A method and system for analyzing the effect of artificial precipitation enhancement operation based on cloud physical parameter analysis

Abstract

The application relates to the technical field of meteorological services, and discloses an artificial rain enhancement operation effect analysis method and system based on cloud physical parameter analysis, which can dynamically select a conversion relationship closest to ground measured precipitation data by acquiring a preset conversion relationship of multiple groups of radar observation data and precipitation rates, comparing radar observation data and ground measured precipitation data of an operation area, and adjusting the selected conversion relationship according to real-time changes of the radar observation data and the ground measured precipitation data. The technical scheme effectively solves the problem in the prior art that when there is a significant difference between the physical form of cloud precipitation particles and a preset liquid rain assumption, the conversion relationship of the traditional radar echo intensity and the precipitation rate is distorted, and the evaluation result is seriously deviated from the ground measured data.

Description

Technical Field

[0001] This invention relates to the field of meteorological service technology, and in particular to a method and system for analyzing the effects of artificial rain enhancement operations based on cloud physical parameter analysis. Background Technology

[0002] In the field of meteorological services, artificial rain enhancement is an important weather control tool. Its core objective is to increase precipitation by altering the microphysical processes of clouds through the seeding of catalysts. Accurately assessing the actual effectiveness of each operation is crucial for scientific decision-making and rational resource allocation. Typically, effectiveness assessment relies on the analysis of cloud physical parameters, such as using meteorological radar echo data to estimate precipitation intensity. However, the conversion relationship between these radar echo intensities and precipitation rates is often based on statistical patterns of liquid precipitation observed over a long period locally. When the physical morphology of precipitation particles within clouds differs significantly from these pre-set assumptions about liquid precipitation, traditional assessment methods face challenges and may lead to severely distorted assessment results. Summary of the Invention

[0003] This invention provides a method and system for analyzing the effects of artificial rain enhancement operations based on cloud physical parameter analysis. It aims to solve the problem in existing artificial rain enhancement operation effect evaluation methods where the conversion relationship between radar echo intensity and precipitation rate is distorted when the physical morphology of precipitation particles in clouds is abnormal, resulting in a huge deviation between the evaluation results and ground-measured data.

[0004] Firstly, in order to solve the above-mentioned technical problems, the present invention provides a method for analyzing the effect of artificial rain enhancement operations based on cloud physical parameter analysis, comprising: Acquire the preset conversion relationship between multiple sets of radar observation data and precipitation rate, and acquire radar observation data of the operational area; The radar observation data is applied to the preset conversion relationship to obtain multiple sets of candidate precipitation rate estimation results; Obtain the measured precipitation data of the work area, compare the estimated results of the multiple candidate precipitation rates with the measured precipitation data of the ground, and obtain the comparison results; Based on the comparison results, select the conversion relationship that is closest to the measured precipitation data on the ground; The selected conversion relationship is adjusted based on the changes in the radar observation data and the measured ground precipitation data.

[0005] Preferably, the step of selecting the conversion relationship closest to the measured ground precipitation data based on the comparison results includes: Spatial clustering is performed on the multi-dimensional polarization parameters of the radar observation data to divide the operating area into multiple regions with consistent physical processes. The measured ground precipitation data are correlated with the corresponding physical process regions; Based on the comparison results, the transformation relationship with the smallest error is selected as the adaptation rule for the consistency region of the physical process.

[0006] Preferably, adjusting the selected conversion relationship based on changes in the radar observation data and the measured ground precipitation data includes: Real-time acquisition of radar multi-dimensional polarization parameter data and measured ground precipitation data within the operational area; The measured ground precipitation data is matched with the corresponding radar multi-dimensional polarization parameter data to construct a real-time true value anchor point set; Calculate the similarity between the radar multidimensional polarization parameters and the radar multidimensional polarization parameters of each anchor point in the true anchor point set; Based on the similarity, the ground-measured precipitation data corresponding to each anchor point in the set of true anchor points are weighted to estimate the precipitation rate. The conversion relationship of the work area is dynamically adjusted based on the precipitation rate.

[0007] Preferably, the step of dynamically adjusting the conversion relationship of the work area based on the estimated precipitation rate includes: Identify whether the radar's multi-dimensional polarization parameters are outside the range of the feature space covered by the set of truth anchor points; If so, the precipitation rate is corrected based on the proximity of the radar multi-dimensional polarization parameters to the characteristic space boundary. The conversion relationship of the work area is dynamically adjusted based on the corrected precipitation rate.

[0008] Preferably, identifying whether the radar multi-dimensional polarization parameters of the radar detection unit are outside the feature space range covered by the set of truth anchor points includes: The statistics of each polarization parameter in the set of truth anchor points are dynamically calculated; The boundary definition of the feature space is updated in real time based on the statistics. When a new truth anchor is added to the set of truth anchors, the coverage of the feature space is re-evaluated. If the boundary of the feature space expands outward, then the judgment threshold for areas outside the range is adjusted. Based on the adjusted judgment threshold, it is determined whether the radar multi-dimensional polarization parameters are outside the feature space range covered by the set of truth anchor points.

[0009] Preferably, if so, the instantaneously estimated precipitation rate is corrected based on the proximity of the radar multi-dimensional polarization parameters to the characteristic spatial boundary, including: Calculate the similarity between the radar's multi-dimensional polarization parameters and the feature space boundary; Based on the similarity, and combined with a preset correction curve or correction function, the instantaneously estimated precipitation rate is nonlinearly corrected.

[0010] Preferably, the nonlinear correction of the real-time estimated precipitation rate by combining a preset correction curve or correction function includes: Real-time monitoring of the dynamic changes in the nonlinear relationship between the radar's multi-dimensional polarization parameters and the measured ground precipitation data; Identify whether the nonlinear relationship deviates significantly from the preset correction curve or correction function; If so, the parameters of the preset correction curve or the correction function are adjusted according to the degree and trend of the deviation. If the preset correction curve or the correction function cannot be effectively fitted, a new correction curve or correction function is generated based on the real-time observation data. The generated correction curve or correction function is used to correct the real-time estimated precipitation rate.

[0011] Preferably, the real-time monitoring of the dynamic changes in the nonlinear relationship between the radar's multi-dimensional polarization parameters and the measured ground precipitation data includes: The work area is divided into multiple sub-areas; Real-time monitoring of changes in the nonlinear relationship characteristic parameters of each sub-region; Based on the trend and magnitude of change, identify local or instantaneous nonlinear dynamic changes.

[0012] Secondly, the present invention provides a system for analyzing the effects of artificial rain enhancement operations, comprising: The acquisition module is used to acquire multiple sets of radar observation data and the preset conversion relationship between precipitation rate, and to acquire radar observation data of the operating area; The estimation module is used to apply the radar observation data to the preset transformation relationship to obtain multiple sets of candidate precipitation rate estimation results; The comparison module is used to acquire the measured precipitation data of the operation area, compare the multiple sets of candidate precipitation rate estimation results with the measured precipitation data of the ground, and obtain the comparison results. The selection module is used to select the conversion relationship that is closest to the measured precipitation data on the ground, based on the comparison results. The adjustment module is used to adjust the selected conversion relationship based on changes in the radar observation data and the measured ground precipitation data.

[0013] Thirdly, the present invention provides a system for analyzing the effects of artificial rain enhancement operations, comprising: The input end is used to acquire the preset conversion relationship between multiple sets of radar observation data and precipitation rate, and to acquire radar observation data of the operating area; The processing unit applies the radar observation data to the preset conversion relationship to obtain multiple sets of candidate precipitation rate estimation results; acquires the ground-measured precipitation data of the operation area, compares the multiple sets of candidate precipitation rate estimation results with the ground-measured precipitation data to obtain a comparison result; and selects the conversion relationship that is closest to the ground-measured precipitation data based on the comparison result. The output terminal is used to adjust the selected conversion relationship based on the changes in the radar observation data and the measured ground precipitation data.

[0014] This invention provides a method and system for analyzing the effectiveness of artificial rain enhancement operations based on cloud physics parameter analysis. By acquiring multiple sets of radar observation data and preset conversion relationships between precipitation rates, and comparing these with radar observation data and measured ground precipitation data from the operational area, the method dynamically selects the conversion relationship closest to the measured ground precipitation data. Furthermore, the method can adjust the selected conversion relationship based on real-time changes in both radar observation data and measured ground precipitation data. This technical solution effectively solves the problem in existing technologies where, when the physical morphology of precipitation particles within clouds differs significantly from the preset assumption of liquid rainfall, the traditional radar echo intensity-precipitation rate conversion relationship becomes distorted, leading to a significant deviation between the evaluation results and measured ground data. By introducing measured ground data as a truth reference and dynamically optimizing the conversion relationship, this application significantly improves the accuracy and reliability of artificial rain enhancement operation effectiveness evaluation, avoids disputes caused by distorted evaluation results, and provides solid data support for scientific decision-making and rational resource allocation. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method for analyzing the effect of artificial rain enhancement operations based on cloud physical parameter analysis, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of a system structure for analyzing the effect of artificial rain enhancement operations based on cloud physical parameter analysis, provided by an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Reference Figure 1 The present invention provides a flowchart of a method for analyzing the effect of artificial rain enhancement operations based on cloud physical parameter analysis, comprising the following steps: S11: Obtain the preset conversion relationship between multiple sets of radar observation data and precipitation rate, and obtain radar observation data of the operation area; S12, The radar observation data is applied to the preset conversion relationship to obtain multiple sets of candidate precipitation rate estimation results; S13, Obtain the measured precipitation data of the ground in the operation area, compare the multiple sets of candidate precipitation rate estimation results with the measured precipitation data of the ground, and obtain the comparison results; S14. Based on the comparison results, select the conversion relationship that is closest to the measured ground precipitation data. S15, adjust the selected conversion relationship based on the changes in the radar observation data and the measured ground precipitation data.

[0018] First, multiple sets of radar observation data and preset conversion relationships between precipitation rates are acquired, along with radar observation data for the operational area. These preset conversion relationships can be obtained through statistical analysis of historical meteorological data. For example, multiple sets of relationships (ZR relationships) between radar reflectivity factor Z and precipitation rate R can be pre-established based on long-term observation data from different seasons, geographical regions, and cloud types (such as stratiform clouds and convective clouds). These relationships can be stored in a database for later retrieval. For instance, these relationships can be configured manually based on experience or historical data, or generated through offline training models. The radar observation data for the operational area can be acquired in real-time through a meteorological radar system. This can be achieved through a direct data stream interface of the radar station or by periodically downloading from a radar data server. For example, a fixed time interval (such as every 5 or 10 minutes) can be set for data collection, or data acquisition can be manually triggered before and during the operation.

[0019] Secondly, the radar observation data is applied to the preset transformation relationship to obtain multiple sets of candidate precipitation rate estimation results. Specifically, the received radar observation data (e.g., radar reflectivity factor) can be substituted point-by-point or region-by-region into each set of preset ZR transformation relationships to calculate the corresponding precipitation rate. For example, for each set of preset relationships, the system will traverse the echo data of all radar detection units and calculate a precipitation rate estimate based on the relationship.

[0020] Next, ground-measured precipitation data of the work area is acquired, and the multiple sets of candidate precipitation rate estimation results are compared with the ground-measured precipitation data to obtain the comparison results. The ground-measured precipitation data can be obtained through a network of ground rain gauges deployed within the work area. These rain gauges can be tipping bucket rain gauges, weighing rain gauges, etc., and their data can be transmitted to the central processing system via wired or wireless networks. For example, cumulative precipitation data can be collected periodically (e.g., hourly) from each rain gauge and converted into instantaneous precipitation rate. Comparing the radar-estimated precipitation rate results with the ground-measured precipitation data can employ various error analysis methods, such as calculating the root mean square error, mean absolute error, or correlation coefficient. For example, the radar estimation results can be interpolated at the ground rain gauge locations and then compared point-to-point with the corresponding measured data.

[0021] Then, based on the comparison results, the transformation relationship that is closest to the measured precipitation data is selected. Specifically, based on the comparison results, the preset transformation relationship with the smallest error or the highest correlation is selected as the best fit relationship for the current time period or region. For example, the error index between each group of candidate precipitation rate estimates and the measured precipitation data can be calculated, and then the transformation relationship with the smallest error can be selected.

[0022] Finally, the selected transformation relationship is adjusted based on changes in the radar observation data and the measured ground precipitation data. As cloud physics and precipitation characteristics change during the operation, the radar observation data and measured ground precipitation data will also change. To maintain the accuracy of the assessment, the selected transformation relationship needs to be adjusted according to these changes. For example, the above comparison and selection process can be periodically re-executed, or the parameters of the transformation relationship can be fine-tuned based on new data trends. This adjustment can be a linear or non-linear correction of the transformation relationship parameters to better fit the real-time observation data.

[0023] Through the above technical solution, this application can dynamically select and adjust the radar precipitation rate conversion relationship based on actual observation data, thereby effectively solving the problem of evaluation distortion under complex cloud physics conditions using traditional methods. Therefore, this application significantly improves the accuracy and reliability of artificial rain enhancement operation effect evaluation, providing more precise data support for meteorological services and decision-making.

[0024] Specifically, the above-mentioned selection of the transformation relationship that most closely matches the measured precipitation data based on the comparison results can be further achieved through the following steps: Spatial clustering of multi-dimensional polarization parameters of radar observation data divides the operating area into multiple regions with consistent physical processes; Correlate measured precipitation data with corresponding regions of physical process consistency; Based on the comparison results, the transformation relationship with the smallest error is selected as the adaptation rule for the physical process consistency region.

[0025] The multi-dimensional polarization parameters of radar observation data can include, but are not limited to, horizontal reflectivity factor Z_H, differential reflectivity Z_DR, differential propagation phase Φ_DP, relative differential propagation phase K_DP, and correlation coefficient ρ_HV. These parameters reflect the microphysical characteristics of precipitation particles, such as shape, size, phase, and concentration. Spatial clustering refers to using clustering algorithms (such as K-means, DBSCAN, hierarchical clustering, etc.) to spatially group these multi-dimensional polarization parameters to identify regions with similar microphysical process characteristics. Through spatial clustering, the entire operating area can be divided into several regions with consistent physical processes; for example, stratiform cloud precipitation regions, convective cloud precipitation regions, and mixed-phase precipitation regions can be identified.

[0026] Ground-based measured precipitation data is typically acquired using equipment such as rain gauges and raindrop spectrometers, providing localized, accurate precipitation information. Correlating ground-based measured precipitation data with corresponding congruent regions of the physical processes involved in the measurement process. This means matching the precipitation data measured by a specific rain gauge or raindrop spectrometer with the congruent region of the physical process in which that measurement point is located. For example, if a rain gauge is located in an area identified as having convective cloud precipitation characteristics, then the rain gauge's data will be correlated with that convective cloud precipitation area.

[0027] The comparison result refers to the error or similarity index obtained by comparing multiple sets of candidate precipitation rate estimates with ground-measured precipitation data. Selecting the transformation relationship with the smallest error as the adaptation rule for the physical process consistency zone means that for each physical process consistency zone, the system will select the transformation relationship that minimizes the error between the radar-estimated precipitation rate and the ground-measured precipitation data from a set of preset transformation relationships within that region. This transformation relationship will serve as the optimal adaptation rule for that specific physical process consistency zone and will be used for subsequent precipitation rate estimations.

[0028] This application's solution utilizes spatial clustering of multi-dimensional polarization parameters from radar observation data to meticulously divide complex operational areas into multiple sub-regions with similar microphysical characteristics. Because precipitation microphysical processes can differ significantly across regions—for example, the characteristics of precipitation particles differ between stratiform clouds and convective clouds—using a single radar precipitation rate conversion relationship is often insufficient to accurately estimate precipitation across the entire region. By correlating ground-measured precipitation data with these consistent regions of physical processes, a realistic precipitation reference can be provided for each specific region. Furthermore, for each consistent region of physical processes, the conversion relationship with the smallest error is independently selected as its adaptation rule, thereby achieving refined matching of different precipitation microphysical processes and effectively improving the local accuracy of precipitation rate estimation.

[0029] The above technical solution overcomes the problem of insufficient accuracy in precipitation rate estimation caused by the use of a single transformation relationship in traditional methods. By subdividing the operational area into multiple regions with consistent physical processes and selecting the most suitable transformation relationship for each region, the local accuracy and overall reliability of radar precipitation rate estimation are significantly improved. This refined adaptation strategy makes the analysis of the effects of artificial rain enhancement operations more precise, providing more reliable data support for operational decisions, thereby enhancing the scientific nature and effectiveness of artificial rain enhancement operations.

[0030] In some embodiments described above, while adjustments to the selected conversion relationship are proposed based on changes in radar observation data and measured ground precipitation data, in practical applications, the relationship between radar observation data and measured ground precipitation data may be affected by various complex factors, and this relationship is not static but exhibits dynamic characteristics. Failure to capture and adjust this dynamic change in real time and with precision may lead to a decrease in the accuracy of precipitation rate estimation, thereby affecting the accurate assessment of the effectiveness of artificial rain enhancement operations. Therefore, this application further proposes a more refined and dynamic method for adjusting the conversion relationship to improve the real-time performance and accuracy of precipitation rate estimation.

[0031] The above-mentioned adjustment of the selected transformation relationship specifically includes: Real-time acquisition of radar multi-dimensional polarization parameter data and measured ground precipitation data within the operational area; The measured ground precipitation data is matched with the corresponding radar multi-dimensional polarization parameter data to construct a real-time true value anchor point set; Calculate the similarity between the radar multidimensional polarization parameters and the radar multidimensional polarization parameters of each anchor point in the true anchor point set; Based on the similarity, the ground-measured precipitation data corresponding to each anchor point in the set of true anchor points are weighted to estimate the precipitation rate. The conversion relationship of the work area is dynamically adjusted based on the precipitation rate.

[0032] Specifically, real-time acquisition of radar multi-dimensional polarization parameter data and measured ground precipitation data within the operational area means that the system continuously receives the latest multi-dimensional polarization parameter data from the radar system, such as reflectivity factor (Z), differential reflectivity (ZDR), and specific propagation phase shift (KDP), while simultaneously acquiring real-time precipitation data from ground rain gauges or ground observation networks. These multi-dimensional polarization parameters can more comprehensively reflect the shape, size, and phase information of precipitation particles, providing richer and more accurate physical evidence for precipitation rate estimation.

[0033] Specifically, the measured ground precipitation data is matched with the corresponding radar multi-dimensional polarization parameter data to construct a real-time truth anchor point set. This can be understood as associating radar observation data and measured ground precipitation data at the same time and spatial location to form a series of reliable "truth points." These truth points serve as the basis for calibration and adjustment of the conversion relationship, aiming to provide real-time and accurate references to address the discrepancies between radar estimates and actual precipitation.

[0034] In practical applications, the similarity between the radar's multi-dimensional polarization parameters and the radar's multi-dimensional polarization parameters of each anchor point in the set of ground truth anchor points is calculated. For example, Euclidean distance, cosine similarity, or other suitable distance metrics can be used to quantify the similarity between the current radar observation data and historical or recent ground truth anchor points. The purpose is to identify the reference data that is closest to the current meteorological conditions and precipitation characteristics, thereby providing the most relevant basis for subsequent precipitation rate estimation.

[0035] Furthermore, based on the similarity, the measured precipitation data corresponding to each anchor point in the set of truth anchor points are weighted to estimate the precipitation rate. This means that the higher the similarity of a truth anchor point, the greater the weight of its corresponding measured precipitation data in estimating the current precipitation rate. For example, the reciprocal or exponential function of the similarity can be used as a weighting factor. By using a weighted average, the precipitation rate at the current moment can be estimated more accurately. The aim is to fully utilize the information from real-time truth anchor points to improve the accuracy and robustness of precipitation rate estimation.

[0036] Therefore, dynamically adjusting the conversion relationship of the operational area based on the precipitation rate means that the system can adaptively correct or optimize the radar precipitation rate conversion relationship based on the real-time estimated precipitation rate. This adjustment can be a fine-tuning of parameters or an update in the form of a conversion function. The purpose is to ensure that the radar estimation results are highly consistent with the ground-measured precipitation data, thereby achieving continuous and accurate evaluation of the effectiveness of artificial rain enhancement operations.

[0037] This application's solution overcomes the limitations of traditional methods in handling dynamic changes in radar precipitation rate conversion relationships by introducing real-time radar multi-dimensional polarization parameter data and ground-measured precipitation data, and constructing a real-time ground truth anchor set. Specifically, when radar observation data and ground-measured precipitation data change, the system can capture these changes in real time and provide a reliable reference using the ground truth anchor set. By calculating the similarity between the current radar multi-dimensional polarization parameters and each anchor point in the ground truth anchor set, the system can intelligently identify the historical or recent observation data that best matches the current meteorological conditions. Subsequently, based on this similarity, the ground-measured precipitation data corresponding to the ground truth anchor points are weighted to obtain a more accurate real-time precipitation rate estimate. It is precisely because of this real-time, similarity-based weighted estimation mechanism that the system can dynamically and adaptively adjust the radar precipitation rate conversion relationship in the operating area according to the latest precipitation rate estimation results, thereby ensuring that the conversion relationship always remains synchronized with the actual meteorological conditions and effectively copes with the complexity and variability of precipitation processes.

[0038] Through the above technical solution, this application enables real-time, dynamic, and refined adjustment of the radar precipitation rate conversion relationship. Compared to the potentially coarse or lagging adjustment methods in the aforementioned schemes, this application significantly improves the real-time performance and accuracy of precipitation rate estimation by introducing multi-dimensional radar polarization parameters and a real-time ground truth anchor set, combined with similarity-weighted estimation. This dynamic adjustment mechanism allows the system to better adapt to the influence of different weather conditions, different precipitation types, and changes in radar characteristics, thereby effectively reducing the deviation between radar-estimated precipitation rates and ground-measured precipitation. Ultimately, this helps to more accurately evaluate the actual effects of artificial rain enhancement operations, provides more reliable data support for operational decisions, and enhances the scientific rigor and effectiveness of artificial rain enhancement operations.

[0039] In some preferred embodiments, a specific example is given below. Suppose that during an artificial rain enhancement operation, the system needs to evaluate the precipitation effect in real time. At a certain moment, the radar system acquires radar multi-dimensional polarization parameter data of a detection unit within the operation area, such as a reflectivity factor Z of 35 dBZ, differential reflectivity ZDR of 1.5 dB, and specific propagation phase shift KDP of 0.5 deg / km. Simultaneously, a ground rain gauge measures a precipitation rate of 5 mm / h at the ground location corresponding to the detection unit in real time. The system uses this matched radar parameter and the measured ground precipitation rate as new truth anchor points and adds them to the real-time truth anchor point set.

[0040] When the system acquires new radar multi-dimensional polarization parameter data in subsequent moments, such as Z = 36 dBZ, ZDR = 1.6 dB, and KDP = 0.55 deg / km, the system calculates the similarity between this new radar parameter set and all anchor points in the real-time ground truth anchor point set (including newly added anchor points and previously accumulated anchor points). For example, if there is a historical anchor point in the set with radar parameters Z = 34 dBZ, ZDR = 1.4 dB, KDP = 0.48 deg / km, and a corresponding measured ground precipitation rate of 4.8 mm / h, calculations show that it has a high similarity to the newly acquired radar parameters (e.g., a smaller Euclidean distance).

[0041] The system weights the measured ground precipitation data corresponding to each anchor point in the ground truth anchor point set based on the calculated similarity. For example, the anchor point with the highest similarity is assigned the largest weight, while anchor points with lower similarity are assigned smaller weights. Through this weighted average, the system estimates the precipitation rate at the current moment, for example, an estimate of 5.1 mm / h. Finally, based on this real-time estimated precipitation rate of 5.1 mm / h, the system dynamically adjusts the radar precipitation rate conversion relationship used in the current operating area (e.g., adjusting the coefficients or exponents in the ZR relationship), so that the adjusted conversion relationship can more accurately estimate the precipitation rate of 5.1 mm / h when applied to the current radar parameters. This continuous real-time adjustment ensures the accuracy of precipitation rate estimation and rapid response to actual precipitation events.

[0042] In some embodiments described above, this application proposes a method for dynamically adjusting the selected transformation relationship based on changes in radar observation data and measured ground precipitation data. However, in practical applications, when the real-time acquired radar multi-dimensional polarization parameter data is outside the feature space covered by the current set of ground truth anchor points, the method of estimating precipitation rate based on the similarity weighting of the existing set of ground truth anchor points may face the challenge of decreased accuracy. This may lead to insufficient precision in adjusting the transformation relationship when facing new or extreme weather conditions, thereby affecting the reliability of the analysis of artificial rain enhancement operations. To address this, this application further proposes an optimization scheme aimed at improving the accuracy of precipitation rate estimation and the robustness of transformation relationship adjustment when radar parameters exceed the known feature space range.

[0043] The above-mentioned dynamic adjustment of the conversion relationship of the work area based on the estimated precipitation rate specifically includes: Identify whether the radar's multi-dimensional polarization parameters are outside the range of the feature space covered by the set of truth anchor points; If so, the precipitation rate is corrected based on the proximity of the radar multi-dimensional polarization parameters to the characteristic space boundary. The conversion relationship of the work area is dynamically adjusted based on the corrected precipitation rate.

[0044] Specifically, identifying whether the radar multi-dimensional polarization parameters are outside the feature space covered by the set of truth anchors means that the system continuously monitors the radar multi-dimensional polarization parameters acquired in real time and compares them with the feature space defined by the currently constructed set of truth anchors. This feature space can be understood as the distribution area or boundary formed by all radar multi-dimensional polarization parameters in the set of truth anchors in a multi-dimensional space. When a new radar multi-dimensional polarization parameter falls outside this area, it is identified as being outside the feature space.

[0045] If so, the precipitation rate is corrected based on the proximity of the radar multi-dimensional polarization parameters to the feature space boundary. This means that once the radar multi-dimensional polarization parameters are identified as being outside the feature space range, the system will no longer rely entirely on internal similarity weighting, but will instead correct based on the distance or similarity between the parameters and the feature space boundary. The proximity can be quantified by calculating Euclidean distance, Mahalanobis distance, or other suitable distance metrics. The purpose of the correction is to obtain a more reliable precipitation rate estimate by extrapolation or reasonable adjustment based on boundary information in the absence of a direct truth anchor.

[0046] Therefore, dynamically adjusting the conversion relationship of the operational area based on the corrected precipitation rate means using the corrected precipitation rate as a more accurate input to further optimize and adjust the radar precipitation rate conversion relationship used in the current operational area. This adjustment ensures that the conversion relationship maintains its effectiveness and accuracy even when encountering meteorological conditions that have not been fully observed.

[0047] This application's solution effectively addresses the problem of decreased precipitation rate estimation accuracy when radar parameters exceed a known range by introducing a mechanism to identify whether radar multi-dimensional polarization parameters fall outside the feature space covered by the set of truth anchor points. Specifically, when real-time radar multi-dimensional polarization parameters are identified as exceeding the feature space defined by the current set of truth anchor points, traditional similarity-weighted precipitation rate estimation methods may no longer be applicable or their accuracy may significantly decrease. In this case, this solution no longer blindly applies internal similarity but instead assesses the proximity of the out-of-range radar parameters to the known feature space boundary. By utilizing this proximity information, the real-time estimated precipitation rate is corrected, for example, by using a distance-based attenuation function or an extrapolation model. This correction mechanism allows the system to provide a reasonably adjusted precipitation rate estimate even when facing new, unobserved meteorological conditions, thus avoiding estimation bias caused by data exceeding the known range. Ultimately, based on this corrected precipitation rate, the system can more accurately and robustly dynamically adjust the radar precipitation rate conversion relationship in the operational area, ensuring analytical accuracy under various complex meteorological conditions.

[0048] Through the above technical solution, this application can significantly improve the robustness and accuracy of artificial rain enhancement operation effect analysis, especially when facing complex or extreme weather conditions where radar multi-dimensional polarization parameters exceed the known characteristic space range. This solution effectively avoids large errors that may occur in traditional methods when extrapolating data by intelligently identifying and correcting precipitation rate estimates that exceed the range, thus ensuring the accuracy and reliability of radar precipitation rate conversion relationship adjustments. Therefore, even under variable weather conditions or the emergence of new characteristics, more accurate precipitation rate estimates can be obtained, leading to a more scientific and reliable evaluation of the effects of artificial rain enhancement operations, and improving the adaptability and practicality of the entire analysis system.

[0049] In some preferred embodiments, a specific example is given below. Suppose that during a certain artificial rain enhancement operation, the multi-dimensional polarization parameters of the radar monitored in real time (e.g., reflectivity factor, differential reflectivity, correlation coefficient, etc.) show a new combination feature that has never appeared in the feature space covered by the currently constructed set of truth anchors, i.e., it is outside the range of that feature space.

[0050] Specifically, the steps described above for identifying whether the multi-dimensional polarization parameters of the radar are outside the feature space covered by the set of truth anchor points can be further refined into the following operations: The statistics of each polarization parameter in the set of truth anchor points are dynamically calculated; The boundary definition of the feature space is updated in real time based on the statistics. When a new truth anchor is added to the set of truth anchors, the coverage of the feature space is re-evaluated. If the boundary of the feature space expands outward, then the judgment threshold for areas outside the range is adjusted. Based on the adjusted judgment threshold, it is determined whether the radar multi-dimensional polarization parameters are outside the feature space range covered by the set of truth anchor points.

[0051] The dynamic calculation of statistics for each polarization parameter in the true anchor point set refers to the real-time analysis of existing radar multi-dimensional polarization parameter data within the set to obtain statistical characteristics such as mean, variance, and covariance. These statistics can reflect the central trend and dispersion of the cloud physical state represented by the current true anchor point set.

[0052] The boundary definition of the feature space is updated in real time based on the calculated statistics, meaning that the effective boundary of the feature space is dynamically defined or adjusted. For example, the confidence interval of a multidimensional Gaussian distribution, the decision boundary of a support vector machine (SVM), or the cluster boundary formed by clustering algorithms (such as K-means, DBSCAN) can be used to define the feature space. This dynamic update ensures that the feature space accurately reflects the range of the currently observed real cloud physical state.

[0053] When new ground truth anchors are added to the ground truth anchor set, the coverage of the feature space is reassessed. This means that as new measured precipitation data and corresponding radar multidimensional polarization parameter data are matched and added to the ground truth anchor set, the statistics of the entire set need to be recalculated, and the actual coverage area of ​​the feature space needs to be reassessed accordingly. This ensures that the feature space always contains the latest, verified real data points.

[0054] If the boundary of the feature space expands outward, adjusting the judgment threshold for areas outside the defined range means that when the feature space becomes larger due to the addition of new data, the standard for judging whether a radar multi-dimensional polarization parameter is "out of range" should also be adjusted accordingly. For example, if the feature space boundary expands outward, the judgment threshold can be appropriately relaxed to avoid misjudging parameters that are normal but located within the newly expanded region as abnormal. Conversely, if the feature space shrinks, the threshold can be tightened.

[0055] Determining whether the radar's multi-dimensional polarization parameters fall outside the feature space covered by the set of true anchor points, based on the adjusted judgment threshold, involves comparing the real-time acquired radar multi-dimensional polarization parameters with the updated threshold to accurately determine whether they fall within or exceed the currently defined feature space. This step is crucial for ensuring the accuracy of subsequent precipitation rate corrections.

[0056] The proposed solution dynamically calculates the statistics of the true anchor set and updates the feature space boundary in real time, ensuring that the definition of the feature space can adaptively adjust as actual observation data changes. When a new true anchor point is added, the coverage of the feature space is reassessed, and the judgment threshold is adjusted according to the expansion of the boundary. This makes the judgment on whether the radar's multi-dimensional polarization parameters are outside the feature space more accurate and robust. This dynamic adjustment mechanism avoids misjudgments that may be caused by using fixed boundaries. Especially when cloud physics processes evolve or the operating area environment changes, it can more accurately identify radar data that needs correction, thus providing a reliable basis for subsequent precipitation rate correction.

[0057] The above technical solution enables precise and adaptive judgment on whether the multi-dimensional polarization parameters of radar exceed the feature space range covered by the true anchor point set. This significantly improves the accuracy of precipitation rate estimation in complex and ever-changing artificial rain enhancement operation environments. Especially when cloud system evolution, meteorological conditions change, or the operation effect is uncertain, this method can dynamically adapt to new data distributions, effectively avoiding precipitation rate estimation deviations caused by inaccurate feature space definition, thereby improving the reliability and accuracy of artificial rain enhancement operation effect analysis.

[0058] In some embodiments described above in this application, when the radar multidimensional polarization parameters are outside the feature space covered by the set of true anchor points, it is necessary to correct the instantaneously estimated precipitation rate. However, simply correcting based on the proximity of the radar multidimensional polarization parameters to the feature space boundary may not adequately capture the complex nonlinear relationship between radar parameters and precipitation rate, especially in areas at or outside the feature space, where this relationship may become more complex and uncertain, thus affecting the accuracy and robustness of the correction.

[0059] In this regard, this application further proposes steps for correcting the real-time estimated precipitation rate, including: Calculate the similarity between the radar's multi-dimensional polarization parameters and the feature space boundary; Based on the similarity, and combined with a preset correction curve or correction function, the instantaneously estimated precipitation rate is nonlinearly corrected.

[0060] Specifically, calculating the similarity between radar multi-dimensional polarization parameters and the feature space boundary refers to quantifying the degree of closeness between the currently observed multi-dimensional polarization parameters and the known feature space boundary defined by a set of ground truth anchor points. This similarity can be calculated using various metrics, such as Euclidean distance, Mahalanobis distance, cosine similarity, or Gaussian kernel function. The feature space boundary can be understood as the outer contour or statistical boundary of the multi-dimensional data points formed by the set of ground truth anchor points. For example, it could be the convex hull, minimum bounding box, or confidence interval boundary defined based on statistical methods (such as mean and standard deviation). By calculating the similarity, the degree to which the current radar observations deviate from the range of known reliable data can be quantified.

[0061] Furthermore, based on the similarity, and in conjunction with a preset correction curve or function, the instantaneously estimated precipitation rate is nonlinearly corrected. The preset correction curve or function is established in advance based on historical data, physical models, or expert experience, and is used to describe the variation of precipitation rate estimation error when radar multi-dimensional polarization parameters deviate from the characteristic space boundary. Nonlinear correction means that the correction amount is not a simple linear relationship with the similarity, but rather adapts to the complex, nonlinear physical process between radar parameters and precipitation rate. For example, when radar parameters only slightly exceed the boundary, the correction amount may be small; while when they significantly deviate from the boundary, the correction amount may increase exponentially or follow other nonlinear patterns to more accurately reflect the actual precipitation situation. These correction curves or functions can be expressed as polynomial functions, exponential functions, logarithmic functions, sigmoid functions, or other nonlinear models.

[0062] This application's solution effectively addresses the problem of decreased accuracy in precipitation rate estimation when radar parameters fall outside the known feature space range by introducing similarity calculations between radar multi-dimensional polarization parameters and feature space boundaries, combined with preset nonlinear correction curves or functions. When radar multi-dimensional polarization parameters exceed the feature space covered by the set of true anchor points, traditional precipitation rate estimation methods may suffer from increased errors due to a lack of sufficient training data or physical model support. By calculating the similarity between the current radar parameters and the feature space boundaries, the system can quantify the degree of deviation from the known reliable data range. This quantification allows the system to finely adjust the real-time estimated precipitation rate based on the degree of deviation using preset nonlinear correction curves or functions. This nonlinear correction better simulates the complex physical relationship between radar parameters and precipitation rate in the actual atmosphere, especially under extreme or atypical weather conditions, thus ensuring more accurate and robust precipitation rate estimation results even in data-sparse or anomalous regions.

[0063] Through the above technical solution, this application can significantly improve the accuracy and robustness of precipitation rate estimation in the analysis of artificial rain enhancement operations, especially when the radar's multi-dimensional polarization parameters are outside the feature space covered by the true value anchor point set. By introducing similarity quantification and nonlinear correction mechanisms, this solution can more accurately capture the complex nonlinear relationship between radar parameters and precipitation rate, effectively compensating for the shortcomings of traditional methods in handling boundary or anomalous data. Therefore, it can reduce the bias in operation effect evaluation caused by estimation errors, providing more reliable data support for the decision-making and optimization of artificial rain enhancement operations.

[0064] In some preferred embodiments, a specific example is given below. Suppose that during a rain enhancement operation, the radar system detects that certain radar multi-dimensional polarization parameters (e.g., reflectivity factor Z, differential reflectivity Zdr, differential phase shift Kdp, etc.) within the operation area exceed the feature space range defined by the historical ground truth anchor set. For example, an abnormally high Zdr value for a certain detection unit indicates the possible presence of large ice crystals or graupel. In this case, the system first calculates the similarity between the radar multi-dimensional polarization parameters of the detection unit and the feature space boundary. Specifically, the Euclidean distance from the parameter point to the nearest point on the feature space boundary can be calculated and normalized into a similarity value. For example, if the similarity value is 0.8 (indicating a close proximity to the boundary), the system will correct the instantaneously estimated precipitation rate according to a preset correction function (e.g., an exponential decay function). This correction function may specify that when the similarity is 0.8, the precipitation rate needs to be corrected downwards by 5%. If the similarity value is 0.2 (indicating a large distance from the boundary), the correction function may specify a downward correction of 20% to reflect the increased uncertainty in estimations in unknown areas. This nonlinear correction allows for more reasonable precipitation rate estimates, even under conditions of abnormal radar parameters, thereby improving the accuracy of artificial rain enhancement operation effectiveness analysis.

[0065] In some embodiments described above, a nonlinear correction is proposed to be applied to the real-time estimated precipitation rate using a preset correction curve or correction function. However, in practical applications, the nonlinear relationship between radar multi-dimensional polarization parameters and measured ground precipitation data may not be constant, but rather dynamically evolves with factors such as meteorological conditions, cloud characteristics, and operational phases. Relying solely on a preset fixed correction curve or correction function may fail to accurately capture this dynamic change, leading to deviations in precipitation rate estimation and affecting the accuracy of artificial rain enhancement operation effectiveness analysis.

[0066] In response, this application further proposes a nonlinear correction to the real-time estimated precipitation rate by combining a preset correction curve or correction function, including: Real-time monitoring of the dynamic changes in the nonlinear relationship between the radar's multi-dimensional polarization parameters and the measured ground precipitation data; Identify whether the nonlinear relationship deviates significantly from the preset correction curve or correction function; If so, the parameters of the preset correction curve or the correction function are adjusted according to the degree and trend of the deviation. If the preset correction curve or the correction function cannot be effectively fitted, a new correction curve or correction function is generated based on the real-time observation data. The generated correction curve or correction function is used to correct the real-time estimated precipitation rate.

[0067] Specifically, real-time monitoring of the dynamic changes in the nonlinear relationship between the radar's multi-dimensional polarization parameters and the measured ground precipitation data can be understood as continuously collecting and analyzing radar multi-dimensional polarization parameter data and measured ground precipitation data, and using statistical methods or machine learning models, such as regression analysis and time series analysis, to track the evolution of the mapping relationship between the two. The aim is to promptly detect the changing trends and patterns of the nonlinear relationship. Identifying whether the nonlinear relationship deviates significantly from a preset correction curve or function can be done by calculating the error (e.g., root mean square error, mean absolute error, etc.) between the actual observed data and the predicted value of the preset curve or function, and comparing it with a preset threshold. If the error exceeds the threshold, a significant deviation is considered to exist. The purpose is to determine whether the current preset correction model is still applicable.

[0068] Furthermore, when a significant deviation is identified, the parameters of the preset correction curve or correction function are adjusted according to the degree and trend of the deviation. Optimization algorithms (such as gradient descent, least squares, etc.) can be used to iteratively update the coefficients, weights, and other parameters of the correction curve or function to better fit the current observation data. The purpose is to enable the existing model to adapt to new data characteristics.

[0069] Furthermore, if the preset correction curve or correction function cannot effectively fit the data, for example, when the deviation is too large or the data distribution changes fundamentally, a new correction curve or correction function is generated based on the real-time observation data. This may involve retraining a completely new model, such as using a nonlinear regression model, neural network, support vector machine, etc., to construct a mapping relationship that better reflects the current situation. The aim is to ensure accurate corrections are still provided under extreme or complex conditions.

[0070] Finally, the generated correction curve or correction function is applied to correct the real-time estimated precipitation rate, ensuring the real-time nature and accuracy of the correction process.

[0071] This application's solution effectively addresses the limitations of preset correction models when facing complex and variable meteorological conditions by introducing a dynamic monitoring and adaptive adjustment mechanism for the nonlinear relationship between radar multi-dimensional polarization parameters and measured ground precipitation data. Specifically, when a dynamic change in the nonlinear relationship is detected, the system can promptly identify this change and its deviation from the preset model. If the deviation is within an acceptable range, the parameters of the preset model are adjusted to adapt to the new data characteristics; if the deviation is too large or the preset model can no longer effectively fit the data, a completely new correction model can be generated based on real-time observation data. This ensures that precipitation rate estimation is always based on the correction relationship that best reflects the current situation, thereby significantly improving estimation accuracy.

[0072] The aforementioned technical solution enhances the environmental adaptability and robustness of artificial rain enhancement operation effectiveness analysis methods. This solution dynamically captures the complex nonlinear relationship between radar observation data and surface precipitation, avoiding estimation errors caused by fixed models. Especially in situations of rapidly changing meteorological conditions or the presence of multiple cloud types, it provides more accurate precipitation rate estimates. This represents a significant technological advancement for accurately evaluating the actual effectiveness of artificial rain enhancement operations and optimizing operational strategies.

[0073] In some preferred embodiments, a specific example is given below. Suppose that during an artificial rain enhancement operation, the initial nonlinear relationship between radar multidimensional polarization parameters and measured ground precipitation data conforms to a preset correction curve A. As the operation progresses, cloud characteristics change, for example, from stratus clouds to stratocumulus mixed clouds, causing a change in the relationship between radar echo characteristics and ground precipitation. At this point, the system monitors this dynamic change in nonlinear relationship in real time and identifies a significant deviation between the current observed data and correction curve A. If the deviation can be compensated for by parameter adjustment, the system will automatically adjust the parameters of correction curve A to better fit the new data. However, if the deviation is too large, for example, correction curve A can no longer effectively describe the new nonlinear relationship, the system will generate a completely new correction curve B based on the real-time acquired radar multidimensional polarization parameter data and measured ground precipitation data, using a machine learning algorithm (e.g., training a new neural network model). Subsequently, all real-time estimated precipitation rates will be corrected using this new correction curve B, thereby ensuring that the precipitation rate estimation remains highly accurate despite changes in cloud characteristics.

[0074] In some embodiments described above in this application, a method for real-time monitoring of the dynamic changes in the nonlinear relationship between radar multi-dimensional polarization parameters and measured ground precipitation data is proposed. Specifically, the real-time monitoring of the dynamic changes in the nonlinear relationship between the radar multi-dimensional polarization parameters and the measured ground precipitation data includes: The work area is divided into multiple sub-areas; Real-time monitoring of changes in the nonlinear relationship characteristic parameters of each sub-region; Based on the trend and magnitude of change, identify local or instantaneous nonlinear dynamic changes.

[0075] Specifically, dividing the operational area into multiple sub-regions refers to subdividing the entire rain enhancement operational area into several smaller, relatively uniform geographical units based on factors such as geographical features, meteorological conditions, radar coverage, or historical data distribution. For example, this division can be based on topography, vegetation cover, hydrological conditions, or the spatial resolution of radar beams. Each sub-region can be considered an independent analysis unit to more precisely capture precipitation characteristics and nonlinear relationships within the region.

[0076] The real-time monitoring of changes in the nonlinear relationship characteristic parameters of each sub-region can be understood as continuously collecting and analyzing radar multi-dimensional polarization parameters and measured ground precipitation data within each defined sub-region, and extracting key parameters that characterize the nonlinear relationship between the two. These characteristic parameters may include, but are not limited to, correlation coefficients, residual distributions of regression models, coefficients of nonlinear fitting functions, or the instantaneous ratio of polarization parameters to precipitation rate in a specific precipitation event. By continuously monitoring these parameters, changes in the nonlinear relationship can be detected in a timely manner.

[0077] In practical applications, identifying local or instantaneous dynamic changes in nonlinear relationships based on trends and magnitudes specifically refers to performing time series analysis on the monitored nonlinear relationship characteristic parameters. For example, methods such as moving averages, exponential smoothing, and Kalman filtering can be used to smooth the data and identify trends. When the rate of change (trend) of the characteristic parameter exceeds a preset threshold, or the magnitude of change reaches a significant level, it can be determined that a local or instantaneous dynamic change in a nonlinear relationship has occurred. This identification helps distinguish between normal fluctuations and actual physical process changes, thereby allowing for more accurate adjustment and correction of the model.

[0078] This application's solution overcomes the limitations of traditional overall monitoring by refining the operational area into multiple sub-regions and independently and in real-time monitoring the nonlinear relationship between radar multi-dimensional polarization parameters and measured precipitation data within each sub-region. Specifically, due to different geographical locations and meteorological conditions, the nonlinear relationship between radar observation data and actual precipitation may exhibit spatial heterogeneity, meaning it is not uniform across the entire operational area. By dividing the area into sub-regions, these local differences can be captured more precisely, avoiding the masking of important local changes due to regional averaging effects. Furthermore, by monitoring the changes in the nonlinear relationship characteristic parameters of each sub-region in real time and identifying local or instantaneous dynamic changes based on their trends and magnitudes, the system can respond promptly to the rapid evolution of precipitation physical processes within the region, such as the formation and dissipation of convective cells or changes in precipitation phase, thereby providing a more accurate and timely basis for the dynamic adjustment of subsequent correction curves or correction functions.

[0079] The aforementioned technical solution significantly improves the monitoring accuracy and response speed for dynamic changes in the nonlinear relationship between radar multi-dimensional polarization parameters and measured ground precipitation data. Compared to only conducting global monitoring, this solution, by refining to the sub-regional level, can more effectively identify and capture changes in nonlinear relationships occurring within local areas or over short periods, such as changes in microscopic physical processes caused by local convective activity, topographic effects, or the artificial rain enhancement operation itself. This ensures more accurate and timely adjustments to preset correction curves or functions, resulting in real-time estimated precipitation rates that more closely approximate the actual situation, thereby improving the accuracy and reliability of artificial rain enhancement operation effect analysis. This refined monitoring mechanism helps to gain a deeper understanding of the complexity of precipitation processes and provides strong support for the refined management of rain enhancement operations.

[0080] Reference Figure 2 This invention provides a schematic diagram of a system for analyzing the effects of artificial rain enhancement operations, comprising: The acquisition module is used to acquire multiple sets of radar observation data and the preset conversion relationship between precipitation rate, and to acquire radar observation data of the operating area; The estimation module is used to apply the radar observation data to the preset transformation relationship to obtain multiple sets of candidate precipitation rate estimation results; The comparison module is used to acquire the measured precipitation data of the operation area, compare the multiple sets of candidate precipitation rate estimation results with the measured precipitation data of the ground, and obtain the comparison results. The selection module is used to select the conversion relationship that is closest to the measured precipitation data on the ground, based on the comparison results. The adjustment module is used to adjust the selected conversion relationship based on changes in the radar observation data and the measured ground precipitation data.

[0081] Also includes: The input end is used to acquire the preset conversion relationship between multiple sets of radar observation data and precipitation rate, and to acquire radar observation data of the operating area; The processing unit applies the radar observation data to the preset conversion relationship to obtain multiple sets of candidate precipitation rate estimation results; acquires the ground-measured precipitation data of the operation area, compares the multiple sets of candidate precipitation rate estimation results with the ground-measured precipitation data to obtain a comparison result; and selects the conversion relationship that is closest to the ground-measured precipitation data based on the comparison result. The output terminal is used to adjust the selected conversion relationship based on the changes in the radar observation data and the measured ground precipitation data.

[0082] It should be noted that the artificial rain enhancement operation effect analysis system provided in this embodiment of the invention is used to execute all the process steps of the artificial rain enhancement operation effect analysis method in the above embodiment. The working principle and beneficial effect of the two are one-to-one, so they will not be described again.

[0083] This invention also provides a terminal device. The terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps described in the embodiments of the artificial rain enhancement operation effect analysis methods, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above system embodiments.

[0084] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0085] The terminal device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of terminal devices and do not constitute a limitation on the terminal device. It may include more or fewer components than described above, or a combination of certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0086] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0087] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0088] Wherein, if the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0089] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; 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. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0090] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for analyzing the effects of artificial rain enhancement operations based on cloud physical parameter analysis, characterized in that, include: Acquire the preset conversion relationship between multiple sets of radar observation data and precipitation rate, and acquire radar observation data of the operational area; The radar observation data is applied to the preset conversion relationship to obtain multiple sets of candidate precipitation rate estimation results; Obtain the measured precipitation data of the work area, compare the estimated results of the multiple candidate precipitation rates with the measured precipitation data of the ground, and obtain the comparison results; Based on the comparison results, the transformation relationship closest to the measured precipitation data is selected, and the multi-dimensional polarization parameters of the radar observation data are spatially clustered to divide the operating area into multiple physical process consistency zones. The measured precipitation data is then associated with the corresponding physical process consistency zones. Based on the comparison results, the transformation relationship with the smallest error is selected as the adaptation rule for the physical process consistency zones. Based on the changes in the radar observation data and the measured ground precipitation data, the selected conversion relationship is adjusted to acquire radar multi-dimensional polarization parameter data and measured ground precipitation data in the operational area in real time. The measured ground precipitation data is matched with the corresponding radar multi-dimensional polarization parameter data to construct a real-time ground truth anchor point set. The similarity between the radar multi-dimensional polarization parameters and the radar multi-dimensional polarization parameters of each anchor point in the ground truth anchor point set is calculated. Based on the similarity, the measured ground precipitation data corresponding to each anchor point in the ground truth anchor point set is weighted to estimate the precipitation rate. Based on the precipitation rate, the conversion relationship of the operational area is dynamically adjusted.

2. The method for analyzing the effect of artificial rain enhancement operations according to claim 1, characterized in that, The step of dynamically adjusting the conversion relationship of the work area based on the estimated precipitation rate includes: Identify whether the radar's multi-dimensional polarization parameters are outside the range of the feature space covered by the set of truth anchor points; If so, the precipitation rate is corrected based on the proximity of the radar's multi-dimensional polarization parameters to the characteristic space boundary. The conversion relationship of the work area is dynamically adjusted based on the corrected precipitation rate.

3. The method for analyzing the effect of artificial rain enhancement operations according to claim 2, characterized in that, Whether the radar multi-dimensional polarization parameters of the identification radar detection unit are outside the feature space range covered by the set of truth anchor points includes: The statistics of each polarization parameter in the set of truth anchor points are dynamically calculated; The boundary definition of the feature space is updated in real time based on the statistics. When a new truth anchor is added to the set of truth anchors, the coverage of the feature space is re-evaluated. If the boundary of the feature space expands outward, the judgment threshold for areas outside the range is adjusted. Based on the adjusted judgment threshold, it is determined whether the radar multi-dimensional polarization parameters are outside the feature space range covered by the set of truth anchor points.

4. The method for analyzing the effect of artificial rain enhancement operations according to claim 2, characterized in that, If so, the instantaneously estimated precipitation rate is corrected based on the proximity of the radar multi-dimensional polarization parameters to the characteristic space boundary, including: Calculate the similarity between the radar's multi-dimensional polarization parameters and the feature space boundary; Based on the similarity, and combined with a preset correction curve or correction function, the instantaneously estimated precipitation rate is nonlinearly corrected.

5. The method for analyzing the effect of artificial rain enhancement operations according to claim 4, characterized in that, The nonlinear correction of the real-time estimated precipitation rate by combining a preset correction curve or correction function includes: Real-time monitoring of the dynamic changes in the nonlinear relationship between the radar's multi-dimensional polarization parameters and the measured ground precipitation data; Identify whether the nonlinear relationship deviates significantly from the preset correction curve or correction function; If so, the parameters of the preset correction curve or the correction function are adjusted according to the degree and trend of the deviation. If the preset correction curve or the correction function cannot be effectively fitted, a new correction curve or correction function is generated based on the real-time observation data. The generated correction curve or correction function is used to correct the real-time estimated precipitation rate.

6. The method for analyzing the effect of artificial rain enhancement operations according to claim 5, characterized in that, The real-time monitoring of the dynamic changes in the nonlinear relationship between the radar's multi-dimensional polarization parameters and the measured ground precipitation data includes: The work area is divided into multiple sub-areas; Real-time monitoring of changes in the nonlinear relationship characteristic parameters of each sub-region; Based on the trend and magnitude of change, identify local or instantaneous nonlinear dynamic changes.

7. A system for analyzing the effects of artificial rain enhancement operations, characterized in that, The system includes: The acquisition module is used to acquire multiple sets of radar observation data and the preset conversion relationship between precipitation rate, and to acquire radar observation data of the operating area; The estimation module is used to apply the radar observation data to the preset transformation relationship to obtain multiple sets of candidate precipitation rate estimation results; The comparison module is used to acquire the measured precipitation data of the operation area, compare the multiple sets of candidate precipitation rate estimation results with the measured precipitation data of the ground, and obtain the comparison results. The selection module is used to select the transformation relationship that is closest to the measured precipitation data on the ground based on the comparison results; to perform spatial clustering on the multi-dimensional polarization parameters of the radar observation data; to divide the operating area into multiple physical process consistency zones; to associate the measured precipitation data on the ground with the corresponding physical process consistency zones; and to select the transformation relationship with the smallest error as the adaptation rule for the physical process consistency zone based on the comparison results. The adjustment module is used to adjust the selected conversion relationship based on changes in the radar observation data and the measured ground precipitation data; to acquire radar multi-dimensional polarization parameter data and measured ground precipitation data in the operating area in real time; to match the measured ground precipitation data with the corresponding radar multi-dimensional polarization parameter data to construct a real-time ground truth anchor point set; to calculate the similarity between the radar multi-dimensional polarization parameters and the radar multi-dimensional polarization parameters of each anchor point in the ground truth anchor point set; to weight the measured ground precipitation data corresponding to each anchor point in the ground truth anchor point set based on the similarity to estimate the precipitation rate; and to dynamically adjust the conversion relationship of the operating area based on the precipitation rate.

8. A system for analyzing the effects of artificial rain enhancement operations, characterized in that, The system includes: The input end is used to acquire the preset conversion relationship between multiple sets of radar observation data and precipitation rate, and to acquire radar observation data of the operating area; The processing unit applies the radar observation data to the preset conversion relationship to obtain multiple sets of candidate precipitation rate estimation results; acquires ground-measured precipitation data of the operation area, compares the multiple sets of candidate precipitation rate estimation results with the ground-measured precipitation data to obtain comparison results; based on the comparison results, selects the conversion relationship closest to the ground-measured precipitation data, performs spatial clustering on the multi-dimensional polarization parameters of the radar observation data, and divides the operation area into multiple physical process consistency zones; associates the ground-measured precipitation data with the corresponding physical process consistency zones; and selects the conversion relationship with the smallest error as the adaptation rule for the physical process consistency zone based on the comparison results. The output terminal is used to adjust the selected conversion relationship based on changes in the radar observation data and the measured ground precipitation data, and to acquire radar multi-dimensional polarization parameter data and measured ground precipitation data in the operating area in real time; to match the measured ground precipitation data with the corresponding radar multi-dimensional polarization parameter data to construct a real-time ground truth anchor point set; to calculate the similarity between the radar multi-dimensional polarization parameters and the radar multi-dimensional polarization parameters of each anchor point in the ground truth anchor point set; to weight the measured ground precipitation data corresponding to each anchor point in the ground truth anchor point set according to the similarity, and to estimate the precipitation rate; and to dynamically adjust the conversion relationship of the operating area according to the precipitation rate.

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