Method and device for determining Doppler velocity of airflow in clouds

Abstract

The present invention provides a method and device for determining the Doppler velocity of airflow in a cloud, which relates to the field of data processing technology and includes the following steps: performing denoising processing on radar power spectrum data corresponding to a distance library of a target cloud cluster to obtain denoised radar power spectrum density data; the target cloud cluster is a cloud cluster containing both raindrops and hail; 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; and calculating a first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value based on the logarithmic unit radar power spectrum density data to determine the Doppler velocity of the airflow in the cloud cluster of the target cloud cluster based on the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

Description

Technical Field

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

[0002] Weather radars are devices that quantitatively detect the physical properties of precipitation particles in clouds. Using the intensity of the cloud's echoes, they can quantitatively measure the sum of the backscattered cross sections of precipitation particles within a given volume of space, providing comprehensive information on the size and number concentration of precipitation particles. Weather radars equipped with Doppler capabilities can also measure the Doppler velocity of precipitation particles within clouds, i.e., the speed at which different parts of a cloud approach or retreat from the radar.

[0003] Weather radar has been used for decades to detect the Doppler velocity of precipitation particles in clouds. The Doppler velocity of precipitation particles in clouds, obtained by radar beam detection at relatively low elevation angles, is assumed to approximate the Doppler velocity of the horizontal airflow within the cloud. This holds true in most cases, but it does not hold true for larger particles, such as hailstones with diameters on the order of 2 cm or larger.

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

[0005] The present invention provides a method and device for determining the 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] Denoising the radar power spectrum data of the target cloud cluster corresponding to the distance library to obtain denoised radar power spectrum density data; the target cloud cluster is a cloud cluster containing both raindrops and hail;

[0008] Perform 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;

[0009] A first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value are calculated based on 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 cluster based on the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

[0010] 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:

[0011] ;

[0012] The second speed weighted average value is specifically:

[0013] ;

[0014] The third speed weighted average value is specifically:

[0015] ;

[0016] 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.

[0017] According to a method for determining the Doppler velocity of airflow in a cloud provided by the present invention, the Doppler velocity of airflow in a target cloud cluster is determined based on the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value, including:

[0018] ;

[0019] ;

[0020] ;

[0021] Where C=V3, is the Doppler velocity of the airflow in the target cloud.

[0022] 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 target cloud cluster further includes:

[0023] In the case where V1-C and V2-C have different signs, let V * = .

[0024] According to a method for determining Doppler velocity of airflow in clouds provided by the present invention, radar power spectrum density data of a target cloud cluster corresponding to a distance library is subjected to denoising processing to obtain denoised radar power spectrum density data, including:

[0025] 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 to the mean of the remaining data is less than a certain threshold, and the maximum value of these remaining data is used as the noise level;

[0026] 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.

[0027] 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 the power spectrum density of the echo.

[0028] The present invention also provides a device for determining Doppler velocity of airflow in a cloud, comprising the following modules:

[0029] a denoising module for denoising radar power spectrum data of a target cloud cluster corresponding to a distance library to obtain denoised radar power spectrum density data; the target cloud cluster is a cloud cluster containing both raindrops and hail;

[0030] A conversion module 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;

[0031] a determination module, configured to calculate a first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value based on the logarithmic unit radar power spectral density data, and determine the Doppler velocity of the airflow in the cloud of the target cloud based on the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

[0032] 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, any of the above-described methods for determining the Doppler velocity of airflow in a cloud is implemented.

[0033] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for determining the Doppler velocity of airflow in a cloud as described above is implemented.

[0034] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned methods for determining the Doppler velocity of airflow in a cloud.

[0035] The method and device for determining the Doppler velocity of airflow in clouds, provided by the present invention, denoise radar power spectrum data to remove electronic noise and other interference, thereby obtaining a more accurate signal. The denoised radar power spectrum density data typically requires logarithmic transformation to improve the data's dynamic range and facilitate subsequent processing. Based on the logarithmic radar power spectrum density data, a first velocity weighted average (V1), a second velocity weighted average (V2), and a third velocity weighted average (V3) can be calculated. These weighted averages are calculated by considering the power spectrum densities corresponding to different velocities, resulting in a comprehensive velocity value that better represents the true velocity of the airflow in the cloud. By analyzing V1, V2, and V3, the Doppler velocity of the target cloud's airflow can be determined, which can be used to infer the cloud's motion direction and velocity, and thus predict the cloud's evolution and impact. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to 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.

[0037] Figure 1 Schematic diagram of simulation results under ideal conditions in related technologies.

[0038] Figure 2 This is a conceptual diagram of the radar power spectrum containing both small raindrops and hail in the related art.

[0039] Figure 3 It is a flow chart of the method for determining the Doppler velocity of airflow in clouds provided by the present invention.

[0040] 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, as provided in this application.

[0041] Figure 5 A schematic diagram of the approximate relationship provided in the embodiments of the present application.

[0042] Figure 6 Schematic diagram for comparing multiple groups of wind speed detection in the embodiment of the present application.

[0043] Figure 7 Schematic diagram of the power spectrum components in this application.

[0044] Figure 8 This is a schematic diagram of the relationship between the various intermediate calculation quantities and calculation results provided in this application.

[0045] Figure 9Schematic diagram of the power spectrum components input by the present invention.

[0046] Figure 10 This is a schematic diagram of the relationship between the intermediate calculation amount and the calculation results provided by the present invention.

[0047] Figure 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.

[0048] Figure 12 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. 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.

[0050] 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.

[0051] Because the radar operates at a low elevation angle during operational operation, its beam is approximately horizontal. Therefore, the Doppler velocity of precipitation cloud particles measured by the radar can be used to monitor the horizontal movement of precipitation clouds. Furthermore, the Doppler velocity of precipitation cloud particles is assumed to be approximately the Doppler velocity of the airflow within the cloud, so this Doppler velocity is also used to derive horizontal wind information and even three-dimensional wind field information within the cloud.

[0052] Weather radars are typically classified into X, C, and S bands. Radar transmission waves in these bands typically generate significant and quantifiable backscattered waves from cloud hydrometeorites with diameters greater than approximately 0.1 mm, which are then received by the radar. After accumulating a certain amount of echo signals, the radar can use signal processing methods to obtain the power spectrum of the echo within a unit detection volume. The X-axis of this power spectrum represents velocity, and the Y-axis represents echo power. By performing a weighted average calculation of the Doppler velocity on this power spectrum, the Doppler velocity value within that unit detection volume can be obtained. The principle formula is as follows:

[0053] (1)

[0054] Where V is the final Doppler velocity value within the unit detection space, which usually corresponds to the detection result of a range bin on a radar beam.v is the value of the X-axis in the power spectrum; F( v ) is the power spectrum v The power spectrum density corresponding to the point. The denominator of formula (1) represents the direct integration of the entire power spectrum, and the numerator represents the first-order moment integral of the power spectrum. The essence of the entire formula is to find the power-weighted average speed.

[0055] 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 detecting 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 the differences in the physical properties of the particles themselves).

[0056] When the horizontal wind speed of the precipitation particles in the cloud is inconsistent with that of the 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. In a few seconds or less, they will reach a falling state with balanced vertical forces. For example, the final falling speed of a raindrop is 10 0 ms -1 Taking into account the 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. At this time, 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 direction at different heights in the cloud are not consistent, the direction of the horizontal force on the particle will constantly change during the fall, making it difficult to achieve equilibrium. Moreover, due to inertia, it is difficult to be accelerated to a large 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.

[0057] Figure 1 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 , consistent with liquid particles, and hail (0.8×10 3 kg m -3 The difference between the hailstones and the melting hailstones is not large, 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 s level. Considering that the final falling speed of hail is 10 1 ms -1 magnitude, which is 10 2 Over such large vertical distances, especially within the complex dynamic structures of severe convective clouds that produce hail, horizontal wind speed and direction are inevitably inconsistent. Furthermore, radar echo power is proportional to the sixth power of particle diameter. This means that larger particles not only have a different Doppler velocity than smaller particles, but also contribute significantly more to the power spectrum. Ultimately, the weighted average velocity along the power spectrum masks the contribution of smaller particles, making it impossible to obtain a result consistent with the Doppler velocity of the airflow.

[0058] Figure 2 FIG. 1 is a conceptual diagram of the radar power spectrum containing both small raindrops and hail in the related art, as shown in FIG. Figure 2 As shown, the blue line in the figure is the power spectrum of the raindrop, and its center is at 5 ms -1 , which is consistent with the Doppler velocity of the airflow in the cloud, and is affected by the turbulence in the cloud, the peak of its power spectrum generally has a certain broadening. 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 significantly larger than that of raindrops. The combination of the two plus a certain amount of noise forms the black line in the figure, which is a clearly skewed distribution. The Doppler velocity calculated by weighted average is 2.33 ms. -1 , and there is an obvious large deviation between the airflow Doppler velocity corresponding to the small raindrops.

[0059] Hail, a weather hazard, has garnered increasing attention in recent years due to the need for disaster prevention and mitigation. The airflow structure within hail clouds has also become a topic of particular interest. Accurately determining the Doppler velocity of airflow within hail clouds is essential for more reliable understanding of horizontal and even three-dimensional wind information within hail clouds, and is essential for more effective research related to hail formation and artificial hail suppression.

[0060] 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:

[0061] Step 310: De-noising the radar power spectrum data of the target cloud cluster corresponding to the distance library to obtain de-noised radar power spectrum density data; the target cloud cluster is a cloud cluster containing both raindrops and hail;

[0062] In the present invention, the radar distance library can be considered as a point on a radar radial line. 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 line and the radial distance corresponding to the distance library.

[0063] More specifically, the radar power spectrum of a range library is a one-dimensional array. For example, using a line graph, the X-axis represents velocity, and the Y-axis represents the power spectrum density of the echo. The Y-axis can be in engineering units related to the radar system, but this is not a limitation of the present invention.

[0064] In this paper, radar power spectrum data for the target cloud's corresponding range bins is information collected by the radar system at specific range bins (range bins) about the velocity distribution of particles within the cloud. This data is crucial for analyzing the cloud's physical properties and motion state. This is especially true when the target cloud contains hail. Accurate radar power spectrum data can help us better understand and predict cloud behavior.

[0065] More specifically, when processing radar power spectrum data, denoising is performed to improve data quality and the accuracy of subsequent analysis. The purpose of denoising is to separate the true signal and background noise from the power spectrum, thereby obtaining denoised radar power spectrum density data.

[0066] In an alternative embodiment, the power spectrum data is divided into eight equal parts. For each data segment, the standard deviation and mean are calculated. The ratio of the standard deviation to the mean is calculated. The segment with the smallest ratio is selected, and the maximum value of this segment is taken as the noise level. This estimated noise level is then subtracted from the entire power spectrum.

[0067] 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.

[0068] Step 320 , performing logarithmic conversion on the radar power spectrum density data corresponding to the denoised radar power spectrum data to obtain logarithmic radar power spectrum density data;

[0069] 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 (take log 10 ). Then, linearly scale the data to start from 0. For example, if the original unit has a storage precision of two decimal places, the minimum value after conversion to logarithmic units is approximately -2. In this case, add 2 to the data.

[0070] Step 330 : Calculate a first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value based on the logarithmic unit radar power spectral density data, and determine the Doppler velocity of the airflow in the target cloud cluster based on the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

[0071] Specifically, the first speed weighted average value is:

[0072] ;

[0073] The second speed weighted average value is specifically:

[0074] ;

[0075] The third speed weighted average value is specifically:

[0076] ;

[0077] 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.

[0078] Determining the Doppler velocity of the airflow in 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:

[0079] ;

[0080] ;

[0081] ;

[0082] Where C=V3, is the Doppler velocity of the airflow in the target cloud.

[0083] The method for determining the Doppler velocity of the airflow in the target cloud cluster further includes:

[0084] In the case where V1-C and V2-C have different signs, let V*= .

[0085] More specifically, this application explains the effectiveness of the solution of this application from the perspective of algorithm verification and experiments.

[0086] 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 This application provides an example of the changes in the graph caused by raising the power spectrum of the skewed distribution to a higher power, such as 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 , the other has a power of 3 and an average of 1 ms -1 , standard deviation is 1 ms -1 , both composite graphs have a skewed blue line. 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 speed value is closer to the mean of the peak component with higher power, that is, 1 ms -1 .

[0087] Then, the power n of F(v) can form a relationship similar to a natural exponential function with the weighted average velocity V(n) calculated by the nth power of F(v). Following the steps above to calculate V1~V3, A, B, C, and V*, it is equivalent to determining a natural exponential function relationship by raising F(v) to the 1st, 2nd, and 3rd power, as shown in formula (2):

[0088] (2)

[0089] Where Y is the velocity value, and X is F(v) raised to the power n.

[0090] Moreover, the extension line of this natural exponential function in the direction of n=0, at the position of n=0, is roughly approximate to the velocity value corresponding to the single peak component with smaller power. Figure 5 The schematic diagram of the approximate relationship provided in the embodiment of this 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 -1Obviously, by finding the parameters A, B, and C in equation (2) and substituting them into the independent variable X = 0, we can find a rough estimate of the velocity value corresponding to the single peak component with the smaller power. This is also the essence of the algorithm in the above embodiment.

[0091] On the other hand, the necessity of taking the logarithm in this application lies in that if, in linear units, the total power of the larger single-peak component is more than two orders of magnitude greater than that of the smaller single-peak component, the smaller single-peak component will essentially be negligible, and the above steps will be difficult to implement. However, taking the logarithm of the power spectrum value can eliminate the magnitude difference in the total power of the two peak components.

[0092] 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.

[0093] The above steps are not only applicable to the case of a mixture of two peak components, but can also 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. In this case, the parameter B is approximately equal to 0, and the final calculated result V * For 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 * 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.

[0094] Finally, when V1-C and V2-C have different signs, we assume that V1, V2, and V3 are approximately equal and directly let V * = V1 because only when the input data approximates an unbiased distribution will V1, V2, and V3 change non-monotonically under the influence of noise, resulting in different signs for V1-C and V2-C. In this case, it is reasonable to set V*=V1.

[0095] In the embodiments of this application, the present invention uses the 1st, 2nd, and 3rd powers of the logarithmic and simply scaled power spectral density to calculate the weighted average of the velocity. A simple algebraic calculation scheme proposed in this invention then yields the desired corrected velocity. The computational complexity is comparable to that of existing techniques, and the existing computational process can be easily adapted or modified, making it easy to deploy the present invention in existing radar data processing software after upgrading or modifying it.

[0096] Finally, the effects of the present invention are demonstrated in three sections. Section (1) is an overall statistical analysis of the calculation results of multiple sets of ideal experiments. Section (2) is an example of a typical bimodal case. Section (3) is an example of a typical unbiased unimodal case. By demonstrating these overall effects and the application effects of typical cases, the superiority and applicability of the calculation results of the present invention can be demonstrated.

[0097] (1) Statistics of results of multiple ideal experiments

[0098] 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 normally distributed spectra. The power of the spectrum with larger power is from 10 1 Enumerate to 10 3 (Engineering unit), index interval 0.2, a total of 11, standard deviation is 1 ms -1 , the mean value is 0ms -1 The power of the spectrum with smaller power is 1 (engineering unit), and the standard deviation is 2 ms -1 Enumeration to 4 ms -1 , interval 0.1 ms -1 , a total of 21, with an average value ranging 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.

[0099] Figure 6 This is a comparative diagram of multiple wind speed detection groups in the embodiment of the present application. Figure 6 The figure shows the comparison results of the traditional observation values and the correction values of the present invention in the above-mentioned multiple sets of ideal wind speed detection experiments. Here, the true value of the wind speed is the mean of the spectral components 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 components with larger power. Regardless of the true value of the wind speed, the results obtained are all between 0 and 1 ms. -1 , it is difficult to reflect the true wind speed value; only the sign, that is, the wind direction, is the same as the true wind speed value. The blue dots in the figure are the results of the calculation by our method, and the box plot is their distribution statistics (the horizontal lines from bottom to top are the minimum value, 25th percentile, median, 75th percentile, and maximum value, respectively). It can be seen that although they are not completely consistent with the true wind speed value, they are all distributed around the true wind speed value, which is significantly better than the traditional observation value.

[0100] It is also necessary to explain that, Figure 6The resulting graph is only affected by the relative difference in the mean values of the two power spectrum components and has nothing to do with the absolute magnitude of their respective mean values. This is because the translation and scaling of the values on the X-axis of the power spectrum do not affect the abstract form of the integral in the above calculation formula and therefore have 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 enumeration range of the mean value of the spectrum with smaller power is from -10 s -1 ~ 10 s -1 Change to 0 ms -1 ~ 20 ms -1 , the result graph is still the same Figure 6 The mean values of the spectrum with larger power are not enumerated here, which does not affect the adequacy of the demonstration of the effect of the present invention.

[0101] (2) Case 1: When the peaks of the two power spectrum components are far apart

[0102] 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 the larger power is 10 (engineering units) 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 their combined power spectrum are shown in the figure below: Figure 7 The power spectrum components in this application are shown in the following figure: Figure 7 shown.

[0103] Figure 8 A schematic diagram of the relationship between various intermediate calculation quantities and calculation results provided in this application, such as Figure 8 As shown in FIG1, the relationship between various intermediate calculation amounts 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 dotted line in the figure) further obtained from 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 cloud airflow velocity in the detection space of mixed hail and rain.

[0104] The above is a typical "double-peak" power case. For the case where the peaks of the two power spectrum components are close to each other, forming 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.

[0105] (3) Case 2: When there is only an unbiased single peak

[0106] 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. Figure 9 Schematic diagram of the power spectrum components input by the present invention, with two average values of 1 ms -1 However, the power spectrum components with different standard deviations and powers and the power spectrum graphs of the two composites are as follows: Figure 9 As shown, Figure 10 The relationship between the intermediate calculation amount and the calculation result provided by the present invention is shown in FIG. Figure 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 demonstrates that the present invention is applicable even when there is only a single unbiased peak, and does not introduce additional bias when traditional observations accurately reflect airflow velocity. This indicates that even when there is no mixed hail and rain in the radar detection space, and only general rainfall, the present invention can still obtain accurate cloud airflow detection results, so there is no need to determine whether to enable the present invention's calculation scheme in actual applications.

[0107] The following describes a device for determining the Doppler velocity of a mid-air flow provided by the present invention. 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 in correspondence with each other.

[0108] Figure 11 The schematic diagram of the structure of the Doppler velocity determination device for cloud airflow provided in this application is as follows: Figure 11 Shown, including:

[0109] 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;

[0110] 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;

[0111] The determination module 113 is configured to calculate a first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value based on the logarithmic unit radar power spectral density data, and determine the Doppler velocity of the airflow in the target cloud cluster based on the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

[0112] In the embodiments of the present application, 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 typically requires logarithmic transformation to improve the data's dynamic range and facilitate subsequent processing. Based on the logarithmic radar power spectrum density data, a first velocity weighted average value (V1), a second velocity weighted average value (V2), and a third velocity weighted average value (V3) can be calculated. These weighted averages are calculated by considering the power spectrum densities corresponding to different velocities, resulting in a comprehensive velocity value that better represents the true velocity of the airflow within the cloud. By analyzing V1, V2, and V3, the Doppler velocity of the target cloud's airflow can be determined, which can be used to infer the cloud's motion direction and velocity, thereby predicting the cloud's evolution and impact.

[0113] Figure 12 Schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 12 As shown, the electronic device may include: a processor 1210, a communications interface 1220, a memory 1230, and a communications bus 1240, wherein the processor 1210, the communications interface 1220, and the memory 1230 communicate with each other via the communications bus 1240. The processor 1210 may call logic instructions in the memory 1230 to execute a method for determining Doppler velocity of airflow in a cloud, the method comprising: performing denoising 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;

[0114] Perform 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;

[0115] A first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value are calculated based on 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 cluster based on the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

[0116] Furthermore, the logic instructions in the aforementioned memory 1230 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0117] In another aspect, the present invention further provides a computer program product, comprising a computer program that can be stored on 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 a cloud provided by each of the above methods, the method comprising: performing denoising on radar power spectrum data corresponding to a range library of a target cloud cluster to obtain denoised radar power spectrum data; the target cloud cluster is a cloud cluster containing both raindrops and hail;

[0118] Perform 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;

[0119] A first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value are calculated based on 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 cluster based on the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

[0120] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for determining the Doppler velocity of airflow in a cloud provided by the above methods, the method comprising: denoising radar power spectrum data corresponding to a range library of a target cloud cluster to obtain denoised radar power spectrum data; the target cloud cluster is a cloud cluster containing both raindrops and hail;

[0121] Perform 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;

[0122] A first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value are calculated based on 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 cluster based on the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value.

[0123] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0124] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0125] 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 various 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's corresponding distance library to obtain the de-noised radar power spectrum data; The target cloud is a cloud containing both raindrops and hail; Perform 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; calculating a first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value based on the logarithmic unit radar power spectral density data, and determining the Doppler velocity of the airflow in the target cloud based on the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value; 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 logarithmic unit radar power spectrum density data; Determining the Doppler velocity of the airflow in 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: B = ln(V1-C)-ln(V2-C); When V1-C and V2-C have the same sign, V * =A+C; Where C = V3, V * is the Doppler velocity of the airflow in the target cloud.

2. The method for determining Doppler velocity of airflow in clouds according to claim 1, 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 * =V1.

3. 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 to the mean of the remaining data is less than a certain threshold, and the maximum value of these 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.

4. 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 velocity and the Y-axis is the power spectrum density of the echo.

5. 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 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; a determination module, configured to calculate a first velocity weighted average value, a second velocity weighted average value, and a third velocity weighted average value based on the logarithmic unit radar power spectral density data, and determine the Doppler velocity of the airflow in the cloud of the target cloud based on the first velocity weighted average value, the second velocity weighted average value, and the third velocity weighted average value; 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 logarithmic unit radar power spectrum density data; Determining the Doppler velocity of the airflow in 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: B = ln(V1-C)-ln(V2-C); In the case where V1-C and V2-C have different signs, V * =A+C; Where C = V3, V * is the Doppler velocity of the airflow in the target cloud.

6. 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 4 is implemented.

7. 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 4 is implemented.

8. 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 4 is implemented.

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