Light scattering signal identification method and device, electronic equipment and storage medium

Abstract

This invention provides a method, apparatus, electronic device, and storage medium for Mie scattering signal identification, belonging to the field of signal processing technology. The method includes: acquiring X-band dual-polarization radar data and S-band radar reflectivity; constructing an attenuation correction model based on differential propagation phase and undetermined correction coefficients; determining an objective function based on the attenuation correction model and S-band radar reflectivity; solving the objective function to obtain optimal correction coefficients; correcting the X-band reflectivity based on the optimal correction coefficients and the attenuation correction model to obtain the optimal corrected X-band reflectivity; and determining the Mie scattering signal based on the interpolated S-band radar reflectivity and the optimal corrected X-band reflectivity. This invention determines the optimal correction coefficients by solving the objective function and uses the optimal correction coefficients and the attenuation correction model to achieve accurate attenuation correction of the X-band reflectivity, thereby obtaining the difference between the corrected S-band and X-band reflectivities in the strong echo region, and thus achieving accurate identification of the Mie scattering signal.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and in particular to a method, apparatus, electronic device, and storage medium for identifying Mie scattering signals. Background Technology

[0002] When electromagnetic waves propagate through the atmosphere and encounter particles such as cloud droplets, raindrops, ice crystals, snowflakes, or hail, scattering occurs. Taking hail detection as an example, S-band radar typically operates in the Rayleigh scattering region, while X-band radar, due to its shorter wavelength, easily enters the Mie scattering region, thus generating a significant Mie scattering signal. Based on this difference in characteristics exhibited by S / X-band radar in hail detection, hail particles can be effectively identified, improving the monitoring and early warning capabilities for severe convective weather.

[0003] Accurate identification of Mie scattering signals is crucial for S / X dual-band radar to detect and identify particles such as hail. Existing technologies typically employ an attenuation correction model that relies on empirical coefficients for Mie scattering signal identification. However, the stability of this attenuation correction model is easily affected by precipitation type, which can introduce significant correction errors in practical applications, resulting in insufficient accuracy in Mie scattering signal identification. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for identifying Mie scattering signals, which addresses the shortcomings of existing attenuation correction models that rely on empirical coefficients, where stability is easily affected by precipitation type and significant correction errors are readily introduced. This invention enables accurate identification of Mie scattering signals and improves the accuracy and reliability of Mie scattering signal identification.

[0005] This invention provides a method for identifying Mie scattering signals, comprising the following steps:

[0006] Acquire X-band dual-polarization radar data and S-band radar reflectivity of the detection area. The X-band dual-polarization radar data includes differential propagation phase and X-band reflectivity. The differential propagation phase is the cumulative phase difference between the horizontally polarized wave and the vertically polarized wave emitted by the X-band dual-polarization radar on the propagation path.

[0007] The S-band radar reflectivity is interpolated into the coordinate system of the X-band dual-polarization radar data to obtain the interpolated S-band radar reflectivity.

[0008] An attenuation correction model is constructed based on the differential propagation phase and the undetermined correction coefficients; within the first reflectivity interval, the target function is determined based on the attenuation correction model and the interpolated S-band radar reflectivity; the target function is solved to obtain the optimal correction coefficients; the reflectivity in the first reflectivity interval satisfies the Rayleigh scattering condition.

[0009] The X-band reflectivity is corrected based on the optimal correction coefficient and the attenuation correction model to obtain the optimal corrected X-band reflectivity.

[0010] Within the second reflectivity interval, the Mie scattering signal is determined based on the difference between the interpolated S-band radar reflectivity and the optimal corrected X-band reflectivity; the reflectivity of the second reflectivity interval is greater than a preset reflectivity threshold.

[0011] According to the Mie scattering signal identification method provided by the present invention, the step of constructing an attenuation correction model based on the differential propagation phase and the undetermined correction coefficients includes:

[0012] Obtain the initial phase of the X-band dual-polarization radar transmitted signal;

[0013] Based on the undetermined correction coefficients, the differential propagation phase, and the initial phase, the attenuation compensation amount is determined;

[0014] The attenuation correction model is constructed based on the X-band reflectivity and the attenuation compensation amount.

[0015] According to the Mie scattering signal identification method provided by the present invention, the attenuation correction model is as follows:

[0016] ;

[0017] in, For range radar r X-band corrected reflectivity at that location For range radar r X-band reflectivity at that location The undetermined correction coefficient; For range radar r Differential propagation phase at the point, This represents the initial phase of the X-band dual-polarization radar transmitted signal.

[0018] According to the present invention, a method for identifying Mie scattering signals includes determining an objective function based on the attenuation correction model and the interpolated S-band radar reflectivity; solving the objective function to obtain the optimal correction coefficients includes:

[0019] The X-band corrected reflectivity is calculated based on the attenuation correction model containing the undetermined correction coefficients.

[0020] Construct an objective function that characterizes the difference between the X-band corrected reflectivity and the interpolated S-band radar reflectivity, wherein the objective function is a function with the undetermined correction coefficient as the independent variable;

[0021] Within a preset numerical range, the undetermined correction coefficients are iterated through and values ​​are taken according to a preset step size. The objective function value corresponding to the undetermined correction coefficients is calculated, and the value of the undetermined correction coefficient corresponding to the minimum objective function value is selected as the optimal correction coefficient.

[0022] According to the Mie scattering signal identification method provided by the present invention, the objective function is:

[0023] ;

[0024] in, For the difference value, For range radar r Interpolated S-band radar reflectivity at the location, For range radar r X-band reflectivity at that location The undetermined correction coefficient; For range radar r Differential propagation phase at the point, This represents the initial phase of the X-band dual-polarization radar transmitted signal.

[0025] According to the Mie scattering signal identification method provided by the present invention, the step of correcting the X-band reflectivity based on the optimal correction coefficient and the attenuation correction model to obtain the optimal corrected X-band reflectivity includes:

[0026] Based on the optimal correction coefficient, the differential propagation phase, and the initial phase of the X-band dual-polarization radar transmitted signal, the optimal attenuation compensation amount on the radar detection path is calculated.

[0027] Based on the optimal attenuation compensation amount and the X-band reflectivity, the optimal corrected reflectivity of the X-band is obtained.

[0028] According to the Mie scattering signal identification method provided by the present invention, the differential propagation phase is obtained based on the following steps:

[0029] Acquire the raw phase observation values ​​obtained from X-band dual-polarization radar detection;

[0030] Obtain the backscattering differential phase contained in the original phase observation values;

[0031] The backscattering differential phase is filtered out from the original phase observation to obtain the differential propagation phase.

[0032] The present invention also provides a Mie scattering signal identification device, comprising the following modules:

[0033] The data acquisition unit is used to acquire X-band dual-polarization radar data and S-band radar reflectivity of the detection area. The X-band dual-polarization radar data includes differential propagation phase and X-band reflectivity. The differential propagation phase is the cumulative phase difference generated by the horizontally polarized wave and the vertically polarized wave emitted by the X-band dual-polarization radar on the propagation path.

[0034] The data mapping unit is used to interpolate the S-band radar reflectivity to the coordinate system of the X-band dual-polarization radar data to obtain the interpolated S-band radar reflectivity.

[0035] An attenuation correction unit is used to construct an attenuation correction model based on the differential propagation phase and undetermined correction coefficients; within a first reflectivity interval, a target function is determined based on the attenuation correction model and the interpolated S-band radar reflectivity; the target function is solved to obtain the optimal correction coefficients; the reflectivity in the first reflectivity interval satisfies the Rayleigh scattering condition; the X-band reflectivity is corrected based on the optimal correction coefficients and the attenuation model to obtain the optimal corrected X-band reflectivity;

[0036] The signal identification unit is used to determine the Mie scattering signal within the second reflectivity interval based on the difference between the interpolated S-band radar reflectivity and the optimal corrected X-band reflectivity; the reflectivity of the second reflectivity interval is greater than a preset reflectivity threshold.

[0037] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the Mie scattering signal identification method as described above.

[0038] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the Mie scattering signal identification method as described above.

[0039] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the Mie scattering signal identification method as described above.

[0040] The Mie scattering signal identification method, device, electronic device, and storage medium provided by this invention utilize the differential propagation phase of an X-band dual-polarization radar to construct an attenuation correction model. By solving the objective function within the first reflectivity interval that satisfies the Rayleigh scattering condition, the optimal correction coefficient that conforms to the current precipitation characteristics is determined, thereby eliminating the dependence on empirical parameters. The optimal correction coefficient is used to achieve accurate attenuation compensation for the X-band reflectivity, ensuring that the difference between the optimal corrected reflectivity of the S-band and X-band in the second reflectivity interval can truly reflect the Mie scattering signal, thus achieving accurate identification of the Mie scattering signal. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the existing Mie scattering signal identification method.

[0043] Figure 2 This is one of the flowcharts illustrating the Mie scattering signal identification method provided by the present invention.

[0044] Figure 3 This is a schematic diagram of the process for constructing an attenuation correction model based on differential propagation phase and undetermined correction coefficients provided by the present invention.

[0045] Figure 4 This is a schematic diagram of the process for determining the optimal correction coefficient provided by the present invention.

[0046] Figure 5 This is a schematic diagram showing the relationship between the total phase difference and the differential propagation phase provided by the present invention.

[0047] Figure 6 This is the second flowchart of the Mie scattering signal identification method provided by the present invention.

[0048] Figure 7 This is a schematic diagram illustrating the relationship between the difference value and the correction coefficient provided by the present invention.

[0049] Figure 8 This is a schematic diagram showing the relationship between the optimal corrected reflectivity of the X-band and the radar reflectivity of the S-band provided by this invention.

[0050] Figure 9 This is a schematic diagram of the probability density distribution of X-band reflectivity, X-band optimal corrected reflectivity, prior art corrected reflectivity, and S-band radar reflectivity provided by the present invention.

[0051] Figure 10 This is a schematic diagram of the hail-affected area identification based on the fuzzy logic method provided by existing technology.

[0052] Figure 11 This is a schematic diagram of the identification of the Mie scattering region provided by existing technology.

[0053] Figure 12 This is a schematic diagram of the identification of the Mie scattering region provided by the present invention.

[0054] Figure 13This is a schematic diagram of the structure of the Mie scattering signal identification device provided by the present invention.

[0055] Figure 14 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0057] It should be noted that, in the description of this invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0058] To facilitate a full understanding of the technical solution of this application, the following content is hereby introduced:

[0059] When electromagnetic waves propagate through the atmosphere and encounter particles such as cloud droplets, raindrops, ice crystals, snowflakes, or hail, scattering occurs. When the particle diameter is much smaller than the wavelength, Rayleigh scattering is the primary phenomenon, with its intensity proportional to the sixth power of the particle diameter. When the particle diameter is comparable to the wavelength, Mie scattering occurs, producing a Mie scattering (MIE) signal. Taking hail detection as an example, S-band radar typically operates within the Rayleigh scattering region, while X-band radar, due to its shorter wavelength, easily enters the Mie scattering region, thus generating a significant Mie scattering signal. Based on this difference in characteristics exhibited by S / X-band dual-band radar in hail detection, hail particles can be effectively identified, improving the monitoring and early warning capabilities for severe convective weather. Therefore, accurately identifying Mie scattering signals is crucial for S / X-band dual-band radar to detect and identify hail.

[0060] Figure 1 This is a flowchart illustrating the existing Mie scattering signal identification method, such as... Figure 1As shown, the existing method first obtains the S / X-band radar reflectivity and performs beam matching processing. Then, assuming all particles along the entire ray path are in the Rayleigh scattering region, a uniform weighted attenuation correction is applied to the reflectivity by minimizing the cost function (the weight coefficient for the Rayleigh scattering region is 1), resulting in a uniformly weighted MIE signal estimate. Next, a non-uniform weighting coefficient is generated using this uniformly weighted MIE signal estimate (the weight coefficient for the Rayleigh scattering region is 1, and the weight coefficient for the Mie scattering region is 0), and this non-uniform weighting coefficient is used again for non-uniform weighted attenuation correction to obtain a non-uniformly weighted MIE signal.

[0061] Furthermore, the weighting coefficients are updated based on the obtained non-uniform weighted MIE signal to divide the Rayleigh scattering region and the Mie scattering region of the entire ray. Based on this, the updated weighting coefficients are used to perform segmented attenuation correction on the X-band radar reflectivity, generating segmented MIE signals. Finally, the non-uniform weighted MIE signal and the segmented MIE signal are fused to obtain the final MIE signal recognition result.

[0062] However, existing technologies have the following drawbacks: First, the attenuation correction relationship used in existing technologies... This method heavily relies on empirical coefficients, and its stability is easily affected by precipitation type. Z (Reflecting the particle backscattering cross section) to estimate the attenuation rate A (Reflecting energy loss along the propagation path), the coefficient in its attenuation correction formula a and b It varies significantly with precipitation type. For example, in the microwave band. b The value range is usually [0.6, 0.9]. This large range of variation can easily introduce significant correction errors in practical applications.

[0063] Secondly, existing technologies rely excessively on prior knowledge in the first step of uniform weight attenuation correction, affecting the accuracy of subsequent processing results. This uniform weight attenuation correction step depends on an initial empirical estimate of the Mie scattering region, which is highly subjective. Since subsequent non-uniform weight correction and segmentation are based on the results of the first step, the deviation of the initial estimate will be further propagated and amplified, thereby reducing the accuracy and reliability of the final Mie scattering signal identification.

[0064] Therefore, this invention proposes a Mie scattering signal identification method, device, electronic device, and storage medium. It adopts an X-band dual-polarization radar reflectivity correction method based on differential propagation phase and avoids reliance on prior knowledge, making the identified Mie scattering region more accurate and reasonable. It can effectively identify and warn of larger hailstones with a diameter of 6 mm or more.

[0065] The following is combined Figures 2-14This invention describes the Mie scattering signal identification method, apparatus, electronic device, and storage medium provided by the present invention.

[0066] Figure 2 This is one of the flowcharts illustrating the Mie scattering signal identification method provided by the present invention, such as... Figure 2 As shown, the execution subject of the Mie scattering signal identification method provided by the present invention can be a radar signal processor, an industrial control computer, a server, a cloud computing platform, or a computer capable of executing the method of the present invention, etc. Unless otherwise specified, the radar signal processor will be used as an example in the following embodiments.

[0067] As an optional embodiment, the Mie scattering signal identification method mainly includes, but is not limited to, the following steps:

[0068] Step 210: Obtain X-band dual-polarization radar data and S-band radar reflectivity of the detection area. The X-band dual-polarization radar data includes differential propagation phase and X-band reflectivity. The differential propagation phase is the cumulative phase difference between the horizontally polarized wave and the vertically polarized wave emitted by the X-band dual-polarization radar on the propagation path.

[0069] X-band dual-polarization radar data refers to observational data obtained by using a dual-polarization radar operating in the X-band (typically 8-12 GHz) to detect target areas. For example, X-band dual-polarization radar data can be observations in polar coordinates stored in a radar base data file after signal preprocessing.

[0070] S-band radar reflectivity data refers to reflectivity factor data obtained by using radar operating in the S-band (typically 2-4 GHz) to detect target areas. For example, S-band radar reflectivity data could be the horizontal polarization reflectivity factor in volumetric scan data generated by an S-band weather radar scan.

[0071] Differential propagation phase refers to the cumulative phase difference of an electromagnetic wave along its propagation path, caused solely by the different propagation speeds of horizontally and vertically polarized waves due to non-spherical particles. It reflects the concentration and phase information of precipitation particles along the path and increases monotonically with distance. For example, differential propagation phase is a smoothed phase curve after removing backscattering phase perturbations, which can accurately reflect the forward scattering effect of electromagnetic waves during propagation.

[0072] X-band reflectivity refers to the reflectivity factor obtained by converting the backscattered echo power of a target received by an X-band radar using radar equations. For example, X-band reflectivity can be the horizontal polarization reflectivity factor without attenuation correction.

[0073] X-band dual-polarization radar data and S-band radar reflectivity data can be obtained by directly reading real-time observation data through the radar data acquisition interface, or by reading stored radar base data files from a historical database.

[0074] Given that S-band radar has a longer wavelength and typically operates in the Rayleigh scattering region when detecting hail, while X-band radar has a shorter wavelength and tends to enter the Mie scattering region when detecting large particles, the two exhibit significant differences in scattering characteristics when detecting the same target. Therefore, this invention acquires X-band dual-polarization radar data and S-band radar reflectivity data for the same detection area, thereby utilizing these differences in scattering characteristics between frequency bands to provide a data foundation for subsequent identification of Mie scattering signals.

[0075] Step 220: Interpolate the S-band radar reflectivity into the coordinate system of the X-band dual-polarization radar data to obtain the interpolated S-band radar reflectivity.

[0076] It should be noted that, for the actual deployment of S-band and X-band dual-band radar systems, there are typically two operating modes: coaxial scanning detection and disparate cooperative detection. Coaxial scanning detection mode refers to the two radars being deployed in the same location; while disparate cooperative detection mode refers to the two radars being placed in different locations, with the scanning range of the S-band radar covering the scanning area of ​​the X-band radar. Given that disparate cooperative detection mode is more widely used in meteorological observation networks, this embodiment primarily focuses on the design of disparate cooperative detection mode. In disparate cooperative detection mode, due to the different spatial positions of the two radars, in order to achieve joint data analysis, it is necessary to interpolate and map the S-band radar reflectivity data to the three-dimensional coordinate system of the X-band dual-polarization radar through a data mapping unit, thereby ensuring that the data from the two bands are strictly aligned spatially during subsequent processing.

[0077] The coordinate system of X-band dual-polarization radar data refers to the spatial coordinate system used by X-band radar during scanning and detection. It is usually a spherical coordinate system with the radar antenna phase center as the origin. For example, the coordinate system of X-band dual-polarization radar data can uniquely determine the position of each detection data point in space using three parameters: azimuth angle, elevation angle, and radial distance.

[0078] Specifically, because S-band and X-band radars are typically deployed in different geographical locations, or even if deployed at the same location, their scanning strategies may differ, resulting in spatially indirect correspondence between their observation data. Therefore, it is necessary to interpolate the S-band radar reflectivity data onto the three-dimensional coordinate grid of the X-band dual-polarization radar to achieve spatial alignment.

[0079] For example, based on the longitude, latitude, and altitude of each range library of the X-band radar, the corresponding nearest neighbor point can be found in the polar coordinate data of the S-band radar or trilinear interpolation can be performed to obtain the S-band reflectivity value that corresponds one-to-one with each observation point of the X-band radar.

[0080] Step 230: Construct an attenuation correction model based on the differential propagation phase and the undetermined correction coefficients; within the first reflectivity interval, determine the target function based on the attenuation correction model and the interpolated S-band radar reflectivity; solve the target function to obtain the optimal correction coefficients; the reflectivity in the first reflectivity interval satisfies the Rayleigh scattering condition; correct the X-band reflectivity based on the optimal correction coefficients and the attenuation correction model to obtain the optimal corrected X-band reflectivity.

[0081] The undetermined correction coefficient refers to the coefficient used in the attenuation correction formula to characterize the proportional relationship between the attenuation rate and the differential propagation phase rate.

[0082] An attenuation correction model is a mathematical expression that describes how radar reflectivity factor is compensated for by attenuation effects along the propagation path. For example, an attenuation correction model can be a linear equation based on differential propagation phase, used to calculate the true reflectivity after path attenuation compensation.

[0083] The first reflectivity range refers to a specific range of reflectivity values ​​selected according to Rayleigh scattering theory. Within this range, both S-band and X-band radars should satisfy the Rayleigh scattering condition, and theoretically, their reflectivities should be essentially the same. For example, the reflectivity of the first reflectivity range is in the range of 40 dBZ to 45 dBZ.

[0084] The optimal correction coefficient refers to the correction coefficient value that minimizes the objective function value. For example, an iterative search algorithm can be used to traverse the correction coefficients within a certain range, and the coefficient value that makes the objective function converge to the minimum value can be taken as the optimal correction coefficient.

[0085] Attenuation correction refers to the process of compensating for the energy loss caused by the absorption and scattering of radar electromagnetic waves during propagation by precipitation particles. For example, attenuation correction can be performed point-by-point by using the calculated path attenuation to correct the reflectivity data of each range station.

[0086] The optimal X-band corrected reflectance refers to the final reflectance data obtained by compensating for full-path attenuation of the original X-band reflectance along the entire ray using a determined optimal correction coefficient. For example, the optimal X-band corrected reflectance can reflect the most accurate physical properties of precipitation particles and eliminate the influence of wavelength attenuation effects.

[0087] It should be noted that, according to the national standard "Hail Classification" (GB / T 27957-2011), hail is classified into four categories based on diameter: small hail (diameter D<5mm), medium hail (5mm≤D<20mm), large hail (20mm≤D<50mm), and extra-large hail (D≥50mm). Existing meteorological research indicates that when the echo top height of 45dBZ is not less than 7km, the probability of hail formation from convective clouds is relatively high. Therefore, 45dBZ is generally considered an important reflectivity threshold for identifying hail clouds.

[0088] Meanwhile, existing research indicates that for X-band radar, significant Mie scattering signals are only generated when precipitation particles exceed 6 mm in diameter. This means that in echo regions with reflectivity below 45 dBZ, precipitation particles are relatively small, making the likelihood of Mie scattering signals in X-band dual-polarization radar echoes extremely low. Under these conditions, both S-band and X-band electromagnetic waves are in the Rayleigh scattering region, and theoretically, after proper attenuation correction, the X-band reflectivity should be essentially the same as the S-band reflectivity.

[0089] Based on the aforementioned physical characteristics, this embodiment employs a segmented processing and iterative optimization strategy: [40, 45] dBZ is selected as the first reflectivity interval. Within this interval, the correction coefficient of the X-band reflectivity is continuously adjusted using an iterative optimization method to minimize the difference between the corrected X-band reflectivity and the S-band reflectivity, thereby determining the optimal correction coefficient. Subsequently, this optimal correction coefficient is used to correct the entire path. In the strong echo interval above 45 dBZ (i.e., areas where large hailstones may exist), the significant difference between the S-band reflectivity and the optimal corrected X-band reflectivity is identified as Mie scattering signals. This embodiment, through this method, can overcome the dependence on empirical formula coefficients and adaptively obtain the correction parameters that best match the characteristics of the current precipitation process using measured data, significantly improving the accuracy and stability of attenuation correction.

[0090] Step 240: Within the second reflectivity interval, the Mie scattering signal is determined based on the difference between the interpolated S-band radar reflectivity and the optimal corrected X-band reflectivity; the reflectivity of the second reflectivity interval is greater than a preset reflectivity threshold.

[0091] Specifically, after obtaining the optimal corrected reflectivity of the X-band, it is compared point by point with the interpolated S-band radar reflectivity. Since the S-band radar is still in the Rayleigh scattering region when detecting large particles (such as hail), while the X-band radar enters the Mie scattering region, resulting in a decrease in reflectivity, the difference between the two directly reflects the intensity of the Mie scattering effect.

[0092] The second reflectivity range can be the echo region with a reflectivity greater than 45 dBZ.

[0093] For example, the interpolated S-band reflectance minus the optimal corrected X-band reflectance can be calculated for each distance. When the difference is greater than a preset threshold or when there is a significant deviation in the echo region with a reflectance greater than 45 dBZ, it can be determined that there is a Mie scattering signal in the echo region, thereby enabling the identification of large particles such as hail.

[0094] The Mie scattering signal identification method provided by this invention utilizes the differential propagation phase of an X-band dual-polarization radar to construct an attenuation correction model. By solving the objective function within the first reflectivity interval that satisfies the Rayleigh scattering condition, the optimal correction coefficient that conforms to the current precipitation characteristics is determined, thereby eliminating the dependence on empirical parameters. The optimal correction coefficient is used to achieve accurate attenuation compensation for the X-band reflectivity, ensuring that the difference between the optimal corrected reflectivity of the S-band and X-band in the second reflectivity interval can truly reflect the Mie scattering signal, thus achieving accurate identification of the Mie scattering signal.

[0095] Figure 3 This is a schematic diagram of the process for constructing an attenuation correction model based on differential propagation phase and correction coefficients, as provided by the present invention. Figure 3 As shown, as another optional embodiment provided by the present invention, an attenuation correction model is constructed based on the differential propagation phase and the undetermined correction coefficients, including but not limited to the following steps:

[0096] Step 310: Obtain the initial phase of the X-band dual-polarization radar transmitted signal.

[0097] The initial phase of an X-band dual-polarization radar transmitted signal refers to the initial phase state of the radar system at the moment of the transmitted pulse, typically corresponding to the phase value at zero range from the radar. For example, the initial phase can be obtained by acquiring the system's initial phase constant recorded in the radar system calibration parameters.

[0098] Step 320: Determine the attenuation compensation amount based on the undetermined correction coefficients, differential propagation phase, and initial phase.

[0099] Specifically, there is a linear relationship between the attenuation rate of electromagnetic waves along the propagation path and the differential propagation phase rate (i.e., the derivative of the differential propagation phase with respect to distance). According to the principles of calculus, integrating the differential propagation phase rate along the propagation path is mathematically equivalent to calculating the cumulative change in the differential propagation phase. Therefore, the attenuation compensation (i.e., the cumulative attenuation along the radar detection path) can be determined as the product of the correction coefficient and the total increment of the differential propagation phase along that path (i.e., the difference between the differential propagation phase at the current distance and the initial phase).

[0100] Step 330: Based on the X-band reflectivity and attenuation compensation, construct an attenuation correction model.

[0101] Specifically, the attenuation correction model can recover the reflectivity signal weakened by path attenuation. For example, the attenuation correction model can be: the corrected X-band reflectivity is equal to the sum of the observed original X-band reflectivity and the attenuation compensation.

[0102] As an optional embodiment, the attenuation correction model used in this invention can be obtained through the following derivation process:

[0103] First, the basic correction formula for X-band radar reflectivity is shown in formula (1):

[0104] (1)

[0105] in, For range radar r X-band corrected reflectivity at that location For range radar r The original X-band reflectivity at that location The attenuation rate along the propagation path, This is the integration path variable.

[0106] Considering the attenuation rate With differential propagation phase rate (That is, the derivative of the differential propagation phase with respect to distance) has a linear relationship, therefore it can be determined by the undetermined correction coefficients. a Connecting them, that is Based on this relationship, formula (1) can be expressed as formula (2) on a discrete radar range library:

[0107] (2)

[0108] in, For range radar r X-band corrected reflectivity at that location For range radar r The original X-band reflectivity at that location a For the undetermined correction factor, For the distance from the warehouse, n r For the corresponding distance is r Radar detection range database K DP ( k ) is the first k The differential propagation phase rate of each distance library.

[0109] Furthermore, according to the definition of differential propagation phase, the integral of the differential propagation phase rate along the path is the cumulative increment of the differential propagation phase. Equation (2) can be rewritten as Equation (3), i.e., the attenuation correction model:

[0110] (3)

[0111] in, For range radar r Corrected X-band reflectivity For range radar r The original X-band reflectivity at that location These are the correction factors to be determined. For range radar r Differential propagation phase at the point, This represents the initial phase of the X-band dual-polarization radar transmitted signal.

[0112] The Mie scattering signal identification method provided by this invention introduces the initial phase of the X-band dual-polarization radar transmitted signal and constructs an attenuation correction model by accurately calculating the attenuation compensation amount based on the undetermined correction coefficients, differential propagation phase, and initial phase. It can utilize the physical relationship between the differential propagation phase and the attenuation rate to transform the complex path integral attenuation calculation into direct compensation based on the phase difference, thereby simplifying the model calculation process while ensuring the physical basis and accuracy of attenuation correction.

[0113] Figure 4 This is a flowchart illustrating the process for determining the optimal correction coefficients provided by the present invention, as shown below. Figure 4 As shown, as another optional embodiment provided by the present invention, the objective function is determined based on the attenuation correction model and the interpolated S-band radar reflectivity; the optimal correction coefficient is obtained by solving the objective function, including but not limited to the following steps:

[0114] Step 410: Calculate the X-band corrected reflectivity based on the attenuation correction model containing undetermined correction coefficients.

[0115] X-band corrected reflectance refers to the compensated reflectance value calculated by inputting the observed original X-band reflectance into the attenuation correction model and combining it with the undetermined correction coefficients and differential propagation phase.

[0116] Step 420: Construct an objective function to characterize the difference between the X-band corrected reflectivity and the interpolated S-band radar reflectivity. The objective function is a function with undetermined correction coefficients as independent variables.

[0117] The objective function refers to the difference between the interpolated S-band radar reflectivity and the X-band corrected reflectivity calculated by the attenuation correction model within the first reflectivity interval.

[0118] Step 430: Within a preset numerical range, the values ​​of the to-be-corrected correction coefficients are iterated and selected according to a preset step size. The objective function value corresponding to the to-be-corrected correction coefficients is calculated, and the value of the to-be-corrected correction coefficient corresponding to the minimum objective function value is selected as the optimal correction coefficient.

[0119] The preset numerical range refers to the possible value range of the undetermined correction coefficient, set based on experience or physical laws. For example, the preset numerical range can be from 0 to 0.3.

[0120] Iterative value selection refers to selecting different coefficient values ​​sequentially within a preset numerical range according to a certain step size. For example, the step size can be set to 0.001, starting from 0 and gradually increasing to 0.3.

[0121] To calculate the objective function value corresponding to the undetermined correction coefficients, first input the currently selected coefficient value into the attenuation correction model to obtain the X-band corrected reflectivity value output by the attenuation correction model, then calculate the absolute difference between the S-band radar reflectivity and the X-band reflectivity corrected by the attenuation correction model, and finally sum the absolute differences of all data points in the first reflectivity interval to obtain the objective function value.

[0122] The value of the undetermined correction coefficient corresponding to the minimum objective function value can be found by comparing the difference values ​​calculated from all traversal values. As a preferred embodiment, in a specific detection process, after traversal calculation, the calculated difference value is the smallest when the correction coefficient is 0.162. Therefore, 0.162 can be determined as the optimal correction coefficient in this detection.

[0123] Considering that both the S-band and X-band should satisfy the Rayleigh scattering condition within the first reflectivity range (e.g., 40-45 dBZ), and theoretically their reflectivities should be highly consistent, this invention selects the value of the correction coefficient corresponding to the smallest difference as the optimal correction coefficient. This allows for the automatic finding of the attenuation parameter that best matches the physical characteristics of the current precipitation process using measured data, ensuring the accuracy of attenuation correction and laying the foundation for accurate identification of Mie scattering signals in the future.

[0124] As an alternative embodiment, the objective function can be as shown in formula (4):

[0125] (4)

[0126] in, For the difference value, For range radar r Interpolated S-band radar reflectivity at the location, For range radar r X-band reflectivity at that location The undetermined correction coefficient; For range radar r Differential propagation phase at the point, This represents the initial phase of the X-band dual-polarization radar transmitted signal.

[0127] The Mie scattering signal identification method provided by this invention calculates the sum of the absolute differences between the S-band and the corrected X-band reflectance within a first reflectance interval as a difference measure, and searches for the correction coefficient that minimizes this difference value within a preset numerical range. By using numerical iterative optimization, it can automatically and objectively find the correction parameters that best match the consistency of the two-band data in the Rayleigh scattering region without prior knowledge, thereby ensuring the accuracy and robustness of the optimal correction coefficient.

[0128] In another embodiment of the present invention, the X-band reflectivity is corrected based on the optimal correction coefficient and the attenuation correction model to obtain the optimal corrected X-band reflectivity, including: calculating the optimal attenuation compensation amount on the radar detection path based on the optimal correction coefficient, the differential propagation phase and the initial phase of the X-band dual-polarization radar transmitted signal; and obtaining the optimal corrected X-band reflectivity based on the optimal attenuation compensation amount and the X-band reflectivity.

[0129] Specifically, once the optimal correction coefficients are determined, they can be substituted into the attenuation correction model. Combined with differential propagation phase data along the entire path, the optimal attenuation compensation for each distance column can be calculated. Subsequently, this optimal attenuation compensation is added to the original observed X-band reflectance to obtain the optimal corrected X-band reflectance, free from path attenuation effects.

[0130] As an optional embodiment, the optimal corrected reflectivity of the X-band can be calculated as shown in formula (5):

[0131] (5)

[0132] in, For range radar r The optimal corrected reflectivity in the X-band at that location, For range radar r The original X-band reflectivity at that location The optimal correction coefficient; For range radar r Differential propagation phase at the point, This represents the initial phase of the X-band dual-polarization radar transmitted signal.

[0133] The Mie scattering signal identification method provided by this invention accurately calculates the optimal attenuation compensation amount on the radar detection path based on the optimal correction coefficient, differential propagation phase and initial phase, and applies it to the correction of X-band reflectivity. This enables accurate compensation for the attenuation effect throughout the path, thereby obtaining the optimal corrected X-band reflectivity that truly reflects the physical properties of precipitation particles, providing a high-quality data foundation for the subsequent accurate extraction of Mie scattering signals.

[0134] In another embodiment of the present invention, the differential propagation phase is obtained based on the following steps: obtaining the original phase observation value obtained by X-band dual polarization radar detection; obtaining the backscatter differential phase contained in the original phase observation value; filtering out the backscatter differential phase from the original phase observation value to obtain the differential propagation phase.

[0135] The original phase observation refers to the sum of the differential propagation phase and the backscattering differential phase generated by the electromagnetic wave in the propagation path due to the different propagation constants of horizontal and vertical polarized waves caused by non-spherical particles.

[0136] Backscattering differential phase refers to the additional phase difference caused by the Mie scattering particles (such as large hailstones) themselves in the backscattering direction. It usually manifests as a localized, sharp peak or bulge in the original phase observation. For example, when a radar beam passes through an area containing large hailstones, the original phase observation will be superimposed with a non-monotonic phase perturbation, which is the backscattering differential phase.

[0137] Obtaining the backscattered differential phase can be achieved by identifying anomalous fluctuations in the original phase observations as a function of distance. For example, iterative filtering algorithms can be used to detect local peaks in the original phase observation sequence that deviate from a monotonically increasing trend, and these peaks can be identified as the backscattered differential phase.

[0138] Filtering refers to the process of removing high-frequency noise, abnormal fluctuations, or specific interference components from data using signal processing algorithms. For example, filtering can employ iterative filtering algorithms or wavelet transforms to separate and remove backscattered differential phase caused by the Mie scattering effect, as well as random measurement noise, from the original phase observations.

[0139] Filtering out the backscattered differential phase from the original phase observations can be achieved by subtracting the identified backscattered differential phase from the original phase observations. For example, after removing local peak interference from the original phase observations, the remaining smooth, monotonically increasing phase curve is the true differential propagation phase.

[0140] As an optional embodiment, the original phase observations can be calculated as shown in formula (6):

[0141] (6)

[0142] in, These are the original phase observations. For differential propagation phase, This is the backscattering differential phase.

[0143] The Mie scattering signal identification method provided by this invention can eliminate the influence of non-monotonic phase perturbation on the attenuation correction model by identifying and filtering out the backscattering differential phase caused by Mie scattering particles in the original phase observation value, thereby extracting the differential propagation phase that truly reflects the path propagation effect and ensuring the accuracy of subsequent attenuation correction calculations.

[0144] As an optional embodiment, when S-band and X-band radars detect the same target within the first reflectivity range (40-45 dBZ), theoretically, the optimal corrected reflectivity of the X-band is... With S-band reflectivity They should be basically the same. At this point, based on formula (5), the method for calculating the S-band reflectivity can be as shown in formula (7):

[0145] (7)

[0146] in, For range radar r S-band reflectivity at that location For range radar r The original X-band reflectivity at that location The optimal correction coefficient; For range radar r Differential propagation phase at the point, This represents the initial phase of the X-band dual-polarization radar transmitted signal.

[0147] It should be noted that under Mie scattering conditions (e.g., when precipitation particles contain large hailstones), X-band radar will exhibit Mie scattering, resulting in a lower observed reflectance. In this case, the S-band reflectance can be calculated as shown in formula (8):

[0148] (8)

[0149] in, For range radar r S-band reflectivity at that location For range radar r The original X-band reflectivity at that location The optimal correction coefficient; For range radar r Differential propagation phase at the point, This represents the initial phase of the X-band dual-polarization radar transmitted signal. For range radar r The Mie scattering signal at that location.

[0150] Therefore, the Mie scattering signal can be obtained by calculating the difference between the S-band reflectivity and the optimal corrected reflectivity of the X-band. The method for calculating the Mie scattering signal is shown in formula (9):

[0151] (9)

[0152] in, For range radar r The Mi scattering signal at that location, For range radar r S-band reflectivity at that location For range radar r The original X-band reflectivity at that location The optimal correction coefficient; For range radar r Differential propagation phase at the point, This represents the initial phase of the X-band dual-polarization radar transmitted signal.

[0153] Furthermore, combining formulas (9) and (5), the method for calculating the Mie scattering signal can be shown in formula (10):

[0154] (10)

[0155] in, For range radar r The Mi scattering signal at that location, For range radar r S-band reflectivity at that location For range radar r The optimal corrected reflectance in the X-band at that location.

[0156] Figure 5 This is a schematic diagram illustrating the relationship between the total phase difference and the differential propagation phase provided by the present invention, as shown below. Figure 5 The diagram illustrates the variation of phase data along the radar detection path with distance at an azimuth angle of 248°. The horizontal axis represents the distance to the radar (in km), and the vertical axis represents the phase angle (in °). The dashed line represents the observed total phase difference, and the solid line represents the differential propagation phase obtained after filtering. At a distance of 45-50 km from the radar, the presence of Mie scattering particles (such as large hailstones) causes a significant backscattering differential phase to superimpose on the total phase difference (dashed line), resulting in sharp bulges and violent fluctuations in the curve. After the filtering process of this invention, the influence of the backscattering differential phase on the total phase difference is eliminated, resulting in a smooth, monotonically increasing differential propagation phase (solid line), thus obtaining accurate propagation phase information and providing an accurate data basis for subsequent attenuation correction.

[0157] Figure 6 This is a second schematic flowchart of the Mie scattering signal identification method provided by the present invention, as shown below. Figure 6As shown, first input the X-band dual-polarization radar data and the S-band radar reflectivity. The total phase difference in the X-band dual-polarization radar data... Filtering is performed to obtain the differential propagation phase. Simultaneously, the S-band radar reflectivity is interpolated into the three-dimensional coordinate system of the X-band radar to obtain the S-band reflectivity aligned with the X-band space. .

[0158] Next, differential propagation phase is used. and correction factor Constructing an attenuation correction model ( ), calculate the X-band corrected reflectance. Within the first reflectance range (e.g., 40-45 dBZ), through Calculate S-band reflection X-band corrected reflectivity The difference between them. The correction coefficients are iterated within a preset numerical range (0-0.3) in a specific step size (0.001). Find the value that minimizes the difference. The optimal correction factor is used to calculate the optimal corrected X-band radar reflectivity. Finally, based on S-band reflectivity With the optimal corrected reflectivity of the X-band The difference is used to obtain the final MIE signal recognition result.

[0159] Figure 7 This is a schematic diagram illustrating the relationship between the difference value and the correction coefficient provided by the present invention, as shown below. Figure 7 As shown, the difference between the S-band radar reflectivity and the X-band corrected reflectivity varies with the correction factor within the first reflectivity range. A changing curve. Figure 7 The horizontal axis represents the correction factor. (Unit: dB deg) -1 The vertical axis represents the difference value (unit: dBZ).

[0160] from Figure 7 As can be seen, the difference value first decreases and then increases with the change of the correction coefficient, exhibiting a clear minimum point. This minimum point can be precisely located using the iterative optimization algorithm of this invention. For example, when the correction coefficient... The difference reaches its minimum at approximately 0.162, indicating that the consistency between the attenuation-corrected X-band reflectance and S-band reflectance within the Rayleigh scattering region is best at this value. Therefore, the value corresponding to this correction factor is the determined optimal correction factor. Using this optimal correction factor for full-path attenuation correction can obtain the most accurate X-band reflectance data.

[0161] Figure 8 This is a schematic diagram illustrating the relationship between the optimal corrected reflectivity of the X-band and the radar reflectivity of the S-band provided by this invention, as shown below. Figure 8 As shown in the figure, the correspondence between the optimal corrected reflectivity of the X-band (vertical axis, unit: dBZ) and the radar reflectivity of the S-band (horizontal axis, unit: dBZ) obtained by the method of the present invention is displayed in the form of a scatter plot. The color scale on the right represents the probability density of the data points.

[0162] from Figure 8 As can be seen, the data points are mainly concentrated near the diagonal, indicating a high degree of consistency between the optimal X-band reflectivity and the S-band radar reflectivity obtained by the method of this invention. Particularly in the Rayleigh scattering range of 40-45 dBZ, the consistency is extremely high, verifying the accuracy of the optimal correction coefficients. However, in the strong echo range above 45 dBZ, some data points deviate from the diagonal, showing that the S-band reflectivity is higher than the X-band reflectivity. This is due to the Mie scattering effect caused by large particles, thus proving that the method of this invention can effectively preserve and identify Mie scattering signals.

[0163] Figure 9 This is a schematic diagram illustrating the probability density distribution of X-band reflectivity, optimally corrected X-band reflectivity, prior art corrected reflectivity, and S-band radar reflectivity provided by this invention. Figure 9 As shown, the horizontal axis represents reflectivity (unit: dBZ), and the vertical axis represents probability density. The four curves represent the original X-band radar reflectivity. (Solid line hollow circle) The optimal corrected reflectivity of the X-band obtained by the method of this invention (Dashed lines and solid squares), existing technology (Junyent method) for correcting reflectance (Dotted line solid triangle) and S-band radar reflectivity (Solid line, solid rhombus). Compared to the original reflectivity. In comparison, both the correction method of this invention and the correction method of the prior art reduce the probability density in the weak echo range (10-40 dBZ) and increase the probability density in the strong echo range (greater than 45 dBZ), reflecting the effectiveness of the attenuation correction. It is worth noting that in the strong echo range, The probability density distribution curve is closer to The distribution curves show that the attenuation correction method proposed in this invention has a better correction effect than existing technologies in the strong echo region, and can more accurately recover the attenuated signal, thereby providing more reliable data support for the subsequent identification of Mie scattering signals.

[0164] Table 1 shows the optimal corrected X-band radar reflectivity ( ) and existing technology corrected reflectivity ( Error statistics in the strong echo range.

[0165] Table 1 Optimal Corrected X-band Radar Reflectivity ( ) and existing technology corrected reflectivity ( Error statistics in the strong echo region

[0166] As shown in Table 1, the error statistics indicate that in the strong echo range, Compare It is closer to the reflectivity of S-band radar. Specifically, this invention uses Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Bias Error (MBE) as evaluation indicators, and their definitions are shown in Equations (11), (12), and (13), respectively:

[0167] (11)

[0168] (12)

[0169] (13)

[0170] in, n For the sample size, The corrected X-band radar reflectivity. This refers to the reflectivity of the S-band radar.

[0171] As can be seen from the data in Table 1, All error indicators are significantly smaller than ,For example, of MAE It is 6.7191. RMSE It is 9.4129. MBE It is -4.7822; while of MAE It is 8.1443. RMSE It is 11.3522. MBE The value is -7.4911. This indicates that existing methods have a significant undercorrection problem in the strong echo range. MBE The relatively large value inevitably leads to an overestimation of the identified Mie scattering region. In contrast, the method of this invention can perform attenuation correction more accurately, thereby improving the accuracy of Mie scattering signal identification.

[0172] Figure 10 This is a schematic diagram of existing technology for identifying hail areas based on fuzzy logic methods, such as... Figure 10The figure shows the distribution of hail-affected areas identified using a widely used fuzzy logic algorithm during a severe convective weather event (e.g., observation time: 21:39:01, May 13, 2025, elevation angle: 1.5°). Both the horizontal and vertical axes represent distance (unit: km), with dark areas indicating identified hail-affected areas and light areas indicating non-hail-affected areas. According to existing research, significant Mie scattering signals only appear on X-band radar when hailstone diameter exceeds 6 mm. Therefore, theoretically, the distribution range of the Mie scattering signal should be smaller than the actual hail-affected area. Figure 10 The fuzzy logic recognition results shown are generally regarded as a relatively reliable reference standard for evaluating the accuracy of other recognition methods.

[0173] Figure 11 This is a schematic diagram of the identification of the Mie scattering region provided by existing technology, such as... Figure 11 As shown, the results of Mie scattering signal identification after processing radar observation data at 21:39:01 on May 13, 2025 (elevation angle 1.5°) using existing technical methods are presented. The horizontal and vertical axes represent the distance from the radar (unit: km). The light gray area is the Rayleigh scattering region, and the dark gray area is the Mie scattering region.

[0174] from Figure 11 As can be seen, the Mie scattering region (dark gray area) identified by existing technical methods exhibits a large-scale, continuous distribution, which is inconsistent with the typically scattered and fragmented spatial distribution of large hailstones. In particular... Figure 11 The area circled by the black ellipse in the image illustrates a misjudgment phenomenon in existing methods. This area is identified as a significant Mie scattering signal, but compared to hail identification results based on fuzzy logic (i.e., a generally accepted and more accurate reference standard), the Mie scattering area identified by existing methods is significantly larger, even... Figure 11 The misjudged area was larger than the actual hail range determined by fuzzy logic, which obviously violates the physical principle that "significant Mie scattering signals are only generated when the hail diameter exceeds a certain threshold (such as 6 mm), so the range of Mie scattering signals should theoretically be smaller than the hail range." This phenomenon also further confirms the error statistics conclusion in Table 1 above, that is, the existing technical method has an undercorrection problem in the strong echo range, which leads to a larger reflectivity difference, thus causing misjudgment and expansion of the Mie scattering area.

[0175] Figure 12 This is a schematic diagram of the identification of the Mie scattering region provided by the present invention, as shown below. Figure 12As shown, the results of processing radar data at the same time (21:39:01 on May 13, 2025) with an elevation angle of 1.5° using the Mie scattering signal identification method proposed in this invention are presented. Both the horizontal and vertical axes represent distance (unit: km). The light gray area represents the Rayleigh scattering region, and the dark gray area represents the Mie scattering region.

[0176] and Figure 11 In stark contrast to the results of the prior art shown, Figure 12 The identified Mie scattering regions (dark gray patches) no longer exhibit large, patchy spatial distributions, but rather show more scattered and dispersed characteristics, with a significantly reduced overall size. Specific observations... Figure 12 The black ellipse marks areas A and B. Based on the local meteorological bureau's ground observation records, large hailstorms occurred in areas A and B at that time. This height correspondence indicates that the identification results of the Mie scattering signal identification method provided by this invention have a very high degree of consistency with the actual ground conditions.

[0177] also, Figure 12 The range of Mie scattering signals identified by this invention is smaller than the hail range determined by fuzzy logic. This is consistent with the physical law that large-diameter hail (diameter greater than 6 mm) is usually only distributed in the core region of convective clouds. This proves that the method of this invention can effectively eliminate false signals in the Rayleigh scattering region through precise attenuation correction, and achieve accurate positioning and identification of Mie scattering signals, avoiding the problem of misjudgment in the prior art.

[0178] Figure 13 This is a schematic diagram of the structure of the Mie scattering signal identification device provided by the present invention, as shown below. Figure 13 As shown, it mainly includes, but is not limited to:

[0179] The data acquisition unit 1310 is used to acquire X-band dual-polarization radar data and S-band radar reflectivity of the detection area. The X-band dual-polarization radar data includes differential propagation phase and X-band reflectivity. The differential propagation phase is the cumulative phase difference generated by the horizontally polarized wave and the vertically polarized wave emitted by the X-band dual-polarization radar on the propagation path.

[0180] The data mapping unit 1320 is used to interpolate the S-band radar reflectivity into the coordinate system of the X-band dual-polarization radar data to obtain the interpolated S-band radar reflectivity.

[0181] The attenuation correction unit 1330 is used to construct an attenuation correction model based on the differential propagation phase and undetermined correction coefficients; within the first reflectivity interval, the target function is determined based on the attenuation correction model and the interpolated S-band radar reflectivity; the target function is solved to obtain the optimal correction coefficients; the reflectivity in the first reflectivity interval satisfies the Rayleigh scattering condition; the X-band reflectivity is corrected based on the optimal correction coefficients and the attenuation correction model to obtain the optimal corrected X-band reflectivity.

[0182] The signal identification unit 1340 is used to determine the Mie scattering signal within the second reflectivity interval based on the difference between the interpolated S-band radar reflectivity and the optimal corrected X-band reflectivity; the reflectivity of the second reflectivity interval is greater than a preset reflectivity threshold.

[0183] It should be noted that the Mie scattering signal identification device provided by the present invention can execute the Mie scattering signal identification method described in any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0184] The Mie scattering signal identification device provided by this invention utilizes the differential propagation phase of an X-band dual-polarization radar to construct an attenuation correction model. By solving the objective function within the first reflectivity interval that satisfies the Rayleigh scattering condition, the optimal correction coefficient that conforms to the current precipitation characteristics is determined, thereby eliminating the dependence on empirical parameters. The optimal correction coefficient is used to achieve accurate attenuation compensation for the X-band reflectivity, ensuring that the difference between the optimal corrected reflectivity of the S-band and X-band in the second reflectivity interval can truly reflect the Mie scattering signal, thus achieving accurate identification of the Mie scattering signal.

[0185] Figure 14 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 14As shown, the electronic device may include: a processor 1410, a communication interface 1420, a memory 1430, and a communication bus 1440. The processor 1410, communication interface 1420, and memory 1430 communicate with each other via the communication bus 1440. The processor 1410 can call logical instructions in the memory 1430 to execute a Mie scattering signal identification method. This method includes: acquiring X-band dual-polarization radar data and S-band radar reflectivity of the detection area. The X-band dual-polarization radar data includes differential propagation phase and X-band reflectivity. The differential propagation phase is the cumulative phase difference between the horizontally polarized wave and the vertically polarized wave emitted by the X-band dual-polarization radar on the propagation path; interpolating the S-band radar reflectivity into the coordinate system of the X-band dual-polarization radar data to obtain the interpolated S-band radar reflectivity; and based on the differential propagation phase and the unknown... A correction coefficient is used to construct an attenuation correction model; within the first reflectivity interval, the objective function is determined based on the attenuation correction model and the interpolated S-band radar reflectivity; the objective function is solved to obtain the optimal correction coefficient; the reflectivity in the first reflectivity interval satisfies the Rayleigh scattering condition; the X-band reflectivity is corrected based on the optimal correction coefficient and the attenuation correction model to obtain the optimal corrected X-band reflectivity; within the second reflectivity interval, the Mie scattering signal is determined based on the difference between the interpolated S-band radar reflectivity and the optimal corrected X-band reflectivity; the reflectivity in the second reflectivity interval is greater than a preset reflectivity threshold.

[0186] Furthermore, the logical instructions in the aforementioned memory 1430 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, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0187] On the other hand, the present invention also provides a computer program product, which includes 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 Mie scattering signal identification method provided by the above methods. The method includes: acquiring X-band dual-polarization radar data and S-band radar reflectivity of the detection area, wherein the X-band dual-polarization radar data includes differential propagation phase and X-band reflectivity, and the differential propagation phase is the cumulative phase difference generated by the horizontally polarized wave and the vertically polarized wave emitted by the X-band dual-polarization radar on the propagation path; interpolating the S-band radar reflectivity to the X-band dual-polarization radar... In the coordinate system of the data, the interpolated S-band radar reflectivity is obtained; an attenuation correction model is constructed based on the differential propagation phase and undetermined correction coefficients; within the first reflectivity interval, the objective function is determined based on the attenuation correction model and the interpolated S-band radar reflectivity; the objective function is solved to obtain the optimal correction coefficients; the reflectivity in the first reflectivity interval satisfies the Rayleigh scattering condition; the X-band reflectivity is corrected based on the optimal correction coefficients and the attenuation correction model to obtain the optimal corrected X-band reflectivity; within the second reflectivity interval, the Mie scattering signal is determined based on the difference between the interpolated S-band radar reflectivity and the optimal corrected X-band reflectivity; the reflectivity in the second reflectivity interval is greater than a preset reflectivity threshold.

[0188] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the Mie scattering signal identification method provided by the methods described above. This method includes: acquiring X-band dual-polarization radar data and S-band radar reflectivity of a detection area; the X-band dual-polarization radar data including differential propagation phase and X-band reflectivity; the differential propagation phase being the cumulative phase difference between the horizontally polarized wave and the vertically polarized wave emitted by the X-band dual-polarization radar along their propagation paths; and interpolating the S-band radar reflectivity into the coordinate system of the X-band dual-polarization radar data to obtain the interpolated S-band reflectivity. Radar reflectivity; an attenuation correction model is constructed based on differential propagation phase and undetermined correction coefficients; within the first reflectivity interval, the objective function is determined based on the attenuation correction model and the interpolated S-band radar reflectivity; the objective function is solved to obtain the optimal correction coefficients; the reflectivity in the first reflectivity interval satisfies the Rayleigh scattering condition; the X-band reflectivity is corrected based on the optimal correction coefficients and the attenuation correction model to obtain the optimal corrected X-band reflectivity; within the second reflectivity interval, the Mie scattering signal is determined based on the difference between the interpolated S-band radar reflectivity and the optimal corrected X-band reflectivity; the reflectivity in the second reflectivity interval is greater than a preset reflectivity threshold.

[0189] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0190] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part 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, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying Mie scattering signals, characterized in that, include: Acquire X-band dual-polarization radar data and S-band radar reflectivity of the detection area. The X-band dual-polarization radar data includes differential propagation phase and X-band reflectivity. The differential propagation phase is the cumulative phase difference between the horizontally polarized wave and the vertically polarized wave emitted by the X-band dual-polarization radar on the propagation path. The S-band radar reflectivity is interpolated into the coordinate system of the X-band dual-polarization radar data to obtain the interpolated S-band radar reflectivity. An attenuation correction model is constructed based on the differential propagation phase and the undetermined correction coefficients; within the first reflectivity interval, the target function is determined based on the attenuation correction model and the interpolated S-band radar reflectivity; the target function is solved to obtain the optimal correction coefficients; the reflectivity in the first reflectivity interval satisfies the Rayleigh scattering condition. The X-band reflectivity is corrected based on the optimal correction coefficient and the attenuation correction model to obtain the optimal corrected X-band reflectivity. Within the second reflectivity interval, the Mie scattering signal is determined based on the difference between the interpolated S-band radar reflectivity and the optimal corrected X-band reflectivity; the reflectivity of the second reflectivity interval is greater than a preset reflectivity threshold.

2. The Mie scattering signal identification method according to claim 1, characterized in that, The construction of the attenuation correction model based on the differential propagation phase and the undetermined correction coefficients includes: Obtain the initial phase of the X-band dual-polarization radar transmitted signal; Based on the undetermined correction coefficients, the differential propagation phase, and the initial phase, the attenuation compensation amount is determined; The attenuation correction model is constructed based on the X-band reflectivity and the attenuation compensation amount.

3. The Mie scattering signal identification method according to claim 2, characterized in that, The attenuation correction model is as follows: ； in, For range radar r X-band corrected reflectivity at that location For range radar r X-band reflectivity at that location The undetermined correction coefficient; For range radar r Differential propagation phase at the point, This represents the initial phase of the X-band dual-polarization radar transmitted signal.

4. The Mie scattering signal identification method according to claim 1, characterized in that, The objective function is determined based on the attenuation correction model and the interpolated S-band radar reflectivity; the optimal correction coefficients are obtained by solving the objective function, including: The X-band corrected reflectivity is calculated based on the attenuation correction model containing the undetermined correction coefficients. Construct an objective function that characterizes the difference between the X-band corrected reflectivity and the interpolated S-band radar reflectivity, wherein the objective function is a function with the undetermined correction coefficient as the independent variable; Within a preset numerical range, the undetermined correction coefficients are iterated through and values ​​are taken according to a preset step size. The objective function value corresponding to the undetermined correction coefficients is calculated, and the value of the undetermined correction coefficient corresponding to the minimum objective function value is selected as the optimal correction coefficient.

5. The Mie scattering signal identification method according to claim 4, characterized in that, The objective function is: ； in, For the difference value, For range radar r Interpolated S-band radar reflectivity at the location, For range radar r X-band reflectivity at that location The undetermined correction coefficient; For range radar r Differential propagation phase at the point, This represents the initial phase of the X-band dual-polarization radar transmitted signal.

6. The Mie scattering signal identification method according to claim 1, characterized in that, The step of correcting the X-band reflectivity based on the optimal correction coefficient and the attenuation correction model to obtain the optimal corrected X-band reflectivity includes: Based on the optimal correction coefficient, the differential propagation phase, and the initial phase of the X-band dual-polarization radar transmitted signal, the optimal attenuation compensation amount on the radar detection path is calculated. Based on the optimal attenuation compensation amount and the X-band reflectivity, the optimal corrected reflectivity of the X-band is obtained.

7. The Mie scattering signal identification method according to claim 1, characterized in that, The differential propagation phase is obtained based on the following steps: Acquire the raw phase observation values ​​obtained from X-band dual-polarization radar detection; Obtain the backscattering differential phase contained in the original phase observation values; The backscattering differential phase is filtered out from the original phase observation to obtain the differential propagation phase.

8. A Mie scattering signal identification device, characterized in that, include: The data acquisition unit is used to acquire X-band dual-polarization radar data and S-band radar reflectivity of the detection area. The X-band dual-polarization radar data includes differential propagation phase and X-band reflectivity. The differential propagation phase is the cumulative phase difference generated by the horizontally polarized wave and the vertically polarized wave emitted by the X-band dual-polarization radar on the propagation path. The data mapping unit is used to interpolate the S-band radar reflectivity to the coordinate system of the X-band dual-polarization radar data to obtain the interpolated S-band radar reflectivity. An attenuation correction unit is used to construct an attenuation correction model based on the differential propagation phase and undetermined correction coefficients; within a first reflectivity interval, a target function is determined based on the attenuation correction model and the interpolated S-band radar reflectivity; the target function is solved to obtain the optimal correction coefficients; the reflectivity in the first reflectivity interval satisfies the Rayleigh scattering condition; the X-band reflectivity is corrected based on the optimal correction coefficients and the attenuation model to obtain the optimal corrected X-band reflectivity; The signal identification unit is used to determine the Mie scattering signal within the second reflectivity interval based on the difference between the interpolated S-band radar reflectivity and the optimal corrected X-band reflectivity; the reflectivity of the second reflectivity interval is greater than a preset reflectivity threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the Mie scattering signal identification method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the Mie scattering signal identification method as described in any one of claims 1 to 7.

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