X-band weather radar data processing method and device

Abstract

Embodiments of the present disclosure provide an X-band weather radar data processing method and device, which are applied to the technical field of meteorological observation and disaster warning. The method comprises the following steps: acquiring and storing base data of an X-band weather radar; performing real-time quality control on the base data based on a preset quality control algorithm, performing real-time quality evaluation on the base data after quality control, generating a quality evaluation index, and feeding back the quality control process in real time according to the quality evaluation index to dynamically adjust parameters of the quality control algorithm; processing the base data after quality control based on the quality control algorithm with dynamically adjusted parameters to generate radar products; and performing warning based on the radar products. In this way, the problems of unstable data quality, lagging parameter adjustment, insufficient product and warning precision in the traditional method can be solved, and a complete technical link from data acquisition, dynamic quality control, accurate inversion to efficient warning is formed.

Description

Technical Field

[0001] This disclosure relates to the field of meteorological observation and disaster early warning technology, and in particular to an X-band weather radar data processing method and device. Background Technology

[0002] In meteorological disaster prevention and mitigation, weather radar serves as a core device for monitoring severe convective weather, heavy rain, hail, and other hazardous weather events. Its data quality and processing efficiency directly determine the timeliness and accuracy of early warnings. X-band weather radar, with its advantages of high resolution, high mobility, and relatively low deployment cost, is widely used in regional meteorological monitoring. However, limited by band characteristics and complex observation environments, its base data is susceptible to interference from ground objects, radial interference, and instrument system deviations. Furthermore, traditional quality control algorithms often use fixed parameters, making it difficult to adapt to the dynamic data characteristics under different weather scenarios (such as clear skies and heavy precipitation), leading to significant data distortion. In addition, existing processing methods lack a connection between quality control and quality assessment, failing to optimize parameters in real time, thus affecting the accuracy of subsequent product inversion and the speed of early warning response, making it difficult to meet the demands of refined meteorological services for highly reliable data and rapid early warnings. Summary of the Invention

[0003] This disclosure provides an X-band weather radar data processing method and apparatus, which solves the technical problems of unstable data quality, delayed parameter adjustment, and insufficient product and early warning accuracy in traditional methods.

[0004] According to a first aspect of this disclosure, an X-band weather radar data processing method is provided. The method includes:

[0005] Acquire and store the base data from the X-band weather radar;

[0006] The baseline data is subjected to real-time quality control based on a preset quality control algorithm, and the baseline data after quality control is evaluated in real-time to generate quality evaluation indicators. The quality control process is fed back in real-time based on the quality evaluation indicators to dynamically adjust the parameters of the quality control algorithm.

[0007] Based on the base data processed by the quality control algorithm with dynamically adjusted parameters, product inversion is performed to generate radar products.

[0008] Early warning is based on the aforementioned radar product.

[0009] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes:

[0010] After acquiring X-band weather radar base data, reflectivity factor deviation calibration is performed on the base data based on the dual-wavelength ratio characteristics of clear-sky echoes.

[0011] In addition to the aspects and any possible implementations described above, an implementation is further provided in which the quality control algorithm includes at least one of the following:

[0012] Radial interference echo quality control algorithm, ground feature and non-precipitation echo quality control algorithm, zero-degree layer bright band identification and correction algorithm, differential reflectivity filtering algorithm, differential propagation phase shift quality control algorithm, differential propagation phase shift rate estimation algorithm, radial velocity deblurring algorithm, radial velocity double PRF singular value processing algorithm, reflectivity factor attenuation correction algorithm, differential reflectivity factor attenuation correction algorithm, and distance deblurring algorithm.

[0013] In addition to the aspects described above and any possible implementations, a further implementation is provided in which the execution logic of the quality control algorithm satisfies:

[0014] The radial interference echo quality control algorithm is executed before the ground feature and non-precipitation echo quality control algorithm.

[0015] The differential propagation phase shift quality control algorithm is executed before the differential propagation phase shift rate estimation algorithm;

[0016] The radial velocity defuzzification algorithm is executed preferentially over the radial velocity double PRF singular value processing algorithm.

[0017] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the real-time quality assessment of the baseline data after quality control, the generation of quality assessment indicators, and the real-time feedback of the quality control process based on the quality assessment indicators to dynamically adjust the parameters of the quality control algorithm include:

[0018] Radar base data samples were collected under clear sky and precipitation conditions, respectively. The ground cover suppression ratio and the impact of ground cover suppression on precipitation echo were calculated to generate ground cover suppression assessment index.

[0019] The base data is preprocessed to remove non-precipitation echoes, and the two-dimensional probability distributions of differential reflectance, correlation coefficient and signal-to-noise ratio, and statistical characteristic values ​​of correlation coefficient are calculated. The standard deviation of reflectance factor is also calculated through self-consistent relational formula to generate dual polarization data quality assessment index.

[0020] The radial velocity field is read from the base data, the radial velocity data is preprocessed, the radial velocity standard deviation of each distance library is calculated, and the radial velocity standard deviations of all distance libraries are sorted and statistically analyzed as a percentage. The upper quartile is selected as the radial velocity quality evaluation index.

[0021] Each quality assessment indicator is fed back to the quality control process in real time, and the identification threshold and filtering intensity of the algorithms related to the ground object suppression link, the system bias of the algorithms related to the dual polarization radar calibration link, and the algorithm parameters of the algorithms related to the radial velocity processing link are dynamically adjusted.

[0022] As described above and in any possible implementation, a further implementation is provided, wherein the step of generating radar products by performing product inversion based on base data processed by a quality control algorithm with dynamically adjusted parameters includes:

[0023] Based on the product type and algorithm parameters configured by the user, radar products including reflectivity factor, radial velocity, spectral width, differential reflectivity, differential phase shift, echo top height, vertical integral liquid water, and storm tracking information are generated.

[0024] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes the step of calculating the profile of vertical meteorological elements based on the radar product, specifically including:

[0025] Acquire the generated radar product and its corresponding volume scan data;

[0026] Geometric modeling is performed on the volume scan data, and the coverage of the radar beam is determined for each pre-divided vertical layer.

[0027] Extract radar beams that meet the coverage conditions and their corresponding radar product data, and divide the radar product data into different statistical intervals according to the preset value range.

[0028] The volume sum of radar product data within each statistical interval is calculated. Based on the volume sum, feature values ​​are extracted to obtain the profile values ​​for each vertical layer, thereby forming a complete vertical meteorological element profile; wherein,

[0029] The extracted profile values ​​are optimized by Gaussian fitting or volume-weighted averaging to improve profile accuracy.

[0030] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the early warning based on the radar product includes:

[0031] Strong convective features were extracted from radar products using storm structure analysis, hail index, and mesoscale cyclone identification algorithms.

[0032] By combining the reflectivity gradient and liquid water content variation characteristics in the vertical profile, the accuracy of identifying severe convective weather can be enhanced.

[0033] Generate a list of alarm signals that includes latitude and longitude location, identification probability, and storm attributes.

[0034] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes:

[0035] Distribute radar and data, radar products and early warning information.

[0036] According to a second aspect of this disclosure, an X-band weather radar data processing apparatus is provided. The apparatus includes:

[0037] The base data acquisition module is used to acquire and store the base data of the X-band weather radar.

[0038] The dynamic quality control module is used to perform real-time quality control on the base data based on a preset quality control algorithm, and to perform real-time quality evaluation on the base data after quality control, generate quality evaluation indicators, and provide real-time feedback on the quality control process based on the quality evaluation indicators, so as to dynamically adjust the parameters of the quality control algorithm.

[0039] The product inversion module is used to perform product inversion based on the base data processed by the quality control algorithm after dynamic parameter adjustment, and generate radar products.

[0040] The early warning generation and output module is used to generate early warnings based on the radar product.

[0041] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0042] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the methods according to the first and / or second aspects of this disclosure.

[0043] In this disclosure, X-band weather radar base data is acquired and stored to provide a complete data foundation for subsequent processing. Real-time quality control is then performed based on a preset quality control algorithm, while real-time quality assessment generates evaluation indicators. These indicators are used to dynamically feed back and adjust the parameters of the quality control algorithm, forming a closed-loop processing mechanism of "quality control-assessment-parameter optimization." Compared to traditional fixed-parameter quality control methods, this approach can accurately adapt to data characteristics under different weather scenarios, effectively filter radial interference, ground object echoes, and other noise, correct data deviations, and significantly improve the purity and reliability of the base data. Product inversion based on the high-quality base data processed by the dynamically optimized quality control algorithm generates more accurate radar products, avoiding the impact of inferior data on product accuracy and providing reliable data support for subsequent early warnings. Finally, early warnings are issued based on the optimized radar products, significantly improving the accuracy of severe weather identification and the timeliness of early warnings, effectively shortening the early warning response time, providing more preparation time for disaster prevention and mitigation, and reducing casualties and property losses caused by disasters.

[0044] The entire process forms a complete technical chain from data acquisition, dynamic quality control, accurate inversion to efficient early warning. It not only solves the problems of unstable data quality, lagging parameter adjustment, and insufficient product and early warning accuracy in traditional methods, but also continuously improves the adaptability and reliability of radar data processing through a closed-loop dynamic optimization mechanism, giving full play to the fine observation advantages of X-band weather radar.

[0045] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0046] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0047] Figure 1 A flowchart illustrating an X-band weather radar data processing method provided by an embodiment of this disclosure is shown.

[0048] Figure 2 A structural diagram of an X-band weather radar data processing apparatus provided in an embodiment of the present disclosure is shown;

[0049] Figure 3 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0051] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0052] In this disclosure, X-band weather radar base data is acquired and stored to provide a complete data foundation for subsequent processing. Real-time quality control is then performed based on a preset quality control algorithm, while real-time quality assessment generates evaluation indicators. These indicators are used to dynamically feed back and adjust the parameters of the quality control algorithm, forming a closed-loop processing mechanism of "quality control-assessment-parameter optimization." Compared to traditional fixed-parameter quality control methods, this approach can accurately adapt to data characteristics under different weather scenarios, effectively filter radial interference, ground object echoes, and other noise, correct data deviations, and significantly improve the purity and reliability of the base data. Product inversion based on the high-quality base data processed by the dynamically optimized quality control algorithm generates more accurate radar products, avoiding the impact of inferior data on product accuracy and providing reliable data support for subsequent early warnings. Finally, early warnings are issued based on the optimized radar products, significantly improving the accuracy of severe weather identification and the timeliness of early warnings, effectively shortening the early warning response time, providing more preparation time for disaster prevention and mitigation, and reducing casualties and property losses caused by disasters.

[0053] The entire process forms a complete technical chain from data acquisition, dynamic quality control, accurate inversion to efficient early warning. It not only solves the problems of unstable data quality, lagging parameter adjustment, and insufficient product and early warning accuracy in traditional methods, but also continuously improves the adaptability and reliability of radar data processing through a closed-loop dynamic optimization mechanism, giving full play to the fine observation advantages of X-band weather radar.

[0054] Figure 1 A flowchart illustrating an X-band weather radar data processing method provided by an embodiment of this disclosure is shown, such as... Figure 1 As shown, an X-band weather radar data processing method 100 may include the following steps:

[0055] S110 acquires and stores the base data from the X-band weather radar.

[0056] In some embodiments, the method further includes:

[0057] After acquiring the X-band weather radar base data, the reflectivity factor deviation of the base data is calibrated based on the dual-wavelength ratio characteristics of clear-sky echoes.

[0058] Specifically, the system collects standard-format base data, streaming data, and elevation angle data in real time from the radar data acquisition system and centralized management storage devices by monitoring designated folders and using network protocols such as HTTP and FTP. It can also automatically retrieve required historical standard-format base data from centralized management storage in response to product request commands, or import historical standard-format base data from a specified path via a human-machine interface. Reserved interfaces also support the compatible collection of other types of related observation data. After acquiring various types of data, the system categorizes and stores the data according to the archiving path information pre-configured via the human-machine interface (including local storage, FTP storage, etc.; for FTP storage, the corresponding IP address, port number, username, password, and storage path must be set in advance, supporting the addition, modification, and deletion of paths). Real-time base data exceeding the system's set expiration time in local storage is automatically deleted and replaced with newly received data according to the "earliest overdue" principle. Historical case-specific base data is permanently stored. The entire data acquisition and storage process generates detailed operation logs, which are synchronously sent to the system monitoring module for process traceability and status monitoring.

[0059] Specifically, after acquiring the X-band weather radar baseline data, reflectivity factor deviation calibration is performed on the baseline data based on the dual-wavelength ratio characteristics of clear-sky echoes. Specifically, clear-sky echoes mainly originate from atmospheric aerosol scattering and molecular scattering, and their dual-wavelength ratio exhibits significant stability and predictability. By simultaneously acquiring clear-sky echo data from a reference band radar within the same observation area, and combining this with atmospheric scattering theory models, the theoretical baseline value of the dual-wavelength ratio under this scenario is calculated. Then, the deviation between the measured dual-wavelength ratio of the X-band radar clear-sky echoes and the theoretical baseline value is compared to establish a dynamic calibration model based on a range-wise and elevation-wise database (e.g., linear calibration). A fitting model or piecewise polynomial calibration model is used to correct the reflectivity factor in the X-band baseline data. This model also considers the influence of regional aerosol distribution differences, seasonal variations, and altitude on the characteristics of clear-sky echoes. The theoretical baseline parameters are updated regularly using atmospheric sounding data to ensure the adaptability of the calibration model. Finally, the model corrects the systematic deviation of the reflectivity factor caused by radar hardware drift, beam distortion, or initial errors in atmospheric attenuation. This makes the calibrated baseline data more closely match the real atmospheric scattering state, providing high-quality data support for the accurate execution of subsequent quality control algorithms and the reliable inversion of radar products.

[0060] S120 performs real-time quality control on the base data based on a preset quality control algorithm, and performs real-time quality assessment on the base data after quality control, generates quality assessment indicators, and provides real-time feedback on the quality control process based on the quality assessment indicators, so as to dynamically adjust the parameters of the quality control algorithm.

[0061] Specifically, the quality control algorithm can receive user-configured parameter information through the human-computer interaction interface, including selecting one or more target quality control algorithms, inputting and adjusting algorithm threshold parameters. If the user does not perform active configuration, the system will automatically adopt the default quality control algorithm and parameter standards preset by the system. Furthermore, it is necessary to conduct algorithm applicability assessment and targeted improvements based on the detection characteristics of radars from different manufacturers to ensure the compatibility of the algorithm with the radar hardware system.

[0062] In some embodiments, the quality control algorithm includes at least one of the following:

[0063] Radial interference echo quality control algorithm, ground feature and non-precipitation echo quality control algorithm, zero-degree layer bright band identification and correction algorithm, differential reflectivity filtering algorithm, differential propagation phase shift quality control algorithm, differential propagation phase shift rate estimation algorithm, radial velocity deblurring algorithm, radial velocity double PRF singular value processing algorithm, reflectivity factor attenuation correction algorithm, differential reflectivity factor attenuation correction algorithm, and distance deblurring algorithm.

[0064] In some embodiments, the execution logic of the quality control algorithm satisfies:

[0065] The radial interference echo quality control algorithm is executed before the ground feature and non-precipitation echo quality control algorithms.

[0066] The differential propagation phase shift quality control algorithm is executed before the differential propagation phase shift rate estimation algorithm;

[0067] The radial velocity defuzzification algorithm is executed before the radial velocity double PRF singular value processing algorithm.

[0068] In some embodiments, real-time quality assessment is performed on the baseline data after quality control to generate quality assessment indicators. The quality control process is then fed back in real-time based on these indicators to dynamically adjust the parameters of the quality control algorithm.

[0069] Radar base data samples were collected under clear sky and precipitation conditions, respectively. The ground cover suppression ratio and the impact of ground cover suppression on precipitation echo were calculated to generate ground cover suppression assessment index.

[0070] The base data is preprocessed to remove non-precipitation echoes, and the two-dimensional probability distributions of differential reflectance, correlation coefficient and signal-to-noise ratio, and statistical characteristic values ​​of correlation coefficient are calculated. The standard deviation of reflectance factor is also calculated through self-consistent relational formula to generate dual polarization data quality assessment index.

[0071] The radial velocity field is read from the base data, the radial velocity data is preprocessed, the radial velocity standard deviation of each distance library is calculated, and the radial velocity standard deviations of all distance libraries are sorted and statistically analyzed as percentages. The upper quartile is selected as the radial velocity quality assessment index.

[0072] Each quality assessment indicator is fed back to the quality control process in real time, and the identification threshold and filtering intensity of the algorithms related to the ground object suppression link, the system bias of the algorithms related to the dual polarization radar calibration link, and the algorithm parameters of the algorithms related to the radial velocity processing link are dynamically adjusted.

[0073] Specifically, in the ground feature suppression stage, the radial interference echo quality control algorithm is initiated before the ground feature and non-precipitation echo quality control algorithm. The former extracts the characteristic spectrum of narrowband interference signals through frequency domain Fourier transform and combines it with time domain pulse amplitude change detection technology to accurately identify and eliminate radial interference echoes generated by power lines, communication base stations, etc., thus avoiding interference signals from confusing the identification boundary between ground features and precipitation echoes. The latter integrates the terrain elevation data provided by the digital elevation model (DEM), the dynamic threshold of reflectivity factor, and the joint constraints of dual polarization correlation coefficient (CC) and differential reflectivity (ZDR) to construct a multi-dimensional ground feature and non-precipitation echo identification model. By comparing the spatial matching degree between observed echoes and terrain contours and analyzing the intensity variation law of echoes at different elevation angles, it efficiently separates ground feature echoes such as mountains and buildings from clear air aerosol non-precipitation echoes. At the same time, it sets a weak precipitation protection threshold to ensure that weak precipitation echoes are not mistakenly filtered.

[0074] In the dual-polarization data quality control stage, the differential propagation phase shift (PHIDP) quality control algorithm is executed before the differential propagation phase shift rate estimation algorithm. PHIDP quality control uses a sliding window to smooth the data, detects and removes abnormal PHIDP jump points caused by noise, and supplements effective data through linear interpolation or polynomial fitting to ensure the continuity and stability of PHIDP, laying the foundation for the accurate estimation of the subsequent differential propagation phase shift rate (KDP). The differential propagation phase shift rate estimation is based on the quality-controlled PHIDP data, using the cumulative difference method combined with sliding window linear fitting to remove systematic errors and random noise in the accumulation process.

[0075] For radial velocity data processing, the radial velocity defuzzification algorithm is executed before the radial velocity dual PRF singular value processing algorithm. The defuzzification algorithm combines the average wind field information inverted by the velocity-azimuth display (VAD) technique and uses the region filling method to correct the folded velocity. At the same time, it introduces velocity fuzziness interval constraints of dual PRF parameters to improve the accuracy of defuzzification. The radial velocity dual PRF singular value processing compares the velocity observations under dual PRF mode and combines the neighborhood velocity consistency check to remove singular values ​​that exceed the reasonable velocity range and replace them with the mean or median of the effective velocities in the neighborhood to ensure the overall consistency of the velocity field.

[0076] Furthermore, addressing the significant attenuation of X-band radar signals with distance, the reflectivity factor (Z) attenuation correction algorithm and the differential reflectivity factor (ZDR) attenuation correction algorithm, based on quality-controlled PHIDP data, establish a dynamically changing attenuation model with distance to achieve attenuation compensation at different distances and correct the underestimation of long-distance precipitation echo intensity. The zero-degree layer bright band identification and correction algorithm combines the temperature profile provided by radiosonde data to locate the altitude range corresponding to the 0°C isotherm. By analyzing the peak characteristics of the vertical gradient of reflectivity factor and the distribution of high-value areas of ZDR, it accurately identifies the location of the zero-degree layer bright band. It uses reflectivity factor attenuation correction in the bright band area and vertical interpolation reconstruction to correct the influence of the bright band on precipitation intensity estimation. The range defuzzification algorithm utilizes the geometric relationship of radar observation, the azimuth continuity constraint of volume scan data, and the range matching relationship of adjacent elevation angle echoes to identify and correct the range folding problem caused by pulse repetition frequency limitation at long distances, restoring the true echo range information.

[0077] Specifically, after completing real-time quality control, the processed base data is simultaneously evaluated in real-time to generate multi-dimensional quality evaluation indicators. Specifically, the weather conditions (such as ground precipitation observation and cloud image data) recorded synchronously by radar observation are used to determine clear sky and precipitation conditions. Base data samples with a preset volume scan cycle are collected in real time under both scenarios. The ground object suppression ratio is obtained by calculating the ratio of the integrated energy of the ground object echo after quality control to the integrated energy of the ground object echo before quality control. The closer the ratio is to 0, the better the ground object suppression effect. The impact of ground object suppression on precipitation echo is obtained by comparing the change in the integrated energy of the precipitation area before and after quality control with the ratio of the integrated energy of the precipitation area before quality control. This ratio is considered to be within ±5% as a good precipitation echo retention effect. The two together constitute the ground object suppression evaluation index, which comprehensively quantifies the ground object filtering effect and the degree of precipitation echo protection.

[0078] For the quality assessment of dual-polarization data, a joint threshold preprocessing method is first used to remove non-precipitation echo interference by using a reflectivity factor ≥10 dBZ and a correlation coefficient ≥0.8. Then, the standard deviation of differential reflectivity (ZDR) within the effective sample range is calculated, and the two-dimensional probability distribution of correlation coefficient (CC) and signal-to-noise ratio (SNR) is statistically analyzed. Statistical features such as the mean, median, and 95th quantile of CC are extracted. At the same time, the theoretical standard deviation of reflectivity factor is inversely calculated using the self-consistent consistency relationship of dual-polarization parameters. By comparing the deviation between theoretical and measured values, a dual-polarization data quality assessment index is generated to comprehensively reflect the stability, consistency, and systematic deviation of dual-polarization parameters.

[0079] For radial velocity quality assessment, after reading the complete radial velocity field from the base data, extreme noise points are first removed using the 3σ criterion. Then, the standard deviation of radial velocity for each distance library is calculated for 5-10 adjacent sampling points. The standard deviations of all valid distance libraries are sorted in ascending order and percentage statistics are performed. The upper quartile is selected as the radial velocity quality assessment index. This index can effectively avoid the interference of extreme outliers and objectively reflect the smoothness and reliability of the radial velocity field.

[0080] Subsequently, the aforementioned quality assessment indicators are fed back to the quality control process in real time, establishing a dynamic parameter adjustment closed-loop mechanism: if the ground object suppression ratio is higher than the first preset threshold, the reflectivity identification threshold of the ground object and non-precipitation echo quality control algorithm is dynamically increased or the strength of the filter kernel is enhanced; if the absolute value of the impact of ground object suppression on precipitation echo exceeds the second preset threshold, the reflectivity identification threshold is correspondingly reduced or the filtering strength is weakened; if the dual-polarization data quality assessment indicators show that the ZDR standard deviation exceeds 0.3dB, the CC mean is lower than the third preset threshold, or the difference between the theoretical and measured standard deviation of the reflectivity factor is too large, the system deviation parameters of the relevant algorithms in the dual-polarization radar calibration link and the sliding window size of the differential reflectivity filtering algorithm are adjusted; if the radial velocity quality assessment indicator exceeds the fourth preset threshold, the area search window size of the radial velocity defuzzification algorithm and the judgment threshold of the radial velocity dual PRF singular value processing algorithm are optimized. Through real-time iteration of "quality control-assessment-parameter adjustment", the quality control algorithm parameters are ensured to dynamically adapt to different weather scenarios and complex observation environments such as clear sky, weak precipitation, and strong convection, continuously ensuring the high-quality output of base data.

[0081] S130 performs product inversion based on the base data processed by the quality control algorithm with dynamically adjusted parameters, and generates radar products.

[0082] In some embodiments, product inversion is performed based on the base data processed by a quality control algorithm with dynamically adjusted parameters to generate radar products, including:

[0083] Based on the product type and algorithm parameters configured by the user, radar products including reflectivity factor, radial velocity, spectral width, differential reflectivity, differential phase shift, echo top height, vertical integral liquid water, and storm tracking information are generated.

[0084] Specifically, the system first receives the quality-controlled memory data, then obtains the user-configured product type, algorithm threshold parameters, and environmental threshold parameters through a human-computer interaction interface (if the user has not configured these parameters, the system's default parameter standards are used). It also supports accessing user-defined algorithm libraries through the system's public interface. For radars from different manufacturers with varying detection ranges and resolutions, product inversion is performed based on the radar's original resolution and detection range. Corresponding radar products can be automatically generated through routine requests or in response to one-time product requests. The generated products cover basic products (including reflectivity factor, radial velocity, spectral width, differential reflectivity, differential phase shift, etc.) and physical quantity products (including echo top height, echo bottom height, cumulative precipitation, vertical integral liquid water, strongest echo height, etc.). The product categories include: wind field products (including velocity and azimuth display, wind profile, wind field inversion, storm relative radial velocity, etc.), severe weather identification products (including storm structure analysis, hail index, storm tracking information, mesoscale cyclones, tornado vortex characteristics, etc.), and polarization data products (including particle phase identification, melt layer identification, dual-polarization quantitative precipitation estimation, etc.). The product format must support both standard and old formats, and the naming convention must be consistent with the current operational radar specifications. During the generation process, the polar coordinate product memory data will be transmitted to the product distribution stage. If the user has gridding requirements, the polar coordinate product can be converted into an equal latitude and longitude grid product according to the pre-configured grid range, resolution, and other adaptation information. The entire product generation process will generate detailed operation logs, which will be sent to the system monitoring module for storage and monitoring.

[0085] In some embodiments, the method further includes a step of calculating vertical meteorological feature profiles based on radar products, specifically including:

[0086] Acquire the generated radar product and its corresponding volume scan data;

[0087] Geometric modeling is performed on the volume scan data, and the coverage of the radar beam is determined for each pre-divided vertical layer.

[0088] Extract radar beams that meet the coverage conditions and their corresponding radar product data, and divide the radar product data into different statistical intervals according to the preset value range.

[0089] The volumetric sum of radar product data within each statistical interval is calculated. Feature values ​​are extracted based on these volumetric sums to obtain the profile values ​​for each vertical layer, thus forming a complete vertical meteorological element profile.

[0090] The extracted profile values ​​are optimized by Gaussian fitting or volume-weighted averaging to improve profile accuracy.

[0091] Specifically, the data of various radar products and their corresponding complete volume scan data are acquired simultaneously. The volume scan data includes the original observation information of each elevation angle and azimuth angle, as well as the physical quantity data after quality control, to ensure the integrity of the data source for profile calculation.

[0092] Subsequently, geometric modeling was performed on the volume scan data, converting the beam observation data in polar coordinates into a spatial grid in a three-dimensional Cartesian coordinate system. Vertical layers were pre-divided according to meteorological analysis requirements. For each vertical layer, the spatial coverage range and overlap of the radar beam within that layer were calculated to determine the beam coverage. The coverage area ratio was set to be no less than the fifth preset threshold or the beam center height fell within the layer interval as an effective coverage condition. Layer data with insufficient coverage were removed to avoid profile distortion.

[0093] Next, all radar beams that meet the coverage conditions and their corresponding radar product data are extracted. Based on the physical characteristics of different meteorological elements, reasonable preset value ranges are set, and the extracted data is divided into multiple continuous statistical intervals. The interval between statistical intervals is set according to the accuracy requirements of the elements.

[0094] Then, the spatial volume sum corresponding to the radar product data within each statistical interval is calculated. Based on this volume sum, the feature values ​​of each vertical layer are extracted. The feature values ​​can be selected as mean, median or peak value according to the feature type. For example, the reflectivity profile selects the volume-weighted mean of each layer, and the radial velocity profile selects the median to reduce the influence of extreme values, thereby obtaining the original profile values ​​of each vertical layer and forming a complete vertical meteorological feature profile.

[0095] Specifically, to further improve the accuracy of the profile, Gaussian fitting or volume weighted averaging is used to optimize the original profile values. Gaussian fitting uses a Gaussian function model to fit the vertical trend of element changes, smoothing out profile fluctuations caused by random noise. Volume weighted averaging uses the coverage volume of each beam within the vertical layer as the weight for weighted calculation, highlighting the contribution of effective observation data. Finally, a smooth, accurate meteorological element profile that truly reflects the vertical structure of the atmosphere is generated, providing key vertical structure support for subsequent severe convective weather identification and early warning.

[0096] S140 is a radar-based early warning system.

[0097] In some embodiments, early warning based on radar products includes:

[0098] Strong convective features were extracted from radar products using storm structure analysis, hail index, and mesoscale cyclone identification algorithms.

[0099] By combining the reflectivity gradient and liquid water content variation characteristics in the vertical profile, the accuracy of identifying severe convective weather can be enhanced.

[0100] Generate a list of alarm signals that includes latitude and longitude location, identification probability, and storm attributes.

[0101] In some embodiments, the method further includes:

[0102] Distribute radar and data, radar products and early warning information.

[0103] Specifically, by integrating storm structure analysis, hail index (HB) algorithm, mesoscale cyclone identification algorithm, and tornado vortex feature (TVS) identification technology, the core features of severe convective weather are systematically extracted from 3D radar products:

[0104] Storm structure analysis will focus on the height, vertical thickness, horizontal scale and morphological characteristics of strong echo nuclei in the three-dimensional field of reflectivity factor, and combine the spatial distribution law of differential reflectivity (ZDR) to determine the convective intensity and particle phase of the storm.

[0105] The hail index algorithm will estimate the hail diameter and the probability of the landing area based on key parameters such as the echo top height (reflectivity factor ≥ the sixth preset threshold), vertical integral liquid water (VIL) density, and the proportion of VIL above the 0°C layer, using empirical formulas and statistical models.

[0106] Mesoscale cyclone identification targets the radial velocity field, using velocity-azimuth display (VAD) technology to invert wind field shear. By detecting the spatial configuration of positive and negative velocity pairs, rotational speed, and vertical extension height, it identifies the initial, development, and dissipation stages of mesoscale cyclones, while simultaneously capturing the characteristics of strong convective core subsystems such as TVS.

[0107] Specifically, to further enhance the accuracy of severe convective weather identification, key information from vertical meteorological element profiles will be deeply integrated: the reflectivity gradient at a height of 1-3 km in the reflectivity factor profile will be analyzed in detail, combined with the peak position and vertical change rate of the liquid water content profile, to help determine the intensity of the storm's upward motion and precipitation efficiency, effectively distinguishing different types of severe convective weather such as heavy rainfall, hail, and thunderstorms; at the same time, the abrupt change characteristics of the correlation coefficient (CC) profile will be introduced to eliminate false strong echo signals caused by non-precipitation particles and reduce the false alarm rate.

[0108] Specifically, after feature extraction and verification, a standardized list of alarm signals is generated. The list accurately includes the latitude and longitude coordinates of the storm center, the type of severe convection, the identification probability, the core attributes of the storm, and the expected period and range of impact. For high-risk scenarios, the urgency level (blue, yellow, orange, red) and key points of defense suggestions will be additionally marked to ensure the practicality and guidance of the warning information. At the same time, it supports sending SMS messages and voice notifications in real time to pre-configured designated user groups (such as aircraft operators, forecasters, etc.) based on SMS modems and cloud communication. The specific content of the alarm information and the notification recipients can be customized through the human-computer interaction interface to ensure that relevant personnel receive the warning information in a timely manner.

[0109] Specifically, after the early warning process is completed, the radar and raw data, the radar products generated by inversion, and the final early warning information will be distributed through multiple channels simultaneously. The distribution will follow meteorological data transmission standards, supporting real-time push to professional user terminals such as meteorological department business platforms, emergency management command systems, and transportation and shipping dispatch centers. At the same time, simplified early warning information will be accurately pushed to affected areas through public channels such as mobile phone text messages, meteorological apps, radio and television broadcasts, and outdoor early warning screens. The data format will be adapted to different terminal needs, providing professional formats such as HDF5 and NetCDF as well as lightweight formats such as JSON and XML. A distribution status feedback mechanism and a data encryption transmission channel will be established to ensure the timeliness, security, and accessibility of early warning information. At the same time, users can obtain data products with different levels of detail according to their permissions, meeting the diverse needs of professional judgment and public risk avoidance.

[0110] Specifically, during the distribution process, for data and information that are not successfully distributed due to communication interruption, the system will automatically store them for 24 hours. Once communication is restored, the system can respond to terminal requests for passive resending. If the failure to send data is an occasional one within 2 hours, the system will automatically complete the resending. The entire process of generating and distributing early warning information will generate detailed operation logs, which will be sent to the system monitoring module in real time for storage and monitoring, ensuring that the early warning and distribution process is traceable and verifiable.

[0111] According to the embodiments of this disclosure, the following technical effects are achieved:

[0112] (1) Significantly improved the quality and reliability of X-band weather radar base data. By introducing integrity verification, invalid data filtering mechanism and standard format storage in the data acquisition stage, the integrity and traceability of base data are guaranteed. Combined with reflectivity factor deviation calibration based on the dual wavelength ratio characteristics of clear sky echo, the system deviation caused by radar hardware drift, beam distortion and initial atmospheric attenuation error is accurately corrected, making the base data more consistent with the real atmospheric observation state, and providing high-quality basic support for subsequent full-link data processing.

[0113] (2) The quality control algorithm has achieved dynamic adaptation and efficient quality control. By constructing a closed-loop mechanism of "real-time quality control - multi-dimensional evaluation - dynamic parameter adjustment", combined with optimized algorithm execution logic, the quality control algorithm can adaptively adjust parameters such as identification threshold and filtering intensity according to different weather scenarios such as clear sky, light precipitation, and strong convection. It not only efficiently removes noise signals such as radial interference, ground object echo, velocity folding, and abnormal jumps, but also retains effective information such as light precipitation echo to the maximum extent, which greatly improves the purity and consistency of the processed data and solves the pain point of poor adaptability of traditional fixed parameter quality control algorithms.

[0114] (3) The inversion accuracy and diversity of radar products and vertical meteorological element profiles have been significantly optimized. Users can customize product types, output resolution and algorithm parameters according to actual needs. Through accurate interpolation conversion from polar coordinates to Cartesian coordinates, diversified products such as reflectivity factor, echo top height, vertical integral liquid water, and storm tracking information are generated. In the calculation of vertical meteorological element profiles, through geometric modeling, effective beam filtering, statistical interval division and Gaussian fitting / volume weighted average optimization, the vertical distribution characteristics of meteorological elements are accurately captured, and the profile accuracy is effectively improved, providing key vertical structure data support for the identification of severe convective weather.

[0115] (4) The accuracy and timeliness of severe convective weather identification have been greatly improved. By integrating multiple algorithms such as storm structure analysis, hail index, and mesoscale cyclone identification, the core features of severe convection are extracted. The key information such as reflectivity gradient and liquid water content changes in the vertical profile are deeply combined to effectively distinguish different types of severe convective weather such as heavy rainfall, hail, and thunderstorms, reducing the false alarm rate and missed alarm rate. After the warning information is generated, it is accurately distributed through multiple channels and in multiple formats to reduce end-to-end delay. This not only meets the in-depth analysis needs of professional users, but also enables the public to quickly avoid risks, thus gaining sufficient response time for disaster prevention and mitigation.

[0116] (5) A highly efficient and collaborative radar data processing and application system has been built, which seamlessly connects all aspects from basic data acquisition, calibration, quality control, product inversion, early warning to distribution. The data format follows meteorological industry standards and supports hierarchical management of permissions and feedback on distribution status. This ensures the security and standardization of data transmission and meets the diverse needs of meteorological business, emergency management, transportation and shipping, etc., thereby enhancing the comprehensive application value and business implementation capability of X-band weather radar data.

[0117] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0118] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0119] Figure 2 A structural diagram of an X-band weather radar data processing apparatus provided in an embodiment of this disclosure is shown, as follows: Figure 2 As shown, an X-band weather radar data processing device 200 may include:

[0120] The base data acquisition module 210 is used to acquire and store the base data of the X-band weather radar.

[0121] The dynamic quality control module 220 is used to perform real-time quality control on the base data based on a preset quality control algorithm, and to perform real-time quality evaluation on the base data after quality control, generate quality evaluation indicators, and provide real-time feedback on the quality control process based on the quality evaluation indicators, so as to dynamically adjust the parameters of the quality control algorithm.

[0122] Product inversion module 230 is used to perform product inversion based on the base data processed by the quality control algorithm after dynamic parameter adjustment, and generate radar products.

[0123] The early warning generation and output module 240 is used to generate early warnings based on the radar product.

[0124] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0125] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0126] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0127] Figure 3A schematic block diagram of an electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0128] Electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in ROM 302 or a computer program loaded into RAM 303 from storage unit 308. RAM 303 can also store various programs and data required for the operation of electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O interface 305 is also connected to bus 304.

[0129] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0130] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0131] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0132] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0133] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0134] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0135] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0136] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0137] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0138] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for processing X-band weather radar data, characterized in that, include: Acquire and store the base data from the X-band weather radar; The baseline data is subjected to real-time quality control based on a preset quality control algorithm, and the controlled baseline data is then evaluated in real-time to generate quality evaluation indicators. These indicators are then used to provide real-time feedback to the quality control process, thereby dynamically adjusting the parameters of the quality control algorithm. The quality control algorithm includes at least one of the following: The following algorithms are included: radial interference echo quality control algorithm and ground object and non-precipitation echo quality control algorithm (belonging to the ground object suppression stage); differential propagation phase shift quality control algorithm and differential propagation phase shift rate estimation algorithm (belonging to the dual polarization radar calibration stage); radial velocity defuzzification algorithm and radial velocity dual PRF singular value processing algorithm (belonging to the radial velocity processing stage); and zero-degree layer bright band identification and correction algorithm, differential reflectivity filtering algorithm, reflectivity factor attenuation correction algorithm, differential reflectivity factor attenuation correction algorithm, and range defuzzification algorithm. The execution logic of the quality control algorithm satisfies: The radial interference echo quality control algorithm is executed before the ground feature and non-precipitation echo quality control algorithm. The differential propagation phase shift quality control algorithm is executed before the differential propagation phase shift rate estimation algorithm; The radial velocity defuzzification algorithm is executed preferentially over the radial velocity dual PRF singular value processing algorithm; The process of performing real-time quality assessment on the baseline data after quality control, generating quality assessment indicators, and providing real-time feedback on the quality control process based on these indicators to dynamically adjust the parameters of the quality control algorithm includes: Radar base data samples were collected under clear sky and precipitation conditions, respectively. The ground cover suppression ratio and the impact of ground cover suppression on precipitation echo were calculated to generate ground cover suppression assessment index. The base data is preprocessed to remove non-precipitation echoes, and the two-dimensional probability distributions of differential reflectance, correlation coefficient and signal-to-noise ratio, and statistical characteristic values ​​of correlation coefficient are calculated. The standard deviation of reflectance factor is also calculated through self-consistent relational formula to generate dual polarization data quality assessment index. The radial velocity field is read from the base data, the radial velocity data is preprocessed, the radial velocity standard deviation of each distance library is calculated, and the radial velocity standard deviations of all distance libraries are sorted and statistically analyzed as a percentage. The upper quartile is selected as the radial velocity quality evaluation index. Each quality assessment indicator is fed back to the quality control process in real time, and the identification threshold and filtering intensity of the algorithms related to the ground object suppression link, the system deviation of the algorithms related to the dual polarization radar calibration link, and the algorithm parameters of the algorithms related to the radial velocity processing link are dynamically adjusted. Based on the base data processed by the quality control algorithm with dynamically adjusted parameters, product inversion is performed to generate radar products. Early warning is based on the aforementioned radar product.

2. The method according to claim 1, characterized in that, The method further includes: After acquiring X-band weather radar base data, reflectivity factor deviation calibration is performed on the base data based on the dual-wavelength ratio characteristics of clear-sky echoes.

3. The method according to claim 1, characterized in that, The process of generating radar products by inverting the base data processed by a quality control algorithm with dynamically adjusted parameters includes: Based on the product type and algorithm parameters configured by the user, radar products including reflectivity factor, radial velocity, spectral width, differential reflectivity, differential phase shift, echo top height, vertical integral liquid water, and storm tracking information are generated.

4. The method according to claim 3, characterized in that, The method further includes a step of calculating the profile of vertical meteorological elements based on the radar product, specifically including: Acquire the generated radar product and its corresponding volume scan data; Geometric modeling is performed on the volume scan data, and the coverage of the radar beam is determined for each pre-divided vertical layer. Extract radar beams that meet the coverage conditions and their corresponding radar product data, and divide the radar product data into different statistical intervals according to the preset value range. The volume sum of radar product data within each statistical interval is calculated. Based on the volume sum, feature values ​​are extracted to obtain the profile values ​​for each vertical layer, thereby forming a complete vertical meteorological element profile; wherein, The extracted profile values ​​are optimized by Gaussian fitting or volume-weighted averaging to improve profile accuracy.

5. The method according to claim 4, characterized in that, The early warning based on the radar product includes: Strong convective features were extracted from radar products using storm structure analysis, hail index, and mesoscale cyclone identification algorithms. By combining the reflectivity gradient and liquid water content variation characteristics in the vertical profile, the accuracy of identifying severe convective weather can be enhanced. Generate a list of alarm signals that includes latitude and longitude location, identification probability, and storm attributes.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Distribute radar and data, radar products and early warning information.

7. An X-band weather radar data processing device, characterized in that, include: The base data acquisition module is used to acquire and store the base data of the X-band weather radar. A dynamic quality control module is used to perform real-time quality control on the base data based on a preset quality control algorithm, and to perform real-time quality evaluation on the base data after quality control, generating quality evaluation indicators. The module then provides real-time feedback on the quality control process based on these indicators to dynamically adjust the parameters of the quality control algorithm. The quality control algorithm includes at least one of the following: The following algorithms are included: radial interference echo quality control algorithm and ground object and non-precipitation echo quality control algorithm (belonging to the ground object suppression stage); differential propagation phase shift quality control algorithm and differential propagation phase shift rate estimation algorithm (belonging to the dual polarization radar calibration stage); radial velocity defuzzification algorithm and radial velocity dual PRF singular value processing algorithm (belonging to the radial velocity processing stage); and zero-degree layer bright band identification and correction algorithm, differential reflectivity filtering algorithm, reflectivity factor attenuation correction algorithm, differential reflectivity factor attenuation correction algorithm, and range defuzzification algorithm. The execution logic of the quality control algorithm satisfies: The radial interference echo quality control algorithm is executed before the ground feature and non-precipitation echo quality control algorithm. The differential propagation phase shift quality control algorithm is executed before the differential propagation phase shift rate estimation algorithm; The radial velocity defuzzification algorithm is executed preferentially over the radial velocity dual PRF singular value processing algorithm; The process of performing real-time quality assessment on the baseline data after quality control, generating quality assessment indicators, and providing real-time feedback on the quality control process based on these indicators to dynamically adjust the parameters of the quality control algorithm includes: Radar base data samples were collected under clear sky and precipitation conditions, respectively. The ground cover suppression ratio and the impact of ground cover suppression on precipitation echo were calculated to generate ground cover suppression assessment index. The base data is preprocessed to remove non-precipitation echoes, and the two-dimensional probability distributions of differential reflectance, correlation coefficient and signal-to-noise ratio, and statistical characteristic values ​​of correlation coefficient are calculated. The standard deviation of reflectance factor is also calculated through self-consistent relational formula to generate dual polarization data quality assessment index. The radial velocity field is read from the base data, the radial velocity data is preprocessed, the radial velocity standard deviation of each distance library is calculated, and the radial velocity standard deviations of all distance libraries are sorted and statistically analyzed as a percentage. The upper quartile is selected as the radial velocity quality evaluation index. Each quality assessment indicator is fed back to the quality control process in real time, and the identification threshold and filtering intensity of the algorithms related to the ground object suppression link, the system deviation of the algorithms related to the dual polarization radar calibration link, and the algorithm parameters of the algorithms related to the radial velocity processing link are dynamically adjusted. The product inversion module is used to perform product inversion based on the base data processed by the quality control algorithm after dynamic parameter adjustment, and generate radar products. The early warning generation and output module is used to generate early warnings based on the radar product.

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