Method for calculating three-dimensional turbulent kinetic energy of typhoon based on laser radar

Abstract

The embodiment of the invention discloses a typhoon three-dimensional turbulent kinetic energy calculation method based on a laser radar. The radial wind speed ur of the typhoon is obtained through laser radar scanning; determining a normalized turbulence energy spectrum of the typhoon according to the radial wind speed; determining the density distribution of the normalized turbulence energy spectrum; and judging whether the density distribution of the normalized turbulent flow energy spectrum meets the inertia subarea of isotropic turbulent flow, and if so, calculating the three-dimensional turbulent flow kinetic energy TKE3d of the typhoon by adopting a preset mode so as to realize efficient calculation of the three-dimensional turbulent flow kinetic energy in the typhoon.

Description

Technical Field

[0001] This specification relates to the technical field of radar data processing, and particularly to a method for calculating the three-dimensional turbulent kinetic energy of a typhoon based on a lidar. Background Art

[0002] Tropical cyclones are one of the most severe meteorological disasters affecting tropical coastal regions globally. Observation of the internal wind field structure and related characteristics of typhoons is particularly important. With the progress of technology, new devices such as Doppler wind lidar (DWL) have been introduced into typhoon observations. Observations using DWL have made significant progress, but currently, the analysis of the high-resolution wind field structure and turbulent characteristics during typhoon landfall by DWL is still insufficient. Currently, the main method of using DWL to detect typhoons is the DBS scanning mode, which mainly studies the wind field and turbulent characteristics from a one-dimensional perspective and cannot directly study the two-dimensional horizontal wind field structure and turbulent characteristics within the boundary layer during typhoon passage. In addition, even if the VAD inversion method is used to obtain the horizontal wind field, the wind speed characteristics in the vertical direction cannot be obtained, making it difficult to directly estimate the three-dimensional turbulent kinetic energy of a typhoon, and the analysis of the high-resolution wind field structure and turbulent characteristics during typhoon landfall is still insufficient.

[0003] Based on this, there is a need for an efficient and simple calculation scheme for the three-dimensional turbulent kinetic energy of a typhoon based on lidar. Summary of the Invention

[0004] Embodiments of this specification provide a calculation scheme for the three-dimensional turbulent kinetic energy of a typhoon based on lidar to solve the following technical problem: There is a need for an efficient and simple calculation scheme for the three-dimensional turbulent kinetic energy of a typhoon based on lidar.

[0005] To solve the above technical problem, one or more embodiments of this specification are implemented as follows:

[0006] In a first aspect, embodiments of this specification provide an efficient and simple method for calculating the three-dimensional turbulent kinetic energy of a typhoon based on lidar, including:

[0007] Using a lidar to scan to obtain the radial wind speed ur of the typhoon;

[0008] Determining the normalized turbulent energy spectrum of the typhoon according to the radial wind speed;

[0009] Determining the density distribution of the normalized turbulent energy spectrum;

[0010] Judging whether the density distribution of the normalized turbulent energy spectrum satisfies the inertial sub-region of isotropic turbulence. If it satisfies, calculate the three-dimensional turbulent kinetic energy TKE of the typhoon in the following manner 3d :

[0011] Among them, is the average value of the radial wind speed on the lidar beam.

[0012] In a second aspect, one or more embodiments of this specification provide an electronic device, including:

[0013] At least one processor; and,

[0014] A memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect.

[0016] One or more of the above technical solutions adopted by the embodiments of this specification can achieve the following beneficial effects: By using lidar scanning to obtain the radial wind speed ur of the typhoon; determining the normalized turbulent energy spectrum of the typhoon according to the radial wind speed; determining the density distribution of the normalized turbulent energy spectrum; judging whether the density distribution of the normalized turbulent energy spectrum satisfies the inertial subrange of isotropic turbulence, and if so, calculating the three-dimensional turbulent kinetic energy TKE of the typhoon in the following manner 3d :

[0017] Among them, is the average value of the radial wind speed on the lidar beam, thereby realizing the efficient calculation of the three-dimensional turbulent kinetic energy inside the typhoon. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic flow chart of a method for calculating the three-dimensional turbulent kinetic energy of a typhoon based on lidar provided by the embodiments of this specification; <LID=

[0020] Figure 2a It is a schematic diagram of the normalized power spectral density of 10 randomly selected radar beams provided by the embodiments of this specification;

[0021] Figure 2b It is a schematic diagram of the comparison between the density distribution and the theoretical expectation provided by the embodiments of this specification;

[0022] Figure 2c Schematic diagram of wind speed fitting provided by the embodiments of this specification;

[0023] Figure 3 Schematic diagram of comparison between the calculation results of this application and other methods provided by the embodiments of this specification;

[0024] Figure 4 TKE varying with time provided by the embodiments of this specification 3d , wind speed and wind direction;

[0025] Figure 5 Schematic diagram of the structure of an electronic device provided by the embodiments of this specification. Detailed implementation manners

[0026] The embodiments of this specification provide a method and a device for calculating the three-dimensional turbulent kinetic energy of a typhoon based on lidar.

[0027] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification with reference to the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0028] As Figure 1 shown, Figure 1 Schematic diagram of the flow of a method for calculating the three-dimensional turbulent kinetic energy of a typhoon based on lidar provided by the embodiments of this specification.

[0029] Figure 1 The flow in

[0030] S102: Use lidar to scan to obtain the radial wind speed ur of the typhoon.

[0031] When using lidar to scan, the radar scanning method is planar position indicator (PPI). Taking the radar as the center, a conical scan with a fixed elevation angle and continuously changing azimuth angle is used. The scanning target is the radial wind speed. The radial resolution is 30 meters. The initial observation radial distance starts from 60 meters. The radar scanning covers the azimuth angle from 30° to 175°, the azimuth resolution is 3°, the elevation angle is 5°, and the maximum observation distance is 5670 meters; however, as the distance increases, the radar wave attenuation and environmental noise also increase, resulting in a rapid increase in the data loss rate. When the radial distance exceeds 2000 meters, the data loss rate reaches more than 80%. Such a high data loss rate will affect the accuracy of the research.

[0032] Therefore, in the present application, a region with a radial distance of 1230 meters and a data loss rate of less than 30% is schematically selected to ensure a sufficiently large research area while maintaining as high data integrity as possible. The external temperature and humidity meter module for humidity measurement is connected to the radar.

[0033] S104. Determine the normalized turbulent energy spectrum of the typhoon according to the radial wind speed.

[0034] The normalized turbulent energy spectrum here refers to the normalized turbulent energy spectrum in the radial direction.

[0035] The detailed steps of the calculation process are as follows: Calculate the one-dimensional Fourier transform of the wind speed data X. Move the zero-frequency component to the center of the spectrum to obtain the shifted Fourier coefficient X[k]. Calculate the squared magnitude of the shifted Fourier coefficient to obtain the power spectral density (PSD):. To eliminate the influence of the average wind speed at different time steps, we divide the PSD at each time point by the square of the horizontal average wind speed at that time for normalization to obtain the normalized energy spectral density.

[0036] That is, the normalized turbulent energy spectrum of the typhoon is determined according to the radial wind speed in the following manner:

[0037] where X is the discrete value of the radial wind speed of the typhoon obtained by scanning in the same radar beam, N is the number of discrete values, P xx [k] is the energy spectral density, P norm is the normalized energy spectral density, is the average horizontal wind speed obtained by fitting based on the radial wind speed.

[0038] Among them, the average horizontal wind speed obtained by fitting based on the radial wind speed is carried out in the following steps:

[0039] Determine the scanning sector and the outlier threshold of the lidar to be fitted (for example, set to 0.15, and values lower than 0.15 are regarded as outliers and excluded from the subsequent fitting calculation), where the sector angle is not less than 60 degrees;

[0040] Use the fitting function to perform a first fit to obtain the fitted wind speed Vf, where y represents the radial wind speed at different azimuth angles in the scanning sector, x is the azimuth angle, A is the amplitude, is the phase; by fitting the data into a sine curve, the maximum value y max (i.e., the amplitude A) can be read out, which represents the two-dimensional horizontal wind speed obtained by the first fit, and at the same time, the x coordinate corresponding to the minimum value y min of the sine curve indicates the wind direction.

[0041] According to the fitted wind speed Vf Calculate the wind speed residual e, calculate the standard deviation σ of the residual, and calculate the standardized residual

[0042] If the calculated standardized residual exceeds a preset threshold, eliminate the corresponding data point; for example, if the absolute value of the standardized residual exceeds 2, consider this point as an outlier and exclude it from the second linear fitting.

[0043] Use the remaining data points to perform fitting again based on the foregoing steps until the fitting result converges.

[0044] As Figure 2c shown, Figure 2c is a schematic diagram of wind speed fitting provided by an embodiment of this specification. Among them, part a in this schematic diagram represents the proportion of missing values at different radial distances; part b is an example of the first fitting, the green curve represents the fitting curve, the gray dashed line represents the signal-to-noise ratio, the red dots are data points that do not meet the standardized residual, and the blue dots are data points that meet the standardized residual; part c is a schematic diagram of the changes in the fitted wind speed and standard deviation at different coverage angles. Among them, the blue solid line is the schematic diagram of the change in the fitted wind speed, and the red dashed line is the schematic diagram of the change in the wind speed standard deviation.

[0045] When inverting the horizontal wind field, the observation data must cover an azimuth angle greater than 60° to accurately fit the horizontal wind field. Through randomly selected examples, it is shown that the average values of the fitted wind speed and the standard deviation (std) wind speed change with the coverage angle. As the coverage angle increases, the average value of the fitted wind speed gradually stabilizes. When the data coverage angle reaches 66° (corresponding to 22 data points), the fitting result tends to be stable. Therefore, 66° (i.e., 22 data points) can be selected for the second fitting step.

[0046] S106. Determine the density distribution of the normalized turbulent energy spectrum.

[0047] To further diagnose the small-scale characteristics of the observed wind field, the density distribution of the normalized turbulent energy spectrum is analyzed. We select the observation data series of the radial wind speed along each radar beam to calculate the power spectral density. This method ensures the reliability of the data and guarantees the synchronization of the data series.

[0048] As Figure 2a and Figure 2b shown, Figure 2a is a schematic diagram of the normalized power spectral density of 10 randomly selected radar beams provided by an embodiment of this specification, Figure 2b is a schematic diagram of the comparison between the density distribution provided by an embodiment of this specification and the theoretical expectation. It can be seen that there are obvious fluctuations between the curves, but most of the spectra still follow the standard line with a slope of -5 / 3.Figure 2b The results in

[0049] The normalized power spectral density distributions of the radial wind speed along all observation beams are given. It can be seen that when all the observation beams are combined, the area-element averaged normalized power spectral density fits very well with the theoretical slope of -5 / 3, indicating that the homogeneous isotropy of the typhoon boundary layer turbulence is still well maintained. This finding is consistent with the results of the standardized turbulent energy spectra of tropical cyclones "Hagupit" and "Pearl" studied using tower observations.

[0050] In addition, Figure 2a the fluctuation characteristics in Figure 2b and the relatively wide upper and lower edges in the box plot in Figure 2b [[ID= which indicate that there is a certain degree of dispersion in the spectral lines, with the deviation from the average line reaching up to an order of magnitude. This shows that on the instantaneous scale, the turbulent vortices still exhibit significant inhomogeneity. However, this instantaneous inhomogeneity can be effectively eliminated through time (or ensemble) averaging. Then, in Figure 2b the wavelength range corresponding to the inertial subrange has been highlighted with a gray shadow. We performed a linear regression analysis on this part of the data and calculated the confidence intervals of the average energy spectral density slopes corresponding to different wavelengths. The regression slope is -1.6824, and the 95% confidence interval is [-1.7045, -1.6604], indicating that the regression result is very close to the theoretical slope of -5 / 3 (i.e., -1.6667). This shows that the spectral distribution within the inertial subrange closely follows the expected -5 / 3 slope, confirming that the turbulence within this wavelength range can be considered isotropic.

[0051] S108, determine whether the density distribution of the normalized turbulent energy spectrum satisfies the inertial subrange of isotropic turbulence. If it does, calculate the three-dimensional turbulent kinetic energy TKE of the typhoon in the following way 3d .

[0052] Since the radial wind spectrum calculated in this figure includes scales outside the inertial subrange, it cannot be directly assumed to represent isotropic turbulence. For these larger-scale turbulent motions, due to current technical limitations, we cannot analyze them separately. However, the variance of the radial velocity can be decomposed into the u, v, and w components of the meteorological wind vector, providing a reference for inferring 3D TKE using r and further providing a calculation method for TKE in the PPI scan mode of DWL: At specific radar parameters (e.g., elevation angle = 35.3°), TKE r can be estimated by multiplying by a factor of 3 3d .

[0053] This result further supports the idea that in the inertial subrange of isotropic turbulence, turbulent fluctuations have characteristics consistent with turbulence theory. Therefore, since the wavelengths in this range follow a law, the turbulent kinetic energy (TKE) of the radial wind speed, by multiplying the TKE r by 3, the TKE of the three-dimensional wind can be reasonably estimated 3d .

[0054] That is, the TKE of the radial wind speed is calculated along the radial wind speed of each radar beam

[0055] where is the average value of the radial wind speed on the lidar beam. The final estimate using three times the TKEr is not strictly theoretical, but it is the best approximation so far

[0056] As Figure 3 shown Figure 3 is a schematic diagram comparing the calculation results of the present application provided in the embodiments of this specification with other methods. In this schematic diagram, parts a and d characterize the relationship period between wind speed and TKE during the entire observation process; parts b and e are before the typhoon center passes; parts c and f are after the typhoon center passes. The gray dashed line represents the radial TKE r , while the red dashed line represents the solid line represents the three-dimensional TKE 3d .

[0057] The green squares, purple inverted triangles, orange circles, and blue triangles in parts a, b, and c represent the TKE 3d values at heights of 10 m, 40 m, 160 m, and 320 m during typhoon passage. According to the schematic diagram of He et al, 2022

[0058] The orange triangles and green stars in parts d, e, and f represent the TKE 3d values at heights of 70 m and 110 m during typhoon passage, and the labels at the bottom of the boxes indicate the effective number of data in each wind speed interval [[ID=3,6]]

[0059] In addition Figure 3 it also includes the TKE data studied by He et al (2022) and Chen (2022), providing valuable comparative analysis. He et al obtained data from a 356-meter-high tower located on the southeast coast of China during the passage of super typhoons Hato (2017) and Mangkhut (2018). At the same time, Chen collected 20Hz turbulent covariance data at different heights to examine the relationship between TKE and wind speed during the landing of super typhoon Maria in 2018. Their observation areas are all located at the land-sea boundary, and the terrain is similar to the research area of our study, making the comparison highly relevant

[0060] From Figure 3 a, it can be clearly seen that the TKE increases gradually with the wind speed. (The red solid line, observed at about 80 m) falls between the TKE values reported by He et al. (2022) at 40 m and 160 m heights, showing good agreement. For wind speeds between 15 and 20 m / s, our TKE 3d values are consistent with the observations of He et al. (2022) at 40 m. When the wind speed exceeds 20 m / s,. However, both our results and those of He et al. show that as the wind speed increases, the TKE tends to level off.

[0061] Our observations are consistent with those of He et al. (2022), confirming that the DWL observation data reliably reflect the correlation between TKE and wind speed. In addition, it is worth noting that the tower of He et al. (2022) is also located in a coastal area with small hills and water bodies, similar to the terrain in this study.

[0062] In Figure 3 (d - f), we compared our data with the results of Chen (2022). Similar to the trend observed by He et al. (2022), the TKE increases with the wind speed. However, the relationship between the TKE values at 70 m and 110 m heights and the wind speed in Chen (2022) is more similar to our results. Before the typhoon center arrives, Figure 3 parts b and 3e show that even at the same wind speed, the TKE is significantly higher than that after the center passes.

[0063] This difference is consistent with the land - sea contrast observed in this application study because the turbulence characteristics are usually stronger on land than on water. The data of Chen (2022) also show a similar pattern of TKE change before and after the passage of the typhoon center, further supporting the solution of this application.

[0064] Due to the land - sea change of the underlying surface, the characteristics of the turbulence footprint area vary significantly with different wind directions ( Figure 3 b - c and 3e - f). It is found that the TKE observed before the typhoon center arrives at the same wind speed is much higher when the footprint is on land after the typhoon center passes over land than when the footprint is on water after the typhoon center passes over water, which is reasonable in theory.

[0065] Figure 4 Schematic diagrams of the time - varying TKE 3d , wind speed and wind direction provided for the embodiments of this specification. Figure 4Furthermore, as the typhoon center passes by, the wind direction changes rapidly. At the same wind speed, the corresponding TKE decreases significantly compared to before the typhoon passes. These results are consistent with those obtained by Shi et al. (2022) using ground-based DWL and DBS5 wind profile techniques during Typhoon Lekima. Overall, these results strongly demonstrate that the TKE obtained from lidar observations is reliable.

[0066] Obtain the radial wind speed ur of the typhoon by using lidar scanning; determine the normalized turbulent energy spectrum of the typhoon according to the radial wind speed; determine the density distribution of the normalized turbulent energy spectrum; judge whether the density distribution of the normalized turbulent energy spectrum satisfies the inertial subrange of isotropic turbulence. If it satisfies, use the radial-based turbulent kinetic energy estimation to calculate the three-dimensional turbulent kinetic energy TKE of the typhoon 3d , thereby realizing the efficient calculation of the three-dimensional turbulent kinetic energy inside the typhoon.

[0067] In addition, specific experiments were also carried out on the solution of this application. The ground wind field characteristics in the fan-shaped area of Typhoon "In-fa, 2021" were observed using a Doppler wind lidar (DWL), and the two-dimensional wind field was inverted using the method of the direct observation radial wind field with a resolution of 𝟑𝟎m, obtaining the wind speed, wind direction, vorticity, and divergence distribution of Typhoon "In-fa". Combining with the turbulence energy spectrum, the characteristics of turbulent kinetic energy were analyzed.

[0068] The wind field structure in the study area conforms to the typical typhoon circulation characteristics, and the observed maximum wind speed exceeds 36m / s. In addition, this study found that the maximum wind speed in the fan-shaped scan is more likely to occur at sea, while the minimum wind speed in the fan-shaped scan is more often observed on land, which may be due to the stronger friction effect of the land terrain compared to the water surface.

[0069] Through the analysis of the vorticity and divergence distribution patterns, vortices with a scale of ∼100m were detected in the outer circulation area of the typhoon. As the typhoon center approaches, the boundary layer vortices gradually weaken, and this finding is consistent with previous studies on typhoon boundary layer roll-up vortices by Wu et al. (2018, 2019), etc.

[0070] After being averaged by the wind speed box, the normalized turbulent energy spectrum of the DWL-observed radial wind strictly follows the theoretical -5 / 3 law. This result indicates, on the one hand, that the turbulence generally maintains uniformity and isotropy, and on the other hand, it also confirms the reliability of the high-resolution observations of the DWL (Doppler wind lidar). In addition, the relationship between the turbulent kinetic energy (TKE) and the wind speed is consistent with the studies of He et al (2022) and Chen (2022). Moreover, the variation of the TKE values observed by the DWL is consistent with the underlying surface characteristics - larger (smaller) TKE is observed when the turbulent footprint is located over land (water), and the variation of TKE before and after the passage of the typhoon center is also consistent with the results of Shi et al (2021). These results provide strong evidence for the reliability of the TKE observed by the DWL.

[0071] Although the observation time of Typhoon Yingfa in this application is relatively short (from 13:40 on July 24th to 11:00), previous studies on typhoon boundary layers also relied on relatively short observation periods. For example, Li et al (2017) observed Typhoon Vicente for 18 hours, and He et al (2022) observed Typhoon Hato and Mangkhut for 11 - 36 hours.

[0072] Similarly, Yi et al (2022) used wind measurement towers (DHT towers) to monitor Typhoon Haitang, Matsa, and Khanun. The observation durations for Haitang and Matsa were 47 hours and 50 minutes, and the observation duration for Khanun was 23 hours and 50 minutes. These studies show that the typhoon observation period is usually shorter than three days. These findings indicate that although the observation time in this study is relatively short, it is comprehensive and provides valuable insights into understanding the meteorological changes caused by typhoons in the scanned area. The research results have important reference value for future studies on typhoon boundary layers.

[0073] It should be noted that the topographic features of the typhoon landing area observed in this application are indeed relatively complex, just like the land-sea boundary areas studied in previous typhoon landing studies. However, in this application, we not only analyzed the detailed wind distribution and its relationship with the terrain, but also studied the vortex structure and TKE characteristics, which provide new ideas for understanding the TC boundary layer dynamics during landing.

[0074] In addition, due to technical limitations, the effective observation range of the DWL in this study is limited to a radial distance of 1230 meters and cannot capture the wind field in a larger area. However, with the progress of DWL technology and the future collaborative observations of multiple radars, wind field data in a larger area can be obtained, further revealing the large-scale characteristics of typhoon landfall.

[0075] In a second aspect, the embodiments of this specification also provide an electronic device. As Figure 5 shown,Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of this specification. The device includes:

[0076] At least one processor; and,

[0077] A memory communicatively connected to the at least one processor; wherein,

[0078] The memory stores instructions executable by the at least one processor. When the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method described in the first aspect.

[0079] Based on the same idea, an embodiment of this specification also provides a non-volatile computer storage medium corresponding to the above method, storing computer-executable instructions. When a computer reads the computer-executable instructions in the storage medium, the instructions cause one or more processors to execute the method described in the first aspect.

[0080] The various embodiments in this specification are all described in a progressive manner. For the same or similar parts among the various embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the apparatus, device, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the corresponding parts of the method embodiments for the relevant content.

[0081] The above specifically describes certain embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0082] The above is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, there can be various modifications and changes to one or more embodiments of this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A method for calculating the three-dimensional turbulent kinetic energy of a typhoon based on lidar, comprising: Scanning with lidar to obtain the radial wind speed ur of the typhoon; Determining the normalized turbulent energy spectrum of the typhoon according to the radial wind speed; Determining the density distribution of the normalized turbulent energy spectrum; Determine whether the density distribution of the normalized turbulent energy spectrum satisfies the inertial subrange of isotropic turbulence. If it does, calculate the three-dimensional turbulent kinetic energy TKE of the typhoon in the following manner 3d : Among them, is the average value of the radial wind speed on the lidar beam.

2. The method according to claim 1, wherein, Determining the normalized turbulent energy spectrum of the typhoon according to the radial wind speed, including: Calculating the normalized turbulent energy spectrum of the typhoon determined according to the radial wind speed in the following manner: Among them, x is a discrete value of the radial wind speed of the typhoon obtained by scanning in the same radar beam, N is the number of discrete values, and P xx [k] is the spectral density, and P norm is the normalized spectral density, is the average horizontal wind speed obtained by fitting based on the radial wind speed.

3. The method according to claim 2, wherein The average wind speed obtained by fitting based on the radial wind speed is carried out in the following steps: Determining the scanning sector and the outlier threshold of the lidar to be fitted, wherein the sector angle is not less than 60 degrees; Using a fitting function Perform a single fitting to obtain the fitted wind speed V f , where y represents the radial wind speed at different azimuth angles in the scanning sector, x is the azimuth angle, A is the amplitude, is the phase; According to the fitted wind speed V f calculate the wind speed residual e, calculate the standard deviation σ of the residual, and calculate the standardized residual If the calculated standardized residual exceeds the preset threshold, the corresponding data points are removed; Using the remaining data points to perform fitting again based on the foregoing steps until the fitting result converges.

4. The method according to claim 1, wherein Judging whether the density distribution of the normalized turbulent energy spectrum satisfies the inertial sub-region of isotropic turbulence, including: Performing linear regression on the density distribution of the normalized turbulent energy spectrum, and calculating the confidence interval of the slope of the density of the average energy spectrum corresponding to different wavelengths; When the confidence interval of the slope is close to the expected -5 / 3, it is determined that the density distribution of the normalized turbulent energy spectrum satisfies the inertial sub-region of isotropic turbulence.

5. An electronic device, comprising: At least one processor; And, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 4.

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