A weather radar echo intensity online calibration method

Abstract

The application discloses a weather radar echo intensity online calibration method and relates to the technical field of weather radar detection. A radar collects environmental signals and processes the environmental signals to obtain effective clutter data; a relative calibration baseline is determined according to the effective clutter data and the relative calibration baseline to determine a relative calibration offset of the weather radar; and the weather radar is calibrated based on the relative calibration offset. The application creatively proposes to use ground clutter to online and real-time calibrate the weather radar, reduces the dependence on external equipment, significantly improves the accuracy and timeliness of the radar echo intensity measurement, reduces the maintenance cost and operation difficulty, reduces the calibration inconsistency caused by manual operation errors, significantly reduces the calibration cost of the weather radar, ensures the continuous high-quality output of data, significantly improves the accuracy and reliability of the weather radar data collection, meets the higher demand of modern meteorological observation and prediction, provides strong support for meteorological observation and early warning and prediction, guarantees the long-term consistency of data and has wide adaptability.

Description

Technical Field

[0001] This invention relates to the field of weather radar detection technology, and more specifically, to a method for online calibration of weather radar echo intensity. Background Technology

[0002] Currently, weather radar plays a crucial 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 the echoes scattered by targets. Among these, radar echo intensity (dBZ) is a key parameter for measuring precipitation intensity, and its accuracy directly affects the accuracy of weather forecasts and climate studies. However, radar systems may be affected by various factors during long-term operation, leading to drift in their measurement errors. Common influencing factors include:

[0003] 1) Hardware aging: Over time, the radar's transmitter, receiver, antenna, and other hardware will gradually age, leading to a decline in system performance.

[0004] 2) Environmental changes: Changes in ambient temperature, humidity and atmospheric conditions may also affect radar performance, especially on electronic equipment.

[0005] 3) Insufficient maintenance and calibration frequency: Radar systems are typically deployed in remote areas far from cities, making regular maintenance and calibration difficult. Traditional calibration methods usually rely on periodic manual calibration, which is not only time-consuming and labor-intensive but also limited by environmental conditions and personnel skill levels. Furthermore, calibration methods often employ in-system calibration monitoring, but this cannot cover the entire radar link. Summary of the Invention

[0006] In view of the above problems, this invention proposes an online calibration method for weather radar echo intensity to overcome the shortcomings of the prior art.

[0007] This invention provides an online calibration method for weather radar echo intensity. The online calibration method for weather radar echo intensity includes:

[0008] Collect and process environmental signals to obtain effective clutter data;

[0009] Based on the effective clutter data and the relative calibration baseline, the relative calibration offset of the weather radar is determined;

[0010] The weather radar is calibrated based on the relative calibration offset.

[0011] Optionally, environmental signals are acquired and processed to obtain effective clutter data, including:

[0012] The weather radar is used to collect environmental signals to obtain unfiltered echo intensity data.

[0013] The unfiltered echo intensity data is processed to obtain the data corresponding to all ground clutter signals in the unfiltered echo intensity data;

[0014] Based on the data corresponding to all ground clutter signals, data resampling is performed according to a preset strategy to obtain daily unfiltered ground object echo intensity matching data.

[0015] Based on the daily unfiltered ground object echo intensity matching data, stable ground clutter data is extracted to obtain the effective clutter data.

[0016] Optionally, the weather radar is used to collect environmental signals to obtain unfiltered echo intensity data, including:

[0017] The weather radar is used to perform a three-dimensional observation scan every 6 minutes to obtain the intensity data of unfiltered ground clutter echoes at the lowest elevation angle.

[0018] Optionally, the unfiltered echo intensity data is processed to obtain data corresponding to all ground clutter signals in the unfiltered echo intensity data, including:

[0019] The unfiltered echo intensity data is processed using a ground clutter identification algorithm to identify ground clutter from meteorological echoes, unstable ground clutter, and anomalous propagation echoes, thereby obtaining the data corresponding to all ground clutter signals.

[0020] The ground clutter identification algorithm includes:

[0021] Using the principles of spatial proximity and compactness testing, the unfiltered echo intensity data is filtered to remove the meteorological echoes and the anomalous propagation echoes.

[0022] The spatial proximity refers to the characteristic that the spatial continuity of the meteorological echo is much greater than that of ground clutter.

[0023] The compactness test principle refers to the characteristic that the ground clutter has a different area or perimeter relative to the meteorological echo and the anomalous propagation echo.

[0024] Optionally, based on the data corresponding to all the ground clutter signals, data resampling is performed according to a preset strategy to obtain daily unfiltered ground object echo intensity matching data, including:

[0025] Based on the data corresponding to all the ground clutter signals, the weather radar is used to obtain new ground clutter echo intensity data with 3600 fixed dimensions and azimuths at the lowest elevation angle every 6 minutes with an azimuth interval of 0.1°.

[0026] The data of the echo intensity of the new ground clutter signal generated every 6 minutes are accumulated hourly to obtain 1 hour of unfiltered ground object echo intensity matching data.

[0027] Based on the unfiltered ground object echo intensity matching data for each 6-minute interval, the data is accumulated daily to obtain the daily unfiltered ground object echo intensity matching data.

[0028] Optionally, based on the daily unfiltered ground object echo intensity matching data, stable ground clutter data is extracted to obtain the effective clutter data, including:

[0029] Construct a clutter map mask;

[0030] Using the clutter mask, the unfiltered ground object echo intensity matching data for each time period in the daily unfiltered ground object echo intensity matching data corresponding to the target location is marked. Echo intensity greater than a threshold is marked as 1, otherwise it is marked as 0, thus obtaining the mask matrix corresponding to each time period of the target location. M ( i, j, k ):

[0031]

[0032] In the above formula Z ( i, j, k The echo intensity matrix is ​​generated from the daily unfiltered ground object echo intensity matching data at the target location. i For elevation angle index, j For directional index, k For distance index;

[0033] Using 24-hour intervals, the mask matrix corresponding to each time interval of the target location is calculated. M ( i, j, k The summation and division by the number of scans yields the frequency at which the echo intensity at the target location exceeds the threshold within 24 hours. F ( i， j, k ):

[0034]

[0035] In the above formula, N That is the number of scans. M S ( i, j, k ) No. S The mask matrix corresponding to the next scan;

[0036] If the occurrence frequency F ( i, j, kIf the value of ) is greater than 50%, then the ground clutter corresponding to the target location is marked as stable ground clutter, and the stable ground clutter is extracted as the effective clutter data;

[0037] If the occurrence frequency F ( i, j, k If the value of ) is not greater than 50%, then the ground clutter corresponding to the target location is marked as unstable ground clutter and is not extracted.

[0038] Optionally, constructing the clutter map mask includes:

[0039] Based on all PPI unfiltered echo data at low elevation angles for 24 consecutive hours under no precipitation conditions within the radar's preset range, a fixed polar coordinate grid with a radar radial resolution of 250 m × 0.1° is created, with 0.1° as the azimuth interval, for a total of 3600 azimuths, to construct the clutter mask.

[0040] Optionally, the relative calibration offset of the weather radar is determined based on the effective clutter data and the relative calibration baseline, including:

[0041] The 95th percentile value of the cumulative probability distribution of ground clutter echo intensity is obtained based on the effective clutter data. ,in, , Z C = P r +20 logR +2 aR + C ,in, P r The receiving power of the weather radar, a For unidirectional gas attenuation, R Echo distance, C This is a constant of the weather radar;

[0042] The difference between the 95th percentile value and the relative calibration baseline is used as the relative calibration offset. RAC ,Right now:

[0043] .

[0044] In the above formula, This is the relative calibration baseline.

[0045] Optionally, the 95th percentile value of the cumulative probability distribution of ground clutter echo intensity is obtained based on the effective clutter data, including:

[0046] The effective clutter data is classified according to echo intensity values, the frequency of occurrence of each level within 24 hours is statistically analyzed, and the cumulative frequency is calculated based on the statistical results of the occurrence frequency to form the cumulative probability density curve of ground clutter echo intensity within 24 hours.

[0047] Based on the cumulative probability density curve of the ground clutter echo intensity over 24 hours, the 95th percentile value of the cumulative probability distribution of the ground clutter echo intensity is determined.

[0048] Optionally, the relative calibration baseline is calibrated using a metal sphere to calibrate the weather radar, and the 95th percentile values ​​of the cumulative probability distribution of unfiltered ground clutter echo intensity obtained from continuous scanning are determined, specifically including:

[0049] The radius of the metal sphere is selected based on the radar wavelength, satisfying the conditions for the Mie scattering oscillation region. The calibration calculation formula for the metal sphere is as follows:

[0050]

[0051] In the above two equations, Z For equivalent echo intensity, R Echo distance, For weather radar operating wavelength, r The radius of the metal sphere is... For horizontal beamwidth, Vertical beamwidth The pulse width. q For the complex refractive index term, q 2 =0.93, c The speed of light;

[0052] The ground clutter echo intensity data scanned after radar calibration are classified according to their corresponding echo intensity values. The frequency of occurrence of each level within 24 hours is statistically analyzed, and the cumulative frequency is calculated based on the frequency statistics to form the cumulative probability density curve of the equivalent echo intensity within 24 hours.

[0053] Based on the cumulative probability density curve of the equivalent echo intensity over 24 hours, the 95th percentile value of the cumulative probability distribution of the equivalent echo intensity is determined and used as the relative calibration baseline.

[0054] The online calibration method for weather radar echo intensity proposed in this invention first processes the collected data to obtain effective clutter data; then, based on the effective clutter data and the relative calibration baseline, the relative calibration offset of the weather radar is determined; finally, the weather radar is calibrated based on the relative calibration offset.

[0055] This invention proposes an online calibration method that creatively utilizes ground clutter to calibrate weather radar. By leveraging the stability and universality of ground clutter echoes, a real-time online calibration scheme is proposed, reducing reliance on the knowledge level of technicians and external equipment. By using ground clutter as a stable reference target, minute system performance variations in weather radar can be detected and corrected, significantly improving the accuracy of radar echo intensity measurements. This is crucial for quantitative precipitation measurement and weather forecasting accuracy. It can detect subtle changes in weather radar system performance, which may be caused by hardware aging, environmental changes, etc., and through timely monitoring and calibration, avoids the impact of system performance degradation on data quality.

[0056] By analyzing the cumulative probability density distribution of ground clutter echo intensity data, the system performance of the weather radar is automatically detected and adjusted. This automated calibration process reduces the need for manual operation, lowers maintenance costs and operational complexity, and minimizes calibration inconsistencies caused by human error. Since this method eliminates the need for periodic manual intervention, it significantly reduces the calibration cost of the weather radar. Automated calibration reduces reliance on manual operation, thereby reducing human resource investment and related expenses. Through automated system design and integration, technicians can operate the system without special training, simplifying the operation process and reducing the risk of calibration inaccuracies due to operational errors.

[0057] Meanwhile, weather radar can collect and analyze data daily or even more frequently, enabling near real-time radar calibration. This real-time capability is crucial for responding to sudden weather events and long-term climate monitoring, ensuring a continuous output of high-quality data. By monitoring and adjusting the system performance of weather radar in real time, the accuracy and reliability of the collected data can be significantly improved, meeting the higher demands of modern meteorological observation and forecasting, and providing strong support for meteorological observation and early warning.

[0058] Through long-term monitoring and automatic adjustment, the performance of weather radar systems can be maintained stably, thus ensuring long-term data consistency. This is of great significance for the reliability of short-term weather warnings and forecasts, as well as long-term meteorological observation data. It not only enables system performance calibration but also allows for the early detection of potential performance problems in weather radars through long-term data analysis, providing data support for preventative maintenance. This helps extend the service life of weather radars and avoid sudden failures. Furthermore, it has broad adaptability, applicable to ground-based radar systems of different bands (S / C / X) and different technical systems, and can be extended to other types of radar monitoring networks. Its good scalability and applicability provide flexible solutions for different application scenarios, offering broad application prospects and high practicality. Attached Figure Description

[0059] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0060] Figure 1 This is a flowchart of an online calibration method for weather radar echo intensity according to an embodiment of this application;

[0061] Figure 2 This is a graph showing the 95th percentile numerical offset of the cumulative probability distribution of the weather radar key parameters (after calibration) and ground clutter echo intensity for a location from September 1st to 7th in an embodiment of this application.

[0062] Figure 3 This is a graph showing the 95th percentile offset of the cumulative probability distribution of ground clutter echo intensity and key parameters (after calibration) of a weather radar in another location for a certain month in this application embodiment. Detailed Implementation

[0063] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention, and are only some, not all, embodiments of the present invention, and are not intended to limit the present invention.

[0064] The inventors discovered that radar systems, during long-term operation, may be affected by various factors, potentially causing drift in their measurement errors. Further analysis of common influencing factors and their causes revealed the following major categories:

[0065] 1) Hardware aging: Radar hardware such as transmitters, receivers, and antennas gradually age over time, leading to a decline in system performance. The inventors discovered that this change is likely gradual, and unless caused by a sudden failure, this gradual change is difficult to detect in a timely manner using conventional detection methods.

[0066] 2) Environmental Changes: Changes in ambient temperature, humidity, and atmospheric conditions can also affect radar performance, particularly impacting electronic equipment. The inventors found that the impact of environmental changes on weather radar performance increases the complexity of monitoring and calibration.

[0067] 3) Insufficient maintenance and calibration frequency: Radar systems are typically deployed in remote areas far from cities, making regular maintenance and calibration difficult. Traditional calibration methods usually rely on periodic manual calibration, which is not only time-consuming and labor-intensive but also limited by environmental conditions and personnel skill levels. Furthermore, calibration methods often employ in-system calibration monitoring, but this cannot cover the entire radar link.

[0068] Point 3 above is a critical influencing factor. Since weather radar systems are usually deployed in remote or hard-to-reach areas, regular calibration is difficult and time-consuming. Limited calibration is usually adopted, such as weekly, monthly, or annual maintenance and calibration (most weather radars are maintained and calibrated weekly, monthly, or annually, and further increasing the frequency of maintenance and calibration will bring extremely high costs).

[0069] Furthermore, manual calibration relies on the operator's experience and skill level, which can lead to subjectivity and inconsistency in calibration results. Radar systems typically require monitoring of key radar parameters, such as transmit power, pulse width, and receiver channel gain changes, and automatic correction of echo intensity in response to changes in these parameters. However, traditional in-system calibration only covers some parameters and gain changes in some links of the radar system, lacking full-link calibration methods, and therefore has certain limitations.

[0070] Furthermore, with increasing societal demands, weather forecasting and climate research require high-precision, automated, and real-time data, which places higher demands on radar system calibration, including automation and real-time performance. However, the aforementioned traditional calibration methods struggle to meet these automation and real-time requirements.

[0071] To address the aforementioned problems, the inventors, after extensive research, creatively proposed the online calibration method for weather radar echo intensity according to this invention. The technical solution of this invention will be explained and described in detail below.

[0072] In response to the problems existing in traditional weather radar calibration, the inventors, after extensive research, creatively proposed a method for relative calibration of weather radar systems using ground clutter signals, which is completely different from the current traditional calibration methods.

[0073] Ground clutter refers to the echoes received by radar from ground targets during scanning. These echoes typically include scattered signals from buildings, terrain, and vegetation. Although ground clutter is generally considered interference signals in radar observation, it possesses the following characteristics, making it potentially valuable for monitoring the system performance of radar systems:

[0074] Stability: Ground clutter echoes are relatively stable and do not change significantly with weather conditions. This makes ground clutter a reliable reference target.

[0075] Widespread presence: Ground clutter is prevalent in radar's lowest elevation angle scans, making it easy to acquire large amounts of data.

[0076] High signal-to-noise ratio: Ground clutter usually has a high signal-to-noise ratio, which makes it observable in noisy environments.

[0077] Based on the above characteristics, using ground clutter signals to monitor radar system performance changes becomes a feasible solution. Especially under stable ground clutter signal conditions, the inventors creatively proposed analyzing radar system performance changes through the cumulative distribution function of ground clutter echo intensity, thereby achieving automated calibration. Furthermore, data processing for ground clutter echo intensity can be automated, reducing the need for manual intervention.

[0078] The present invention provides an online calibration method for weather radar echo intensity, referencing... Figure 1 The flowchart shown illustrates the online calibration method for weather radar echo intensity, which includes:

[0079] Step 101: Collect and process environmental signals to obtain effective clutter data.

[0080] The online calibration method for weather radar echo intensity proposed in this invention utilizes ground clutter. Therefore, it is first necessary to collect environmental signals and process them to obtain effective clutter data. Generally, the echo signals received by weather radar include: meteorological echoes, ground clutter, and anomalous propagation echoes. Meteorological echoes are scattered echoes from precipitation, wind speed, clouds, and other meteorological phenomena; ground clutter may include scattered echoes from buildings, terrain, vegetation, and animals; anomalous propagation echoes are usually false echoes. All these echoes are received by the weather radar, and stable ground clutter, i.e., effective ground clutter data, is required for accurate calibration of the weather radar.

[0081] A preferred method for acquiring and processing environmental signals to obtain effective clutter data includes:

[0082] Step S1: Use weather radar to collect environmental signals to obtain unfiltered echo intensity data.

[0083] First, environmental signals are directly collected from the weather radar receiver to obtain unfiltered echo intensity data; that is, all echo signals that can be received by the weather radar are considered as unfiltered echo intensity data. Data from the lowest elevation angle during normal radar scanning is selected, which typically contains a large amount of ground clutter.

[0084] Step S2: Perform calculations on the unfiltered echo intensity data to obtain the data corresponding to all ground clutter signals in the unfiltered echo intensity data.

[0085] After obtaining the unfiltered echo intensity data, ground clutter is needed. Therefore, the unfiltered echo intensity data is processed to filter out the data corresponding to ground clutter signals from all the unfiltered echo intensity data. A preferred filtering method includes: processing the unfiltered echo intensity data using a ground clutter identification algorithm to identify ground clutter from meteorological echoes, unstable ground clutter, and anomalous propagation echoes, thereby obtaining the data corresponding to the ground clutter signals. The ground clutter identification algorithm includes:

[0086] Using the principles of spatial proximity and compactness, the unfiltered echo intensity data is filtered to remove meteorological echoes and the aforementioned anomalous propagation echoes. Spatial proximity refers to the fact that the spatial continuity of meteorological echoes is much greater than that of ground clutter; the compactness principle refers to the fact that ground clutter has a different area or perimeter than meteorological echoes and anomalous propagation echoes. In other words, ground clutter echoes are filtered out by utilizing the different characteristics between them and meteorological and anomalous propagation echoes.

[0087] For example, using a 5x5 sliding window with a distance of 5 dB from the current radar source, trvar (the threshold for the difference between the echoes of the current source and its neighboring sources) is set to 6 dB; np (the total number of neighboring sources) is set to 8, representing an 8-neighborhood (i.e., the source contacts in eight possible directions, including any of the diagonal directions); tr is the ratio of the minimum number of neighboring sources identified as meteorological echo features to np. When the minimum number of neighboring sources for meteorological echo features is 11, tr is set to 1.3 after taking the value. This method can identify ground clutter in radar data.

[0088] Step S3: Based on the data corresponding to the identified ground clutter signal, data resampling is performed according to a preset strategy to obtain daily unfiltered ground object echo intensity matching data.

[0089] Because most weather radars currently use a fixed pulse accumulation number signal processing method to collect and process echo signals, the number of azimuth angles in the initial radar scan is not fixed. The number of azimuth and radial angles is usually more than 360, but may be less than 360, but generally less than 400 (e.g., the number of azimuth and radial angles may be 361, 365, 367, etc.). Therefore, the size of the radar echo intensity matrix (the matrix formed by the echo intensity data) and the azimuth corresponding to the index are not fixed. As a result, it is difficult to obtain stable echo intensity data by directly statistically analyzing the ground clutter signals corresponding to different non-fixed locations obtained from the original echo intensity data. This will affect the statistics of the distribution of ground clutter data.

[0090] To solve this problem, the above data needs to be resampled. A better approach is to resample the data based on a preset strategy. On the basis of the data corresponding to the identified ground clutter signal, the data is resampled every 6 minutes with an azimuth interval of 0.1° using weather radar to obtain new ground clutter echo intensity data with 3600 fixed dimensions and azimuths.

[0091] The newly generated ground clutter echo intensity data from each time period are then accumulated hourly to obtain one hour of unfiltered ground feature echo intensity matching data, where each time period is 6 minutes. That is, for the same location, there are a total of 10 unfiltered ground feature echo intensity matching data points in one hour of unfiltered ground feature echo intensity matching data. Then, based on the unfiltered ground feature echo intensity matching data for the entire day, it is accumulated daily to obtain daily unfiltered ground feature echo intensity matching data. That is, for the same location, there are a total of 24 * 10 = 240 unfiltered ground feature echo intensity matching data points in the daily unfiltered ground feature echo intensity matching data.

[0092] Step S4: Based on the daily unfiltered ground object echo intensity matching data, extract stable ground clutter data to obtain effective clutter data.

[0093] After obtaining daily unfiltered ground object echo intensity matching data, stable ground clutter data is extracted based on this data, thus obtaining effective clutter data. Specifically:

[0094] Construct a clutter mask; the optimal method for constructing this clutter mask is as follows: Select all unfiltered echo data of all PPIs (Plan Position Indicator) at low elevation angles for 24 consecutive hours under no precipitation conditions within a preset radar range (the preset range is selected according to actual needs; the larger the range, the more data is available, and the more accurate the constructed clutter mask). Based on this data, create a fixed polar coordinate grid with a radar radial resolution of 250 meters × 0.1°, with 0.1° as the azimuth interval, for a total of 3600 azimuths, and construct the clutter mask.

[0095] After constructing the clutter mask, this mask is used to mark the unfiltered ground object echo intensity matching data for each time period in the daily unfiltered ground object echo intensity matching data corresponding to the target location. Echo intensity greater than a threshold (e.g., greater than 50 dBZ) is marked as 1, otherwise it is marked as 0. This yields the mask matrix corresponding to each time period (i.e., data collected and processed every 6 minutes) for the target location. M ( i, j, k ):

[0096]

[0097] In the above formula Z ( i, j, k The echo intensity matrix is ​​generated from daily unfiltered ground object echo intensity matching data at the target location. i For elevation angle index, j For directional index, k This is the distance index.

[0098] Obtain the mask matrix M ( i, j, k Then, in 24-hour increments, the mask matrices corresponding to each time interval of the target location are generated. M ( i, j, k Add the results together and divide by the number of scans (scanning once every 6 minutes, so 10 scans per hour, and a total of 240 scans in 24 hours) to get the frequency at which the echo intensity at the target location exceeds the threshold within 24 hours. F ( i, j, k ):

[0099]

[0100] In the above formula, N It refers to the number of scans. M S ( i, j, k ) No. S The mask matrix corresponding to each scan. If the frequency occurs F ( i, j, k If the value of ) is greater than 50%, the ground clutter corresponding to the target location is marked as stable ground clutter, and this stable ground clutter is extracted as effective clutter data. For example, among 240 ground clutter data points at a distance of 250 meters and 0.5 degrees from the weather radar, a total of 130 ground clutter data points have ground clutter echo intensities greater than the threshold. That is, out of 240 scans performed at a distance of 250 meters and 0.5 degrees from the weather radar, 130 scans yielded ground clutter echo intensities greater than the threshold. Therefore, the ground clutter corresponding to this distance of 250 meters and 0.5 degrees from the weather radar is considered stable ground clutter, and this stable ground clutter is extracted as effective clutter data.

[0101] It is understandable that if the frequency of occurrence... F ( i, j, k If the value of ) is not greater than 50%, then the ground clutter corresponding to the target location is marked as unstable ground clutter and is not extracted.

[0102] The effective clutter data obtained by the above labeling can be expressed by the corresponding labeling matrix:

[0103]

[0104] In the above formula C 1 ( i, j, k This is the final effective clutter label matrix.

[0105] Step 102: Determine the relative calibration offset of the weather radar based on the effective clutter data and the relative calibration baseline.

[0106] Once effective clutter data is obtained, 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 involves 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 aR + C Among these, P r This refers to the receiving power of the weather radar, measured in dBm. a For unidirectional gas attenuation, the unit is dB / km, with 0.0055 dB / km for S-band and 0.008 dB / km for C-band; R Echo distance, in km; C This is a constant value for weather radar, measured in dB.

[0107] Through the above Z C From the formula, we can see that since the received power is a known value, or can be considered a fixed value, any change in the received echo intensity of a weather radar with the same received power is related to the echo distance. R unidirectional gas attenuation a It is related to the constants of weather radar. And the echo distance... R For the same echo distance, the impact on the echo intensity of weather radar is the same. Therefore, for weather radars with the same receiving power, any change in the received echo intensity over time must be attributed to the radar constant. C and unidirectional gas attenuation a The changes in radar constants. C The change is caused by the calibration change, therefore, based on this, the distance influence factor can be removed, that is... .

[0108] The 95th percentile value of the cumulative probability distribution of ground clutter echo intensity is determined by classifying effective clutter data according to echo intensity values, statistically analyzing the frequency of occurrence of each level within 24 hours, calculating the cumulative frequency based on the frequency statistics, forming the 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.

[0109] Finally, the difference between the 95th percentile of the cumulative probability distribution of ground clutter echo intensity and the relative calibration baseline is taken as the relative calibration offset (RCA), i.e.:

[0110] .

[0111] In the above formula, This is the relative calibration baseline.

[0112] For relative calibration baselines, a preferred method is to calibrate weather radar using a metal sphere. The metal sphere generates a strong scattered echo to the radar, and since the backscattering cross section of the metal sphere is readily known, it can be used for accurate detection and calibration of radar echo intensity. Because the RCS (radar cross section) oscillates with the ratio of the metal sphere's radius to the radar wavelength, to obtain a stable RCS, the radius of the metal sphere is generally selected based on the radar wavelength, satisfying the conditions for the Mie scattering oscillation region. The formula for calculating the metal sphere calibration is as follows:

[0113]

[0114] In the above two equations, Z Equivalent echo intensity, expressed in millimeters to the power of 6 per cubic meter (mm). 6 / m 3 ); R Echo distance, in meters (m); The wavelength of the weather radar is measured in centimeters (cm). r The radius of the metal sphere is in centimeters (cm). Horizontal beamwidth, in degrees (°); Vertical beamwidth, in degrees (°); The pulse width is expressed in microseconds (μs). q For the complex refractive index term, q 2 =0.93, c The speed of light is typically taken as 2.99792458 × 10⁻⁶. 8 The unit is meters per second (m / s).

[0115] The weather radar is calibrated using a metal sphere. After calibration, the ground clutter echo intensity data collected by the radar are classified according to their corresponding echo intensity values. The frequency of each level occurring within 24 hours is statistically analyzed, and the cumulative frequency is calculated based on the frequency statistics to form an equivalent echo intensity. Z Cumulative probability density curve over 24 hours; ground clutter echo intensity after radar metal ball calibration. Z The equivalent echo intensity is determined by the cumulative probability density curve over 24 hours. Z The 95th percentile value of the cumulative probability distribution is used as the relative calibration baseline.

[0116] Step 103: Calibrate the weather radar based on the relative calibration offset.

[0117] After obtaining the relative calibration offset through the aforementioned two steps, the weather radar can be calibrated based on the obtained relative calibration offset.

[0118] Furthermore, because real-time radar data collected by the weather radar, as well as data from each calibration and adjustment, can be recorded, a long-term testing and monitoring database can be created. This data can be used for subsequent analysis and system optimization. By analyzing long-term data, potential performance problems of the weather radar can be predicted and identified in advance, maintenance and calibration plans can be arranged, and even historical data can be corrected, thereby further optimizing the calibration results of the weather radar.

[0119] To verify the effectiveness of the online calibration method for weather radar echo intensity proposed in this invention, unfiltered echo intensity data collected by weather radars in multiple locations and time periods were used as an example to calibrate the weather radars using the calibration method proposed in this invention. (Refer to...) Figure 2 The figure shows the variation curves of the 95th percentile numerical offset of the cumulative probability distribution of ground clutter echo intensity for key weather radar parameters (after calibration) and local clutter echo intensity from September 1st to 7th. Figure 2 The horizontal axis represents the date ( Figure 2 The left vertical axis represents the 95th percentile numerical offset of the cumulative probability distribution of ground clutter echo intensity, from September 1st to September 7th. Figure 2 The value is represented by RCA Value in the middle, and the right vertical axis represents the real-time key parameters of the weather radar (RCA Value). Figure 2 (Using Syscal in Chinese); Curve 1 shows the variation curves of key parameters within the weather radar, and Curve 2 shows the variation curve of the 95th percentile numerical offset of the cumulative probability distribution of ground clutter echo intensity. Figure 2 The data shows that the values ​​of key parameters of the weather radar changed significantly on September 4. Similarly, the 95th percentile value of the cumulative probability distribution of ground clutter echo intensity on that day decreased significantly.

[0120] On September 5th, technicians conducted a routine maintenance and calibration of the weather radar. Measurements revealed that the low-noise amplifier injection power in the test channel decreased by 0.38 dB. The internal power display showed 665 kW, while the power meter reading was 692 kW, a difference of 0.17 dB. This adjustment resulted in a 0.48 dB change in the key parameters of the weather radar. Furthermore, analysis of the 95th percentile of the cumulative probability distribution of ground clutter echo intensity on September 5th showed a decrease of 0.7 dB compared to September 3rd. Details are shown in the table below.

[0121]

[0122] Therefore, the online calibration method for weather radar echo intensity proposed in this invention successfully utilizes ground clutter to calibrate the weather radar, replacing the manual periodic calibration scheme and reducing reliance on external equipment. By using ground clutter as a stable reference target, minute system performance changes in the weather radar can be detected and corrected, 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, lowers maintenance costs and operational difficulty, and reduces calibration inconsistencies caused by human error.

[0123] Reference Figure 3 The graph shown is a curve illustrating the variation of the 95th percentile numerical offset of the cumulative probability distribution of ground clutter echo intensity for key parameters (after calibration) from a weather radar in another location for a certain month. Figure 3 Meaning of horizontal and vertical axes and Figure 2 The same applies, so I won't elaborate further. Curve 1 shows the variation curves of key parameters within the weather radar, and curve 2 shows the variation curve of the 95th percentile numerical offset of the cumulative probability distribution of ground clutter echo intensity. From... Figure 3 The data shows that on the 7th of that month, the 95th percentile of the cumulative probability distribution of ground clutter echo intensity changed significantly by 2.8 dB. The corresponding key parameters of the weather radar also changed accordingly, almost coinciding with the changes. The parameter changes after the technicians completed testing and parameter adjustments on the 15th of that month are shown in the table below:

[0124]

[0125] As shown in the table above, the radar antenna gain decreased by 1.34 dB, the elevation beamwidth increased by 0.04°, and the key parameters of the weather radar increased by 2.33 dB. Analysis of the ground clutter echo intensity values ​​acquired by the radar after calibration showed that the 95th percentile of the cumulative probability distribution of the ground clutter echo intensity increased by 2.79 dB. Furthermore, the transmit and receive branch losses of the weather radar were measured on the same day, revealing a significant increase in the loss of the TR discharge tube, exceeding the normal value. The tube was replaced, and retesting was performed. The weather radar system parameters were corrected to the measured transmit and receive branch losses. This demonstrates that the online calibration method for weather radar echo intensity proposed in this invention can effectively solve the problem of low echo intensity accuracy caused by component factors.

[0126] The above-mentioned experimental verification demonstrates the effectiveness and practicality of the online calibration method for weather radar echo intensity proposed in this invention.

[0127] In summary, the online calibration method for weather radar echo intensity proposed in this invention first collects and processes environmental signals to obtain effective clutter data; then, based on the effective clutter data and the relative calibration baseline, determines the relative calibration offset of the weather radar; and finally, calibrates the weather radar based on the relative calibration offset.

[0128] The online calibration method proposed in this invention creatively utilizes ground clutter to calibrate weather radar. By leveraging the stability and universality of ground clutter echoes, a near real-time online calibration scheme is proposed, reducing reliance on the knowledge level of technicians and external equipment. By using ground clutter as a stable reference target, minute system performance changes in weather radar can be detected and corrected, significantly improving the accuracy of radar echo intensity measurements. This is crucial for quantitative precipitation measurement and weather forecasting accuracy. It can detect subtle changes in weather radar system performance, which may be caused by hardware aging, environmental changes, etc., and through timely monitoring and calibration, avoids the impact of system performance degradation on data quality.

[0129] By analyzing the cumulative probability density distribution of ground clutter echo intensity data, the system performance of the weather radar is automatically detected and adjusted. This automated calibration process reduces the need for manual operation, lowers maintenance costs and operational complexity, and minimizes calibration inconsistencies caused by human error. Since this method eliminates the need for periodic manual intervention, it significantly reduces the calibration cost of the weather radar. Automated calibration reduces reliance on manual operation, thereby reducing human resource investment and related expenses. Through automated system design and integration, technicians can operate the system without special training, simplifying the operation process and reducing the risk of calibration inaccuracies due to operational errors.

[0130] Meanwhile, weather radar can collect and analyze data daily or even more frequently, enabling near real-time radar calibration. This real-time capability is crucial for responding to sudden weather events and long-term climate monitoring, ensuring a continuous output of high-quality data. By monitoring and adjusting the system performance of weather radar in real time, the accuracy and reliability of the collected data can be significantly improved, meeting the higher demands of modern meteorological observation and forecasting, and providing strong support for meteorological observation and early warning.

[0131] Through long-term monitoring and automatic adjustment, the performance of weather radar systems can be maintained stably, thus ensuring long-term data consistency. This is of great significance for the reliability of short-term weather warnings and forecasts, as well as long-term meteorological observation data. It not only enables system performance calibration but also allows for the early detection of potential performance problems in weather radars through long-term data analysis, providing data support for preventative maintenance. This helps extend the service life of weather radars and avoid sudden failures. Furthermore, it has broad adaptability, applicable to ground-based radar systems of different bands (S / C / X) and different technical systems, and can be extended to other types of radar monitoring networks. Its good scalability and applicability provide flexible solutions for different application scenarios, offering broad application prospects and high practicality.

[0132] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0133] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0134] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for online calibration of weather radar echo intensity, characterized in that, The online calibration method for weather radar echo intensity includes: The weather radar is used to collect environmental signals to obtain unfiltered echo intensity data; the unfiltered echo intensity data is processed to obtain data corresponding to all ground clutter signals in the unfiltered echo intensity data; based on the data corresponding to all ground clutter signals, data resampling is performed according to a preset strategy to obtain daily unfiltered ground object echo intensity matching data; based on the daily unfiltered ground object echo intensity matching data, stable ground clutter data is extracted to obtain effective clutter data. Based on the effective clutter data and the relative calibration baseline, the relative calibration offset of the weather radar is determined; The weather radar is calibrated based on the relative calibration offset; Specifically, based on the daily unfiltered ground object echo intensity matching data, stable ground clutter data is extracted to obtain the effective clutter data, including: Construct a clutter map mask; using the clutter map mask, mark the unfiltered ground object echo intensity matching data for each time period in the daily unfiltered ground object echo intensity matching data corresponding to the target location, marking 1 for echo intensity greater than a threshold and 0 for otherwise, thus obtaining the mask matrix corresponding to each time period of the target location. M ( i, j, k ): In the above formula Z ( i, j, k The echo intensity matrix is ​​generated from the daily unfiltered ground object echo intensity matching data at the target location. i For elevation angle index, j For directional index, k For distance index; Using 24-hour intervals, the mask matrix corresponding to each time interval of the target location is calculated. M ( i, j, k The summation and division by the number of scans yields the frequency at which the echo intensity at the target location exceeds the threshold within 24 hours. F ( i, j, k ): In the above formula, N It refers to the number of scans. M S ( i, j, k ) No. S The mask matrix corresponding to the next scan; If the occurrence frequency F ( i, j, k If the value of ) is greater than 50%, then the ground clutter corresponding to the target location is marked as stable ground clutter, and this stable ground clutter is extracted as the effective clutter data; if the occurrence frequency F ( i, j, k If the value of ) is not greater than 50%, then the ground clutter corresponding to the target location is marked as unstable ground clutter and is not extracted.

2. The online calibration method for weather radar echo intensity according to claim 1, 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 intensity data of unfiltered ground clutter echoes at the lowest elevation angle.

3. The online calibration method for weather radar echo intensity according to claim 1, characterized in that, The unfiltered echo intensity data is processed to obtain data corresponding to all ground clutter signals in the unfiltered echo intensity data, including: The unfiltered echo intensity data is processed using a ground clutter identification algorithm to identify ground clutter from meteorological echoes, unstable ground clutter, and anomalous propagation echoes, thereby obtaining the data corresponding to all ground clutter signals. The ground clutter identification algorithm includes: Using the principles of spatial proximity and compactness testing, the unfiltered echo intensity data is filtered to remove the meteorological echoes and the anomalous propagation echoes. The spatial proximity refers to the characteristic that the spatial continuity of the meteorological echo is much greater than that of ground clutter. The compactness test principle refers to the characteristic that the ground clutter has a different area or perimeter relative to the meteorological echo and the anomalous propagation echo.

4. The online calibration method for weather radar echo intensity according to claim 1, characterized in that, Based on the data corresponding to all the ground clutter signals, data resampling is performed according to 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, the weather radar is used to obtain new ground clutter echo intensity data with 3600 fixed dimensions and azimuths at the lowest elevation angle every 6 minutes with an azimuth interval of 0.1°. The data of the echo intensity of the new ground clutter signal generated every 6 minutes are accumulated hourly to obtain 1 hour of unfiltered ground object echo intensity matching data. Based on the unfiltered ground object echo intensity matching data for each 6-minute interval, the data is accumulated daily to obtain the daily unfiltered ground object echo intensity matching data.

5. The online calibration method for weather radar echo intensity according to claim 1, characterized in that, Constructing a clutter map mask includes: Based on all PPI unfiltered echo data at low elevation angles for 24 consecutive hours under no precipitation conditions within the radar's preset range, a fixed polar coordinate grid with a radar radial resolution of 250 m × 0.1° is created, with 0.1° as the azimuth interval, for a total of 3600 azimuths, to construct the clutter mask.

6. The online calibration method for weather radar echo intensity according to claim 5, characterized in that, Based on the effective clutter data and the relative calibration baseline, the relative calibration offset of the weather radar is determined, including: The 95th percentile value of the cumulative probability distribution of ground clutter echo intensity is obtained based on the effective clutter data. ,in, , Z C = P r +20 logR +2 aR + C ,in, P r The receiving power of the weather radar, a For unidirectional gas attenuation, R Echo distance, C This is a constant of the weather radar; The difference between the 95th percentile value and the relative calibration baseline is used as the relative calibration offset. RAC ,Right now: In the above formula, This is the relative calibration baseline.

7. The online calibration method for weather radar echo intensity according to claim 6, characterized in that, Based on the effective clutter data, the 95th percentile value of the cumulative probability distribution of ground clutter echo intensity is obtained, including: The effective clutter data is classified according to echo intensity values, the frequency of occurrence of each level within 24 hours is statistically analyzed, and the cumulative frequency is calculated based on the statistical results of the occurrence frequency to form the cumulative probability density curve of ground clutter echo intensity within 24 hours. Based on the cumulative probability density curve of the ground clutter echo intensity over 24 hours, the 95th percentile value of the cumulative probability distribution of the ground clutter echo intensity is determined.

8. The online calibration method for weather radar echo intensity according to claim 6, characterized in that, The relative calibration baseline is calibrated using a metal sphere to calibrate the weather radar. The 95th percentile values ​​of the cumulative probability distribution of unfiltered ground clutter echo intensity obtained from continuous scanning are used to determine the baseline. Specifically, this includes: The radius of the metal sphere is selected based on the radar wavelength, satisfying the conditions for the Mie scattering oscillation region. The calibration calculation formula for the metal sphere is as follows: In the above two equations, Z For equivalent echo intensity, R Echo distance, For weather radar operating wavelength, r The radius of the metal sphere is... For horizontal beamwidth, Vertical beamwidth The pulse width. q For the complex refractive index term, q 2 =0.93, c The speed of light; The ground clutter echo intensity data scanned after radar calibration are classified according to their corresponding echo intensity values. The frequency of occurrence of each level within 24 hours is statistically analyzed, and the cumulative frequency is calculated based on the frequency statistics 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 over 24 hours, the 95th percentile value of the cumulative probability distribution of the equivalent echo intensity is determined and used as the relative calibration baseline.

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