and B are related to stability. The average values ​​of coefficients A and B for different Monin Obukhov length intervals are taken as reference values ​​for the corresponding geostrophic parameters. Table 3 provides a reference table using the calculation results of the Xilinhot data. There are no significant differences between weakly unstable, moderately unstable, and extremely unstable conditions, and subsequent wind profile simulations also show similar patterns. Therefore, these three conditions are classified together.

[0119] Table 3 Geostrophic parameters

[0120] parameter Unstable neutral Weak stability Moderately stable Extremely stable A 2.82 2.40 1.06 0.72 0.88 B 2.63 5.01 13.83 16.88 20.78

[0121] The final geostrophic wind is calculated using the surface roughness, the surface friction velocity and the dimensionless coefficient.

[0122] Specifically, by observing the surface friction velocity and surface roughness, and adding coefficients A and B, the final geostrophic wind can be obtained through the geostrophic resistance law. df ,have:

[0123]

[0124] Optionally, selecting a wind profiler estimation model according to the key parameter set includes:

[0125] When the absolute value of the Monin Obukhov length is greater than the second threshold, selecting a preset logarithmic wind profile model or a preset DH wind profile model according to the surface wind speed;

[0126] When the Monin-Obukhov length is less than the first threshold and the absolute value of the Monin-Obukhov length is less than or equal to the second threshold, selecting the preset Gryning model or the logarithmic wind profile model;

[0127] When the Monin-Obukhov length is greater than the first threshold and the absolute value of the Monin-Obukhov length is less than or equal to the second threshold, selecting the Gryning model or the preset LOG-Ekman two-layer model;

[0128] The wind profile calculation model includes the logarithmic wind profile model, the DH wind profile model, the Gryning model and the LOG-Ekman two-layer model.

[0129] Specifically, the wind profile model is selected based on the stability and wind speed conditions. First, determine the atmospheric stability. According to Table 1, it is divided into 7 stability conditions according to the Monin Obukhov length. Under neutral conditions, first determine the surface wind speed. For light wind conditions, that is, the surface wind speed is less than 8m / s, the LOG model is simple to calculate and is a suitable choice. The calculation effect is as follows: Figure 5 shown. Figure 6 (a) shows the average results of each wind profile model when the wind speed at 4m height is less than 8m / s. Figure 6 (b) is the result when the wind speed at 4m height is higher than 8m / s. Under high wind conditions, the DH wind profiler model is more accurate in estimating high-altitude wind speed. Under strong wind conditions, the DH wind profiler model is selected. Under unstable conditions, the Gryning and LOG models perform well, such as Figure 7 As shown. In the three stable cases, one of the two-layer models, Gryning and LOG-Ekman, can be selected, as Figure 8 shown. Figure 8(a) and (b) are the average results of the wind profile models for the weak and moderate stability cases, respectively. The roughness, boundary layer height, and geostrophic wind used in the models are calculated using the methods and parameters corresponding to their respective stability conditions.

[0130] Specifically, the logarithmic wind profile model is the logarithmic wind profile wind speed calculation method used when calculating roughness; for the wind speed vertical distribution data U calculated by the DH wind profile model DH (h obs ),have:

[0131]

[0132] Wherein, H is the boundary height, and H is the first height H1 or the second height H2 according to the situation.

[0133] Optionally, selecting the Gryning model to calculate the vertical distribution data of wind speed includes:

[0134] According to the Gryning Obukhov length and the first threshold and the second threshold, the parameters of the Gryning model are adjusted to calculate the vertical distribution data of the wind speed.

[0135] Specifically, when using the Gryning model to calculate the vertical distribution data of wind speed, the middle layer height is calculated first. For the middle layer height L MBL ,have:

[0136]

[0137] According to Table 1, the first threshold can be 0 and the second threshold can be 500. Therefore, for the vertical distribution data of wind speed U calculated by Gryning model, Gryning (h obs ), when the absolute value of the Monin Obukhov length is greater than 500, that is, under neutral conditions, we have:

[0138]

[0139] When the Monin Obukhov length is greater than 0 and less than or equal to 500, that is, under stable conditions, we have:

[0140]

[0141] When the Monin Obukhov length is less than 0 and greater than -500, that is, under unstable conditions, we have:

[0142]

[0143] Where, is the correction function describing the stability effect in the Gryning model. have:

[0144]

[0145] Where,

[0146] Optionally, the LOG-Ekman two-layer model calculates the vertical distribution data of wind speed including:

[0147] Obtain the estimated altitude and the top of the near-surface layer altitude;

[0148] According to the estimated height and the height of the near-ground layer, the corresponding wind profile expression of the LOG-Ekman two-layer model is selected to calculate the vertical distribution data of the wind speed

[0149] Specifically, the wind profile expression of the LOG-Ekman two-layer model is bounded by the turning height, which is the height of the top of the near-surface layer h. ASL Therefore, for the vertical distribution data of wind speed U calculated by the LOG-Ekman two-layer model Two (h obs ), when the observation height is less than the height of the top of the ground layer, we have:

[0150]

[0151] When the estimated height is greater than or equal to the top of the ground layer, there are:

[0152]

[0153] Among them, the height of the top of the near-surface layer is 0.3 times the height of the boundary layer, z T is the height from the top of the ground layer. For the angle β between the surface wind and the geostrophic wind at this time, we have:

[0154]

[0155] In the formula, the coefficient In neutral case:

[0156]

[0157] In stable conditions:

[0158]

[0159] Where a2, b2, c2, and d2 are constant coefficients, and their values ​​can be the same as those of a1, b1, c1, and d1. In unstable situations:

[0160]

[0161] It should be noted that, in the accompanying drawings of the present invention, Figure 3、 Figure 5 、 Figure 6 、 Figure 7 and Figure 8 The horizontal axis represents the ratio of wind speed to friction speed and is dimensionless. Any embodiment of the present invention may be applied as long as the objectives of the present invention are achieved. The foregoing is merely an exemplary embodiment of the present invention and is not intended to limit the scope of the present invention.

[0162] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. Those skilled in the art will readily conceive of other embodiments of the present invention after considering the disclosure of the specification and practical truths. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary technical means in the art not described herein.

Claims

1. A method for rapidly estimating high-altitude wind speed based on surface observations, characterized in that: The method comprises: Acquiring surface high-frequency ultrasonic data and tower wind speed data, wherein the surface high-frequency ultrasonic data includes three-dimensional wind speed and temperature; Performing quality control and analysis on the surface high-frequency ultrasonic data to extract a set of key parameters, wherein the set of key parameters includes surface wind speed, surface friction speed, and Monin-Obukhov length; Calculating the surface roughness based on the tower wind speed data, the surface friction velocity and the Monin Obukhov length; Observe whether there is microwave radiometer data and whether the effective data of the microwave radiometer can cover the boundary layer height; if so, calculate the boundary layer height based on the microwave radiometer data; if not, calculate the boundary layer height based on the surface friction velocity and the Monin Obukhov length; The meteorological element information above the boundary layer height is obtained based on the preset reanalysis data, the dimensionless coefficient in the geostrophic drag law is derived, and the geostrophic wind is calculated; Selecting a wind profiler estimation model according to the key parameter set; Based on the selected wind profile calculation model, the surface roughness, the boundary layer height, and the geostrophic wind, vertical wind speed distribution data is calculated.

2. The method for rapidly estimating high-altitude wind speed based on surface observation according to claim 1, characterized in that: The acquisition of surface high-frequency ultrasonic data and tower wind speed data includes: The ultrasonic anemometer and the tower wind speed measuring device are set up at the same preset location; The ultrasonic anemometer is placed at a preset height on the ground and horizontally calibrated; Using the ultrasonic anemometer to obtain surface high-frequency ultrasonic data, and using the tower wind speed measuring device to obtain tower wind speed data; The ultrasonic anemometer and one of the layers of the tower wind speed measuring device are at the same height, and the average wind speed deviation within a preset time window does not exceed a preset deviation threshold.

3. The method for rapidly estimating high-altitude wind speed based on surface observation according to claim 1, characterized in that: The quality control of the surface high-frequency ultrasonic data includes: Performing physical rationality judgment on the three-dimensional wind speed and temperature and eliminating abnormal data; Performing amplitude resolution test, rigidity test, high-order statistics test and stationarity test on the surface high-frequency ultrasonic data to eliminate invalid data; The coordinates of the three-dimensional wind speed are rotated to eliminate measurement errors.

4. The method for rapidly estimating high-altitude wind speed based on surface observation according to claim 2, characterized in that: The key parameter set obtained by extraction includes: Analyzing the surface high-frequency ultrasonic data after quality control to obtain the pulsation component of the three-dimensional wind speed corresponding to the coordinate axis direction and the temperature pulsation; Calculating the surface friction velocity according to the pulsation component; The Monin Obukhov length is calculated using the pulsation component, the temperature pulsation, and the surface friction velocity.

5. The method for rapidly estimating high-altitude wind speed based on surface observation according to claim 1, characterized in that: The surface roughness obtained by calculation includes: Based on a preset step size, multiple candidate roughnesses are obtained within a preset range; Obtaining the actual observed wind speed of a preset number of floors of the tower according to the tower wind speed data; The theoretical wind speed of the corresponding layer is calculated using the surface friction velocity and Monin Obukhov length; Calculate the relative error between the actual observed wind speed and the theoretical wind speed at the corresponding floor; Summing the relative errors of the preset number of layers of each candidate roughness to obtain the relative error sum of the corresponding candidate roughness; The relative errors and sizes of different candidate roughnesses are compared, and the corresponding candidate roughness is selected according to the comparison result to obtain the surface roughness.

6. The method for rapidly estimating high-altitude wind speed based on surface observation according to claim 1, characterized in that: Calculating the boundary layer height based on the surface friction velocity and the Monin Obukhov length includes: Determine whether the Monin Obukhov length is greater than a preset first threshold and less than or equal to a preset second threshold; If not, calculating a first height according to the surface friction speed, a preset first proportionality coefficient, and a preset Coriolis parameter; If yes, calculating a second height according to the surface friction speed, a preset second proportional coefficient, the Coriolis parameter, and a preset stability correction function; The boundary layer height includes the first height and the second height, and the absolute value of the second threshold is greater than the absolute value of the first threshold.

7. The method for rapidly estimating high-altitude wind speed based on surface observation according to claim 6, characterized in that: The geostrophic wind is calculated by: The meteorological element information above the boundary layer height is obtained based on the preset reanalysis data, and the initial wind speed is calculated; Calculating a dimensionless coefficient based on the initial wind speed; The final geostrophic wind is calculated using the surface roughness, the surface friction velocity and the dimensionless coefficient.

8. The method for rapidly estimating high-altitude wind speed based on surface observation according to claim 7, characterized in that: The selecting of a wind profiler estimation model according to the key parameter set includes: When the absolute value of the Monin Obukhov length is greater than the second threshold, selecting a preset logarithmic wind profile model or a preset DH wind profile model according to the surface wind speed; When the Monin-Obukhov length is less than the first threshold and the absolute value of the Monin-Obukhov length is less than or equal to the second threshold, selecting the preset Gryning model or the logarithmic wind profile model; When the Monin-Obukhov length is greater than the first threshold and the absolute value of the Monin-Obukhov length is less than or equal to the second threshold, selecting the Gryning model or the preset LOG-Ekman two-layer model; The wind profile calculation model includes the logarithmic wind profile model, the DH wind profile model, the Gryning model and the LOG-Ekman two-layer model.

9. The method for rapidly estimating high-altitude wind speed based on surface observation according to claim 8, characterized in that: The method of selecting the Gryning model to calculate the vertical distribution data of wind speed includes: According to the Gryning Obukhov length and the first threshold and the second threshold, the parameters of the Gryning model are adjusted to calculate the vertical distribution data of the wind speed.

10. The method for rapidly estimating high-altitude wind speed based on surface observation according to claim 8, characterized in that: The LOG-Ekman two-layer model calculates the vertical distribution data of wind speed including: Obtain the estimated altitude and the top-of-the-surface altitude; According to the difference between the estimated height and the height of the top of the near-ground layer, the corresponding wind profile expression of the LOG-Ekman two-layer model is selected to calculate the vertical distribution data of the wind speed.

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