Ground-based radar calibration deviation estimation method based on satellite-ground precipitation radar verification

Abstract

The invention discloses a ground-based radar calibration deviation estimation method based on satellite-ground precipitation radar verification, and the method mainly comprises the steps: carrying out the frequency correction of a satellite-borne radar SR reflectivity factor from a Ku wave band to an S wave band, and carrying out the projection conversion of a polar coordinate system with a ground-based radar GR as a center after the correction; the converted SR and GR reflectivity factors are subjected to overlapping matching in time and space which are accurate to seconds; the mean value precision is improved through inverse distance weight, and the precipitation space heterogeneity of SR and GR in the matching space is reduced through the in-pixel non-uniform beam filling quality control technology; a satellite-ground precipitation radar quality control factor threshold standard system is established, whole-process quality control is carried out in the calibration deviation estimation process, and finally ground-based radar GR calibration deviation estimation is realized through comparison verification of spaceborne radar SR and ground-based radar GR reflectivity factors after quality control processing. According to the invention, the error of satellite-ground radar inspection can be effectively reduced, and the precision of rainfall estimation is improved.

Description

Technical Field

[0001] The present invention belongs to the field of satellite ground equipment calibration systems, and particularly relates to a method for estimating the calibration deviation of a ground-based radar based on satellite-ground precipitation radar calibration. Background Art

[0002] With the continuous development of meteorological observation technology, ground-based radars play an increasingly important role in precipitation monitoring. Ground-based radars achieve quantitative estimation of precipitation by detecting the scattering and attenuation of electromagnetic waves by precipitation particles. However, in the actual observation process of ground-based radars, due to the influence of various factors, such as the atmospheric environment and equipment performance, there are certain deviations in their calibration accuracy, which directly affects the accuracy of precipitation estimation. Summary of the Invention

[0003] In order to solve the deficiencies of the prior art, the present invention provides a method for estimating the calibration deviation of a ground-based radar based on satellite-ground precipitation radar calibration. By adopting a new method for frequency correction of all-phase states and all precipitation types, a pixel time difference matching method accurate to the second, and through a variety of factor sensitivity experiments, the present invention details the influence of factors such as precipitation type, bright band, and insufficient attenuation correction on the inspection accuracy, designs the threshold standard of the quality control factor system, reduces the errors caused by different frequencies and relative movements of the instrument, effectively improves the matching accuracy of satellite-ground radars and the product inspection accuracy, realizes the accurate estimation of the calibration deviation of the ground-based radar, and provides strong support for meteorological observation and weather forecasting.

[0004] A method for estimating the calibration deviation of a ground-based radar based on satellite-ground precipitation radar calibration provided by the present invention includes:

[0005] S1. Obtain the original reflectivity factor data of the spaceborne radar SR and the ground-based radar GR, and perform quality control on the original data;

[0006] S2. Perform frequency correction on the quality-controlled SR reflectivity factor from the Ku band to the S band;

[0007] S3. Perform polar coordinate system projection conversion centered on GR on the corrected SR reflectivity factor;

[0008] S4. Correct the horizontal longitude and latitude information of each height layer of SR based on the satellite zenith angle;

[0009] S5. Adopt an improved radar reflectivity factor averaging method to convert the reflectivity factors of SR and GR from logarithmic scale to linear scale respectively;

[0010] S6. Perform precise second-level time and space overlap matching on the converted SR and GR reflectivity factors;

[0011] S7. For the SR and GR reflectivity factors within each matched overlapping spatial range, improve the mean accuracy respectively through inverse distance weighting;

[0012] S8. Reduce the precipitation spatial heterogeneity of SR and GR within the matching space through the in-pixel non-uniform beam filling quality control technique;

[0013] S9. Conduct quality control on the precipitation type, precipitation phase state, and bright band factors of SR within the matching space respectively to reduce the impact on the evaluation accuracy;

[0014] S10. Adopt an improved radar reflectivity factor averaging method to convert the reflectivity factors of SR and GR from linear scale to logarithmic scale respectively;

[0015] S11. Based on the comparison and verification of the converted SR reflectivity factor and GR reflectivity factor, estimate the calibration deviation of the ground-based radar GR.

[0016] Furthermore, for S1, respectively obtain the original data of the reflectivity factors of the spaceborne radar SR and the ground-based radar GR, and conduct quality control on the original data, including:

[0017] S11. Obtain the original data of the radar reflectivity factor of the spaceborne radar SR;

[0018] S12. Conduct quality control on the obtained original data of the radar reflectivity factor of the spaceborne radar SR through dual-frequency observation quality, precipitation classification quality, and dual-frequency precipitation identification parameters;

[0019] S13. Obtain the original data of the radar reflectivity factor of the ground-based radar GR;

[0020] S14. Conduct quality control on the GR reflectivity factor by removing the calibration distance data.

[0021] Furthermore, for S2, conduct frequency correction on the quality-controlled SR reflectivity factor from Ku band to S band, including:

[0022] S21. Obtain the data including the raindrop number concentration Nw, precipitation particle phase state phase, and temperature T from the SR product data; S22. Integrate and calculate the backscattering cross-section σ of the raindrop particles according to the obtained data values b and the corresponding scattering function fz(Dm);

[0023] The scattering function fz(Dm) is:

[0024]

[0025] Wherein, λ is the wavelength of the S-band radar, Kw is the complex refractive index of water, T is the temperature, σb is the backscattering cross-section of the rainfall particles, D is the diameter of the precipitation particles, and Dm is the median diameter of the precipitation particles;

[0026] S23. Combine the raindrop number concentration Nw provided in the product and fz(Dm) calculated in S22 to calculate the radar reflectivity factor Zs of the S-band, realizing frequency correction.

[0027] The corrected radar reflectivity factor Zs of the S-band is:

[0028] Zs = Nwfz(Dm);

[0029] Wherein, Nw is the raindrop number concentration, Dm is the median diameter of the precipitation particles, and fz(Dm) is the backscattering function of the S-band.

[0030] Further, in S4, correct the horizontal longitude and latitude information of each height layer of SR based on the satellite zenith angle, including:

[0031] S41. Calculate the satellite observation field-of-view deviation distance using the satellite zenith angle;

[0032] S42. Calculate the field-of-view distance deviation of the horizontal longitude and latitude of each height layer respectively based on the satellite scan angle and the observation field-of-view deviation distance;

[0033] S43. Subtract this deviation amount from the SR ground horizontal longitude and latitude information, and obtain the horizontal longitude and latitude information of each height layer through correction.

[0034] Further, in S5, adopt an improved radar reflectivity factor averaging method to convert the reflectivity factors of SR and GR from logarithmic scale to linear scale respectively, including:

[0035] Through the expression dBZ = 10logdB, convert the reflectivity factors of SR and GR from the logarithmic scale dBZ to the linear scale dB respectively.

[0036] Further, in S6, perform time and space overlap matching with an accuracy of seconds for the converted SR and GR reflectivity factors, including:

[0037] S61. Screen the SR transit GR matching;

[0038] S62. Extract the SR row-by-row observation time after screening, assign and expand it to the three-dimensional observation distance library time, and record it as TSR;

[0039] S63. Extract the time of each elevation angle and each beam of GR, assign and expand it to the three-dimensional observation distance library time, and record it as TGR;

[0040] S64. Calculate the distance library with the closest distance from the SR to the center of the GR site, and record the SR beam time at this distance as the SR transit GR time TSR0;

[0041] S65. Calculate the average value of the first three elevation scan times of the SR, and mark it as TGR0;

[0042] S66. Calculate the time difference between the GR and the SR Filter from the GR base data The smallest GR base data and perform screening;

[0043] S67. Screen out each overlapping spatial range that matches, calculate the average time of the GR and the SR within the overlapping space respectively, and calculate the time difference between the two The time accuracy is accurate to seconds, and the selected spatial overlapping range is determined.

[0044] Furthermore, in step S7, for the SR and GR reflectivity factors within each matched overlapping spatial range, the mean accuracy is improved by inverse distance weighting respectively, including:

[0045] S71. According to the unified polar coordinate projection of the GR and the SR, first loop through the SR beam distance library, and then loop through the GR elevation angle, and traverse the corresponding fixed GR elevation angle relationship for each distance library;

[0046] S72. Obtain the space where the spaceborne and ground radars' beams cross through looped spatio-temporal matching. Use inverse distance weighted averaging for the matched GR distance library within a 5-kilometer range of the SR footprint area, and use inverse distance weighted averaging for the matched SR distance library within the GR elevation angle beam broadening range.

[0047] Furthermore, in step S8, the precipitation spatial heterogeneity of the SR and the GR within the matching space is reduced by the non-uniform beam filling quality control technology within the pixel, including:

[0048] S81. Improved technology for the non-uniform beam filling effect of the GR

[0049] Set the minimum detection reflectivity factor threshold of the GR as Zg* = 15 dBZ. For each volume-matched sample, record the proportion fg of the GR distance library within this volume that satisfies Zg ≥ Zg*;

[0050] Reduce the influence of non-uniform beam filling of the GR within the matching volume by excluding samples with fg less than the beam filling percentage factor. Here, the non-uniform beam filling factor fg = 0.9.

[0051] S82. Improved technology for the non-uniform precipitation filling effect of the SR

[0052] Set the minimum detection reflectivity factor threshold of SR as Zs* = 15 dBZ. For each volume-matched sample, record the proportion fs of the SR range bins in the volume that satisfy Zs ≥ Zs*;

[0053] Reduce the impact of non-uniform beam filling of SR within the matched volume by excluding samples with fs less than the beam filling percentage factor. Here, the non-uniform beam filling factor fs = 0.9.

[0054] S83. Non-uniform beam filling quality control technology within the pixels of SR

[0055] Calculate the proportion fs_nubf of the range bins with non-uniform beam correction parameter paramNUBF of SR greater than 0.1 in the volume to the SR range bins in the volume;

[0056] Reduce the impact of non-uniform beam filling within the pixel area of SR by excluding samples with fs_nubf less than the beam filling percentage factor. Here, fs_nubf = 1.0.

[0057] Furthermore, for S9, perform quality control on the precipitation type, precipitation phase, and bright band factor of SR within the matched space respectively to reduce the impact on the evaluation accuracy, including:

[0058] S91. For each sample of SR within each matched volume, record the proportion f_stratiform of stratiform precipitation in the volume. Reduce the impact of the precipitation type of SR on the accuracy evaluation by excluding samples with f_stratiform less than the precipitation classification factor;

[0059] S92. For each sample of SR within each matched volume, record the proportion f_liquid of convective precipitation in the volume. Reduce the impact of the precipitation phase of SR within the matched volume by excluding samples with f_liquid less than the phase classification factor;

[0060] S93. For each sample of SR within each matched volume, record the bright band identification factor f_bb in the volume. Reduce the impact of the precipitation bright band observed by SR within the matched volume by excluding samples with f_bb equal to 0.

[0061] The beneficial effects of the present invention are as follows:

[0062] 1. The present invention adopts a new method of frequency correction for all precipitation phases and all precipitation types, and a pixel time difference matching method accurate to 1 s, etc., effectively reducing the errors brought by different frequencies, precipitation intensities, and movement changes of the instrument during the evaluation process;

[0063] 2. Through various factor sensitivity experiments, the present invention analyzed in detail how precipitation type, bright band, insufficient attenuation correction, etc. affect the inspection accuracy, designed the threshold standard of the quality control factor system, and improved the accuracy of satellite-ground radar matching and product inspection accuracy;

[0064] 3. The present invention adopts an improved average method of radar reflectivity factor, converts dBZ to dB for averaging and then converts it back to dBZ, which can correct the BIAS to be about 1.6 dB larger, proving that the direct averaging of previous dBZ results in a lower BIAS, and effectively improving the accuracy of radar calibration bias estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a flowchart of a method for estimating the calibration bias of a ground-based radar based on satellite-ground precipitation radar calibration of the present invention;

[0066] Figure 2 is a sensitivity relationship diagram between the difference ΔZ of the reflectivity factors of the spaceborne radar SR and the ground-based radar GR and the time deviation factor in an embodiment of the present invention;

[0067] Figure 3 is a statistical probability density distribution diagram of the difference ΔZ of the reflectivity factors of three ground-based radars GR and the spaceborne radar SR in the Guangdong-Hong Kong-Macao Greater Bay Area in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] To make the objectives, technical solutions and advantages of the present invention clearer, the following further explains with reference to the drawings and embodiments.

[0069] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0070] As shown in the Figure 1 accompanying drawings, the present invention provides a method for estimating the calibration bias of a ground-based radar based on satellite-ground precipitation radar calibration, which includes the following steps:

[0071] S1. Obtain the original data of the radar reflectivity factors of the spaceborne radar SR and the ground-based radar GR, and perform quality control on the original data respectively, including:

[0072] S11. Obtain the original data of the radar reflectivity factor of the spaceborne radar SR;

[0073] The Global Precipitation Measurement (GPM) Core Observatory (abbreviated as GPM-CO) was successfully launched on February 27, 2014, aiming to measure global precipitation in space. The GPM-CO operates at an altitude of 407 km, with the polar orbit extended to 65°S–65°N, covering subtropical and tropical regions. The orbital period is approximately 93 minutes, with 15 or 16 orbits per day. The Dual frequency Precipitation Radar (DPR) is the precipitation radar carried on the GPM-CO (hereinafter referred to as the "spaceborne radar SR"). It has a scanning width of 245 km, a beam width of 0.71°, a detection accuracy of 1 dB, a vertical observation height of 19 km, and a spatial resolution of 5.2 km at the sub-satellite point. The DPR (hereinafter referred to as the "spaceborne radar SR") with stable operation and high observation accuracy can provide reference for global ground-based weather radars (hereinafter referred to as "ground-based radars GR") to estimate the calibration deviation of ground-based radars through space-ground precipitation radar calibration.

[0074] S12. Perform quality control on the original data of the radar reflectivity factor of the spaceborne radar SR, including:

[0075] S121. Perform quality control on the Ku-band radar reflectivity factor of the spaceborne radar SR through dual-frequency observation quality control;

[0076] The spaceborne radar SR has two detection bands: Ku at 13.6 GHz and Ka at 35.5 GHz. The KuPR has a vertical resolution of 250 meters and a minimum detection accuracy of 18 dBZ (0.5 mm / h), and the KaPR has a vertical resolution of 250 meters / 500 meters and a minimum detection accuracy of 12 dBZ (0.2 mm / h). For the quality control of the radar reflectivity factor of the Ku band in the original data of the spaceborne radar SR, the accuracy and reliability of the data can be improved through the quality control method of dual-frequency observation. In dual-frequency observation, the Ka band has a shorter wavelength and is suitable for higher-resolution observations, capable of capturing the details of precipitation particles more precisely, especially for light precipitation; the Ku band has a moderate wavelength and is commonly used in weather radars, especially performing well in the observation of moderate-intensity precipitation and snowfall.

[0077] The different attenuation characteristics of these two frequency bands make them complementary for correcting radar data. By comparing the dual-frequency data, the rainfall can be estimated more accurately, and the performance differences of different bands under different precipitation intensities can also be corrected. Especially in the relationship between the radar reflectivity factor and precipitation intensity, through the collaborative processing of data from the two frequency bands, it helps to improve the accuracy of quality control.

[0078] Furthermore, in dual-frequency observations, the quality control of Ku-band radar reflectivity factor data of spaceborne radar SR can be carried out in combination with auxiliary dual-frequency observation quality codes (auxiliary products of spaceborne radar SR). By screening out data with excellent quality grades, effective optimization can be achieved in the quality control process of the Ku-band radar reflectivity factor of the original data of spaceborne radar SR, thereby improving the reliability of the observed data.

[0079] S122. Use precipitation classification quality parameters and dual-frequency precipitation identification parameters to conduct classification quality control on the radar reflectivity factor in the Ku band;

[0080] To improve the accuracy and reliability of precipitation data. When conducting precipitation classification quality control, precipitation classification quality parameters and dual-frequency precipitation identification parameters are used to help identify and eliminate potential measurement errors, noise, and inconsistencies of the radar reflectivity factor in the Ku band, where: Quality control using precipitation classification quality parameters: In the auxiliary products of spaceborne radar SR, the precipitation classification quality parameters are labeled as 1-stratiform, 2-convective, 3-other, -1111-no rain value, -9999.9-missing. By screening out data with parameters of 1-stratiform and 2-convective for quality control, the rest are excluded.

[0081] Quality control using dual-frequency precipitation identification parameters: Dual-frequency radar provides the ability to reflect echo data at two frequencies, which helps improve the accuracy of precipitation identification. Use the optimal data in the dual-frequency precipitation identification parameters for quality control, and exclude the rest.

[0082] By using precipitation classification quality and dual-frequency precipitation identification parameters for classification quality control of the radar reflectivity factor in the Ku band, the accuracy of radar observation data can be effectively improved, and errors caused by meteorological phenomena or technical reasons can be reduced. This method is of great significance for improving precipitation type classification and intensity estimation, especially under complex meteorological conditions, and can help improve the accuracy of precipitation monitoring and early warning.

[0083] S13. Obtain the original data of the radar reflectivity factor of ground-based radar GR;

[0084] The original data of the ground-based radar GR is sourced from the meteorological information system. The newly established Next Generation Weather Radar Network (CINRAD) already has hundreds of weather radars (S-band radars and C-band weather radars). Among them, the S-band dual-polarization weather radars cover four models: SAD, SB, SC, and WSR88D0. They are equipped with a horizontally polarized parabolic antenna, and their azimuth and elevation angles are changed mechanically. The volume scan mode of the S-band dual-polarization radar (ground-based radar GR) includes 9 elevation angles (0.5°, 1.5°, 2.4°, 3.3°, 4.3°, 6.0°, 9.9°, 14.6°, 19.5°), with a beam width of 0.98°, a range bin interval of 250 meters, a volume scan time resolution of 6 minutes, and an effective operational detection range of 230 km. The S-band dual-polarization weather radar can obtain quantitative information such as differential radar reflectivity factor and surface rainfall intensity, and is one of the most important detection means for weather monitoring and early warning operations. Therefore, the calibration and performance maintenance of the S-band dual-polarization weather radar are also quite important, directly determining the accuracy of precipitation measurement.

[0085] S14. Conduct quality control on the original data of the ground-based radar GR;

[0086] Considering that there are certain quality differences in the observations of the ground-based radar GR at close and far distances, conduct quality control on the original data of the ground-based radar GR by excluding the radar reflectivity factor data within the calibration distance (within 15 km and beyond 230 km).

[0087] S2. Conduct frequency correction from Ku band to S band on the reflectivity factor of the spaceborne radar SR after quality control;

[0088] For the same observed target, there are differences in the radar reflectivity factors detected by radars of different wavelengths. Under Rayleigh scattering conditions, the S-band ground-based radar reflectivity factor is the sum of the sixth powers of particle diameters and is independent of the radar wavelength. The satellite Ku-band radar uses the Mie scattering approximation for detecting large-scale precipitation particles. Therefore, it is necessary to correct the Ku band of the spaceborne radar SR to the S band, which is beneficial for subsequent estimation of the calibration deviation of the ground-based radar GR. The corrected data can improve the observation accuracy of the multi-band radar system and ensure the accuracy and consistency in data fusion, precipitation estimation, and meteorological analysis. It includes the following steps:

[0089] S21. Obtain data such as the number concentration of raindrops Nw, the phase of precipitation particles (phase), and temperature T from the secondary products of the dual-frequency precipitation measurement radar DPR carried on the GPM core observation satellite GPM-CO;

[0090] S22. Integrate and calculate the backscattering cross-section σ of the precipitation particles based on the obtained data values b and the corresponding scattering function fz(Dm) values;

[0091] The scattering function fz(Dm) is as follows:

[0092]

[0093] Where λ is the wavelength of the S-band radar, Kw is the complex refractive index of water, T is the temperature, σb is the backscattering cross-section of precipitation particles, D is the diameter of precipitation particles, and Dm is the median diameter of precipitation particles.

[0094] S23. Combine the raindrop number concentration Nw provided in the product and fz(Dm) calculated in S22 to calculate the radar reflectivity factor Z of the S-band s , and achieve frequency correction.

[0095] The corrected radar reflectivity factor Z of the S-band s is:

[0096] Z s = Nwfz(Dm);

[0097] Where Nw is the raindrop number concentration, Dm is the median diameter of precipitation particles, and fz(Dm) is the backscattering function of the S-band.

[0098] Combine Nw and fz(Dm) and calculate Z using the expression s , thereby obtaining the radar reflectivity factor of the S-band and achieving frequency correction.

[0099] S3. Perform a polar coordinate system projection conversion centered on the ground-based radar GR for the corrected SR reflectivity factor, including:

[0100] S31. Determine the coordinate system and parameters of the ground-based radar GR;

[0101] Determine the geographical location (latitude and longitude) of the ground-based radar GR, the altitude when the antenna feedback source is horizontal, and define a polar coordinate system centered on GR, including the radial distance r, the azimuth angle θ, and the elevation angle β.

[0102] S32. Convert the geographical coordinates of the spaceborne radar SR data into polar coordinates centered on GR, including:

[0103] Use the azimuth equal-distance projection coordinate system to project the longitude and latitude of each SR beam range bin with the Earth reference coordinate system WGS84 into relative geographical information centered on the GR site.

[0104] To project the longitude and latitude of each SR beam range bin with the Earth reference coordinate system (WGS84) into relative geographical information centered on the ground radar GR site, a projection can be performed using an azimuth and distance coordinate system (such as a polar coordinate system). The following are the steps to implement this process:

[0105] S321. Define the coordinate system and variables

[0106] GR station: Located at a known longitude and latitude position, with longitude λGR and latitude φGR.

[0107] SR beam: The observation point position of each beam is given by the corresponding longitude and latitude, with longitude λSR and latitude φSR.

[0108] All position data are based on the longitude and latitude of the WGS84 coordinate system.

[0109] S322. Calculate the distance and azimuth between GR and SR beams

[0110] To project the data of the SR beam into the relative coordinate system centered on GR, it is first necessary to calculate the distance and azimuth between the GR station and each SR beam.

[0111] Calculate the distance between the ground radar GR and the observation point: For the longitude and latitude in the WGS84 coordinate system, use the great circle distance formula (Haversine formula) to calculate the spherical distance (i.e., the radial distance) between the ground radar GR station and the spaceborne radar SR beam r) :

[0112]

[0113] where: R is the radius of the earth, approximately 6371 km; Δλ = λSR - λGR is the longitude difference, in radians; Δφ = φSR - φGR is the latitude difference, in radians.

[0114] Calculate the azimuth: To determine the azimuth, that is, the direction from the GR station to the SR beam, the following formula can be used to calculate the azimuth θ:

[0115] θ = arctan2(sin(Δλ)·cos(φ SR ), cos(φ GR )·sin(φ SR ) - sin(φ GR )·cos(φ SR )·cos(Δλ))

[0116] where: arctan2(y,x) is the function to calculate the four-quadrant arctangent, and the output angle θ is the azimuth (positive direction from north to east).

[0117] The azimuth θ gives the direction from the GR station to the SR beam, in radians, and is usually converted to degrees.

[0118] S323. Convert to the relative coordinate system

[0119] After calculating the distance d and azimuth angle θ for each SR beam, these data can be converted to relative positions in the polar coordinate system. Assume that the GR site is the origin of the polar coordinate system (i.e., the distance is 0 and the azimuth angle is 0). Convert the distance and azimuth angle to (x, y) coordinates in the relative coordinate system: x = d * sin(θ); y = d * cos(θ). Where: x is the eastward distance relative to the GR site, and y is the northward distance relative to the GR site.

[0120] In this way, the original geographical coordinates (latitude and longitude) are converted to a relative coordinate system centered on the GR site. Further, the observation positions of each SR beam can also be converted from the geographical coordinate system (WGS84) to a relative coordinate system centered on the GR site.

[0121] S33. Use the antenna height of GR, the geocentric radius of the Earth, and the SR relative geographical information to calculate the coordinates and data on the elevation cone surface of SR relative to GR. The data here are all data including relevant auxiliary parameters such as radar reflectivity factor and time.

[0122] Based on the antenna height of GR and the geocentric radius of the Earth, calculate the elevation angle β of the SR beam relative to GR to further improve the position of the SR beam in the polar coordinate system:

[0123] Calculate the elevation angle β: According to the antenna height of the GR site (which affects the propagation angle of the signal) and the relative position of SR (the polar coordinates converted in S32), calculate the elevation angle of the SR beam. The elevation angle usually refers to the angle between the line connecting the GR site and the SR target and the horizontal plane.

[0124] Geocentric radius of the Earth: Considering the curvature effect of the Earth, use the geocentric radius of the Earth to more accurately calculate the distance and elevation angle to the target to ensure the accuracy of the calculation.

[0125] Calculate the cone surface coordinates: The calculation of the elevation angle is based on spatial geometric relationships, especially on the spherical surface. The elevation angle and distance together determine the spatial position of the SR beam. These data will be further combined with the azimuth angle and radial distance through polar coordinate conversion to obtain the final coordinate information.

[0126] The polar coordinate data converted in S32 provides the necessary basic data for this step, while the antenna height of the GR site and the geocentric radius of the Earth are indispensable physical parameters for calculating the elevation angle and spatial coordinates. Through these parameters, the elevation angle and spatial position of the SR beam are finally calculated. Through these steps, or by obtaining the SR-GR distance through the haversine formula, the geographical coordinate data (WGS84) is effectively converted to a polar coordinate system centered on GR, and precise calculations are carried out in combination with physical parameters such as the antenna height of the radar and the geocentric radius of the Earth, laying a foundation for further spatial analysis and visualization.

[0127] According to the quality control threshold standard system, restrict the data application scope:

[0128] 1. Horizontal distance r factor: Calculate the spherical distance between the spaceborne radar SR beam and the ground-based radar GR site through the Haversine formula, which is the radial distance r. r represents the radial distance in the polar coordinate system centered on GR, that is, the horizontal distance.

[0129] 2. Vertical distance z factor: Based on the geometric relationship of the polar coordinate system, combined with the elevation angle β and the horizontal distance r, calculate the vertical height z: z = r·sin(β) + h GR ;

[0130] Where: β is the elevation angle calculated from the GR antenna height, the earth's curvature, and the relative position of the SR beam (refer to the above content); hGR: the antenna altitude of the ground-based radar GR.

[0131] If the earth's curvature is ignored, it can be simplified to a plane approximation: z ≈ r·tan(β)z.

[0132] According to the quality control system standard, restrict the application scope, that is, only retain the data within the following range for subsequent calculations:

[0133] Horizontal distance r: 15 km < r < 120 km; Vertical height z: 0 km < z < 4 km.

[0134] S4. Correct the horizontal longitude and latitude information on each height layer of SR based on the satellite zenith angle, including:

[0135] S41. Calculate the deviation distance of the satellite observation field of view using the satellite zenith angle;

[0136] To calculate the deviation distance of the satellite observation field of view, the satellite zenith angle and the geometric relationship between the satellite and the ground target can be used. The zenith angle is the angle between the direction from the satellite to the ground target and the straight line directly above the satellite observation point. Generally, the larger the zenith angle, the more the observation distance (i.e., the distance between the satellite and the ground target) will be affected by deviation.

[0137] If the height h of the satellite and the horizontal distance (also known as the "ground projection distance" dz) between the ground point and the directly vertical projection point of the satellite are known, we can describe the field of view deviation through the relationship between the zenith angle and the distance: Assume the satellite is in orbit, the target point is on the ground, the satellite zenith angle is θz, and the deviation distance between the satellite and the target (the deviation along the ground parallel line) can be estimated by the following formula:

[0138] dz = h*tan(θz)

[0139] Where: dz is the deviation distance (horizontal distance) of the satellite observation field of view, h is the height of the satellite from the ground, and θz is the zenith angle of the satellite.

[0140] The larger the zenith angle θz, the greater the horizontal deviation between the satellite and the ground target. This means that the satellite's field of view will deviate more from its vertical direction, resulting in more obvious deviation of the observed data. The deviation distance d of the satellite's field of view is the horizontal distance between the actual position observed by the satellite and the projection point directly below the satellite.

[0141] For example: Assume the height of the satellite is 800 km and the zenith angle is 30°. Then the deviation distance d of the field of view can be calculated as follows:

[0142] d = 800 * tan(30°) = 462.4 km

[0143] Using the zenith angle to calculate the deviation distance of the satellite observation field of view, the key is to know the height of the satellite and the zenith angle, and the deviation distance of the satellite observation field of view can be obtained.

[0144] S42. Based on the satellite scanning angle and the deviation distance of the observation field of view, calculate the field of view distance deviation of the horizontal longitude and latitude at each altitude layer respectively;

[0145] Calculate the deviation in longitude: Since the equatorial circumference of the earth is about 40,075 km (i.e., the total distance of 360° longitude), the field of view deviation in the longitude direction can be calculated by the following formula:

[0146]

[0147] Where: ΔLongitude is the deviation in longitude (unit: degree), d is the deviation distance of the field of view (unit: meter or kilometer), R is the radius of the earth (unit: meter or kilometer, usually 6371 km), and φ is the latitude of the satellite observation point (unit: degree).

[0148] Calculate the deviation in latitude: The distance in the latitude direction changes with the change of latitude. Near the equator, the arc length of the earth is longer, while near the poles, the distance between latitudes is shorter. Therefore, the deviation in the latitude direction is fixed, and the calculation method is:

[0149]

[0150] Where: ΔLatitude is the deviation in the latitude direction (unit: degree).

[0151] Example: Assume there is a satellite with an orbital height of 800 km, a zenith angle of 30°, and the satellite observes above the equator (latitude φ = 0°).

[0152] Calculate the longitude deviation:

[0153]

[0154] Calculate the latitude deviation:

[0155]

[0156] Therefore, for this satellite, the field-of-view deviation will shift approximately 0.0725° in each of the longitude and latitude directions.

[0157] By this method, the horizontal longitude and latitude field-of-view deviations of satellites at different altitude levels can be calculated. For different satellite altitudes, zenith angles, and latitudes of the observation points, the deviations will be different. By this method, the accuracy of satellite observation data and its impact on ground targets can be estimated.

[0158] S43. Subtract this deviation amount from the SR ground horizontal longitude and latitude information, and obtain the horizontal longitude and latitude information of each altitude level through correction.

[0159] Subtracting the calculated deviation amount from the SR ground horizontal longitude and latitude information to perform correction to obtain the horizontal longitude and latitude information of each altitude level is actually to correct the satellite observation error (deviation amount) from the original ground longitude and latitude data. The original ground longitude and latitude can be adjusted according to the previously calculated field-of-view deviation (including deviations in both longitude and latitude directions).

[0160] After calculating the deviation amount, the longitude and latitude information can be updated in the following way:

[0161] Corrected latitude: φcorrected = φ0 - Δφ (Note: Latitude deviation is usually corrected in positive and negative directions, and the specific direction depends on the sign of the deviation amount)

[0162] Corrected longitude: λcorrected = λ0 - Δλ (Similarly, the correction direction depends on the sign of the deviation amount)

[0163] Suppose there is the following situation:

[0164] Original ground longitude and latitude are: φ0 = 10°, λ0 = 20°

[0165] Original ground latitude correction: φcorrected = φ0 - Δφ = 0° - 0.0725° = -0.0725°

[0166] Original ground longitude correction: λcorrected = λ0 - Δλ = 20° - 0.0725° = 19.9275°

[0167] Through these steps, the SR ground longitude and latitude information at any altitude layer can be corrected to obtain a more accurate observation result. In actual operation, especially in places far from the satellite orbit or when the zenith angle is large, correcting the deviation is particularly important.

[0168] S5. Using the improved radar reflectivity factor averaging method, convert the reflectivity factors of SR and GR from logarithmic scale to linear scale respectively;

[0169] The radar reflectivity factor is an important parameter in meteorology, used to represent the reflection intensity of the target object to the radar signal. Usually, it is expressed in logarithmic scale (dBZ) for (Z). The improved radar reflectivity factor averaging method provided by the present invention is: convert to dB through dBZ = 10log(dB), find the average of dB and then convert back to dBZ. That is, first convert (dBZ) back to the linear scale (Z), then take the average, and then convert back to (dBZ) representation.

[0170] For example, assume there are (dBZ) data: [dBZ_1 = 30, dBZ_2 = 35, dBZ_3 = 40]. The average (dBZ) value obtained by using the improved radar reflectivity factor averaging method is 36.74, rather than the directly averaged value (assuming direct averaging is ((30 + 35 + 40) / 3 = 35)). When processing multiple reflectivity data, if directly averaging (dBZ) may lead to deviation. First convert (dBZ) to (Z) for averaging and then convert back to (dBZ), which can provide a more accurate average reflectivity factor.

[0171] Using the improved radar reflectivity factor averaging method, convert the obtained reflectivity factors of SR and GR from logarithmic scale to linear scale respectively.

[0172] S6. Perform time and space overlap matching with precision to the second for the converted SR and GR reflectivity factors;

[0173] Precisely match the observation data of two different radar systems (SR and GR) according to the overlap in time and space, and finally obtain the result that meets the requirements. The core goal of each step is to precisely determine the time difference and space overlap between the two through time and space mapping, combined with the observation information of different radars, so as to assist further data analysis.

[0174] S61. Screen the SR transit GR matching;

[0175] Judge whether the number of effective precipitations within the range of 15 - 230 km of the GR site is greater than 10. If it is less than 10, it is considered local sporadic precipitation, and jump out of the loop for the next transit matching.

[0176] Extract the observation time of the filtered SR line by line, assign and expand it into a three-dimensional observation distance library time, with the time accurate to milliseconds, and record it as T SR 。

[0177] Extracting the observation time of the spaceborne radar SR line by line and assigning and expanding it into a three-dimensional observation distance library time usually involves expanding the two-dimensional scanning process into three-dimensional space in order to obtain observation data at different time and space positions.

[0178] Extract the longitude and latitude information of each orbit (scan line, and each scan line corresponds to a set of longitude and latitude), and assign the time of each scan line to each space position. For example, for each line, according to the radar scanning mode and satellite speed, determine the space position (x1, y1) corresponding to each time point t. Assuming that the radar performs N lines of scans in total, then the time t can be used as the third dimension to construct a three-dimensional observation distance library. Each space point (x1, y1) corresponds to an observation time t and an observation distance D, which can be represented by the following three-dimensional data structure: D(t, x1, y1) = observation distance, the distance changing with time.

[0179] Through the extraction of the line-by-line observation time and the space-time expansion, the two-dimensional scanning data of the spaceborne radar can be converted into three-dimensional space-time data. This can not only help to understand the dynamic process of radar scanning, but also enable more complex observation analysis, such as data processing and analysis at different time and space positions.

[0180] S63、Extract the time of each beam at each elevation angle of GR, assign and expand it into a three-dimensional observation distance library time, with the time accurate to milliseconds, and record it as T GR 。

[0181] Similar to S62, but here it is for the GR radar, extracting the observation time of each beam at each elevation angle. These time data also need to be expanded into an observation library in three-dimensional space and maintain millisecond-level accuracy, recorded as TGR. The GR radar has multiple elevation angles and multiple beams, and each beam corresponds to an observation position and time. Through this step, these information are also converted into a three-dimensional data structure for more accurate time and space matching.

[0182] Assigning the SR line-by-line observation time to the three-dimensional observation distance library time means expanding the scanning time information of each observation line to the area scanned by that line, and assigning a corresponding timestamp to each data point in the three-dimensional data structure. This approach helps to establish a connection between time, space, and observation data, forming a complete spatio-temporal dataset.

[0183] S64、Calculate the distance library with the closest distance from SR to the center of the GR site, and the SR beam time at this distance is the SR transit GR time T SR0 。

[0184] By calculating the central distance between the SR radar and the GR site, the closest distance point is found, and the beam observation time of the SR radar at this point is taken as the time TSR0 when the SR passes over the GR site. This step involves the matching of spatial positions. The site coordinates of SR and GR are compared, and the SR observation point closest to the GR site is selected to determine the transit time when the SR passes over the GR site. This time value TSR0 will be used as the basis for subsequent calculations.

[0185] S65. Calculate the average of the first three elevation scan times of GR and mark it as T GR0 。

[0186] To obtain a higher-precision time match between GR and SR, it is necessary to average the observation times of the first few elevations of SR when it passes over the GR site. This step averages the scan times of the first 3 elevations to obtain TGR0, which serves as the reference time for the time match between GR and SR.

[0187] Usually, the GR radar scans different elevations at regular time intervals. After knowing the scan time or scan period of each elevation, since the GR radar has multiple elevations, the scan times of each elevation may be different. By averaging the times of the first 3 elevations, the influence of accidental factors can be reduced, making the matched time more accurate.

[0188] S66. Calculate the time difference between GR and SR Filter from the GR base data of the previous and next ten minutes The smallest GR base data. Judge whether the selected GR base data is within 6 minutes. If it is greater than 6 minutes, jump out of the match.

[0189] By calculating the time difference between TGR0 and TSR0 Evaluate the time difference between GR and SR. Then, in the base data of the GR site, filter out the base data closest to SR and check whether the time difference is within a reasonable range (within 6 minutes). If the time difference is greater than 6 minutes, jump out of the matching process, indicating that this match is invalid.

[0190] The core of this step is the calculation and rationality judgment of the time difference. If the time difference exceeds 6 minutes, it means that the matching time accuracy is insufficient and there may be a large deviation between the data. Therefore, this matching process needs to be skipped. Through this step, it is ensured that the selected matching data is accurate.

[0191] S67. Calculate the time difference of the spatial matching of the GR and SR observations. Filter out each overlapping spatial range that is matched, calculate the average times of GR and SR within the overlapping space respectively, and calculate the time difference between the two The time accuracy is accurate to seconds.

[0192] Calculate the time difference (with time accuracy reaching the second level) of GR and SR observation data in each space through the overlapping range in space, and screen out the time deviation factor. The overlapping space range within the threshold (-90s < Δt < 90s) is the selected space overlapping range. Through the sensitivity test established by the pre-value system in the present invention, it is found that within 90 seconds of time deviation represents the observation of the same precipitation, which is also one of the purposes of establishing the prefabricated standard system.

[0193] S7. For the SR and GR reflectivity factors in each matching overlapping space range, improve the mean accuracy through inverse distance weighting respectively.

[0194] S71. According to the unified polar coordinate projection of GR and SR, first loop through the SR beam distance library, and then loop through the GR elevation angle. Traverse the relationship between each distance library and the fixed GR elevation angle.

[0195] Project and match the observation data of SR and GR through a unified polar coordinate system to ensure that they can be compared in the same coordinate system, including:

[0196] Unified polar coordinate projection: The observation data of GR and SR are located in different spatial positions and coordinate systems respectively. To ensure their effective comparison and matching, first, it is necessary to unify their coordinate systems and convert them into the same polar coordinate system. The definition of the polar coordinate system is usually based on a central point, usually the GR station or the SR station.

[0197] SR beam distance library loop: The observation data of the SR radar are usually divided into multiple beams according to distance. Each beam has a certain coverage range, and data at different positions can be obtained through the observation information of this beam. The SR beam distance library organizes these beams according to different distances to form a library.

[0198] GR elevation angle loop: The observation data of the GR radar are divided according to the elevation angle. Each elevation angle corresponds to an observation beam to obtain data in a specific area. Therefore, the GR elevation angle loop processes the observation data at different elevation angles one by one.

[0199] Traversal of the fixed GR elevation angle relationship: By traversing the relationship between the beam distance of each SR and the elevation angle of GR, a corresponding relationship between SR and GR can be constructed to ensure that each SR beam distance can be spatially matched with the GR beam at a fixed elevation angle.

[0200] S72. Obtain the space of satellite-ground radar beam intersection through cyclic spatio-temporal matching. Use inverse distance weighted averaging for the matched GR range bins within 5 km of the SR footprint area, and use inverse distance weighted averaging for the matched SR range bins within the GR elevation beam broadening range.

[0201] Perform weighted averaging on the intersection area spatio-temporally matched by SR and GR radars to ensure that the data matched in S71 is more accurate and reliable, including:

[0202] Space of satellite-ground radar beam intersection: During the matching process of SR and GR, their respective observation ranges must be taken into account. Through spatio-temporal matching, the intersection area between SR and GR is obtained, which is the overlapping part of their observation areas. This intersection area determines which data can be used for weighted averaging calculations.

[0203] Use inverse distance weighted averaging for the GR range bins within 5 km of the SR footprint area: After obtaining the intersection area of SR and GR, for the GR radar observation points within 5 km of the SR footprint area (i.e., within the observation area of the SR radar), perform inverse distance weighted averaging on its range bins. Inverse distance weighted averaging weights based on the distance between each data point and the target point: points closer to the target have a greater weight, and vice versa. This method ensures that GR data closer to the target has a greater impact on the result, guaranteeing the spatial consistency of the data.

[0204] Use inverse distance weighted averaging for the SR range bins within the GR elevation beam broadening range: Similar to the GR data, for the SR radar observation data within the elevation beam range of the GR radar, inverse distance weighted averaging also needs to be performed. Here, the "elevation beam broadening range" refers to the area covered by the GR radar due to different elevations, and the SR data within this range will also use inverse distance weighting to ensure the spatial consistency of the weighted result.

[0205] Adopt the method of inverse distance weighted averaging to further refine the matching result of the observation data of SR and GR radars, ensuring more reasonable data weighting within the intersection space, thereby improving the accuracy and reliability of data matching.

[0206] The present invention uses a high-precision time matching method for improvement technology: including the pixel time difference matching method accurate to 1 s. Take the difference between the time means of GR and SR in the independent intersection pixel space obtained by the effective illumination volume method, and the maximum matching time difference ΔT at 230 km is within 1 s.

[0207] High-precision space matching method for improvement technology: including using the inverse distance weight averaging method for SR and GR respectively. The antenna gain at the center of the SR pixel is twice that of the edge, and the mean accuracy of the reflectivity factor of GR and SR within the illumination volume is improved through inverse distance weighting.

[0208] First, in step S6, a method of matching the GR single elevation scan time with the SR time (transit time) closest to GR in terms of distance is adopted. Specifically, it is necessary to extract the scan time of GR stored in the radar data and convert it into the datetime format for easy calculation. At the same time, obtain its transit time from the SR data, that is, the time of the point where SR is closest to GR, and this time can be directly read from the SR dataset. Next, calculate the time difference between each elevation scan time of GR and the SR transit time, and find the GR scan time with the smallest time difference as the matching time point.

[0209] Then, in step S7, a method of pixel time matching is adopted according to the cross - overlapping space of the GR and SR beams. First, determine the overlapping area of GR and SR in space, which can be achieved by calculating their distance and azimuth angle, and project the SR data into the coordinate system centered on GR. Then, for each pixel in the overlapping area of GR and SR, extract its observation time. These time data are usually stored in their respective datasets. Finally, calculate the pixel time mean values of GR and SR in the overlapping area, and find the time difference of these mean values to ensure that the maximum matching time difference ΔT is within 1 second.

[0210] Through these two matching methods, it is possible to achieve the improvement from the traditional transit - time - based matching to the high - precision time matching based on the cross - overlapping space. Step S6 provides a basic matching method, while step S7 further improves the matching accuracy and reliability, ensuring the consistency of GR and SR data in time and space, and providing a more accurate basis for subsequent data analysis and applications.

[0211] S8. Reduce the precipitation spatial heterogeneity of SR and GR in the matching space through the non - uniform beam filling quality control technology within the pixel;

[0212] S81. Improvement technology for the non - uniform beam filling effect of GR

[0213] Set the minimum detection reflectivity factor threshold of GR as Zg* = 15 dBZ. For each volume - matching sample, record the proportion fg of the GR range bins in this volume that satisfy Zg ≥ Zg*.

[0214] Reduce the influence of non - uniform beam filling of GR in the matching volume by excluding the samples where fg is less than the beam filling percentage factor, where fg = 0.9.

[0215] S82. Improvement technology for the non - uniform precipitation filling effect of SR

[0216] Set the minimum detection reflectivity factor threshold of SR as Zs* = 15 dBZ. For each volume-matched sample, record the proportion fs of the SR range bins within the volume that satisfy Zs ≥ Zs*.

[0217] Reduce the impact of non-uniform beam filling of SR within the matched volume by excluding samples with fs less than the beam filling percentage factor, where fs = 0.9.

[0218] In order to minimize the differences caused by the lowest detection sensitivity as much as possible, the present invention uniformly sets the minimum threshold of the reflectivity factor of GR and SR to 15 dBZ.

[0219] S83. Non-uniform beam filling quality control technology within the pixels of SR

[0220] Calculate the proportion fs_nubf of the range bins with the non-uniform beam correction parameter paramNUBF of SR greater than 0.1 within the volume to the SR range bins within the volume.

[0221] Reduce the impact of non-uniform beam filling within the pixel area of SR by excluding samples with fs_nubf less than the beam filling percentage factor, where fs_nubf = 1.0.

[0222] The present invention uses the non-uniform beam filling quality control technology within the pixels, significantly reducing the spatial heterogeneity of precipitation.

[0223] S9. Respectively perform quality control on the precipitation type, precipitation phase state, and bright band factor of SR within the matched space to reduce the impact on the evaluation accuracy;

[0224] S91. Establishment of precipitation type quality control factors.

[0225] The precipitation types observed by SR are divided into convective precipitation and stratiform precipitation. For each sample within the effective illumination volume, record the proportion f_stratiform of stratiform precipitation within the volume.

[0226] Reduce the impact of SR precipitation type on the accuracy evaluation by excluding samples with f_stratiform less than the precipitation classification factor, where f_stratiform = 1.0.

[0227] S92. Establishment of precipitation phase state quality control factors.

[0228] The precipitation phase states observed by SR are divided into liquid, solid, and mixed states. For each sample within the effective illumination volume, record the proportion f_liquid of convective precipitation within the volume.

[0229] Reduce the influence of SR precipitation phase in the matching volume by excluding samples with f_liquid less than the phase classification factor, where f_liquid = 1.0.

[0230] S93. Establishment of bright band quality control factor.

[0231] The precipitation observed by SR is divided into with and without bright bands. If there is a bright band, it is set to 1; if there is no bright band, it is set to 0. For each sample in the effective irradiation volume, record the bright band identification factor f_bb in this volume.

[0232] Reduce the influence of the precipitation bright band observed by SR in the matching volume by excluding samples with f_bb equal to 0, where f_bb = 1.0.

[0233] S10. Adopt the improved radar reflectivity factor averaging method to convert the reflectivity factors of SR and GR from linear scale to logarithmic scale respectively;

[0234] After the above S5 step, the radar reflectivity factor dBZ is converted to dB. Here, the improved radar reflectivity factor averaging method is used to convert the average value of the reflectivity factor dB after S9 from linear scale to logarithmic scale to dBZ through a formula, effectively reducing the calculation deviation caused by direct averaging of dBZ.

[0235] S11. Based on the comparison and verification of the converted SR reflectivity factor and GR reflectivity factor, estimate the calibration deviation of the ground-based radar GR;

[0236] S111. Space-ground precipitation radar consistency. By comparing the data of the averaged GR radar reflectivity factor Zg and SR radar reflectivity factor Zs, ΔZ (ΔZ = Z g -Z s ) is used to evaluate the space-ground precipitation radar consistency.

[0237] S112. Combine the calibration and maintenance records of the ground-based precipitation radar twice a year, usually once before the flood season and once after the flood season. By statistically analyzing the time series of ΔZ during the calibration period of the ground-based radar, jointly evaluate the calibration error of the ground-based radar.

[0238] South China belongs to the subtropical monsoon climate with maritime characteristics. During the flood season (April - October) every year, typhoon, rainstorm and strong convective weather events occur frequently. The three dual-polarization S-band radars (Guangzhou, Shenzhen and Zhaoqing in the Guangdong-Hong Kong-Macao Greater Bay Area, hereinafter referred to as GZ, SZ, ZQ for short) selected in the present invention are arranged in a triangle and are close to each other. The straight-line distances are only 56, 125 and 82 km respectively. Calculated according to the effective observation range of 230 km, the effective observation area of a single radar is 1.7×10 6 km 2, , and the area jointly observed by the three radars is 1.1×10 6 km2 , the common observation area of the three radars is large and the overlap degree is high, which enables them to effectively observe heavy precipitation events in the same area and provide reliable ground verification data for spaceborne radars, thus being suitable for ground-based radar networking verification. It accounts for 65% of the effective observation range of a single radar, indicating that the three radars can approximately represent the observation of heavy precipitation events in the same area and are suitable for using DPR to carry out ground-based radar networking verification. In the present invention, the technical effects of the three-radar networking observation are mainly reflected in the following two points:

[0239] 1. Networking observation: The three radars are arranged in a triangle and are close to each other. The straight-line distances are 56 km (Guangzhou - Shenzhen), 125 km (Guangzhou - Zhaoqing), and 82 km (Shenzhen - Zhaoqing) respectively. This layout makes their observation areas have a large overlap, enabling the formation of a dense observation network, improving the spatio-temporal resolution and monitoring accuracy of the same area;

[0240] 2. Ground verification: The common observation area of the three radars is large and the overlap degree is high, which can provide reliable ground verification data for spaceborne radars. By comparing with spaceborne radar data, the precipitation estimation algorithm of spaceborne radars can be verified and improved.

[0241] In the above technical solution, quality control factors are applied in multiple steps of the present invention for quality control of GR and SR data to reduce estimation bias, including: the application of the horizontal distance r factor in step S32, the application of the vertical distance z factor in step S32, the application of the non-uniform beam filling factor in step S7, the application of the precipitation classification factor in step S81, the application of the precipitation phase factor in step S82, the application of the precipitation bright band factor in step S83, the application of the time deviation factor in step S5, and the application of the reflectivity factors Zg and Zs: When performing space-ground radar matching, because the sensitivities of the two are different, data with intensities below 15 dbz are filtered out before matching. After the space-ground radar spatio-temporal matching is completed, experiments are carried out and it is found that for data above 40 dbz, the differences between the two are large. After research, it is due to instrument reasons, so data above 40 dbz are also excluded. Therefore, the final result is to only compare the reflectivity factors Zg and Zs for data between 15 - 40 dbz. Based on the analysis of the sources of sensitivity errors, through sensitivity experiments on various factors, the threshold standard system of quality control factors for space-ground precipitation radars is determined, and a systematic and scientific quality control standard system is applied to the evaluation dataset to improve the accuracy of the consistency test of the reflectivity factors of space-ground precipitation radars.

[0242] As shown in the following Table 1 of the quality control factor and scheme optimization combination experiment for space-ground precipitation radars:

[0243] Table 1 Quality control factor and scheme optimization combination experiment for space-ground precipitation radars

[0244]

[0245]

[0246] The above table first conducts the quality control factor experiments from 1 to 4 one by one in sequence. On this basis, the quality control factor experiments from 5 to 13 are then conducted in sequence respectively. Finally, the results after the optimization combination of the quality control factors are given.

[0247] Comparing the direct average of dBZ, using the formula dBZ = 10×log(Z) to convert to dB for averaging and then converting back to dBZ, the BIAS increases significantly by 1.6 dB. The reason for the analysis is that dBZ actually compresses the values above 10 dB. Averaging directly without conversion will greatly reduce the actual BIAS value. Similarly, the BIAS of the average dBZ of the SZ radar is -0.67 dB. After converting to dB for averaging and then converting back to dBZ, the BIAS increases to -0.17 dB (table omitted). Converting dBZ to dB for averaging and then converting back to dBZ can correct the BIAS to be about 1.6 dB larger, proving that the previous direct averaging of dBZ leads to a lower BIAS.

[0248] The time deviation factor has the greatest impact on the correlation coefficient. When the time is set within 90 s, the variability of ΔZ decreases significantly, and the CC reaches 0.93. As Figure 2 shows the sensitivity relationship between the difference in reflectivity factors ΔZ (ΔZ = Z g -Z s ) of the space - ground radar and the time deviation ΔT, where: (a) is GZ, (b) is SZ, (c) is ZQ, (d) is the superposition of the three, (e) is stratiform, and (f) is convective. It is found that ΔZ has a large divergence as the time deviation ΔT increases, especially the maximum ΔZ between 200 s and 300 s exceeds 24 dB. Figures e - f reveal that the variability of ΔZ of convective precipitation with time is more significant than that of stratiform precipitation. Obviously, compared with stratiform clouds, the rapid formation and moving speed of convective clouds have an impact on the matching accuracy of the space - ground radar reflectivity factors.

[0249] From the perspective of the inspection indicators classified by phase state, the liquid state is better than the solid state which is better than the mixed state. Therefore, setting the vertical height from 0 to 4 km (the bottom of the bright band in the Greater Bay Area is 4.1 km) has the greatest impact on BIAS. Below 4 km, it is basically liquid precipitation, and the BIAS drops to 0.87. The standard deviation STD of the difference in stratiform precipitation is the smallest at 2.23, and the STD of convective precipitation is the largest at 3.6, indicating that the divergence of ΔZ in unstable convective precipitation is large, and using stable precipitation can significantly reduce the divergence of ΔZ.

[0250] As shown in Table 2 below, the present invention provides a threshold standard system for quality control factors of space-ground precipitation radar. Table 2 is a threshold standard composed of 8 main quality control factors determined after experiments. By setting these parameter thresholds, the reflectivity error of space-ground radar inspection can be effectively reduced.

[0251] Table 2: Threshold Standard System for Quality Control Factors of Space-Ground Precipitation Radar

[0252]

[0253] A ground-based radar calibration deviation estimation method based on space-ground precipitation radar calibration provided by the present invention actually includes two parts: the first is the pre-stage part, that is, the establishment of a threshold standard system for quality control factors of space-ground precipitation radar; the second is the application part, that is, applying the threshold standard system for quality control factors of space-ground precipitation radar to estimate the calibration deviation of ground-based radar based on space-ground precipitation radar calibration.

[0254] After the quality control factor threshold experiment, the threshold standards of 8 types of quality control factors are set, effectively reducing the reflectivity deviation of space-ground radar inspection. As Figure 3 shown, the figure statistically shows the probability density distribution diagram of the difference ΔZ in reflectivity factors between three ground-based radars GR (Guangzhou GZ, Shenzhen SZ, and Zhaoqing ZQ) in the Guangdong-Hong Kong-Macao Greater Bay Area and the spaceborne radar SR using the method of the present invention. Part b in the figure is the probability density distribution diagram of the calibration deviation estimation of the comparison and verification of the reflectivity factors of the spaceborne radar SR and the Guangzhou GZ ground-based radar GR after quality control processing through the optimal quality control factor threshold standard using the method of the present invention; part d in the figure is the probability density distribution diagram of the calibration deviation estimation of the comparison and verification of the reflectivity factors of the spaceborne radar SR and the Shenzhen SZ ground-based radar GR after quality control processing through the optimal quality control factor threshold standard using the method of the present invention; part f in the figure is the probability density distribution diagram of the calibration deviation estimation of the comparison and verification of the reflectivity factors of the spaceborne radar SR and the Zhaoqing ZQ ground-based radar GR after quality control processing through the optimal quality control factor threshold standard using the method of the present invention. It is statistically found that the correlation CC of ΔZ based on the three ground-based radars reaches between 0.9 and 0.95, proving that the combined threshold standard can improve the matching accuracy of space-ground radars and indirectly improve the authenticity inspection accuracy. It is statistically found that the standard deviation STD of the difference of ΔZ based on the three ground-based radars in the Greater Bay Area is as low as between 1.23 and 1.62 dB, and the overall sampling points are stably concentrated near the fitting line, and there are very few scattered sampling points, indicating that the sampling data matching of space-ground radars reaches a relatively high consistency level. For example, the correlation CC of ΔZ based on the GZ radar is 0.95, and the STD is only 1.23 dB, which is effectively improved compared with the traditional statistical conclusion (the correlation CC of ΔZ from 2016 to 2019 is 0.91, and the STD is 2.05 dB).

[0256] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

[0257] The above describes the present invention and its embodiments. Such description is not restrictive. What is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Generally speaking, if those of ordinary skill in the art are inspired by it and, without departing from the purpose of the present invention, design similar structural modes and embodiments to this technical solution without creative efforts, they shall fall within the protection scope of the present invention.

Claims

1. A method for estimating ground-based radar calibration deviation based on satellite-ground precipitation radar calibration, characterized in that: It includes: S1. Obtain the original data of reflectivity factors of space-borne radar SR and ground-based radar GR, and perform quality control on the original data; S2, perform frequency correction from Ku band to S band on the SR reflectivity factor after quality control; S3, performing polar coordinate projection transformation on the corrected SR reflectivity factor with GR as the center; S4. Correct the horizontal longitude and latitude information at each SR altitude layer based on the satellite zenith angle; S5. Using the improved radar reflectivity factor averaging method, the reflectivity factors of SR and GR are converted from logarithmic scale to linear scale respectively; S6, performing temporal and spatial overlap matching of the converted SR and GR reflectivity factors with an accuracy of seconds; S7, for the SR and GR reflectivity factors within each overlapping spatial range of the matching, the mean accuracy is improved by using the inverse distance weight respectively; S8, reduce the spatial heterogeneity of precipitation of SR and GR in the matching space through the intra-pixel non-uniform beam filling quality control technology; S9, perform quality control on the precipitation type, precipitation phase and bright band factor of SR in the matching space to reduce the impact on the evaluation accuracy; S10, using the improved radar reflectivity factor averaging method, the reflectivity factors of SR and GR are converted from linear scale to logarithmic scale respectively; S11. Estimation of the ground-based radar GR calibration deviation is performed based on the comparison and verification of the converted SR reflectivity factor and the GR reflectivity factor.

2. The method for estimating ground-based radar calibration deviation based on satellite-to-ground precipitation radar calibration according to claim 1, characterized in that: S1, respectively obtaining the original data of the reflectivity factor of the space-borne radar SR and the ground-based radar GR, and performing quality control on the original data, including: S11, obtaining the original data of the radar reflectivity factor of the spaceborne radar SR; S12, quality control is performed on the original data of the SR radar reflectivity factor obtained by the spaceborne radar through dual-frequency observation quality, precipitation classification quality and dual-frequency precipitation identification parameters; S13, obtaining original data of radar reflectivity factor of ground-based radar GR; S14. Perform quality control on the GR reflectivity factor by eliminating the calibration distance data.

3. The method for estimating ground-based radar calibration deviation based on satellite-to-ground precipitation radar calibration according to claim 1, characterized in that: S2, performing frequency correction from Ku band to S band on the SR reflectivity factor after quality control, includes: S21, obtaining data including raindrop number concentration Nw, precipitation particle phase phase and temperature T from SR product data; S22. Calculate the backscattering cross section σ of the rainfall particles based on the acquired data value integral b And the corresponding scattering function fz(Dm); The scattering function fz(Dm) is: Where λ is the wavelength of the S-band radar, Kw is the complex refractive index of water, T is the temperature, and σ b is the backscattering cross section of the rainfall particles, D is the diameter of the precipitation particles, and Dm is the median diameter of the precipitation particles; S23, combined with the raindrop number concentration Nw provided in the product and fz(Dm) calculated in S22, calculate the radar reflectivity factor Z of the S band s , realize frequency correction; Corrected S-band radar reflectivity factor Z s for: Z s =Nwfz(Dm); Where Nw is the raindrop number concentration, Dm is the median diameter of precipitation particles, and fz(Dm) is the backscattering function of the S band.

4. The method for estimating ground-based radar calibration deviation based on satellite-to-ground precipitation radar calibration according to claim 1, characterized in that: The S4, correcting the horizontal longitude and latitude information at each SR altitude layer based on the satellite zenith angle, comprises: S41, calculating the satellite observation field deviation distance using the satellite zenith angle; S42, based on the satellite scanning angle and the observed field of view deviation distance, respectively calculating the field of view distance deviation of the horizontal longitude and latitude at each altitude layer; S43. Subtract the deviation from the SR ground horizontal longitude and latitude information, and obtain the horizontal longitude and latitude information of each altitude layer through correction.

5. The method for estimating ground-based radar calibration deviation based on satellite-to-ground precipitation radar calibration according to claim 1, characterized in that: The step S5, using an improved radar reflectivity factor averaging method to convert the reflectivity factors of SR and GR from a logarithmic scale to a linear scale, comprises: The reflectivity factors of SR and GR are converted from logarithmic scale dBZ to linear scale dB by the expression dBZ=10logdB.

6. The method for estimating ground-based radar calibration deviation based on satellite-to-ground precipitation radar calibration according to claim 1, characterized in that: S6, performing temporal and spatial overlap matching of the converted SR and GR reflectivity factors with an accuracy of seconds, comprises: S61, screening SR transit GR matching; S62, extract the SR observation time after screening, assign it to the three-dimensional observation distance library time, and record it as T SR； S63, extract the time of each beam of GR elevation angle, assign it to the three-dimensional observation distance library time, and record it as T GR； S64. Calculate the distance from the SR to the GR site center, and record the SR beam time of this distance as the SR transit GR time T SR0； S65. Calculate the average time of the first three elevation scans of SR, marked as T GR0； S66. Calculate the time difference between GR and SR Filter from GR base data Minimum GR base data and screening; S67, filter out each matching overlapping space range, calculate the average time of GR and SR in the overlapping space, and calculate the time difference between the two The time precision is accurate to seconds, and the selected spatial overlap range is determined.

7. The method for estimating ground-based radar calibration deviation based on satellite-to-ground precipitation radar calibration according to claim 1, characterized in that: S7, for the SR and GR reflectivity factors within each overlapping spatial range of the matching, respectively improving the mean accuracy by using the inverse distance weight, comprises: S71, according to the unified polar coordinate projection of GR and SR, first loop from the SR beam distance library, then loop from the GR elevation angle, and traverse each distance library corresponding to the fixed GR elevation angle relationship; S72. Obtain the space of the satellite-to-ground radar beam intersection through cyclic space-time matching. Use the inverse distance weighted average for the matched GR distance library within the 5-kilometer SR footprint area, and use the inverse distance weighted average for the matched SR distance library within the GR elevation beam broadening range.

8. The method for estimating ground-based radar calibration deviation based on satellite-to-ground precipitation radar calibration according to claim 1, characterized in that: The S8, reducing the precipitation spatial heterogeneity of SR and GR in the matching space by using the intra-pixel non-uniform beam filling quality control technology, includes: S81, GR non-uniform beam filling effect improvement technology; The minimum detection reflectivity factor threshold of GR is set to Zg*=15dBZ. For each volume matching sample, the proportion of GR distance library that satisfies Zg≥Zg* in the volume is recorded as fg; The influence of GR non-uniform beam filling in the matching volume is reduced by excluding samples whose fg is less than the beam filling percentage factor, where the non-uniform beam filling factor fg = 0.9; S82, SR non-uniform precipitation plugging effect improvement technology; The minimum detection reflectivity factor threshold of SR is set to Zs*=15dBZ. For each volume matching sample, the proportion of SR distance libraries satisfying Zs≥Zs* in the volume is recorded as fs; The impact of SR non-uniform beam filling in the matching volume is reduced by excluding samples with fs less than the beam filling percentage factor, where the non-uniform beam filling factor fs = 0.9; S83, SR pixel non-uniform beam filling quality control technology; The ratio of the distance library with the pixel non-uniform beam correction parameter paramNUBF greater than 0.1 to the SR distance library in the volume is fs_nubf; The effect of non-uniform beam filling within the SR pixel area is reduced by excluding samples whose fs_nubf is less than the beam filling percentage factor, where fs_nubf = 1.

0.

9. The method for estimating ground-based radar calibration deviation based on satellite-to-ground precipitation radar calibration according to claim 1, characterized in that: S9, respectively performs quality control on the precipitation type, precipitation phase and bright band factor of the SR in the matching space to reduce the impact on the evaluation accuracy, including: S91. For each sample of SR in the matching volume, record the proportion of stratiform precipitation in the volume as f_stratiform, and reduce the impact of SR precipitation type on accuracy assessment by excluding samples whose f_stratiform is less than the precipitation classification factor; S92, for each SR sample in the matching volume, record the proportion of convective precipitation in the volume as f_liquid; reduce the influence of the SR precipitation phase in the matching volume by excluding samples whose f_liquid is less than the phase classification factor; S93. For each SR sample in the matching volume, record the bright band identification factor in the volume as f_bb, and reduce the influence of the precipitation bright band observed by SR in the matching volume by excluding samples with f_bb equal to 0.

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