Method and device for determining Doppler velocity of airflow in cloud

Abstract

The invention provides a method and a device for determining Doppler velocity of airflow in cloud, and relates to the technical field of data processing. De-noising processing is carried out on the radar power spectrum data of the distance library corresponding to the target cloud cluster to obtain de-noised radar power spectrum density data; the target cloud cluster is a cloud cluster containing raindrops and hail at the same time; performing logarithmic conversion on the radar power spectrum density data corresponding to the de-noised radar power spectrum data to obtain logarithmic unit radar power spectrum density data; calculating according to the logarithmic unit radar power spectral density data to obtain a first speed weighted average value, a second speed weighted average value and a third speed weighted average value, and obtaining a second speed weighted average value according to the first speed weighted average value, the second speed weighted average value and the third speed weighted average value, and determining the in-cloud airflow Doppler velocity of the target cloud cluster.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and device for determining Doppler velocity of airflow in a cloud. Background Art

[0002] Weather radar is a device that quantitatively detects the physical properties of precipitation particles in clouds. Weather radar can quantitatively detect the sum of the backscattering cross sections of precipitation particles in a space volume in the air through the echo intensity of precipitation clouds, and comprehensively reflect the size and number concentration information of precipitation particles. Weather radar with Doppler function can also detect the Doppler velocity of precipitation particles in clouds, that is, the speed at which various parts of precipitation clouds approach or move away from the radar.

[0003] Weather radar has been used for decades to detect Doppler velocity of precipitation particles in clouds. The Doppler velocity of precipitation particles in clouds obtained by radar beam detection at a relatively low elevation angle is assumed to be similar to the Doppler velocity of horizontal airflow in clouds, which is true in most cases, but not for larger particles, such as hail particles with a diameter of 2 cm or even larger.

[0004] Therefore, how to effectively estimate the airflow Doppler velocity corresponding to small raindrops in a power spectrum containing both hail and small raindrops has become a problem that needs to be solved urgently in the industry. Summary of the invention

[0005] The present invention provides a method and device for determining Doppler velocity of airflow in clouds, which are used to solve the problem in the prior art of how to effectively estimate the Doppler velocity of airflow corresponding to small raindrops in a power spectrum containing both hail and small raindrops.

[0006] The present invention provides a method for determining the Doppler velocity of airflow in a cloud, comprising the following steps.

[0007] De-noising the radar power spectrum data of the distance library corresponding to the target cloud cluster to obtain the de-noised radar power spectrum density data; the target cloud cluster is a cloud cluster containing both raindrops and hail; Performing logarithmic transformation on the radar power spectrum density data corresponding to the denoised radar power spectrum data to obtain logarithmic unit radar power spectrum density data; A first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value are calculated according to the logarithmic unit radar power spectral density data, so as to determine the Doppler velocity of the airflow in the cloud of the target cloud according to the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

[0008] According to a method for determining Doppler velocity of airflow in a cloud provided by the present invention, the first velocity weighted average value is specifically: ; The second speed weighted average value is specifically: ; The third speed weighted average value is specifically: ; Where v is the velocity value on the X-axis of the radar power spectrum; min v and max v in the summation symbol represent the left and right boundaries of the X-axis, that is, the upper and lower limits of the Doppler velocity measurement of the radar system, F( v ) is the radar power spectrum density data in logarithmic units.

[0009] According to a method for determining Doppler velocity of airflow in a cloud provided by the present invention, the Doppler velocity of airflow in a cloud of the target cloud is determined according to the first velocity weighted average value, the second velocity weighted average value and the third velocity weighted average value, comprising: ; ; ; Where, C=V3, is the Doppler velocity of the airflow in the target cloud.

[0010] According to a method for determining the Doppler velocity of airflow in a cloud provided by the present invention, the method for determining the Doppler velocity of airflow in a cloud of a target cloud cluster further includes: In the case where V1-C and V2-C have different signs, let V * = .

[0011] According to a method for determining Doppler velocity of airflow in clouds provided by the present invention, radar power spectrum density data of a distance library corresponding to a target cloud group is denoised to obtain denoised radar power spectrum density data, including: The maximum power spectrum density points in the power spectrum density data of each radar are eliminated one by one until the ratio of the standard deviation and the mean of the remaining data is less than a certain threshold, and the maximum value of the remaining data is used as the noise level; The noise level is subtracted from the entire radar power spectrum density data to achieve denoising processing and obtain denoised radar power spectrum density data.

[0012] According to a method for determining Doppler velocity of airflow in clouds provided by the present invention, the radar power spectrum data is a one-dimensional array in which the X-axis is velocity and the Y-axis is power spectrum density of echo.

[0013] The present invention also provides a device for determining Doppler velocity of airflow in a cloud, comprising the following modules: A denoising module is used to perform denoising on the radar power spectrum data of the distance library corresponding to the target cloud cluster to obtain denoised radar power spectrum density data; the target cloud cluster is a cloud cluster containing both raindrops and hail; A conversion module, used for performing logarithmic conversion on radar power spectrum density data corresponding to the denoised radar power spectrum data to obtain logarithmic unit radar power spectrum density data; A determination module is used to calculate a first velocity weighted average value, a second velocity weighted average value and a third velocity weighted average value according to the logarithmic unit radar power spectral density data, so as to determine the Doppler velocity of the airflow in the cloud of the target cloud according to the first velocity weighted average value, the second velocity weighted average value and the third velocity weighted average value.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for determining the Doppler velocity of airflow in a cloud as described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for determining the Doppler velocity of airflow in a cloud as described in any one of the above is implemented.

[0016] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for determining the Doppler velocity of airflow in a cloud as described in any one of the above is implemented.

[0017] The method and device for determining the Doppler velocity of airflow in clouds provided by the present invention perform denoising on radar power spectrum data to remove electronic noise and other interference, thereby obtaining a more accurate signal. The denoised radar power spectrum density data usually needs to be logarithmically transformed to improve the dynamic range of the data and facilitate subsequent processing. According to the logarithmic unit radar power spectrum density data, the first velocity weighted average value (V1), the second velocity weighted average value (V2) and the third velocity weighted average value (V3) can be calculated. These weighted average values ​​are calculated by considering the power spectrum density corresponding to different speeds, thereby obtaining a comprehensive velocity value, which can better represent the true velocity of the airflow in the cloud. By analyzing V1, V2 and V3, the Doppler velocity of the airflow in the cloud of the target cloud can be determined to infer the movement direction and velocity of the cloud, and then predict the evolution and influence of the cloud. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or 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 creative work.

[0019] Figure 1 It is a schematic diagram of simulation results under ideal conditions in related technologies.

[0020] Figure 2 It is a conceptual diagram of the radar power spectrum containing both small raindrops and hail in the related technology.

[0021] Figure 3 It is a flow chart of the method for determining the Doppler velocity of airflow in clouds provided by the present invention.

[0022] Figure 4 This is an example diagram of the changes in the graph caused by raising the power spectrum of the skewed distribution to a higher power provided by the present application.

[0023] Figure 5 A schematic diagram of the approximate relationship provided for an embodiment of the present application.

[0024] Figure 6 It is a comparative schematic diagram of multiple groups of wind speed detection in the embodiment of the present application.

[0025] Figure 7 Schematic diagram of power spectrum components in this application.

[0026] Figure 8 A schematic diagram of the relationship between various intermediate calculation quantities and calculation results provided for this application.

[0027] Fig. 9 Schematic diagram of the power spectrum components input by the present invention.

[0028] Fig.10 A schematic diagram of the relationship between the intermediate calculation amount and the calculation result provided by the present invention.

[0029] Fig.11 This is a schematic diagram of the structure of the device for determining the Doppler velocity of airflow in clouds provided in this application.

[0030] Fig.12 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0032] In related technologies, weather radars used in meteorological services usually adopt a scanning method called "volume scanning", which performs azimuth scanning at a fixed elevation angle within the elevation range of 0.5°~19.5° to monitor precipitation clouds in the air within a radius of tens or even hundreds of kilometers around the radar.

[0033] Since the detection elevation angle of the weather radar is low during its operational operation and the radar beam is approximately horizontal, the Doppler velocity of precipitation cloud particles measured by the weather radar can be used to monitor the horizontal movement of precipitation clouds. Furthermore, the Doppler velocity of the precipitation cloud particles is assumed to be approximately the Doppler velocity of the airflow in the cloud, so this Doppler velocity is also used to solve the horizontal wind information and even the three-dimensional wind field information in the cloud.

[0034] Weather radars are usually divided into X, C, S and other bands. Radar transmission waves in these bands can usually generate obvious and quantifiable backscattered waves in cloud hydrometeor particles with a diameter greater than about 0.1 mm and be received by the radar. After the radar has accumulated a certain amount of echo signals, the power spectrum of the echo in the unit detection space can be obtained through signal processing methods. The X-axis of this power spectrum is the speed, and the Y-axis is the echo power. By performing a weighted average calculation of the Doppler velocity on the power spectrum, the Doppler velocity value in the unit detection space can be finally obtained. The principle formula is as follows: (1) Among them, V is the Doppler velocity value finally obtained in the unit detection space, which usually corresponds to the detection result of a range library of a radar beam; v is the value of the X-axis in the power spectrum; F( v ) is the power spectrum v The denominator of formula (1) represents the direct integration of the entire power spectrum, and the numerator represents the first-order distance integral of the power spectrum. The essence of the whole formula is to find the power-weighted average speed.

[0035] It may be necessary to explain that weather radar uses the weighted average form of formula (1) to calculate the Doppler velocity of precipitation clouds, rather than determining the Doppler velocity by the peak position in the power spectrum like radar that detects aircraft. This is because the movement speed of precipitation particles in a certain detection space may vary (for example, due to the influence of turbulence in the cloud and differences in the physical properties of the particles themselves).

[0036] When the precipitation particles in the cloud are inconsistent with the horizontal wind speed at their location, the particles will be affected by air resistance in the horizontal direction and accelerated until there is no speed difference between them and the horizontal wind speed at the real-time location; at the same time, the precipitation particles in the air are affected by gravity and air resistance in the vertical direction, and will reach a falling state with balanced vertical forces in a few seconds or less. For example, the final falling speed of a raindrop is 10 0 ms -1 Combining the above forces and motion processes in the horizontal and vertical directions, if the time required for a particle to be accelerated to the same speed by the horizontal wind is extremely short, then the distance it falls can be ignored, that is, it is still in an environment with the same horizontal wind, and its Doppler velocity can be considered as the Doppler velocity of the airflow; however, if the time required for a particle to be accelerated to the same speed by the horizontal wind is very long, then it will also fall a long distance. Considering that the horizontal wind speed and wind direction at different heights in the cloud are not consistent, the horizontal force direction of the particle will change continuously during the falling process, and it is difficult to achieve balance. In addition, due to the influence of inertia, it is difficult to be accelerated to a larger horizontal velocity. At this time, the Doppler velocity detected by the radar in the quasi-horizontal direction obviously cannot represent the Doppler velocity of the airflow.

[0037] Figure 1 It is a schematic diagram of simulation results under ideal conditions in related technologies, such as Figure 1 As shown in Figure 2, the time required for spherical particles of different sizes to be accelerated from rest to the horizontal wind speed of their environment under the action of air resistance is simulated. The density of the particles is 10 3 kg m -3 , which is consistent with liquid particles and hail (0.8×10 3 kg m -3 ) and melting hail are not much different, which is representative. The ambient horizontal wind speed is set to the typical wind speed at high altitude, 10 ms -1 , but it should be noted that the magnitude of the ambient horizontal wind speed here has no effect on the conclusion. Figure 1 It can be seen that for common raindrop sizes (0.1 to 6 mm in diameter), the time required for the particles to be accelerated to wind speed is at most 10 1 s level, that is, most raindrops can reach the same horizontal speed as the ambient wind in a short time. However, for the typical size of hail (10~100 mm), especially the particles with a diameter of 50~100 mm, the time required to be accelerated to the wind speed is 10 2 Considering that the final speed of hail is 10 1 ms -1 The magnitude is 10 2In s, it may have fallen several kilometers. Over such a large vertical distance, especially in a strong convective cloud with a complex dynamic structure that produces hail, the horizontal wind speed and direction must be inconsistent. On the other hand, the radar echo power is proportional to the sixth power of the particle diameter, which means that not only are the Doppler velocities of larger particles different from those of smaller particles, but their contribution to the power spectrum is also abnormally large, which will eventually mask the contribution of small particles when calculating the weighted average velocity along the power spectrum, making it impossible to obtain a result consistent with the Doppler velocity of the airflow.

[0038] Figure 2 FIG. 1 is a conceptual diagram of the radar power spectrum in the related art containing both small raindrops and hail. Figure 2 As shown in the figure, the blue line is the power spectrum of the small raindrop, and its center is at 5 ms -1 , which is consistent with the Doppler velocity of the airflow in the cloud, and affected by the turbulence in the cloud, the peak of its power spectrum generally has a certain width. The red line in the figure is the power spectrum of hail. Affected by the inertia of the hail itself, the absolute value of the velocity corresponding to the peak position of the power spectrum is smaller, at 1 ms. -1 The power spectrum peak of hail is obviously much larger than that of small raindrops. The combination of the two plus a certain amount of noise constitutes the black line in the figure, which is an obviously skewed distribution. The Doppler velocity calculated by the weighted average is 2.33 ms -1 , and there is obviously a large deviation between the airflow Doppler velocity corresponding to the small raindrops.

[0039] Hail, as a disastrous weather, has attracted more and more attention in recent years due to the need for disaster prevention and mitigation, and the airflow structure in hail clouds has also become one of the topics worthy of key research. Obtaining the correct Doppler velocity of airflow in hail clouds is the basis for more reliable grasp of horizontal wind and even three-dimensional wind information in hail clouds, and is also a necessary condition for more effective research on hail formation and artificial hail prevention.

[0040] Figure 3 FIG. 1 is a flow chart of a method for determining Doppler velocity of airflow in a cloud provided by the present invention. Figure 3 As shown, the method includes the following: Step 310, performing denoising processing on the radar power spectrum data of the distance library corresponding to the target cloud cluster to obtain denoised radar power spectrum density data; the target cloud cluster is a cloud cluster containing both raindrops and hail; In the present invention, the radar distance library can be considered as a point on a radar radial direction. The size of the spatial range it represents is related to the radar beam width and distance resolution, and the spatial position it represents is related to the elevation angle and azimuth angle of the radar radial direction and the radial distance corresponding to the distance library.

[0041] More specifically, the radar power spectrum of a range library is a one-dimensional array. If a line graph is used as an example, the X-axis is the speed and the Y-axis is the power spectrum density of the echo. The unit of the Y-axis can be an engineering unit related to the radar system, which is not limited in the present invention.

[0042] In the present invention, the radar power spectrum data of the target cloud corresponding to the distance library is the information collected by the radar system on the distribution of particle motion speed inside the cloud at a specific distance unit (distance library). These data are crucial for analyzing the physical characteristics and motion state of the cloud, especially when the target cloud contains hail, accurate radar power spectrum data can help us better understand and predict the behavior of the cloud.

[0043] More specifically, when processing radar power spectrum data, denoising is to improve the quality of the data and the accuracy of subsequent analysis. The purpose of denoising is to separate the real signal and background noise from the power spectrum, thereby obtaining the denoised radar power spectrum density data.

[0044] In an optional embodiment, the power spectrum data is divided into eight equal parts. The standard deviation and mean of each data segment are calculated; the ratio of the standard deviation to the mean is calculated; the data segment with the smallest ratio is selected, and the maximum value of the data segment is taken as the noise level. The estimated noise level is subtracted from the entire power spectrum.

[0045] In another optional embodiment, starting from the point with the largest power spectrum density, the points are eliminated one by one; during the elimination process, the ratio of the standard deviation to the mean of the remaining data is continuously calculated; when the ratio of the standard deviation to the mean is less than a set threshold, the elimination is stopped; the maximum value of the remaining data after elimination is taken as the noise level; and the estimated noise level is subtracted from the entire power spectrum.

[0046] Step 320, performing logarithmic conversion on the radar power spectrum density data corresponding to the denoised radar power spectrum data to obtain logarithmic unit radar power spectrum density data; In the embodiment of the present application, if the value of the power spectrum density at this time is a linear unit (the value starts from 0) rather than a logarithmic unit, then the logarithm is converted to a logarithmic unit (taking log 10 ). Then, the data is linearly scaled to start from 0. For example, if the storage precision of the original unit is two decimal places, the minimum value after conversion to logarithmic units is about -2, and 2 is added to the data as a whole.

[0047] Step 330, a first velocity weighted average value, a second velocity weighted average value and a third velocity weighted average value are calculated according to the logarithmic unit radar power spectral density data, so as to determine the Doppler velocity of the airflow in the cloud of the target cloud according to the first velocity weighted average value, the second velocity weighted average value and the third velocity weighted average value.

[0048] Specifically, the first speed weighted average value is: ; The second speed weighted average value is specifically: ; The third speed weighted average value is specifically: ; Where v is the velocity value on the X-axis of the radar power spectrum; min v and max v in the summation symbol represent the left and right boundaries of the X-axis, that is, the upper and lower limits of the Doppler velocity measurement of the radar system; F(v) is the logarithmic unit radar power spectrum density data.

[0049] Determining the Doppler velocity of the airflow in the cloud of the target cloud according to the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value includes: ; ; ; Where, C=V3, is the Doppler velocity of the airflow in the target cloud.

[0050] The method for determining the Doppler velocity of the airflow in the target cloud cluster further includes: In the case where V1-C and V2-C have different signs, let V* = .

[0051] More specifically, the present application describes and illustrates the effectiveness of the solution of the present application from the perspectives of algorithm verification and experiment.

[0052] First, mathematically, it is obvious that if the spectral density of a skewed peak is raised to a higher power, the graph of the entire spectrum will increasingly approach the central distribution at the original peak position. Figure 4 The example diagram of the change in the graph caused by raising the power spectrum of the skewed distribution to a higher power provided by the present application is as follows: Figure 4 As shown, the original spectrum consists of two unimodal distributions based on the normal model, one of which has a power of 1 and a mean of 5 ms corresponding to the spectrum X. -1 , standard deviation is 2 ms -1 , and another with a power of 3 and an average of 1 ms -1 , standard deviation is 1 ms -1, both composite graphs have skewed blue lines. The higher the power of the blue line, the closer the result is to a central distribution, and the speed value obtained by weighted average is closer to the mean of the single peak component with higher power. For example, the speed obtained by weighted average of the blue line is 2 ms -1 , calculated as 1.25 ms according to the red line -1 , calculated as 1.08 ms according to the black line -1 As the power of F(v) increases, the final weighted average velocity value is closer to the mean of the peak component with higher power, that is, 1 ms. -1 .

[0053] Then, the power n of F(v) can form a relationship similar to a natural exponential function with the weighted average speed V(n) calculated by the nth power of F(v). According to the above steps to calculate V1~V3, A, B, C and V*, it is equivalent to determining a natural exponential function relationship by the 1st, 2nd and 3rd powers of F(v), as shown in formula (2): (2) Among them, Y is the velocity value, and X is F(v) raised to the power of n.

[0054] Moreover, the extension line of this natural exponential function in the direction of n=0, at the position of n=0, just roughly approximates the speed value corresponding to the single peak component with smaller power, Figure 5 The schematic diagram of the approximate relationship provided in the embodiment of the present application is as follows: Figure 5 As shown, the black dotted line as an extension line points to the Y-axis scale of 5 ms -1 Obviously, by finding the parameters A, B, and C in equation (2) and substituting them into the independent variable X=0, we can find the rough estimate of the velocity value corresponding to the single peak component with smaller power, which is also the essence of the algorithm in the above embodiment.

[0055] On the other hand, the necessity of taking the logarithm in the present application is that if the total power of the single peak component with larger power is more than two orders of magnitude larger than that of the single peak component with smaller power in linear units, the smaller single peak component will be negligible in essence, and the above steps will hardly work. However, taking the logarithm of the value of the power spectrum can eliminate the magnitude difference of the total power of the two peak components.

[0056] The necessity of converting the power spectrum values ​​into logarithms and scaling them to start from 0 is that in the above-mentioned step of calculating the speed-weighted average, in the speed-weighted average calculation based on F(v) or its higher power, F(v) must be a non-negative number with a minimum value of 0.

[0057] The above steps are not only applicable to the case of two peak components mixed, but also can be successfully calculated for the case of only one unbiased single peak component or no peak and only noise. For example, when there is only one unbiased single peak component, in the above embodiment, the calculated V1, V2, and V3 are almost equal, and the parameter B solved is approximately equal to 0, and the final calculated result V * and V1. For another example, when only random noise remains in the power spectrum density, V1, V2, and V3 are all close to 0, and the final calculated result V * It is also 0. These situations show that the calculation results of the present invention when there are no large particles such as hail in the detection space are also reasonable and do not conflict with the prior art.

[0058] Finally, when V1-C and V2-C have different signs, we assume that V1, V2, and V3 are approximately equal and directly set V * =V1 is because, when the input data is close to an unbiased distribution, V1, V2, and V3 will not change monotonically under the influence of noise, resulting in different signs of V1-C and V2-C. In this case, it is reasonable to set V*=V1.

[0059] In the embodiment of the present application, the present invention uses the 1st, 2nd, and 3rd powers of the logarithmic and simply scaled power spectrum density to calculate the weighted average of the speed, and then the required corrected speed can be calculated based on the simple algebraic calculation scheme proposed by the present invention. The time complexity of the calculation is at the same order of magnitude as that of the prior art, and the calculation process of the prior art can be realized by simply adjusting or modifying it, which is convenient for deploying the content of the present invention after upgrading and modifying the existing radar data processing software.

[0060] Finally, the effects of the present invention are presented in three sections. Section (1) is an overall statistical analysis of the calculation results of multiple groups of ideal tests. Section (2) is an example of a typical bimodal case. Section (3) is an example of a typical unbiased unimodal case. The presentation of these overall effects and typical application effects illustrates the superiority and applicability of the calculation results of the present invention.

[0061] (1) Statistics of results of multiple groups of ideal experiments In order to demonstrate the overall application effect of the present invention, the parameters of the two input power spectrum components are enumerated below, so as to obtain multiple sets of ideal test results that can cover various situations. The input power spectrum consists of two normal distribution spectra. The power of the spectrum with larger power is from 10 1 Enumerate to 10 3 (Engineering units), exponential interval 0.2, 11 in total, standard deviation 1 ms -1 , the mean value is 0ms -1 ; The power of the spectrum with smaller power is taken as 1 (engineering unit), and the standard deviation is from 2 ms-1 Enumeration to 4 ms -1 , interval 0.1 ms -1 , a total of 21, the average value ranges from -10 ms -1 Enumeration to 10 ms -1 , interval 1 ms -1 , a total of 21. The total number of samples is 11×21×21=4851.

[0062] Figure 6 FIG. 1 is a comparative schematic diagram of multiple wind speed detection groups in the embodiment of the present application. Figure 6 As shown in the figure, the comparison results of the traditional observation values ​​and the correction values ​​of the present invention in the above multiple groups of ideal wind speed detection experiments, where the true value of the wind speed is the mean value of the spectrum component with smaller power. The red dots in the figure are the Doppler velocity values ​​obtained by the traditional observation method. It can be seen that they are affected by the peak component with larger power. No matter how large the true value of the wind speed is, the results obtained are all between 0 and 1 ms. -1 , it is difficult to reflect the true value of wind speed, only the sign, that is, the wind direction, is the same as the true value of wind speed. The blue dots in the figure are the results of the calculation of the present invention, and the box plot is its distribution statistics (the horizontal lines from bottom to top are the minimum value, 25% percentile, median, 75% percentile, and maximum value, respectively). It can be seen that although they are not completely consistent with the true value of wind speed, they are all distributed near the true value of wind speed, which is significantly better than the traditional observation value.

[0063] It is also necessary to explain that Figure 6 The result graph is only affected by the relative difference of the mean values ​​of the two power spectrum components, and has nothing to do with the absolute size of their respective mean values, because the transformation of the value of the power spectrum X-axis such as translation and scaling does not affect the abstract form of the integral in the above calculation formula, so it has no effect on the algorithm of the present invention. Figure 6 In the experiment, the mean value of the spectrum with higher power is from 0 ms -1 Change to 10 ms -1 , the mean value of the spectrum with smaller power is enumerated in the range of -10 s -1 ~ 10 s -1 Change to 0 ms -1 ~ 20 ms -1 , the result graph is still the same Figure 6 It is similar, only the X-axis and the Y-axis will be translated. Therefore, the mean values ​​of the spectrum with larger power are not enumerated here, which does not affect the adequacy of the effect of the present invention.

[0064] (2) Case 1: When the peaks of two power spectrum components are far apart Case 1 shows the situation when the peaks of the two power spectrum components are far apart, that is, the "double peak" situation. The power of the spectrum with larger power is 10 (engineering unit) and the standard deviation is 1 ms. -1 , the average value is 1 ms-1 ; The power of the spectrum with smaller power is 1 (engineering unit), and the standard deviation is 3 ms -1 , the average value is 9 ms -1 The two power spectrum components and the power spectrum of the two components are as follows: Figure 7 The power spectrum components in this application are shown in FIG. Figure 7 shown.

[0065] Figure 8 A schematic diagram of the relationship between various intermediate calculation quantities and calculation results provided for this application, such as Figure 8 As shown, the relationship between various intermediate calculation quantities of Example 1 and the calculation results of the present invention is given. The V1, V2, and V3 calculated by the present invention are 4.71, 3.16, and 2.11 ms respectively. -1 (blue circle in the figure), the natural exponential relationship line (black dashed line in the figure) further obtained by them points to the speed corresponding to the peak with smaller power (red star in the figure), and the final calculated result V * 8.55 ms -1 (green triangle in the figure), which is consistent with the target value (9 ms -1 ) is very close and significantly better than the traditional observation value (1.73 ms -1 , black cross in the figure). In summary, this example shows that the present invention can obtain the velocity corresponding to the peak with smaller power from the bimodal power spectrum, so as to obtain a more objective airflow velocity in the cloud in the detection space where hail and rain are mixed.

[0066] The above is a typical "double peak" power situation. For the situation where the peaks of the two power spectrum components are close to each other and form a skewed single peak, the effect has been demonstrated in the previous text when demonstrating the concept of the algorithm of the present invention, and will not be repeated here.

[0067] (3) Case 2: When there is only an unbiased single peak Case 2 shows the case where the peak positions of the two power spectrum components are consistent, that is, the case of unbiased single peak. Fig. 9 Schematic diagram of the power spectrum components input by the present invention, with two mean values ​​of 1 ms -1 However, the power spectrum components with different standard deviations and powers and the power spectrum graphs of the two synthesized are as follows Fig. 9 As shown, Fig.10 The schematic diagram of the relationship between the intermediate calculation amount and the calculation result provided by the present invention, and the mutual relationship between various intermediate calculation amounts and the calculation results of the present invention are as follows Fig.10 As shown. It can be seen that when there is only a single peak, the V1, V2, V3 calculated by the present invention are respectively the same as the final estimated value V * Both are consistent with the target value, which is 1 ms -1, and the traditional observation value in this case is also 1 ms -1 In summary, this example shows that the present invention is still applicable when there is only one unbiased peak value, and will not introduce additional deviations when the traditional observation value can correctly reflect the airflow velocity. This shows that when there is no hail and rain mixture in the radar detection space, but only general rainfall, the present invention can also obtain correct cloud airflow detection results, so there is no need to additionally determine whether to enable the calculation scheme of the present invention in actual application.

[0068] The device for determining the Doppler velocity of a mid-air flow provided by the present invention is described below. The device for determining the Doppler velocity of a mid-air flow described below and the method for determining the Doppler velocity of a mid-air flow described above can be referred to each other.

[0069] Fig.11 The schematic diagram of the structure of the Doppler velocity determination device for airflow in clouds provided in this application is as follows: Fig.11 As shown, including: The denoising module 111 is used to perform denoising on the radar power spectrum data of the distance library corresponding to the target cloud cluster to obtain denoised radar power spectrum density data; the target cloud cluster is a cloud cluster containing both raindrops and hail; The conversion module 112 is used to perform logarithmic conversion on the radar power spectrum density data corresponding to the denoised radar power spectrum data to obtain logarithmic unit radar power spectrum density data; The determination module 113 is used to calculate a first velocity weighted average value, a second velocity weighted average value and a third velocity weighted average value according to the logarithmic unit radar power spectral density data, so as to determine the Doppler velocity of the airflow in the cloud of the target cloud according to the first velocity weighted average value, the second velocity weighted average value and the third velocity weighted average value.

[0070] In the embodiment of the present application, the radar power spectrum data is denoised to remove electronic noise and other interference, thereby obtaining a more accurate signal. The denoised radar power spectrum density data usually needs to be logarithmically transformed to improve the dynamic range of the data and facilitate subsequent processing. According to the logarithmic unit radar power spectrum density data, the first velocity weighted average value (V1), the second velocity weighted average value (V2) and the third velocity weighted average value (V3) can be calculated. These weighted average values ​​are calculated by considering the power spectrum density corresponding to different speeds, thereby obtaining a comprehensive velocity value, which is more representative of the true velocity of the airflow in the cloud. By analyzing V1, V2 and V3, the Doppler velocity of the airflow in the target cloud can be determined to infer the movement direction and velocity of the cloud, and then predict the evolution and impact of the cloud.

[0071] Fig.12 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Fig.12 As shown, the electronic device may include: a processor 1210, a communications interface 1220, a memory 1230 and a communication bus 1240, wherein the processor 1210, the communications interface 1220 and the memory 1230 communicate with each other through the communication bus 1240. The processor 1210 may call the logic instructions in the memory 1230 to execute the method for determining the Doppler velocity of the airflow in the cloud, the method comprising: performing denoising on the radar power spectrum data of the distance library corresponding to the target cloud cluster to obtain the denoised radar power spectrum data; the target cloud cluster is a cloud cluster containing both raindrops and hail; Performing logarithmic transformation on the radar power spectrum density data corresponding to the denoised radar power spectrum data to obtain logarithmic unit radar power spectrum density data; A first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value are calculated according to the logarithmic unit radar power spectral density data, so as to determine the Doppler velocity of the airflow in the cloud of the target cloud according to the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

[0072] In addition, the logic instructions in the above-mentioned memory 1230 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0073] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the method for determining the Doppler velocity of airflow in the cloud provided by the above methods, the method comprising: performing denoising processing on the radar power spectrum data of the distance library corresponding to the target cloud cluster to obtain the denoised radar power spectrum data; the target cloud cluster is a cloud cluster containing both raindrops and hail; Performing logarithmic transformation on the radar power spectrum density data corresponding to the denoised radar power spectrum data to obtain logarithmic unit radar power spectrum density data; A first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value are calculated according to the logarithmic unit radar power spectral density data, so as to determine the Doppler velocity of the airflow in the cloud of the target cloud according to the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

[0074] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the method for determining the Doppler velocity of airflow in a cloud provided by the above methods, the method comprising: performing denoising processing on radar power spectrum data of a distance library corresponding to a target cloud cluster to obtain denoised radar power spectrum data; the target cloud cluster is a cloud cluster containing both raindrops and hail; Performing logarithmic transformation on the radar power spectrum density data corresponding to the denoised radar power spectrum data to obtain logarithmic unit radar power spectrum density data; A first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value are calculated according to the logarithmic unit radar power spectral density data, so as to determine the Doppler velocity of the airflow in the cloud of the target cloud according to the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

[0075] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0076] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining Doppler velocity of airflow in a cloud, characterized in that: include: De-noising the radar power spectrum data of the target cloud cluster corresponding to the distance library to obtain the de-noised radar power spectrum data; The target cloud is a cloud containing both raindrops and hail; Performing logarithmic transformation on the radar power spectrum density data corresponding to the denoised radar power spectrum data to obtain logarithmic unit radar power spectrum density data; A first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value are calculated according to the logarithmic unit radar power spectral density data, so as to determine the Doppler velocity of the airflow in the cloud of the target cloud according to the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

2. The method for determining Doppler velocity of airflow in clouds according to claim 1, characterized in that: The first speed weighted average value is specifically: ； The second speed weighted average value is specifically: ； The third speed weighted average value is specifically: ； Where v is the velocity value on the X-axis of the radar power spectrum; min v and max v in the summation symbol represent the left and right boundaries of the X-axis, that is, the upper and lower limits of the Doppler velocity measurement of the radar system, F( v ) is the radar power spectrum density data in logarithmic units.

3. The method for determining Doppler velocity of airflow in clouds according to claim 2, characterized in that: Determining the Doppler velocity of the airflow in the cloud of the target cloud according to the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value includes: ； ； ； Where, C=V3, is the Doppler velocity of the airflow in the target cloud.

4. The method for determining Doppler velocity of airflow in clouds according to claim 3, characterized in that: The method for determining the Doppler velocity of the airflow in the target cloud cluster further includes: In the case where V1-C and V2-C have different signs, let V * = .

5. The method for determining Doppler velocity of airflow in clouds according to claim 1, characterized in that: The radar power spectrum density data of the target cloud cluster corresponding to the distance library is denoised to obtain the denoised radar power spectrum density data, including: The maximum power spectrum density points in the power spectrum density data of each radar are eliminated one by one until the ratio of the standard deviation and the mean of the remaining data is less than a certain threshold, and the maximum value of the remaining data is used as the noise level; The noise level is subtracted from the entire radar power spectrum density data to achieve denoising processing and obtain denoised radar power spectrum density data.

6. The method for determining Doppler velocity of airflow in clouds according to claim 1, characterized in that: The radar power spectrum data is a one-dimensional array in which the X-axis is the speed and the Y-axis is the power spectrum density of the echo.

7. A device for determining Doppler velocity of airflow in a cloud, characterized in that: include: The denoising module is used to denoise the radar power spectrum data of the target cloud cluster corresponding to the distance library to obtain the denoised radar power spectrum density data; The target cloud is a cloud containing both raindrops and hail; A conversion module, used for performing logarithmic conversion on radar power spectrum density data corresponding to the denoised radar power spectrum data to obtain logarithmic unit radar power spectrum density data; A determination module is used to calculate a first velocity weighted average value, a second velocity weighted average value and a third velocity weighted average value according to the logarithmic unit radar power spectral density data, so as to determine the Doppler velocity of the airflow in the cloud of the target cloud according to the first velocity weighted average value, the second velocity weighted average value and the third velocity weighted average value.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for determining the Doppler velocity of airflow in a cloud as claimed in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for determining the Doppler velocity of airflow in a cloud as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for determining the Doppler velocity of airflow in a cloud as claimed in any one of claims 1 to 6 is implemented.

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