38 results about "Differential reflectivity" patented technology

The invention discloses a dual-polarization Doppler weather radar ground clutter inhibition method based on fuzzy logic, and the method comprises the steps: processing volume scanning data of a dual-polarization Doppler weather radar into 360 radial lines, wherein the interval between each two adjacent radial lines is one degree, and the data of each radial line comprises seven echo characteristic physical quantities: intensity, a mean Doppler speed, a spectrum width, a difference reflectivity factor, a difference propagation phase shift, and a correlation coefficient; setting the seven physical quantities which respectively comprise the intensity, the mean Doppler speed, the spectrum width, the difference reflectivity factor, the difference propagation phase shift, and the correlation coefficient; enabling seven fuzzy results to be assigned with corresponding weight values, carrying out accumulating and obtaining a ground clutter criterion of one echo point; Determining that the intensity echo of the point is the ground clutter when the ground clutter criterion of one echo point is greater than a threshold value; inhibiting the ground clutter of the dual-polarization Doppler weather radar through the ground clutter criterion of each echo point, and compensating a hole of a current layer after clutter inhibiting through employing the echo intensity value of a corresponding point in an upper layer in the volume scanning data.

Embodiments of the present invention provide for improved estimation of environmental parameters in a dual-polarization radar system. In some embodiments, environmental parameters can be estimated using a linear combination of data received in two orthogonal polarization states. In particular, embodiments of the invention improve ground clutter and noise mitigation in dual polarization radar systems. Moreover, embodiments of the invention also provide for systems to determine the differential reflectivity and/or the magnitude of the co-polar correlation coefficient and the differential phase in a dual polarization radar system.

The invention provides a metallic ball calibration method for an X-band solid dual-polarization weather radar, and the method comprises the steps: respectively selecting a test point in a broad pulse detection range and a narrow pulse detection range of the radar; placing a metallic ball at each test point; calculating a corresponding radar observation elevation angle, an radar observation azimuth angle and a radar observation database number for each metallic ball through employing the GPS information of each metallic ball and the longitude and latitude and height information of the radar; setting a calibration observation mode, and obtaining the observation data, wherein the calibration observation mode comprises the horizontal change range of a radar observation azimuth, the change range of the radar observation elevation angle and a stepping angle, and the observation data comprises a radar reflectivity factor observed value and a difference reflectivity factor observed value; and enabling the radar reflectivity factor observed value and the difference reflectivity factor observed value to be respectively compared with a reflectivity factor theoretical value and a difference reflectivity factor theoretical value, wherein the differences serve as the measurement error of the radar; and correcting the detection result of the radar in a corresponding detection range through the measurement error.

The invention discloses an all-solid-state dual-band dual-polarization Doppler weather radar system, which comprises a control computer, a servo, an antenna pedestal, a Ku-band subsystem and a Ka-band subsystem. The computer controls power-up state and working state of the Ku-band subsystem and the Ka-band subsystem to realize detection of weather targets of cloud, snow, rain and fog and the like in the sky and to obtain weather target height, echo intensity, radial velocity, speed spectrum width and linear depolarization ratio, differential reflectivity, differential transmission phase displacement, differential transmission phase displacement rate and correlation coefficient and the like polarization information. Characteristics of the Ka band and Ku band are combined, and a brand-new method is provided for combined detection of the weather targets of cloud, snow, rain and fog and the like, thereby realizing complete and accurate recording of the whole raining process and rain and wind change information, and ensuring accuracy of radar detection.

The invention discloses a rainfall particle type identification method and a device. The method comprises steps that a polarization parameter set and preset temperature of rainfall particles are acquired, and the polarization parameter set comprises reflectivity, difference reflectivity, a difference propagation phase shift rate and a correlation coefficient; a parameter set corresponding to each rainfall particle type is respectively acquired, and the parameter set comprises a sub parameter set corresponding to each polarization parameter and a sub parameter set corresponding to the preset temperature; probability of a rainfall particle belonging to each rainfall particle type is calculated; the type of the rainfall particle is determined through utilizing probability of the rainfall particle belonging to each rainfall particle type. The method is advantaged in that through polarization parameter analysis processing, probability of the rainfall particle belonging to a certain type is acquired, rainfall particle type identification is realized, bases are provided for making different manual operation modes for different rainfall particle types, and manual influence weather work validity is improved.

The invention discloses a real-time calibration method and system for differential reflectivity factors, and relates to the field of dual polarization system meteorological radars. The method comprises the following steps: waveform signals are generated via a receiver according to calibration signals and excitation signals; the calibration signals are divided into horizontal calibration signals and vertical calibration signals via a feeder line component, and the excitation signals are transmitted to a transmitter; detection signals are transmitted via the transmitter according to the excitation signals, the two paths of calibration signals are processed via the receiver to obtain two paths of intermediate frequency calibration signals, and echo signals which are generated after a target is detected via the detection signals are received; a signal processor is used for processing the two paths of intermediate frequency calibration signals to obtain digital calibration signals, and thedifferential reflectivity factors are obtained according to the echo signals; real-time calibration is performed on the differential reflectivity factors via a computer according to the digital calibration signals. Via the real-time calibration method and system disclosed in the invention, online real-time calibration of the differential reflectivity factors can be realized, and polarization parameter measurement by the dual polarization system meteorological radars can be improved in terms of accuracy.

The invention discloses a dual-polarization weather radar ZDR parameter calibration method. The method comprises the steps of 1 determining the length of a suspension line of an unmanned plane and a metal ball according to the linear distance between a selected calibration site and a radar station, and enabling the unmanned plane and the metal ball not to appear in one radar scanning wave beam atthe same time; 2 determining a scanning elevation angle range value according to the altitude of a radar feed source and the linear distance between the test point and the radar station, so that the unmanned aerial vehicle has at least three elevation angle scans within the flight height for distinguishing the unmanned aerial vehicle from the metal ball; 3 setting a radar scanning mode as a customVCP-sect mode, and adopting a sector scanning combination with a target azimuth of +/-5 degrees and an antenna scanning speed of 4deg/s; and 4 suspending the unmanned aerial vehicle at the position of the test point, scanning according to the radar scanning mode set in the step 3, and completing ZDR calibration of the dual-polarization radar. The calibration method has good stability at high altitude, and can effectively improve the precision and reliability of calibration data for radar ZDR test calibration.

The embodiment of the application discloses a differential reflectivity ZDR calibration method. Complex I/Q signals of a horizontal channel and a vertical channel that correspond to each distance library in each radial direction are obtained; according to complex I/Q signals of the horizontal channel and the vertical channel corresponding to each distance library as well as horizontal channel radial noises, ZDR related data corresponding to each distance library are calculated correspondingly; on the basis of Bragg scattering features and the ZDR related data corresponding to all distance libraries, target distance libraries are screened among all distance libraries and thus a target distance library set is formed; whether the target distance library set meets the ZDR deviation evaluationstandard is determined; and if so, ZDR deviation for calibrating the ZDR is determined based on the ZDR related data corresponding to each target distance library in the target distance library set. When the method is used for calibrating the ZDR, normal services performed by the radar do not need to be stopped and real-time updating of the ZDR deviation is realized.

Provided is a rainfall intensity estimation method, including the steps of: receiving specific differential phases, horizontal reflectivities, and differential reflectivities as scan observation data of the multiple elevation observation of the dual-polarization radar observing the very short distance according to elevation angles observed; calculating horizontal attenuations for scans from the horizontal reflectivities among the multiple elevation observation data; calculating vertical attenuations for the scans from the horizontal reflectivities and the differential reflectivities among the multiple elevation observation data; obtaining a volume mean of the horizontal attenuations for the scans, a volume mean of the vertical attenuations for the scans, a volume mean of the specific differential phases, a volume mean of the specific differential phases, a volume mean of the horizontal reflectivities, and a volume mean of the differential reflectivities; and estimating a rainfall intensity from at least one of the volume means.

The invention relates to a radar echo movement information calculation method and device. The detection data of a double-polarization radar is obtained; generating combined reflectivity data accordingto the echo reflectivity data of each layer in the detection data; the method is based on a set reflectivity factor threshold. Calibrating the combined reflectivity data in a first plane area according to a corresponding detection position and determining an echo intensity center position; according to echo reflectivity, correlation coefficient and differential reflectivity ZDR data in the detection data, determining a ZDR column and ZDR column information in a second plane area corresponding to the detection position; and calculating the movement information of the echo according to the central position of the ZDR column and the central position of the echo intensity in the area in the contour line of the central position of the echo intensity, so as to achieve the purpose of predictingthe movement information of the precipitation echo.

The invention discloses a raindrop spectrum-based dual-polarization weather radar data quality real-time evaluation method. The method comprises the following steps of S1, data acquisition; s2, data preprocessing: cleaning raindrop spectrum data, and marking grid points where polluted radar data are located for dual-polarization weather radar data by adopting a quality control algorithm; s3, data evaluation: inverting the raindrop diameter according to the raindrop spectrum data, and when the particle diameter inverted by the raindrop spectrum data is less than or equal to 1mm, performing real-time evaluation on the dual-polarization weather radar system error by adopting a micro raindrop method; and when the particle diameter of raindrop spectrum data inversion is greater than 1mm, inverting each polarization parameter, and performing deviation analysis on the polarization parameter corresponding to the radar by taking the polarization parameter as a true value, thereby realizing evaluation of the radar data quality. According to the method, the advantages of the micro raindrop method and the advantages of the raindrop spectrum inversion method are integrated, and the real-time evaluation of the differential reflectivity data quality error of the dual-polarization weather radar caused by the radar system deviation can be realized.

The invention discloses a precipitation particle identification method and device based on Kmeans clustering. The method comprises the steps: obtaining the polarization variables of radar observation data, wherein the polarization variables comprise a horizontal reflectivity factor, a differential reflectivity factor and a differential propagation phase shift; reconstructing a ratio differential propagation phase shift according to a physical constraint relationship between the horizontal reflectivity factor and the differential reflectivity factor, and determining reconstructed observation data according to the horizontal reflectivity factor, the differential reflectivity factor and the reconstructed ratio differential propagation phase shift; carrying out precipitation particle identification on the reconstructed observation data by adopting a fuzzy classification algorithm to obtain a first identification result; and carrying out secondary classification on the first identification result by adopting a Kmeans clustering method to obtain a second identification result. According to the invention, through the reconstruction ratio differential propagation phase shift and the adoption of the Kmeans clustering method for secondary classification, the accuracy of precipitation particle identification is improved.

A system for measuring hematocrit in a whole blood sample is provided. An absorbent substrate is adapted to receive a whole blood sample. At least one light source is positioned to illuminate the sample on the substrate at first and second wavelengths. The first and second wavelengths are different from each other. A spectral sensor is positioned to measure a first intensity and a second intensity of light diffusely reflected from the sample at the first and second wavelengths, respectively. The diffusely reflected first and second intensities of light are compared to reference values to generate first and second reflectance values. A controller, coupled to the spectral sensor, is configured to determine a first differential reflectance between the first and second reflectances. The hematocrit level of the sample is determined based on a first stored relationship between hematocrit and a differential reflectance corresponding to the first and second wavelengths.

The invention relates to the technical field of meteorological prediction, in particular to a dual-polarization meteorological radar raindrop spectrum inversion method suitable for rainfall in different climatic states, which classifies rainfall according to climatic state rainfall drop spectrum characteristics in the nature, according to the shape-slope relation of rainfall in different climatic states, respectively corresponds to different relations between horizontal reflectivity and differential reflectivity, and finally calls different formulas to carry out real-time dual-polarization radar raindrop spectrum inversion so as to obtain an actual climatic rainfall condition, thereby solving the problem that a traditional dual-polarization radar raindrop spectrum inversion method can only fixedly use a shape-slope relation of rainfall in one climatic state at a time, and if the method needs to be applied to rainfall in another climatic state, the shape-slope relation corresponding to the rainfall needs to be manually changed. And if a conversion period exists between the rainfall types of the two climatic states, the traditional inversion method cannot timely call respective corresponding shape-slope relations.

The invention discloses a quality control method for a differential phase of a dual-polarization meteorological radar, and the method sequentially comprises the steps: carrying out the preprocessing of a dual-polarization radar actual measurement differential phase; removing a backward differential phase error existing in the actually measured differential phase value by using the relation among the differential phase, the horizontal reflectivity factor and the differential reflectivity factor; carrying out LSTM processing according to the transformation condition of the differential phase in the same radial direction, so that the filtering effect of the differential phase is achieved; traversing dual-polarized radar rays to scan all radial actually measured differential phase values in all directions, and performing LSTM processing; and based on the processed rainfall differential phase value, carrying out attenuation correction of reflectivit by using a reflectivity attenuation correction algorithm. According to the invention, the accuracy problem in reflectivity attenuation correction can be effectively improved, and the stability of attenuation correction is improved.

The invention discloses a method for improving polarization isolation of a dual-polarization phased array weather radar. The method is characterized in that the scheme is as follows: when a PPAR is ina dual-polarization working state, quadrature phase sequence coding is adopted to perform phase modulation on transmitted signals of two polarization channels so as to reduce cross coupling between the two polarization channels, reduce ZDR deviation, ensure that a differential reflectivity error can meet requirements, and reduce the influence on a co-polarization correlation coefficient and a differential phase at the same time. At present, an expected target is realized by virtue of phased-array antenna hardware, and the price is very high. 180-degree phase changes between signal pulses areincreased in transmitted horizontal or vertical ports to reduce differential reflectance errors, however, errors in co-polarization correlation coefficient estimation are increased, and in some cases,errors in differential phase estimation are also increased. Compared with the prior art, the method has the advantages of reasonable scheme design, simple equipment, low implementation cost, excellent performance and the like.

The embodiment of the invention discloses a ground clutter identification method and device, electronic equipment and a storage medium, and the method comprises the steps: extracting a characteristic parameter set (the standard deviation PDPstd of a differential phase, the standard deviation ZDRstd of a differential reflectivity and the vertical gradient DBZg of the reflectivity of an adjacent elevation angle) from a distance library of radar echoes; and inputting the characteristic parameter set into a pre-trained neural network model according to the ratio PR of the coherent power to the incoherent power and the texture structure TDBZ of the reflectivity factor so as to obtain a ground clutter identification result which is output by the neural network model and corresponds to the distance library. The neural network model performs automatic learning according to the sample, the degree of dependence on the experience and level of people is small, and thus the accuracy of ground clutter recognition is improved. Moreover, the characteristic parameters adopted by the scheme of the invention are characteristic quantities with obvious differences under the ground object and rainfall conditions so that the neural network model can be a shallow neural network model, and the machine learning efficiency is improved.