Weather radar echo intensity online calibration method

Abstract

The invention discloses a weather radar echo intensity online calibration method, and relates to the technical field of weather radar detection. The radar collects and processes an environment signal to obtain effective clutter data; determining a relative calibration offset of the weather radar according to the effective clutter data and the relative calibration baseline; and calibrating the weather radar based on the relative calibration offset. According to the method, the weather radar is calibrated online in real time by using the ground clutter creatively, and the dependence on external equipment is reduced. And the precision and timeliness of radar echo intensity measurement are obviously improved. The maintenance cost and the operation difficulty are reduced, and the calibration inconsistency caused by manual operation errors is reduced. The calibration cost of the weather radar is remarkably reduced, continuous high-quality output of data is ensured, the accuracy and reliability of data acquired by the weather radar are remarkably improved, higher requirements of modern meteorological observation and forecasting are met, and powerful support is provided for meteorological observation and early warning and forecasting. And the long-term consistency of the data is ensured. And the method has wide adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of weather radar detection, and in particular to an online calibration method for weather radar echo intensity. Background Art

[0002] At present, weather radar plays a vital role in meteorological observation and short-term forecasting. Weather radar measures meteorological parameters such as precipitation intensity, wind speed and cloud height by emitting electromagnetic waves and receiving echoes scattered by targets. Among them, radar echo intensity (dBZ) is a key parameter for measuring precipitation intensity, and its accuracy directly affects the accuracy of weather forecasts and climate research. However, radar systems may be affected by a variety of factors during long-term operation, resulting in drift in their measurement errors. For example, common influencing factors include: 1) Hardware aging: Radar's transmitter, receiver, antenna and other hardware equipment will gradually age over time, resulting in a decline in system performance.

[0003] 2) Environmental changes: Changes in ambient temperature, humidity and atmospheric conditions may also affect the performance of the radar, especially the electronic equipment.

[0004] 3) Insufficient maintenance and calibration frequency: Radar systems are usually deployed in remote areas far from cities, making regular maintenance and calibration difficult. Traditional calibration methods usually rely on regular manual calibration, which is not only time-consuming and labor-intensive, but also limited by environmental conditions and personnel technical level. In addition, the calibration method usually uses in-machine calibration monitoring, but it cannot cover the entire radar link. Summary of the invention

[0005] In view of the above problems, the present invention proposes an online calibration method for weather radar echo intensity to overcome the shortcomings of the prior art.

[0006] An embodiment of the present invention provides a weather radar echo strength online calibration method. The weather radar echo strength online calibration method includes: Collect environmental signals and process them to obtain effective clutter data; Determining a relative calibration offset of the weather radar according to the effective clutter data and the relative calibration baseline; The weather radar is calibrated based on the relative calibration offset.

[0007] Optionally, environmental signals are collected and processed to obtain effective clutter data, including: Using the weather radar to collect environmental signals to obtain unfiltered echo intensity data; Performing operations on the unfiltered echo intensity data to obtain data corresponding to all ground clutter signals in the unfiltered echo intensity data; Based on the data corresponding to all the ground clutter signals, data resampling is performed based on a preset strategy to obtain daily unfiltered ground object echo intensity matching data; Based on the daily unfiltered ground object echo intensity matching data, the stable ground clutter data is extracted to obtain the effective clutter data.

[0008] Optionally, using the weather radar to collect environmental signals to obtain unfiltered echo intensity data includes: The weather radar is used to perform a three-dimensional observation scan every 6 minutes to obtain the lowest elevation angle unfiltered ground clutter echo intensity data.

[0009] Optionally, performing calculations on the unfiltered echo intensity data to obtain data corresponding to all ground clutter signals in the unfiltered echo intensity data includes: The unfiltered echo intensity data is operated by using a ground clutter identification algorithm to identify the ground clutter from the meteorological echo, the unstable ground clutter and the abnormal propagation echo, and obtain the data corresponding to all the ground clutter signals; Wherein, the ground clutter identification algorithm includes: Filtering the unfiltered echo intensity data by using the spatial proximity and compactness inspection principles to filter out the meteorological echo and the abnormal propagation echo; The spatial proximity refers to the property that the spatial continuity of the weather echo is much greater than that of the ground clutter; The compactness test principle refers to the characteristic that the ground clutter has different areas or perimeters relative to the meteorological echo and the abnormal propagation echo.

[0010] Optionally, based on the data corresponding to all the ground clutter signals, data resampling is performed based on a preset strategy to obtain daily unfiltered ground object echo intensity matching data, including: Based on the data corresponding to all the ground clutter signals, new ground clutter echo intensity data with 3600 fixed dimensions and azimuths are obtained at the lowest elevation angle every 6 minutes and at an azimuth interval of 0.1° using the weather radar; The data of the echo intensity of the new ground clutter signal generated every 6 minutes are accumulated hour by hour to obtain the unfiltered ground object echo intensity matching data for 1 hour; The unfiltered ground object echo intensity matching data of each 6-minute time period is accumulated on a daily basis to obtain the unfiltered ground object echo intensity matching data on a daily basis.

[0011] Optionally, based on the daily unfiltered ground object echo intensity matching data, extracting stable ground clutter data to obtain the effective clutter data includes: Constructing a clutter map mask; Using the clutter map mask, the unfiltered ground object echo intensity matching data of each time in the daily unfiltered ground object echo intensity matching data corresponding to the target position is marked, and the echo intensity greater than the threshold is marked as 1, otherwise it is marked as 0, and the mask matrix corresponding to each time of the target position is obtained. M ( i, j, k ):

[0012] In the above formula Z ( i, j, k ) is an echo intensity matrix, which is generated by the daily unfiltered ground object echo intensity matching data at the target location, i is the elevation index, j is the orientation index, k is the distance index; Taking 24 hours as a unit, the mask matrix corresponding to each time of the target position is M ( i, j, k ) and divided by the number of scans to obtain the frequency of occurrence of the echo intensity exceeding the threshold at the target location within 24 hours. F ( i， j, k ):

[0013] In the above formula, N is the number of scans, M S ( i, j, k ) No. S The mask matrix corresponding to the scan; If the frequency of occurrence F ( i, j, k ) is greater than 50%, marking the ground clutter corresponding to the target position as stable ground clutter, and extracting the stable ground clutter as the effective clutter data; If the frequency of occurrence F ( i, j, k ) is not greater than 50%, the ground clutter corresponding to the target position is marked as unstable ground clutter and is not extracted.

[0014] Optionally, constructing a clutter map mask includes: Based on all the PPI unfiltered echo data at low elevation angles for 24 consecutive hours under non-precipitation conditions within the preset distance range of the radar, a fixed polar coordinate grid with a radar radial resolution of 250 meters × 0.1° is created, with 0.1° as the azimuth interval, a total of 3600 azimuths, to construct the clutter map mask.

[0015] Optionally, determining a relative calibration offset of a weather radar according to the effective clutter data and a relative calibration baseline comprises: The 95th percentile value of the cumulative probability distribution of the corresponding ground clutter echo intensity is obtained according to the effective clutter data. ,in, , Z C = P r +20 logR +2 a + C ,in, P r is the received power of the weather radar, a is a one-way gas attenuation, R is the echo distance, C is the constant of the weather radar; The difference between the 95th percentile value and the relative calibration baseline is taken as the relative calibration offset RAC ,Right now: .

[0016] In the above formula, is the relative calibration baseline.

[0017] Optionally, obtaining a 95th percentile value of a cumulative probability distribution of a corresponding ground clutter echo intensity according to the effective clutter data includes: The effective clutter data is classified according to the echo intensity value, the occurrence frequency of each level within 24 hours is counted, and the cumulative frequency is calculated according to the occurrence frequency statistical result to form a cumulative probability density curve of the ground clutter echo intensity within 24 hours; Based on the cumulative probability density curve of the ground clutter echo intensity within 24 hours, a 95th percentile value of the cumulative probability distribution of the ground clutter echo intensity is determined.

[0018] Optionally, the relative calibration baseline is calibrated using a metal ball to calibrate the weather radar, and the 95th percentile value of the cumulative probability distribution of the unfiltered clutter echo intensity obtained by continuous scanning is determined, specifically including: According to the radar wavelength, the radius value that satisfies the Mie scattering oscillation area condition is selected as the radius of the metal ball. The metal ball calibration calculation formula is:

[0019] In the above two formulas, Z is the equivalent echo intensity, R is the echo distance, is the working wavelength of weather radar, ris the radius of the metal sphere, is the horizontal beam width, is the vertical beam width, is the pulse width, q is the complex refractive index term, q 2 =0.93, c is the speed of light; The ground clutter echo intensity data scanned after radar calibration is classified according to the corresponding echo intensity value, the occurrence frequency of each level within 24 hours is counted, and the cumulative frequency is calculated according to the occurrence frequency statistical result to form the cumulative probability density curve of the equivalent echo intensity within 24 hours; Based on the cumulative probability density curve of the equivalent echo intensity within 24 hours, the 95th percentile value of the cumulative probability distribution of the equivalent echo intensity is determined and determined as the relative calibration baseline.

[0020] The weather radar echo intensity online calibration method proposed by the present invention first processes the collected data to obtain effective clutter data; then determines the relative calibration offset of the weather radar according to the effective clutter data and the relative calibration baseline; and finally calibrates the weather radar based on the relative calibration offset.

[0021] The online calibration method proposed in the present invention creatively proposes to use ground clutter to calibrate weather radar. By utilizing the stability and universality of ground clutter echoes, a real-time online calibration scheme is proposed, which reduces the dependence on the knowledge level of technicians and external equipment. By using ground clutter as a stable reference target, it is possible to detect and correct small system performance changes in weather radars, thereby significantly improving the accuracy of radar echo intensity measurements. This is crucial for quantitative precipitation measurement and weather forecast accuracy. It is possible to detect subtle changes in weather radar system performance, which may be caused by hardware aging, environmental changes, etc., and timely monitoring and calibration can avoid the impact of system performance degradation on data quality.

[0022] By analyzing the distribution of the cumulative probability density of ground clutter echo intensity data, the system performance of the weather radar is automatically detected and adjusted. The automated calibration process reduces the need for manual operation, reduces maintenance costs and operational difficulties, and reduces calibration inconsistencies caused by manual operation errors. Since this method does not require regular manual operation, the calibration cost of the weather radar is significantly reduced. Automated calibration reduces dependence on manual operation, thereby reducing human resource investment and related expenses. Through system automation design and integration, technicians can operate the system without special training, which not only simplifies the operation process, but also reduces the risk of inaccurate calibration due to operational errors.

[0023] At the same time, weather radar can collect and analyze data daily or even more frequently, thus achieving near-real-time radar calibration. This real-time performance is crucial for responding to sudden weather events and long-term climate monitoring, ensuring the continuous high-quality output of data. By monitoring the system performance changes of weather radar in real time and making adjustments, the accuracy and reliability of weather radar data can be significantly improved, meeting the higher requirements of modern meteorological observation and forecasting, and providing strong support for meteorological observation and early warning forecasting.

[0024] Through long-term monitoring and automatic adjustment, the performance of the weather radar system can be maintained stable, thus ensuring the long-term consistency of the data. This is of great significance to the reliability of short-term meteorological warning forecasts and long-term meteorological observation data. Not only can the system performance be calibrated, but also potential performance problems of the weather radar can be discovered in advance through long-term data analysis, providing data support for preventive maintenance. This helps to extend the service life of the weather radar and avoid sudden failures. It also has a wide range of adaptability, suitable for ground-based radar systems with different bands and different technical systems such as S / C / X, and can be promoted and applied to other types of radar monitoring networks. It has good scalability and applicability, which provides flexible solutions for different application scenarios, with broad application prospects and high practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 It is a flow chart of a weather radar echo intensity online calibration method according to an embodiment of the present application; Figure 2 It is a curve diagram of the change of the 95th percentile numerical offset of the key parameters of the weather radar (the key parameters after calibration) and the cumulative probability distribution of the ground clutter echo intensity in one location from September 1 to 7 in the embodiment of the present application; Figure 3 It is a curve diagram of the change of the 95th percentile numerical offset of the key parameters of the weather radar (the key parameters after calibration) and the cumulative probability distribution of the ground clutter echo intensity in another place in a certain month in the embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, are only part of the embodiments of the present invention, not all of the embodiments, and are not used to limit the present invention.

[0027] The inventors have found that the current radar system may be affected by a variety of factors during long-term operation, which may cause the drift of its measurement error. The inventors further specifically analyzed the common influencing factors and their causes, and found the following categories: 1) Hardware aging: Radar transmitters, receivers, antennas and other hardware devices will gradually age over time, resulting in a decline in system performance. The inventors found that this change may be gradual, and unless it is caused by a sudden change due to a fault factor, this gradual change is difficult to detect in time through conventional detection methods.

[0028] 2) Environmental changes: Changes in ambient temperature, humidity and atmospheric conditions may also affect the performance of radar, especially electronic equipment. The inventors have found that the impact of environmental changes on weather radar performance increases the complexity of monitoring and calibration.

[0029] 3) Insufficient maintenance and calibration frequency: Radar systems are usually deployed in remote areas far from cities, making regular maintenance and calibration difficult. Traditional calibration methods usually rely on regular manual calibration, which is not only time-consuming and labor-intensive, but also limited by environmental conditions and personnel technical level. In addition, the calibration method usually uses in-machine calibration monitoring, but it cannot cover the entire radar link.

[0030] The above point 3) is a key influencing factor. Since weather radar systems are usually deployed in remote or hard-to-reach areas, regular calibration becomes difficult and time-consuming. Usually, limited calibration is adopted, such as weekly, monthly, and annual maintenance and calibration (the vast majority of weather radars are maintained and calibrated weekly, monthly, or annually. Further increasing the frequency of maintenance and calibration will bring extremely high costs).

[0031] In addition, the manual calibration process depends on the operator's experience and skill level, which may lead to subjectivity and inconsistency in the calibration results. Radar systems usually need to monitor key radar parameters, such as transmit power, pulse width, and receive channel gain changes, and automatically correct echo strength based on changes in key parameters. However, traditional in-machine calibration can only cover some parameters of the radar system and gain changes of some links, and lacks full-link calibration methods, so it has certain limitations.

[0032] In addition, with the increasing social needs, weather forecasting and climate research require high-precision, automated and real-time data, which puts forward higher requirements for radar system calibration, such as automation and real-time performance. However, the above traditional calibration methods are difficult to achieve automation and real-time requirements.

[0033] In view of the above problems, the inventors have creatively proposed the online calibration method of weather radar echo intensity of the present invention after extensive research. The technical solution of the present invention is explained and illustrated in detail below.

[0034] In view of the problems existing in the traditional weather radar calibration, the inventor has creatively proposed a method of using ground clutter signals to perform relative calibration on the weather radar system after extensive research, which is completely different from the current traditional calibration method.

[0035] Ground clutter refers to the echoes from ground targets received by the radar during scanning. These echoes usually include scattered signals generated by buildings, terrain, and vegetation. Although ground clutter is usually regarded as an interference signal in radar observation, it has the following characteristics, making it potentially valuable for application in the system performance monitoring of radar systems: , Stability: Ground clutter echoes are relatively stable and do not change significantly with changing weather conditions. This makes ground clutter a reliable reference target.

[0036] , Widespread existence: Ground clutter is common in the radar’s lowest elevation angle scan, making it easy to obtain a large amount of data.

[0037] , High signal-to-noise ratio: Ground clutter usually has a high signal-to-noise ratio, making it observable in a noisy environment.

[0038] Based on the above characteristics, using ground clutter signals to monitor radar system performance changes has become a feasible solution. Especially under the condition that the ground clutter signal is stable, the inventor creatively proposes to analyze the performance changes of the radar system through the cumulative distribution function of the ground clutter echo intensity, thereby realizing automatic calibration. In addition, the data processing of the ground clutter echo intensity can be automated, thereby reducing the need for manual intervention.

[0039] The present invention provides an online calibration method for weather radar echo intensity, referring to Figure 1 As shown in the flowchart, the weather radar echo intensity online calibration method includes: Step 101: Collect and process environmental signals to obtain effective clutter data.

[0040] The online calibration method of the weather radar echo intensity proposed in the present invention is realized by using ground clutter. Therefore, it is necessary to first collect environmental signals and process them to obtain effective clutter data. In general, the echo signals received by the weather radar include: meteorological echoes, ground clutter and abnormal propagation echoes. Meteorological echoes are scattered echoes for meteorological conditions such as precipitation, wind speed, and clouds; and ground clutter may include scattered echoes generated by buildings, terrain, vegetation, and animals; abnormal propagation echoes are usually false echoes. These echoes will be received by the weather radar, and in order to accurately calibrate the weather radar, stable ground clutter, that is, effective ground clutter data, is required.

[0041] A better method for collecting environmental signals and processing them to obtain effective clutter data includes: Step S1: using weather radar to collect environmental signals to obtain unfiltered echo intensity data.

[0042] First, the unfiltered echo intensity data is obtained by directly using the receiving end of the weather radar to collect environmental signals, that is, all echo signals that can be received by the weather radar are regarded as unfiltered echo intensity data. The data with the lowest elevation angle in the normal scanning of the radar is selected, which usually contains a large amount of ground clutter.

[0043] Step S2: Calculate the unfiltered echo intensity data to obtain data corresponding to all ground clutter signals in the unfiltered echo intensity data.

[0044] After obtaining the unfiltered echo intensity data, since ground clutter is needed, the unfiltered echo intensity data is calculated, and the data corresponding to the ground clutter signal is screened out from all the unfiltered echo intensity data. A better screening method includes: using a ground clutter identification algorithm to calculate the unfiltered echo intensity data, identifying the ground clutter from meteorological echoes, unstable ground clutter and abnormal propagation echoes, and obtaining the data corresponding to the ground clutter signal. Among them, the so-called ground clutter identification algorithm includes: The unfiltered echo intensity data is filtered using the spatial proximity and compactness test principles to filter out the meteorological echo and the abnormal propagation echo. The so-called spatial proximity refers to the characteristic that the spatial continuity of the meteorological echo is much greater than that of the ground clutter; the so-called compactness test principle refers to the characteristic that the ground clutter has a different area or perimeter relative to the meteorological echo and the abnormal propagation echo. That is, the ground clutter echo is screened out by using the different characteristics between the meteorological echo and the abnormal propagation echo.

[0045] For example: using a sliding window with a 5*5 distance bin size, trvar (the threshold of the echo difference between the current bin and the surrounding adjacent bins) is set to 6dB; np (the total number of adjacent bins) is set to 8, indicating an 8-neighborhood (i.e., the bins touch eight possible directions, including any one of the diagonal directions); tr is the ratio of the minimum number of adjacent bins identified as a weather echo feature to np. When the minimum number of adjacent bins for a weather echo feature is 11, tr is set to 1.3 after taking the number. In this way, ground clutter in radar data can be identified.

[0046] Step S3: Based on the data corresponding to the identified ground clutter signal, data resampling is performed based on a preset strategy to obtain daily unfiltered ground object echo intensity matching data.

[0047] At present, most weather radars and their methods of collecting and processing echo signals adopt a signal processing method with a fixed pulse accumulation number, which will result in the radar starting scanning azimuth angle number being not fixed. Usually, the number of azimuth radials exceeds 360, and may be less than 360, but generally less than 400 (for example, the number of azimuth radials may be 361, 365, 367, etc.). Therefore, it will cause the radar echo intensity matrix (the matrix formed by the echo intensity data) to have an unfixed size and an unfixed azimuth corresponding to the index. Therefore, it is difficult to obtain stable echo intensity data by directly statistically analyzing the data corresponding to the ground clutter signals with different non-fixed positions obtained from the original echo intensity data, which will affect the statistics of the ground clutter data distribution.

[0048] In order to solve this problem, it is necessary to resample the above data. The best method is to resample the data based on a preset strategy. On the basis of the data corresponding to the identified ground clutter signal, the weather radar is used to resample the data every 6 minutes and at an azimuth interval of 0.1° to obtain new ground clutter echo intensity data with 3600 fixed dimensions and azimuths.

[0049] After that, the data of the new ground clutter echo intensity generated at each time is accumulated hourly to obtain 1 hour of unfiltered ground object echo intensity matching data, where each time is 6 minutes. That is, for the same location, there are a total of 10 unfiltered ground object echo intensity matching data in the 1-hour unfiltered ground object echo intensity matching data. Then, based on the unfiltered ground object echo intensity matching data of the whole day, daily accumulation is performed to obtain daily unfiltered ground object echo intensity matching data, that is, for the same location, there are a total of 24*10=240 unfiltered ground object echo intensity matching data in the daily unfiltered ground object echo intensity matching data.

[0050] Step S4: Based on the daily unfiltered ground object echo intensity matching data, the stable ground clutter data is extracted to obtain effective clutter data.

[0051] After obtaining the daily unfiltered ground object echo intensity matching data, the stable ground clutter data is extracted based on the daily unfiltered ground object echo intensity matching data, thereby obtaining effective clutter data. Specifically: Construct a clutter map mask; the optimal way to construct the clutter map mask is to select all PPI (Plan Position Indicator) unfiltered echo data at low elevation angles for 24 consecutive hours without precipitation within the preset distance range of the radar (the preset distance is selected according to actual needs. The larger the distance, the more corresponding data, and the more accurate the constructed clutter map mask) and create a fixed polar coordinate grid with a radar radial resolution of 250 meters × 0.1° based on the data, with 0.1° as the azimuth interval, a total of 3600 azimuths, to construct the clutter map mask.

[0052] After the clutter map mask is constructed, the clutter map mask is used to mark the unfiltered ground object echo intensity matching data of each time in the daily unfiltered ground object echo intensity matching data corresponding to the target position. The echo intensity greater than the threshold (for example, the echo intensity is greater than 50dBZ) is marked as 1, otherwise it is marked as 0, and the mask matrix corresponding to each time of the target position (that is, the data collected and processed every 6 minutes) can be obtained. M ( i, j, k ):

[0053] In the above formula Z ( i, j, k ) is the echo intensity matrix, which is generated by the daily unfiltered ground object echo intensity matching data at the target location. i is the elevation index, j is the orientation index, k is the distance index.

[0054] Get the mask matrix M ( i, j, k ) After that, the mask matrix corresponding to each time of the target position is converted into a 24-hour unit. M ( i, j, k ) and divided by the number of scans (scanning once every 6 minutes, 10 scans per hour, and a total of 240 scans in 24 hours), the frequency of the target position echo intensity exceeding the threshold within 24 hours is obtained. F ( i, j, k ):

[0055] In the above formula, N is the number of scans, M S ( i, j, k) No. S The mask matrix corresponding to the scan. F ( i, j, k ) is greater than 50%, the ground clutter corresponding to the target position is marked as stable ground clutter, and the stable ground clutter is extracted as effective clutter data. For example: among the 240 ground clutter data corresponding to the position of 250 meters and 0.5° from the weather radar, there are a total of 130 ground clutter data corresponding to the ground clutter echo intensity greater than the threshold, that is, among the 240 scans at the position of 250 meters and 0.5° from the weather radar, there are 130 scans with the ground clutter echo intensity greater than the threshold, then the ground clutter corresponding to the position of 250 meters and 0.5° from the weather radar is considered to be stable ground clutter, and the stable ground clutter is extracted as effective clutter data.

[0056] It is understandable that if the frequency F ( i, j, k ) is not greater than 50%, the ground clutter corresponding to the target position is marked as unstable ground clutter and will not be extracted.

[0057] The effective clutter data obtained by the above marking can be expressed by the corresponding marking matrix:

[0058] In the above formula C 1( i, j, k ) is the final effective clutter labeling matrix.

[0059] Step 102: Determine the relative calibration offset of the weather radar according to the effective clutter data and the relative calibration baseline.

[0060] After obtaining the effective clutter data, the relative calibration offset of the weather radar can be determined based on the effective clutter data and the relative calibration baseline. A preferred method includes: obtaining the 95th percentile value of the cumulative probability distribution of the corresponding ground clutter echo intensity based on the effective clutter data ,in, ,and Z C = P r +20 logR +2 a + C , among which, P r is the received power of the weather radar, in dBm; a is the one-way gas attenuation, the unit is dB / km, the S-band is 0.0055dB / km, and the C-band is 0.008dB / km; R is the echo distance, in km;C is a constant for weather radar, in dB.

[0061] Through the above Z C From the formula, we can know that: Since the received power is a clearly known value and can be regarded as a fixed value, for weather radars with the same received power, any change in the received echo intensity is related to the echo distance R , One-way gas attenuation a , weather radar constants. R , for the same echo distance, it has the same effect on the echo intensity of the weather radar, so for the same received power weather radar, any change in the received echo intensity over time must be attributed to the radar constant C and one-way gas attenuation a The radar constant C The change in is caused by the change in calibration, so based on this, the distance effect factor can be removed, that is, .

[0062] The 95th percentile value of the cumulative probability distribution of ground clutter echo intensity is obtained by classifying the effective clutter data according to the echo intensity value, counting the occurrence frequency of each level within 24 hours, and calculating the cumulative frequency based on the occurrence frequency statistics to form a cumulative probability density curve of ground clutter echo intensity within 24 hours; and then determining the 95th percentile value of the cumulative probability distribution of ground clutter echo intensity based on the cumulative probability density curve of ground clutter echo intensity within 24 hours.

[0063] Finally, the difference between the 95th percentile value of the cumulative probability distribution of the ground clutter echo intensity and the relative calibration baseline is taken as the relative calibration offset RCA, that is: .

[0064] In the above formula, This is the relative calibration baseline.

[0065] For the relative calibration baseline, the best one can be obtained by calibrating the weather radar with a metal ball. The metal ball can produce a strong scattered echo to the radar. Since the backscattering cross section of the metal ball is easy to know, it can be used to accurately detect and calibrate the radar echo intensity. Since the RCS (radar cross-sectional area) shows an oscillation trend with the ratio of the metal ball radius to the radar wavelength, in order to obtain a stable RCS, the radius value that meets the Mie scattering oscillation area conditions is generally selected as the metal ball radius according to the radar wavelength. The metal ball calibration calculation formula is:

[0066] In the above two formulas, Zis the equivalent echo intensity, in millimeters to the sixth power per cubic meter (mm 6 / m 3 ); R is the echo distance in meters (m); is the working wavelength of weather radar, in centimeters (cm); r is the radius of the metal ball, in centimeters (cm); is the horizontal beam width, in degrees (°); is the vertical beam width in degrees (°); is the pulse width in microseconds (μs); q is the complex refractive index term, q 2 =0.93, c is the speed of light, generally taken as 2.99792458×10 8 , the unit is meters per second (m / s).

[0067] The weather radar is calibrated by a metal ball. After calibration, the ground clutter echo intensity data collected by the radar is classified according to its corresponding echo intensity value. The frequency of occurrence of each level within 24 hours is counted, and the cumulative frequency is calculated based on the occurrence frequency statistics to form an equivalent echo intensity. Z Cumulative probability density curve within 24 hours; based on the clutter echo intensity after radar metal ball calibration Z Cumulative probability density curve over 24 hours to determine equivalent echo intensity Z The 95th percentile value of the cumulative probability distribution is determined as the relative calibration baseline.

[0068] Step 103: Calibrate the weather radar based on the relative calibration offset.

[0069] After the relative calibration offset is obtained through the above two steps, the weather radar can be calibrated based on the obtained relative calibration offset.

[0070] In addition, since the radar data collected by the weather radar in real time, as well as the data of each calibration and adjustment, can be recorded, a long-term inspection and monitoring database can be formed. These data can be used for subsequent analysis and system optimization. By analyzing long-term data, it is possible to predict and identify performance problems that may occur in the weather radar in advance, arrange maintenance and calibration plans, and even revise historical data, thereby further optimizing the calibration results of the weather radar.

[0071] In order to verify the effectiveness of the online calibration method for weather radar echo intensity proposed in the present invention, the unfiltered echo intensity data collected by weather radars in multiple locations and time periods are taken as an example, and the calibration method proposed in the present invention is used to calibrate the weather radar. Figure 2The figure shows the change curve of the 95th percentile value offset of the weather radar key parameters (calibrated key parameters) and the cumulative probability distribution of the ground clutter echo intensity from September 1 to 7 at a certain location. Figure 2 The horizontal axis is the date ( Figure 2 Day of Month), from September 1 to September 7, the left ordinate is the 95th percentile value offset of the cumulative probability distribution of ground clutter echo intensity ( Figure 2 The vertical axis on the right is the real-time key parameters in the weather radar ( Figure 2 Curve 1 is the variation curve of the key parameters in the weather radar, and Curve 2 is the variation curve of the 95th percentile value offset of the cumulative probability distribution of the ground clutter echo intensity. Figure 2 It shows that the values ​​of key parameters of the weather radar changed significantly on September 4. It is also seen that the 95th percentile value of the cumulative probability distribution of the solar-ground clutter echo intensity has decreased significantly.

[0072] On September 5, technicians conducted a routine maintenance and calibration of the weather radar. The measurement results showed that the low noise amplifier injection power of the weather radar test channel decreased by 0.38dB. The internal power display was 665kW, and the power meter measured the power as 692kW, with a difference of 0.17dB. The adjustment caused the value of the key parameter of the weather radar to change by 0.48dB. The 95th percentile value of the cumulative probability distribution of the ground clutter echo intensity on September 5 was 0.7dB smaller than that on September 3. The results are shown in the following table:

[0073] It can be seen that the online calibration method of weather radar echo intensity proposed by the present invention successfully uses ground clutter to calibrate weather radar, replacing the manual regular calibration scheme and reducing the dependence on external equipment. By using ground clutter as a stable reference target, it is possible to detect and correct small system performance changes in the weather radar, thereby significantly improving the accuracy of radar echo intensity measurement. By analyzing the distribution of the cumulative probability density of ground clutter echo intensity, the system performance of the weather radar is automatically detected and adjusted. The automated calibration process reduces the need for manual operation, reduces maintenance costs and operating difficulties, and reduces calibration inconsistencies caused by manual operation errors.

[0074] Reference Figure 3 The following is a graph showing the variation of the 95th percentile value offset of the weather radar key parameters (key parameters after calibration) and the cumulative probability distribution of the ground clutter echo intensity in another place in a certain month. Figure 3 The meaning of the horizontal and vertical coordinates Figure 2Curve 1 is the variation curve of the key parameters in the weather radar, and Curve 2 is the variation curve of the 95th percentile value offset of the cumulative probability distribution of the ground clutter echo intensity. Figure 3 It can be seen from the display that on the 7th of the month, the 95th percentile value of the cumulative probability distribution of the ground clutter echo intensity changed significantly by 2.8dB. The corresponding key parameters of the weather radar also changed accordingly, and the two almost overlapped. The parameter changes after the technicians completed the test and parameter adjustment on the 15th of the month are shown in the following table:

[0075] From the above table, we can see that the radar antenna gain decreased by 1.34dB, the elevation beam width increased by 0.04°, and the key parameter value in the weather radar increased by 2.33dB. After analyzing the ground clutter echo intensity value collected by the radar after calibration, the 95th percentile value of the cumulative probability distribution of the ground clutter echo intensity increased by 2.79dB. In addition, the transmitting branch loss and receiving branch loss of the weather radar were measured on that day, and it was found that the TR discharge tube loss increased significantly and exceeded the normal value. It was replaced during this process and tested again. The weather radar system parameters were corrected to the measured transmitting and receiving branch loss. It is proved that the online calibration method of weather radar echo intensity proposed by the invention can effectively solve the problem of low echo intensity accuracy caused by device factors.

[0076] The above-mentioned actual measurement verification demonstrates the effectiveness and practicability of the online calibration method for weather radar echo intensity proposed in the present invention.

[0077] In summary, the online calibration method for weather radar echo intensity proposed in the present invention first collects and processes environmental signals to obtain effective clutter data; then determines the relative calibration offset of the weather radar based on the effective clutter data and the relative calibration baseline; finally, calibrates the weather radar based on the relative calibration offset.

[0078] The online calibration method proposed in the present invention creatively proposes to use ground clutter to calibrate weather radar. By utilizing the stability and universality of ground clutter echoes, a near real-time online calibration scheme is proposed, which reduces the dependence on the knowledge level of technicians and external equipment. By using ground clutter as a stable reference target, it is possible to detect and correct small system performance changes in weather radars, thereby significantly improving the accuracy of radar echo intensity measurements. This is crucial for quantitative precipitation measurement and weather forecast accuracy. It is possible to detect subtle changes in weather radar system performance, which may be caused by hardware aging, environmental changes, etc., and timely monitoring and calibration can avoid the impact of system performance degradation on data quality.

[0079] By analyzing the distribution of the cumulative probability density of ground clutter echo intensity data, the system performance of the weather radar is automatically detected and adjusted. The automated calibration process reduces the need for manual operation, reduces maintenance costs and operational difficulties, and reduces calibration inconsistencies caused by manual operation errors. Since this method does not require regular manual operation, the calibration cost of the weather radar is significantly reduced. Automated calibration reduces dependence on manual operation, thereby reducing human resource investment and related expenses. Through system automation design and integration, technicians can operate the system without special training, which not only simplifies the operation process, but also reduces the risk of inaccurate calibration due to operational errors.

[0080] At the same time, weather radar can collect and analyze data daily or even more frequently, thus achieving near-real-time radar calibration. This real-time performance is crucial for responding to sudden weather events and long-term climate monitoring, ensuring the continuous high-quality output of data. By monitoring the system performance changes of weather radar in real time and making adjustments, the accuracy and reliability of weather radar data can be significantly improved, meeting the higher requirements of modern meteorological observation and forecasting, and providing strong support for meteorological observation and early warning forecasting.

[0081] Through long-term monitoring and automatic adjustment, the performance of the weather radar system can be maintained stable, thus ensuring the long-term consistency of the data. This is of great significance to the reliability of short-term meteorological warning forecasts and long-term meteorological observation data. Not only can the system performance be calibrated, but also potential performance problems of the weather radar can be discovered in advance through long-term data analysis, providing data support for preventive maintenance. This helps to extend the service life of the weather radar and avoid sudden failures. It also has a wide range of adaptability, suitable for ground-based radar systems with different bands and different technical systems such as S / C / X, and can be promoted and applied to other types of radar monitoring networks. It has good scalability and applicability, which provides flexible solutions for different application scenarios, with broad application prospects and high practicality.

[0082] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0083] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0084] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for online calibration of weather radar echo intensity, characterized in that: The weather radar echo intensity online calibration method comprises: Collect environmental signals and process them to obtain effective clutter data; Determining a relative calibration offset of the weather radar according to the effective clutter data and the relative calibration baseline; The weather radar is calibrated based on the relative calibration offset.

2. The weather radar echo intensity online calibration method according to claim 1, characterized in that: Collect environmental signals and process them to obtain effective clutter data, including: Using the weather radar to collect environmental signals to obtain unfiltered echo intensity data; Performing operations on the unfiltered echo intensity data to obtain data corresponding to all ground clutter signals in the unfiltered echo intensity data; Based on the data corresponding to all the ground clutter signals, data resampling is performed based on a preset strategy to obtain daily unfiltered ground object echo intensity matching data; Based on the daily unfiltered ground object echo intensity matching data, the stable ground clutter data is extracted to obtain the effective clutter data.

3. The weather radar echo intensity online calibration method according to claim 2 is characterized in that: The weather radar is used to collect environmental signals to obtain unfiltered echo intensity data, including: The weather radar is used to perform a three-dimensional observation scan every 6 minutes to obtain the lowest elevation angle unfiltered ground clutter echo intensity data.

4. The weather radar echo intensity online calibration method according to claim 2 is characterized in that: The unfiltered echo intensity data is operated to obtain data corresponding to all ground clutter signals in the unfiltered echo intensity data, including: The unfiltered echo intensity data is operated by using a ground clutter identification algorithm to identify the ground clutter from the meteorological echo, the unstable ground clutter and the abnormal propagation echo, and obtain the data corresponding to all the ground clutter signals; Wherein, the ground clutter identification algorithm includes: Filtering the unfiltered echo intensity data by using the spatial proximity and compactness inspection principles to filter out the meteorological echo and the abnormal propagation echo; The spatial proximity refers to the property that the spatial continuity of the weather echo is much greater than that of the ground clutter; The compactness test principle refers to the characteristic that the ground clutter has different areas or perimeters relative to the meteorological echo and the abnormal propagation echo.

5. The weather radar echo intensity online calibration method according to claim 2 is characterized in that: Based on the data corresponding to all the ground clutter signals, data resampling is performed based on a preset strategy to obtain daily unfiltered ground object echo intensity matching data, including: Based on the data corresponding to all the ground clutter signals, new ground clutter echo intensity data with 3600 fixed dimensions and azimuths are obtained at the lowest elevation angle every 6 minutes and at an azimuth interval of 0.1° using the weather radar; The data of the echo intensity of the new ground clutter signal generated every 6 minutes are accumulated hour by hour to obtain the unfiltered ground object echo intensity matching data for 1 hour; The unfiltered ground object echo intensity matching data of each 6-minute time period is accumulated on a daily basis to obtain the unfiltered ground object echo intensity matching data on a daily basis.

6. The weather radar echo intensity online calibration method according to claim 2, characterized in that: Based on the daily unfiltered ground object echo intensity matching data, the stable ground clutter data is extracted to obtain the effective clutter data, including: Constructing a clutter map mask; Using the clutter map mask, the unfiltered ground object echo intensity matching data of each time in the daily unfiltered ground object echo intensity matching data corresponding to the target position is marked, and the echo intensity greater than the threshold is marked as 1, otherwise it is marked as 0, and the mask matrix corresponding to each time of the target position is obtained. M ( i, j, k ): In the above formula Z ( i, j, k ) is an echo intensity matrix, which is generated by the daily unfiltered ground object echo intensity matching data at the target location, i is the elevation index, j is the orientation index, k is the distance index; Taking 24 hours as a unit, the mask matrix corresponding to each time of the target position is M ( i, j, k ) and divided by the number of scans to obtain the frequency of occurrence of the echo intensity exceeding the threshold at the target location within 24 hours. F ( i, j, k ): In the above formula, N is the number of scans, M S ( i, j, k ) No. S The mask matrix corresponding to the scan; If the frequency of occurrence F ( i, j, k ) is greater than 50%, marking the ground clutter corresponding to the target position as stable ground clutter, and extracting the stable ground clutter as the effective clutter data; If the frequency of occurrence F ( i, j, k ) is not greater than 50%, the ground clutter corresponding to the target position is marked as unstable ground clutter and is not extracted.

7. The on-line calibration method for weather radar echo intensity according to claim 6, characterized in that: Constructing a clutter map mask includes: Based on all the PPI unfiltered echo data at low elevation angles for 24 consecutive hours under non-precipitation conditions within the preset distance range of the radar, a fixed polar coordinate grid with a radar radial resolution of 250 meters × 0.1° is created, with 0.1° as the azimuth interval, a total of 3600 azimuths, to construct the clutter map mask.

8. The on-line calibration method for weather radar echo intensity according to claim 7, characterized in that: Determining a relative calibration offset of the weather radar according to the effective clutter data and the relative calibration baseline includes: The 95th percentile value of the cumulative probability distribution of the corresponding ground clutter echo intensity is obtained according to the effective clutter data. ,in, , Z C = P r +20 logR +2 a + C ,in, P r is the received power of the weather radar, a is a one-way gas attenuation, R is the echo distance, C is the constant of the weather radar; The difference between the 95th percentile value and the relative calibration baseline is taken as the relative calibration offset RAC ,Right now: 。 In the above formula, is the relative calibration baseline.

9. The on-line calibration method for weather radar echo intensity according to claim 8, characterized in that: Obtaining the 95th percentile value of the cumulative probability distribution of the corresponding ground clutter echo intensity according to the effective clutter data, including: The effective clutter data is classified according to the echo intensity value, the occurrence frequency of each level within 24 hours is counted, and the cumulative frequency is calculated according to the occurrence frequency statistical result to form a cumulative probability density curve of the ground clutter echo intensity within 24 hours; Based on the cumulative probability density curve of the ground clutter echo intensity within 24 hours, a 95th percentile value of the cumulative probability distribution of the ground clutter echo intensity is determined.

10. The on-line calibration method for weather radar echo intensity according to claim 8, characterized in that: The relative calibration baseline is to calibrate the weather radar using a metal ball, and the 95th percentile value of the cumulative probability distribution of the unfiltered ground clutter echo intensity obtained by continuous scanning is determined, specifically including: According to the radar wavelength, the radius value that satisfies the Mie scattering oscillation area condition is selected as the radius of the metal ball. The metal ball calibration calculation formula is: In the above two formulas, Z is the equivalent echo intensity, R is the echo distance, is the working wavelength of weather radar, r is the radius of the metal sphere, is the horizontal beam width, is the vertical beam width, is the pulse width, q is the complex refractive index term, q 2 =0.93, c is the speed of light; The ground clutter echo intensity data scanned after radar calibration is classified according to the corresponding echo intensity value, the occurrence frequency of each level within 24 hours is counted, and the cumulative frequency is calculated according to the occurrence frequency statistical result to form the cumulative probability density curve of the equivalent echo intensity within 24 hours; Based on the cumulative probability density curve of the equivalent echo intensity within 24 hours, the 95th percentile value of the cumulative probability distribution of the equivalent echo intensity is determined and determined as the relative calibration baseline.

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