A method, apparatus, and equipment for identifying ground feature influence zones based on historical radar observation data.

Abstract

This application discloses a method, apparatus, and equipment for identifying the influence area of ​​ground features based on historical radar observation data, relating to the field of radar precipitation detection technology. This application first constructs a historical precipitation dataset from radar detection, performs preprocessing and filtering, and then generates evaluation data through point-to-point cumulative processing. From the evaluation data, the influence areas of terrain and buildings are accurately identified. This application can realize the identification of the influence of ground features in radar observation data, improving the quality of observation data.

Description

Technical Field

[0001] This application relates to the field of radar precipitation detection technology, and in particular to a method, device and equipment for identifying ground feature influence zones based on historical radar observation data. Background Technology

[0002] Dual-polarization radar simultaneously emits horizontally and vertically polarized waves to detect precipitation particles, in addition to the reflectivity factor (Z). H In addition to radial velocity (V), differential reflectivity factor (Z) can also be obtained. DR ), differential propagation phase shift (Φ DP ), differential propagation phase shift rate (K) DP ) and correlation coefficient (ρ) HV Dual polarization quantities, which are related to the size, phase, and concentration of precipitation particles, are of great significance for the monitoring and early warning of severe convective weather. However, good quality is the foundation for the application of these dual polarization quantities, and data quality assessment is a necessary step before operational application.

[0003] To address the impact of ground features (such as mountains and buildings) on radar observation data, existing measures involve using adaptive Gaussian frequency domain (GMAP) filters in weather radar hardware to filter out clutter while recovering the remaining weather echoes through Gaussian fitting. While GMAP filters can remove ground features from the reflectivity factor and recover precipitation information to some extent, they still have limitations. For strong ground feature signals in the reflectivity factor, GMAP filters struggle to completely remove them, and beam obstruction caused by ground features cannot be identified. For smaller-scale dual-polarization quantities with higher consistency requirements between the two channels, GMAP filters are currently unable to recover precipitation information effectively. Therefore, accurately identifying the impact of ground features in radar observation data has become a key technical challenge for improving data accuracy and quality. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, and equipment for identifying ground feature influence areas based on historical radar observation data, so as to identify the influence of ground features in radar observation data and improve the quality of observation data.

[0005] To achieve the above objectives, this application provides the following solution.

[0006] Firstly, this application provides a method for identifying the impact zone of ground features based on historical radar observation data, including:

[0007] Construct a historical precipitation dataset from radar detection;

[0008] The historical precipitation dataset is preprocessed and filtered to obtain the filtered historical precipitation dataset.

[0009] By accumulating the observation data of the same observation point in the selected historical precipitation dataset at different historical times, evaluation data of each observation point within the radar radiation range is obtained.

[0010] Based on the assessment data from each observation point, the land cover impact zone is identified, and the land cover impact zone identification results for each observation point are obtained.

[0011] Secondly, this application provides a device for identifying the impact zone of ground features based on historical radar observation data. This device applies the aforementioned method for identifying the impact zone of ground features based on historical radar observation data. The device includes:

[0012] The historical precipitation dataset construction module is used to build historical precipitation datasets detected by radar.

[0013] The preprocessing and filtering module is used to preprocess and filter the historical precipitation dataset to obtain the filtered historical precipitation dataset.

[0014] The accumulation module is used to accumulate the observation data of the same observation point in the filtered historical precipitation dataset at different historical times to obtain the evaluation data of each observation point within the radar radiation range.

[0015] The feature impact zone identification module is used to identify feature impact zones based on the assessment data of each observation point, and obtain the feature impact zone identification results for each observation point.

[0016] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for identifying ground feature influence areas based on historical radar observation data.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects.

[0018] This application provides a method, apparatus, and equipment for identifying the influence area of ​​ground features based on historical radar observation data. The application first constructs a historical precipitation dataset detected by radar, performs preprocessing and filtering, and then generates evaluation data through point-to-point cumulative processing. The influence areas of terrain and buildings are accurately identified from the evaluation data. This application can realize the identification of the influence of ground features in radar observation data and improve the quality of observation data.

[0019] This application further subdivides the evaluation data into five parts: completely blocked beam area, partially blocked beam area, dual-polarization main lobe contaminated area, dual-polarization sidelobe contaminated area, and unaffected area. Based on the varying degrees of influence of different terrain and building-affected areas on weather radar observation data, a graded quality control scheme was established to preserve usable weather radar observation data to the greatest extent possible. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for identifying ground feature influence zones based on historical radar observation data, provided as an embodiment of this application.

[0022] Figure 2 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0024] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] This application aims to address the data quality issues caused by ground features in current weather radar observation data without relying on high-precision digital elevation models (DEMs).

[0026] In one exemplary embodiment, such as Figure 1 As shown, a method for identifying ground feature influence areas based on historical radar observation data is provided, including the following steps 101 to 104.

[0027] Step 101: Construct a historical precipitation dataset from radar detection.

[0028] Step 102: Preprocess and filter the historical precipitation dataset to obtain the filtered historical precipitation dataset.

[0029] Step 103: Accumulate the observation data of the same observation point in the selected historical precipitation dataset at different historical times to obtain the evaluation data of each observation point within the radar radiation range.

[0030] Step 104: Identify the land cover impact zone based on the assessment data of each observation point, and obtain the land cover impact zone identification results for each observation point.

[0031] By implementing steps 101 to 104 above, the influence of ground features in radar observation data can be identified, thereby improving the quality of the observation data.

[0032] In another exemplary embodiment of this application, in step 101 above, for any S / C / X band dual-polarization weather radar (hereinafter referred to as radar), data with large-area precipitation characteristics are selected from its observation data of more than one year. Specifically, the horizontal structure (PPI) at an elevation angle of 2.4° in the polar coordinate volume scan data at a certain moment is used to calculate its first-layer elevation angle reflectivity factor (Z). H Cases with a concentration greater than 15 dBZ and a correlation coefficient (ρhv) greater than 0.95 are counted as large-area precipitation cases and stored in the historical precipitation dataset of the radar. The specific steps are as follows: Step 201-Step 202.

[0033] Step 201: Obtain observation data for each historical moment within the radar's historical time period.

[0034] Step 202: Select the observation data that meets the precipitation area condition from the observation data of each historical moment within the historical time period to form the radar's historical precipitation dataset; the precipitation area condition is: the observation data with an elevation angle reflectivity factor greater than 15dBZ and a correlation coefficient >0.95 in the first layer is no less than 15% of the total observation data.

[0035] Step 202 is specifically implemented using steps 301-304.

[0036] Step 301: Initialize the value of historical time t to 1.

[0037] Step 302: Determine whether the radar's historical observation data at time t meets the precipitation area condition and obtain the judgment result.

[0038] Step 303: If the judgment result is yes, then the observation data of the radar at the historical time t is added to the historical precipitation dataset; otherwise, the observation data of the radar at the historical time t is not added to the historical precipitation dataset.

[0039] Step 304: Increment the value of t by 1, return to the step of "determine whether the observation data of the radar at the historical time t meets the precipitation area condition and obtain the judgment result", until all historical times within the historical time period have been traversed.

[0040] In another exemplary embodiment, in step 102 above, each data point in the historical precipitation dataset is processed, and the differential propagation phase shift (Φ) is performed. DP Unfold, determine the initial value, and then calculate the standard deviation SD(Φ) of the four points before and after it point by point. DP For S-band weather radar, the observation results are essentially unaffected by attenuation and require no further filtering. However, C-band and X-band weather radars are more susceptible to attenuation, with Φ... DP Using a threshold of <10°, observation data that is basically unaffected by attenuation is selected point by point, specifically including the following steps 401-403.

[0041] Step 401: Obtain the differential propagation phase shift of the observation data at each historical moment in the historical precipitation dataset.

[0042] Step 402: Based on the differential propagation phase shift of the observation data at each historical moment in the historical precipitation dataset, perform radial defolding processing on the observation data at each historical moment in the historical precipitation dataset to obtain the preprocessed historical precipitation dataset.

[0043] Step 403: Select the preprocessed historical precipitation data that meets the condition Φ. DP Observational data with a gradient of <10° were used to form a filtered historical precipitation dataset; among which, Φ DP The differential propagation phase shift of the observed data.

[0044] In another exemplary embodiment, in step 103 above, taking advantage of the minimal spatial and temporal variation of terrain and building echoes, point-to-point accumulation is performed on the historical precipitation dataset after filtering for each weather radar to obtain the reflectivity factor (Z) before radar hardware ground feature suppression. T ), reflectance factor after ground feature suppression (Z) H ), differential reflectivity factor (Z) DR ), correlation coefficient (ρ) hv ), differential propagation phase shift standard deviation (SD(Φ)) DP The evaluation data was used. Two different approaches were employed during the accumulation process, depending on the influencing factors:

[0045] Option A, addressing the beam obstruction phenomenon, uses the minimum measurable radar sensitivity at 100km as Z. H A threshold is set to ensure that the sample is not affected by the sensitivity at different distances.

[0046] Option B, addressing the pollution issue caused by dual polarization, utilizes weak precipitation (Z). H Located between 25 and 35 dBZ) as Z H Thresholds are set to ensure that samples are not affected by hail or non-meteorological echoes.

[0047] For example, the above evaluation data includes first evaluation data and second evaluation data; the above step 103 can be replaced by the following steps 501-504.

[0048] Step 501: Obtain observation data from the filtered historical precipitation dataset whose reflectance factor after ground object suppression is not less than the first reflectance factor threshold, and form the first precipitation dataset to be accumulated; the first reflectance factor threshold is the reflectance factor after ground object suppression corresponding to the minimum measurable sensitivity of radar at 100km.

[0049] Step 502: Obtain observation data from the filtered historical precipitation dataset whose reflectance factor after ground cover suppression is not less than the second reflectance factor threshold, and form the second precipitation dataset to be accumulated; the second reflectance factor threshold is the reflectance factor after ground cover suppression in the range of 25dBZ-35dBZ.

[0050] Step 503: Accumulate the observation data of the same observation point in the first precipitation dataset at different historical times to obtain the first evaluation data of each observation point.

[0051] Step 504: Accumulate the observation data of the same observation point in the second precipitation dataset at different historical times to obtain the second evaluation data of each observation point.

[0052] If the radar historical precipitation dataset contains observation data with multiple scanning modes and different spatiotemporal resolutions, the data with the same scanning mode can be selected and processed separately during accumulation to obtain evaluation data for the same observation point under different scanning modes.

[0053] For example, the above accumulation method can be any data statistics method, such as average calculation, variance calculation, standard deviation calculation, determination of maximum and minimum values, etc. The embodiments of this application preferably use the average calculation method, but other methods can also be selected in some specific implementation processes, which are not limited here.

[0054] In another exemplary embodiment, in step 104 above, by analyzing and identifying the accumulated evaluation data from different observation points point by point, the different types of terrain and building impact areas corresponding to each observation point are determined, as follows:

[0055] A. Areas with complete beam obstruction. In the evaluation data obtained from cumulative scheme A, areas without effective observation data are considered to be caused by terrain or buildings near the radar obstructing beam propagation and are defined as beam obstruction areas.

[0056] B. Partial Beam Obstruction Area. In the evaluation data obtained from cumulative scheme A, if the ZH in a certain radial direction is significantly lower than the average value of the evaluation data, it is considered to be caused by the terrain or buildings near the radar partially obstructing the beam propagation, and is defined as a partial beam obstruction area.

[0057] C. Dual-polarization main lobe contamination region. In the evaluation data obtained from cumulative scheme B, ρ... HV <0.9, SD(Φ DP The double polarization anomaly region > 5° and Z T With Z H The difference (ΔZ) H The superposition of single polarization anomalies exceeding 5dB is considered to be ground objects or buildings appearing near the main lobe of the weather radar, and is defined as a dual polarization main lobe contamination area.

[0058] D. Double polarization sidelobe contamination region. In ρ HV <0.9, SD(Φ DP Within the double polarization anomaly region of >5°, and with Z T With Z H The difference (ΔZ) H If the value is less than 5 dB, it is considered to be caused by electromagnetic waves emitted by the sidelobe of weather radar coming into contact with ground objects or buildings, and is defined as a double polarization sidelobe pollution zone.

[0059] E. No-Impact Zone. All accumulated points in the assessment data that do not fall under the above characteristics are unaffected by terrain and buildings and are defined as no-terrain and no-building-impact zones.

[0060] For example, step 104 above can be replaced by steps 601-605.

[0061] Step 601: Determine whether the first evaluation data of the j-th observation point meets the beam blocking condition. If so, determine that the observation point belongs to the beam blocking area; j = 1, 2, ..., J, where J is the number of observation points within the radar radiation range. The beam blocking condition is: all observation data of the j-th observation point are invalid.

[0062] Step 602: Determine whether the first evaluation data of the j-th observation point meets the beam partial occlusion condition. If so, determine that the j-th observation point belongs to the beam partial occlusion area. The beam partial occlusion condition is: the minimum value of the difference between the reflectivity factor of each radial surface of the observation point after suppressing ground objects and the average value of the reflectivity factor of all radial surfaces of the observation point after suppressing ground objects is less than the deviation threshold.

[0063] Step 603: Determine whether the second observation data of the j-th observation point satisfies the dual-polarization main lobe contamination condition. If so, determine that the j-th observation point belongs to the dual-polarization main lobe contamination region. The dual-polarization main lobe contamination condition is: ρ HV <0.9 and SD(Φ DP )>5° and ΔZ H >5dB, where ρ HV For the correlation coefficient, SD(Φ) DP ) represents the standard deviation of the phase shift in differential propagation, ΔZ H This represents the difference between the reflectance factor before and after ground feature suppression.

[0064] Step 604: Determine whether the second observation data of the j-th observation point satisfies the double polarization sidelobe contamination condition. If so, determine that the j-th observation point belongs to the double polarization sidelobe contamination region; the double polarization sidelobe contamination condition is ρ HV <0.9 and SD(Φ DP )>5° and ΔZ H ≤5dB.

[0065] Step 605: If the first evaluation data of the j-th observation point does not meet the beam partial occlusion condition, and the second evaluation data of the j-th observation point does not meet the dual polarization main lobe contamination condition, and the second evaluation data of the j-th observation point does not meet the dual polarization sidelobe contamination condition, then the j-th observation point is determined to belong to the unaffected area.

[0066] In an exemplary embodiment, step 104 is followed by step 105.

[0067] Step 105: Based on the identification results of the ground feature impact zone at each observation point, perform quality control on the observation data to be processed at each observation point. This observation data can be observation data from observation points in historical or future radar precipitation datasets.

[0068] Step 105 involves adopting different quality control schemes based on the identification results of the evaluation data, taking into account the varying sensitivity and impact of different weather radar observations. The details are as follows:

[0069] A. Area completely blocked by the beam. No observation data is available in this area, and quality control is not required.

[0070] B. Partial beam obstruction area. Observational data in this area is partially obstructed by ground features. H Z DR ρ HV Invalid values ​​are removed from the observations, Φ DP Observations are retained.

[0071] C. Dual polarization main lobe contamination region. This region is close to terrain and buildings, Z H Z DR ρ HV Invalid values ​​are removed from the observations, Φ DP After the observed values ​​are filled with marker values, and the values ​​are removed, K is calculated by interpolation using radial and unaffected area data. DP .

[0072] D. Double polarization sidelobe contamination region; this region is far from terrain and buildings. Z H Observation retention, Z DR ρ HV Invalid values ​​are removed from the observations, Φ DP After the observed values ​​are filled with marker values, and the values ​​are removed, K is calculated by interpolation using radial and unaffected area data. DP .

[0073] For example, step 105 above specifically includes steps 701-703.

[0074] Step 701: Remove the ground object suppression reflectance factor, differential reflectance factor, and correlation coefficient from the observation data of each observation point belonging to the beam partially obscured area;

[0075] Step 702: Remove the ground object suppressed reflectance factor, differential reflectance factor, and correlation coefficient from the unprocessed observation data of each observation point belonging to the dual-polarization main lobe contamination zone. Assign a first differential propagation phase shift filling flag value to the differential propagation phase shift in the unprocessed observation data of each observation point belonging to the dual-polarization main lobe contamination zone, and assign a first differential propagation phase shift rate filling flag value to the differential propagation phase shift rate in the unprocessed observation data of each observation point belonging to the dual-polarization main lobe contamination zone. The first differential propagation phase shift filling flag value is obtained by interpolating the differential propagation phase shift of the observation points in the radially adjacent unaffected areas of the dual-polarization main lobe contamination zone. The first differential propagation phase shift rate filling flag value is calculated based on the first differential propagation phase shift filling flag value.

[0076] Step 703: Remove the differential reflectivity factor and correlation coefficient from the observation data of each observation point belonging to the dual-polarization sidelobe contamination region, and assign a second differential propagation phase shift filling flag value to the differential propagation phase shift of each observation point belonging to the dual-polarization sidelobe contamination region, and assign a second differential propagation phase shift rate filling flag value to the differential propagation phase shift rate of each observation point belonging to the dual-polarization mainlobe contamination region; the second differential propagation phase shift filling flag value is obtained by interpolating the differential propagation phase shift of the radially adjacent unaffected region of the dual-polarization sidelobe contamination region; the second differential propagation phase shift rate filling flag value is calculated based on the second differential propagation phase shift filling flag value.

[0077] Based on the same inventive concept, this application also provides a device for identifying the influence zone of ground features based on historical radar observation data, which is used to implement the above-mentioned method for identifying the influence zone of ground features based on historical radar observation data. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for identifying the influence zone of ground features based on historical radar observation data provided below can be found in the limitations of the method for identifying the influence zone of ground features based on historical radar observation data described above, and will not be repeated here.

[0078] In one exemplary embodiment, a device for identifying ground feature influence zones based on historical radar observation data is provided, comprising the following modules.

[0079] The historical precipitation dataset building module is used to construct historical precipitation datasets detected by radar.

[0080] The preprocessing and filtering module is used to preprocess and filter the historical precipitation dataset to obtain the filtered historical precipitation dataset.

[0081] The accumulation module is used to accumulate the observation data of the same observation point in the filtered historical precipitation dataset at different historical times to obtain the evaluation data of each observation point within the radar radiation range.

[0082] The feature impact zone identification module is used to identify feature impact zones based on the assessment data of each observation point, and obtain the feature impact zone identification results for each observation point.

[0083] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 2 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a method for identifying ground feature influence zones based on historical radar observation data.

[0084] Those skilled in the art will understand that Figure 2 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0085] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0086] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0087] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0088] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for identifying the influence zone of ground features based on historical radar observation data, characterized in that, include: Construct a historical precipitation dataset from radar detection; The historical precipitation dataset is preprocessed and filtered to obtain the filtered historical precipitation dataset. By accumulating the observation data of the same observation point in the selected historical precipitation dataset at different historical times, evaluation data of each observation point within the radar radiation range is obtained. Based on the assessment data from each observation point, the land cover impact zone is identified, and the land cover impact zone identification results for each observation point are obtained. The evaluation data includes first evaluation data and second evaluation data; The process of accumulating observational data from the same observation point at different historical times within the filtered historical precipitation dataset to obtain evaluation data for each observation point within the radar radiation range specifically includes: The first precipitation dataset to be accumulated is composed of the observation data in the filtered historical precipitation dataset whose reflectance factor after ground object suppression is not less than the first reflectance factor threshold; the first reflectance factor threshold is the reflectance factor after ground object suppression corresponding to the minimum measurable sensitivity of the radar at 100km. Observational data whose reflectance factor after land cover suppression is not less than the second reflectance factor threshold are obtained from the filtered historical precipitation dataset to form the second precipitation dataset to be accumulated; the second reflectance factor threshold is the reflectance factor after land cover suppression in the range of 25dBZ-35dBZ. The observation data of the same observation point in the first precipitation dataset to be accumulated are accumulated at different historical times to obtain the first evaluation data of each observation point; The observation data of the same observation point in the second precipitation dataset at different historical times are accumulated to obtain the second evaluation data of each observation point.

2. The method for identifying ground feature influence areas based on historical radar observation data according to claim 1, characterized in that, Constructing a historical precipitation dataset from radar detection, specifically including: Acquire observation data at various historical moments within the radar's historical timeframe; The historical precipitation dataset of the radar is composed of observation data that meet the precipitation area condition from the observation data of each historical moment within the historical time period. The precipitation area condition is that the observation data with an elevation angle reflectivity factor greater than 15 dBZ and a correlation coefficient greater than 0.95 in the first layer is no less than 15% of the total observation data.

3. The method for identifying ground feature influence areas based on historical radar observation data according to claim 2, characterized in that, The historical precipitation dataset for radar is composed of observational data that meets the precipitation area condition from various historical moments within a historical time period. Specifically, it includes: Initialize the value of historical time t to 1; Determine whether the historical observation data of the radar at time t meets the precipitation area condition, and obtain the judgment result; If the judgment result is yes, then the observation data of the radar at the historical time t is added to the historical precipitation dataset; otherwise, the observation data of the radar at the historical time t is not added to the historical precipitation dataset. Increment the value of t by 1, return to the step of "determining whether the observation data of the radar at historical time t meets the precipitation area condition and obtaining the judgment result", until all historical times within the historical time period have been traversed.

4. The method for identifying ground feature influence areas based on historical radar observation data according to claim 1, characterized in that, The historical precipitation dataset is preprocessed and filtered to obtain a filtered historical precipitation dataset, specifically including: Obtain the differential propagation phase shift of observation data at various historical moments in the historical precipitation dataset; Based on the differential propagation phase shift of the observation data at each historical moment in the historical precipitation dataset, radial defolding of the differential propagation phase shift is performed on the observation data at each historical moment in the historical precipitation dataset to obtain the preprocessed historical precipitation dataset. Select the preprocessed historical precipitation data that meets the condition Φ DP Observational data with a gradient of <10° were used to form a filtered historical precipitation dataset; among which, Φ DP The differential propagation phase shift of the observed data.

5. The method for identifying ground feature influence areas based on historical radar observation data according to claim 1, characterized in that, Based on the assessment data from each observation point, the impact zone of ground features is identified, and the identification results of the impact zone of ground features at each observation point are obtained, specifically including: Determine whether the first evaluation data of the j-th observation point meets the beam blocking condition. If so, determine that the observation point belongs to the beam blocking area; j = 1, 2, ..., J, where J is the number of observation points within the radar radiation range. The beam blocking condition is: all observation data of the j-th observation point are invalid. Determine whether the first evaluation data of the j-th observation point meets the beam partial occlusion condition. If so, determine that the j-th observation point belongs to the beam partial occlusion area. The beam partial occlusion condition is: the minimum value of the difference between the reflectivity factor of each radial surface of the observation point after suppressing ground objects and the average value of the reflectivity factor of all radial surfaces of the observation point after suppressing ground objects is less than the deviation threshold. Determine whether the second evaluation data of the j-th observation point satisfies the dual-polarization main lobe contamination condition. If so, determine that the j-th observation point belongs to the dual-polarization main lobe contamination region. The dual-polarization main lobe contamination condition is: ρ HV <0.9 and SD(Φ DP )>5° and ΔZ H >5dB, where ρ HV For the correlation coefficient, SD(Φ) DP ) represents the standard deviation of the phase shift in differential propagation, ΔZ H This represents the difference between the reflectance factor before and after ground feature suppression. Determine whether the second evaluation data of the j-th observation point satisfies the double polarization sidelobe contamination condition. If so, determine that the j-th observation point belongs to the double polarization sidelobe contamination region; the double polarization sidelobe contamination condition is ρ. HV <0.9 and SD(Φ DP )>5° and ΔZ H ≤5dB; If the first evaluation data of the j-th observation point does not meet the beam blocking condition, and the first evaluation data of the j-th observation point does not meet the partial beam blocking condition, and the second evaluation data of the j-th observation point does not meet the dual polarization main lobe contamination condition, and the second evaluation data of the j-th observation point does not meet the dual polarization sidelobe contamination condition, then the j-th observation point is determined to belong to the unaffected area.

6. The method for identifying ground feature influence areas based on historical radar observation data according to claim 1, characterized in that, Based on the assessment data from each observation point, the impact zone of ground features is identified, and the identification results of the impact zone of ground features at each observation point are obtained. This is followed by: Based on the identification results of the ground feature impact areas at each observation point, quality control is performed on the observation data to be processed at each observation point.

7. The method for identifying ground feature influence areas based on historical radar observation data according to claim 6, characterized in that, Based on the identification results of the ground feature impact zones at each observation point, quality control is performed on the unprocessed observation data for each observation point, specifically including: The reflectance factor, differential reflectance factor, and correlation coefficient of the ground object suppression in the observation data of each observation point belonging to the beam partial obstruction area are removed; The reflectance factor, differential reflectance factor, and correlation coefficient after ground object suppression are removed from the unprocessed observation data of each observation point belonging to the dual-polarization main lobe contamination zone. A first differential propagation phase shift filler value is assigned to the differential propagation phase shift in the unprocessed observation data of each observation point belonging to the dual-polarization main lobe contamination zone, and a first differential propagation phase shift rate filler value is assigned to the differential propagation phase shift rate in the unprocessed observation data of each observation point belonging to the dual-polarization main lobe contamination zone. The first differential propagation phase shift filler value is obtained by interpolating the differential propagation phase shift of observation points in the radially adjacent, unaffected areas of the dual-polarization main lobe contamination zone. The first differential propagation phase shift rate filler value is calculated based on the first differential propagation phase shift filler value. The differential reflectivity factor and correlation coefficient are removed from the observation data of each observation point belonging to the dual-polarization sidelobe contamination region. A second differential propagation phase shift filling flag value is assigned to the differential propagation phase shift of the observation data of each observation point belonging to the dual-polarization sidelobe contamination region, and a second differential propagation phase shift rate filling flag value is assigned to the differential propagation phase shift rate of the observation data of each observation point belonging to the dual-polarization sidelobe contamination region. The second differential propagation phase shift filling flag value is obtained by interpolating the differential propagation phase shift of observation points in the radially adjacent, unaffected areas of the dual-polarization sidelobe contamination region. The second differential propagation phase shift rate filling flag value is calculated based on the second differential propagation phase shift filling flag value.

8. A device for identifying the influence zone of ground features based on historical radar observation data, characterized in that, The ground feature impact zone identification device based on radar historical observation data applies the ground feature impact zone identification method based on radar historical observation data according to any one of claims 1-7, and the ground feature impact zone identification device based on radar historical observation data includes: The historical precipitation dataset construction module is used to build historical precipitation datasets detected by radar. The preprocessing and filtering module is used to preprocess and filter the historical precipitation dataset to obtain the filtered historical precipitation dataset. The accumulation module is used to accumulate the observation data of the same observation point in the filtered historical precipitation dataset at different historical times to obtain the evaluation data of each observation point within the radar radiation range. The feature impact zone identification module is used to identify feature impact zones based on the assessment data of each observation point, and obtain the feature impact zone identification results for each observation point.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for identifying ground feature influence zones based on historical radar observation data as described in any one of claims 1-7.

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