36 results about "Differential reflectivity" patented technology

The embodiment of the invention provides an X-band weather radar non-rainfall data quality control method and system. The method is applied to the technical field of weather radars and comprises the following steps: acquiring X-band weather radar data; identifying and eliminating the electromagnetic interference echo according to the continuity characteristic of the electromagnetic interference echo and the distribution condition of the correlation coefficient CC and the differential reflectivity factor ZDR; judging whether the difference value of the reflectivity factors of each echo point before and after hardware filtering is greater than a preset threshold value or not, and if so, marking the echo point as a rainfall echo; if not, fuzzy logic recognition elimination based on characteristic parameters is carried out; wherein the non-precipitation echo probability PNPR and the precipitation echo probability PPR of each echo point are calculated according to the characteristic parameters, whether the PNPR is larger than the PPR or not is judged, and if yes, the echo point is marked as a non-precipitation echo; if not, non-rainfall weak echo discrimination is carried out; whether the echo is a non-rainfall echo is determined by comparing the reflectivity factor mean value # imgabs0 #, the correlation coefficient mean value # imgabs1 # of the elevation angle of the whole layer of the echo and the ratio ValCC of the effective distance library number of the correlation coefficients with a judgment threshold value, and if the echo is the non-rainfall echo, the echo point is marked as the non-rainfall echo; and if not, continuously filtering scatter and plaque noise around the echo point by using the reflectivity factors in the radial direction and the azimuth to obtain the rainfall echo. In this way, the quality of weather radar data can be improved.

The embodiment of the invention provides an X-band dual-polarization radar attenuation correction method based on artificial intelligence. The method is applied to the field of X-band dual-polarization radar attenuation correction. The method comprises the steps of performing space-time matching on existing observation data of an X-band dual-polarization radar and existing observation data of an S / C-band radar in each region to generate a space-time matching data set of each region; according to the space-time matching data set of each region, training an attenuation correction model of the corresponding region, and generating an attenuation correction model of each region; and performing quality control on the observation data of the X-band dual-polarization radar to be subjected to attenuation correction, and inputting the observation data into the attenuation correction model of the corresponding region to obtain the horizontal reflectivity and the differential reflectivity of the X-band dual-polarization radar after attenuation correction. Therefore, the signal attenuation of the horizontal reflectivity and the differential reflectivity of the X-band dual-polarization radar can be effectively corrected, the deviation between the X-band dual-polarization radar and the observation value of the adjacent S-band radar can be reduced, the hardware resource overhead can be reduced, and the method can be efficiently applied nationwide.

The invention relates to the field of meteorological observation, and provides a networking X-band radar full-system differential reflectivity self-adaptive cooperative calibration method and system and a storage medium, and the method comprises the steps: collecting radar base data, and transmitting the radar base data to a data processing center; preprocessing the radar-based data, performing deviation detection on differential reflectivity data of the preprocessed radar-based data, and generating a calibration instruction based on a deviation detection result; based on the calibration instruction, automatically scheduling the radar to be calibrated to execute zenith calibration VPCAL mode scanning; in the scanning process, synchronously collecting VPCAL scanning data and transmitting the VPCAL scanning data to a data processing center; based on VPCAL scanning data, quantitative deviation is calculated and issued to a calibration execution unit; and carrying out automatic parameter adjustment on the X-band radar of which the quantitative deviation exceeds a threshold value, and sending a deviation warning to a user. According to the invention, the monitoring precision, the calibration precision and the quantitative rainfall estimation capability of the weather radar can be improved, and forecasting and early warning services and refined meteorological services are provided.

The invention relates to the technical field of lightning forecasting, and discloses a lightning forecasting method based on mesoscale mode polarization radar data assimilation, which comprises the following steps: acquiring dual-polarization radar data, preprocessing the dual-polarization radar data, and generating an observation error covariance matrix; obtaining reanalysis data as an initial field and a boundary condition of a meteorological mode, constructing a background error covariance matrix as a background field, and performing cyclic assimilation through a reflectivity assimilation observation operator, a radial speed assimilation observation operator and a differential reflectivity assimilation observation operator by adopting a three-dimensional variational assimilation method; and taking the forecast field obtained after each assimilation as a background field of the next time, and finally generating a lightning forecast result. According to the invention, the influence of radar data errors on the accuracy of numerical mode forecasting is reduced, so that the accuracy of lightning forecasting under complex terrains can be improved.

The invention discloses a multi-source heterogeneous data assimilation-driven high-altitude atmospheric motion numerical simulation method, which comprises the following steps of: obtaining satellite microwave radiation brightness temperature data and ground dual-polarization radar differential reflectivity data, inputting the data into a quantum annealing collaborative inversion model, and solving three-dimensional humidity field distribution data; humidity gradient tensor field data are calculated, and the space coordinate range of the frontal area is judged based on a preset humidity gradient modulus length threshold value; carrying out vector included angle calculation on humidity gradient tensor field data of a frontal area and background wind field data, generating a cloud micro-physical parameterization regulation coefficient, and dynamically selecting a cloud phase change dominant mode; and inputting the three-dimensional humidity field distribution data and the cloud microphysical parameterized regulation and control coefficient into a four-dimensional variational assimilation system to solve optimal analysis field data, and outputting high-altitude wind field prediction data in a driving numerical mode. According to the method, the response sensitivity and the physical consistency of the cloud microphysical process in numerical simulation are enhanced, and the cloud phase evolution simulation precision in the severe weather process is improved.

The invention provides a vertical velocity quantitative estimation method and system based on a dual-polarization radar, and the method employs the multi-polarization parameters of the dual-polarization radar, combines a deep learning technology with radar meteorological physical knowledge, builds a nonlinear relation between the multi-polarization parameters of the dual-polarization radar and the vertical velocity, and achieves the quantitative estimation of the vertical velocity. Quantitative estimation of the three-dimensional vertical velocity profile is realized, and a quantitative analysis product is provided for convective weather forecast and early warning. According to the method, a differential reflectivity column, a differential phase column recognition algorithm and a dual-polarization radar hydrogel classification algorithm are combined to screen an upflow area of convective weather, and a data set of dual-polarization radar observation characteristic data and vertical velocity of the upflow area is established. Transform is used as a prediction model, a space-time attention mechanism is added, and multi-time step information is used as input, so that extraction of time features observed by the dual-polarization radar is facilitated, and the vertical velocity prediction accuracy is improved.

The invention discloses an X-band dual-polarization radar differential propagation phase shift quality control method, and relates to the technical field of radar quality control. The method comprises the following steps: on each initial distance library in each radial direction of the X-band dual-polarization radar, determining a plurality of target distance libraries based on a reflectivity factor, differential reflectivity, a first smooth value of a correlation coefficient and textures of the differential reflectivity and differential propagation phase shift, and searching a plurality of effective distance libraries based on an effective value limiting condition; for each effective distance library, determining a second smooth value of the differential propagation phase shift and a final value of the differential propagation phase shift; and if it is determined that the backscattering phase shift exists, determining final values of the differential propagation phase shifts on a plurality of preset distance libraries between the starting distance library and the ending distance library based on second smooth values of the differential propagation phase shifts of the starting distance library and the ending distance library. And the quality control efficiency of the differential propagation phase shift is improved.

The invention discloses a forest fire automatic identification and risk assessment method based on X-band and S-band dual-band joint detection, and the method comprises the steps: deploying an X-band radar and an S-band radar to construct a cooperative detection network, and forming a detection region covering short and long ranges through space-time registration; a Kalman filtering algorithm is adopted to fuse dual-source radar data, and the method comprises the following steps: defining a four-dimensional state vector containing a plane position coordinate and a velocity component; directly mapping the position component of the state vector to a radar reflectivity plane observation value by using a position mapping type observation matrix; fire point identification based on a random forest model comprises the following steps: inputting feature fusion space reflectivity parameters and time dynamic features; calculating the heat release rate per unit area through the linear combination of the dual-polarization radar reflectivity and the differential reflectivity; calculating fire intensity by combining the fire point area; and coupling the fire intensity with the wind field spreading prediction, and generating graded early warning information oriented to power grid safety.

The invention discloses a frequency domain adaptive clutter suppression method and system for a dual-polarization weather radar, and relates to the technical field of weather radar processing. According to the invention, pulse-level quality control and ground feature identification marking are carried out on radar original echoes; calculating power spectrums of the marking units, performing dynamic noise measurement and data quality grading, and screening out effective processing units; constructing a Gaussian mixture model of a power spectrum of an effective unit, and iteratively estimating model parameters through an expectation maximization algorithm to realize complete separation of a ground feature spectrum and a meteorological spectrum in a frequency domain; inverting single-channel parameters based on the separated meteorological spectrum; a cooperative spectrum separation strategy is adopted for horizontal and vertical polarization channels, and dual-polarization parameters such as differential reflectivity, correlation coefficients and differential phases are inverted based on the filtered dual-channel signals; according to the invention, high-precision separation of clutters and meteorological echoes is realized, the loss of meteorological signals is significantly reduced while ground features are effectively suppressed, and the accuracy and reliability of dual-polarization parameter inversion are improved.

The present application relates to the technical field of weather radars, and discloses a fully automatic calibration method for a weather radar. In the method, multiple sets of calibration differential signals are injected into a receiver of a weather radar to be calibrated, wherein at least two sets of calibration differential signals differ in injection power, at least two sets of calibration differential signals differ in frequency offset, and at least two sets of calibration differential signals differ in spectral width; then, by combining the injection power, frequency offset, and spectral width of each set of calibration differential signals and measured values of intensity, velocity, and spectral width under the corresponding calibration differential signals, the unified calibration of the intensity, velocity, spectral width, differential reflectivity factor, and differential propagation phase shift can be accomplished. The method innovatively unifies various radar parameters into a single calibration process while ensuring comprehensive calibration of those radar parameters, thereby greatly reducing calibration time and items, optimizing the calibration process, and improving the calibration efficiency.

The application discloses an X-band dual-polarization radar differential propagation phase shift quality control method, and relates to the technical field of radar quality control.The method comprises the following steps: on each initial distance bin in each radial direction of the X-band dual-polarization radar: determining a plurality of target distance bins based on the first smooth value of the reflectivity factor, the differential reflectivity and the correlation coefficient, and the texture of the differential reflectivity and the differential propagation phase shift, and finding a plurality of effective distance bins based on an effective value limitation condition; for each effective distance bin: determining the second smooth value of the differential propagation phase shift and the final value of the differential propagation phase shift; and if it is determined that the backscattering phase shift exists, determining the final value of the differential propagation phase shift on a plurality of preset distance bins between the start distance bin and the end distance bin based on the second smooth value of the differential propagation phase shift of the start distance bin and the end distance bin. The quality control efficiency of the differential propagation phase shift is improved.

The present application relates to radar signal processing technical field, specifically provide a kind of staggered pulse repetition interval time-frequency domain dual-polarized radar detection variable estimation method, comprising: in frequency domain, the estimation of echo signal-to-noise ratio, reflectivity factor, differential reflectivity of horizontal / vertical polarization based on power spectral density;And the estimation of average Doppler velocity and velocity spectrum width;In time domain, after ground clutter filtering based on odd-even pulse, respectively, odd-even pulse correlation parameter estimation is carried out, so that two detection variables of dual-polarized radar correlation coefficient and differential propagation phase shift of odd-even pulse are obtained respectively, then odd-even combination can obtain the estimation result of accurate correlation coefficient and differential propagation phase shift.The present application realizes the accurate estimation of dual-polarized radar multi-class detection variable, ensures that estimation result is not disturbed by ground clutter interference, simultaneously adapts single pulse repetition interval and staggered pulse repetition interval two working modes, improves the reliability and applicability of radar detection.

The invention provides a dual-polarization weather radar time domain echo simulation method based on Doppler spectrum reconstruction, and the method comprises the steps: constructing a scanning region and a resolution volume grid, dividing a sub-resolution volume grid, and carrying out the interpolation of the sub-resolution volume grid through a trilinear interpolation method; micro-physical information and a scattering matrix of each phase-state particle are calculated, echo intensity contributions are superposed along a speed coordinate axis of a Doppler spectrum and then are sequenced, a horizontal polarization channel Doppler spectrum of each sub-resolution volume grid is obtained, and an antenna pattern is taken as a weight and is superposed, so that a horizontal polarization channel Doppler spectrum of a resolution volume grid is obtained; and obtaining the power spectrum density of the horizontal polarization channel through coordinate transformation, obtaining the IQ echo sequence of the horizontal polarization channel through inverse Fourier transformation, and obtaining the IQ echo sequence of the vertical polarization channel in combination with the differential reflectivity factor, the differential propagation phase shift and the co-polarization correlation coefficient. According to the invention, reliable simulation of time domain echoes in the weather process of any shape spectrum can be realized.

The present invention discloses a method for identifying and classifying Doppler radar clutter based on SE-Res2Net-101, including extracting radar reflectivity Z in the base data H , differential reflectivity Z DR , correlation coefficient ρ HV and differential propagation phase shift rate K DP numerical data; preprocessing the extracted numerical data to generate a data set; constructing an SE-Res2Net-101 model; inputting the training set data into SE-Res2Net-101 for model training; inputting the test data set into the trained model, and making clutter judgments through the prediction probability given by the model. Solve the technical problem of current clutter detection technology that requires digital simulation of ground terrain and the provision of DEM, and establish a clutter prediction model from the variables of Z H , Z DR , ρ HV and K DP A method for identifying clutter in a Doppler radar and classifying biological clutter and ground clutter based on a residual convolutional network with an SE attention mechanism, which improves accuracy and speeds up the prediction speed

The invention provides a weather radar health condition monitoring method based on observation characteristics and natural conditions, and the method comprises the steps: analyzing a state data package through a special data interface for radar control communication, extracting and analyzing a state bit and numerical information in BITE data, and achieving the real-time state monitoring of hardware through the combination of a preset threshold value; extracting a signal quality factor SQI and a uniformity parameter from the base data analyzed by the data interface, and evaluating radar Doppler coherence and polarization performance through multi-dimensional threshold comparison; based on natural observation base data, reflectivity factors, differential reflectivity and correlation coefficients are extracted, and in combination with threshold analysis of solar radiation and rainfall scenes, the polarization balance degree and the polarization isolation degree are diagnosed, and the antenna lobe performance and the system detection sensitivity are monitored; and according to the monitoring result, when all diagnosis items are normal, determining that the radar is healthy, otherwise, triggering an abnormal alarm. The problems of observation pause or interruption and the like caused by monitoring of weather radar performance and health conditions by instruments and meters are solved.

The application discloses a weather radar wind turbine clutter recognition method and system based on fuzzy reasoning, and belongs to the technical field of fuzzy reasoning. The monitoring area of a target weather radar is divided into multiple echo observation subunits according to distance gates and scanning azimuth angles, and the reflectivity factor, radial velocity, spectral width and differential reflectivity of each echo observation subunit are continuously acquired within a preset data acquisition time sequence. By analyzing the discrete characteristics of the reflectivity factor and the radial velocity in the time sequence, echo stability characteristic quantities are constructed, echo shape consistency characteristic quantities are constructed based on the consistency deviation of the spectral width and the differential reflectivity in the neighborhood, the echo stability characteristic quantities and the echo shape consistency characteristic quantities are subjected to fuzzy mapping, and the wind turbine clutter and meteorological echoes are distinguished and recognized through a fuzzy comprehensive judgment quantity, so that the accuracy and robustness of the wind turbine clutter recognition are improved, and the weather radar data quality control under complex background conditions is suitable.

The invention discloses a quantitative prediction method for an icing load of a power transmission line, and the method comprises the following steps: collecting and fusing multi-source meteorological data: employing a dual-polarization radar to invert the particle size distribution of liquid drops through differential reflectivity, calculating the content of supercooled water through a differential phase, and obtaining an atmosphere liquid water path and a vertical temperature profile; according to the measured wind speed, wind direction and wind shear index, an environment parameter field is constructed, and icing early warning is triggered; conducting wire state parameter real-time sensing: monitoring conducting wire three-axis amplitude and galloping frequency, obtaining conducting wire surface temperature gradient, quantifying damping effect of conducting wire galloping on collision efficiency and providing boundary conditions for a thermodynamic equation; mixed phase state dynamic discrimination: constructing a phase state discrimination matrix based on a combination rule of a temperature interval and droplet particle size distribution; dynamic collision efficiency correction: calculating the dynamic collision efficiency by adopting an unsteady state collision formula; thermodynamics-fluid mechanics coupling calculation is carried out; and ice coating load accumulation and output: calculating the ice coating thickness, mass and mechanical load.

The invention discloses a severe convective rainfall short-term and imminent forecasting method based on a dual-polarization radar, electronic equipment and a medium. The method comprises the following steps: acquiring a horizontal reflectivity factor, a differential reflectivity and a ratio differential phase shift through the dual-polarization radar; a display regression model is constructed, and the display regression model takes the horizontal reflectivity factor, the differential reflectivity and the ratio differential phase shift as inputs and takes the precipitation amount as an output; u-Net and ResNet are constructed and trained, and the U-Net and the ResNet take the horizontal reflectivity factor, the differential reflectivity and the ratio differential phase shift at the t moment as input and take corresponding data at the t + 1 moment as output; acquiring real-time horizontal reflectivity factors, differential reflectivity and ratio differential phase shift data, inputting the real-time horizontal reflectivity factors, the differential reflectivity and the ratio differential phase shift data into pre-trained U-Net and ResNet, and averaging prediction results respectively output by the U-Net and the ResNet by using a Bagging strategy to obtain prediction values of the horizontal reflectivity factors, the differential reflectivity and the ratio differential phase shift; and inputting the result into a display regression model to obtain a predicted value of the precipitation.

The application discloses a meteorological radar ground object echo recognition method based on XGBoost, and comprises the following steps: step S1, selecting radar parameters for inputting an XGBoost model, wherein the radar parameters comprise a reflectivity factor, a reflectivity factor texture, a differential reflectivity factor, a change degree of echo intensity along a radial direction, a regional average value of a radial velocity and a correlation coefficient; step S2, initializing the XGBoost model to determine parameters of the initial XGBoost model; step S3, inputting a meteorological radar measured data sample set, and dividing the meteorological radar measured data sample set into a training set and a test set; step S4, performing Bayesian optimization on the initial XGBoost model by using the training set to obtain a final XGBoost optimization model; step S5, inputting the test set into the XGBoost optimization model to perform iteration, and stopping the iteration when a maximum iteration number is reached or a loss function of the XGBoost optimization model reaches an optimal solution of an objective function, thereby completing final recognition and classification. The application is suitable for various weather conditions and can more accurately recognize ground object echoes.

The application provides a ZDR arc automatic recognition random forest modeling method and system, relates to the meteorological radar data processing technical field, and includes the following steps: acquiring dual-polarization radar observation data, extracting a differential reflectivity field and a reflectivity field, determining an arc candidate area based on a spatial position relationship and extracting multi-dimensional feature parameters, calculating a mutual information matrix and a partial correlation coefficient matrix to screen feature pairs and perform tensor product operation to generate high-order interaction features, constructing a training sample set, initializing a basic random forest, and based on feature conditional entropy, reconstructing a sampling probability distribution to complete decision tree growth to obtain a basic classifier, extracting a decision tree discrimination path sequence, converting it into a topology graph, encoding it into a path embedding vector to train a meta-random forest classifier, inputting to-be-recognized data into the basic classifier and the meta-random forest classifier, and outputting a recognition result. The accuracy and automation degree of ZDR arc recognition are effectively improved.

The present application relates to the field of radar technology, and more particularly to a precipitation particle phase state recognition method for an X-band dual-polarization micro-weather radar, which comprises the following steps: Step 1, scanning at a set pitch angle between horizontal and vertical, obtaining spectrum reflectivity, spectrum differential reflectivity and spectrum correlation coefficient along Doppler velocity distribution for each range bin; Step 2, determining a separation boundary according to a valley value formed by first decreasing and then increasing the spectrum correlation coefficient, dividing the range bin along Doppler velocity into a plurality of spectrum components corresponding to single phase states respectively; Step 3, obtaining component differential reflectivity, component correlation coefficient and relative order along fall velocity for each spectrum component, and obtaining phase state spatial distribution by summarizing and splicing. The present application can separate and recognize multiple coexisting phase states in a single resolution volume, and improves the accuracy of phase state recognition.

This invention provides a method for monitoring the health status of weather radar based on observational characteristics and natural conditions. The method includes: parsing status data packets through a dedicated radar control communication data interface, extracting and analyzing status bits and numerical information from BITE data, and combining this with preset thresholds to achieve real-time hardware status monitoring; extracting the signal quality factor (SQI) and uniformity parameters from the base data parsed by the data interface, and evaluating the radar's Doppler coherence and polarization performance through multi-dimensional threshold comparisons; extracting reflectivity factors, differential reflectivity, and correlation coefficients based on natural observation base data, and diagnosing polarization balance and polarization isolation by combining threshold analysis of solar radiation and precipitation scenarios, and monitoring antenna lobe performance and system detection sensitivity; summarizing the monitoring results, determining the radar is healthy when all diagnostic items are normal, otherwise triggering an abnormal alarm. This invention solves the problems of observation pauses or interruptions caused by instrumentation monitoring of weather radar performance and health status.

The invention discloses a weather radar wind turbine clutter identification method and system based on fuzzy reasoning, and belongs to the technical field of fuzzy reasoning. A monitoring area of a target weather radar is divided into a plurality of echo observation subunits according to a range gate and a scanning azimuth angle, and a reflectivity factor, a radial speed, a spectral width and a differential reflectivity of each echo observation subunit are continuously acquired in a preset data acquisition time sequence. By analyzing discrete characteristics of a reflectivity factor and a radial velocity on a time sequence, an echo stable characteristic quantity is constructed, an echo form consistent characteristic quantity is constructed based on consistency deviation of a spectral width and a differential reflectivity in a neighborhood, fuzzy mapping is performed on the echo stable characteristic quantity and the echo form consistent characteristic quantity, and the echo stable characteristic quantity and the echo form consistent characteristic quantity are calculated. The wind turbine clutter and the meteorological echo are distinguished and recognized through the fuzzy comprehensive judgment quantity, the accuracy and robustness of wind turbine clutter recognition are improved, and the method is suitable for weather radar data quality control under the complex background condition.

The present application discloses a fully automatic calibration method for a meteorological radar, and relates to the technical field of meteorological radars. The method sequentially injects multiple groups of calibration differential signals into a receiver of a meteorological radar to be calibrated, wherein at least two groups of calibration differential signals have different injection powers, at least two groups of calibration differential signals have different frequency deviations, and at least two groups of calibration differential signals have different spectral widths. Then, according to the injection powers, frequency deviations, and spectral widths of the calibration differential signals of each group, combined with the measured values ​​of the intensity, velocity, and spectral width under the corresponding calibration differential signals, the calibration of the intensity, velocity, spectral width, differential reflectivity factor, and differential propagation phase shift can be uniformly completed. On the basis of ensuring comprehensive calibration of various radar parameters, the method innovatively unifies these radar parameters into the same calibration process, greatly reducing the calibration time and projects, optimizing the calibration process, and improving the calibration efficiency.

The application discloses a phased array rain measuring radar precipitation estimation method and system based on a hybrid expert model, which comprises the following steps: adaptively correcting double-polarization parameters of a phased array radar to restore reflectivity and differential reflectivity; inputting a sequence of parameters corrected at multiple times into a hybrid expert model to generate expert selection probability by a gate routing, fusing precipitation estimation of multiple expert towers to obtain preliminary surface precipitation estimation; and identifying overtop rain attenuation by using a convolution network, and when overtop rain attenuation is identified, selecting the nearest time when overtop does not occur, and compensating for the precipitation field based on a multi-scale variational optical flow extrapolation to replace it. The application also discloses a corresponding system. The application significantly improves the accuracy, regional adaptability and time sequence continuity of precipitation estimation under different weather and terrain conditions, and effectively solves the near-range underestimation problem caused by X-band attenuation and overtop rain attenuation.

The present application discloses an automatic analysis method for the quality of dual-polarization weather radar base data, which relates to the field of dual-polarization weather radar. The method uses a fuzzy logic method to identify and remove ground object echoes in the volume scanning base data of the dual-polarization weather radar. After completing the data preprocessing of the volume scanning base data to obtain precipitation echo base data, data statistics and evaluation are performed according to the reflectivity factor, differential reflectivity, and differential phase. The base data quality is analyzed from multiple dimensions such as radar detection power, radial continuity detection results, initial differential phase stability, and layered cloud precipitation echo detection quantity. This method can help radar debugging and installation personnel accurately evaluate the quality of radar base data, and can also provide support for researchers engaged in radar data application to quickly understand the quality of radar base data.

The invention belongs to the technical field of meteorological detection, and discloses a dual-polarization radar built-in online real-time calibration system design method, and the method comprises the steps: carrying out the built-in calibration: through a built-in calibration source, a numerical control attenuator, a noise source and the like, achieving the built-in calibration function of parameters such as noise, intensity, speed, spectral width, differential reflectivity, differential propagation phase shift and the like, and achieving the real-time calibration of the parameters. The built-in signal source is equivalent to the built-in signal source, the signal switch and frequency of the calibration source are controlled through the upper computer software, the attenuation value of the numerical control attenuator is controlled through the upper computer software so as to control the intensity of the calibration signal, and the built-in source can be used for replacing the built-in instrument for testing after the built-in source and the built-in instrument are mutually calibrated. Other faults or errors possibly caused by periodic shutdown test maintenance and frequent disassembly are avoided, the maintenance cost is reduced, and the maintenance efficiency is improved.

The invention relates to the technical field of forest fire monitoring, and discloses a forest fire monitoring method based on a polarization radar, and the method comprises the steps: S10, obtaining the body scanning data of a monitoring point through the polarization radar, the body scanning data comprising polarization variables, and the polarization variables comprising a horizontal reflectivity, a differential reflectivity, a cross correlation coefficient and a radial speed standard deviation; s20, the polarization variables are preprocessed, the preprocessing comprises normalization and abnormal value cutting processing, and the polarization variables with effective pixel masks are constructed; s30, performing initial classification on the preprocessed polarization variables based on a fuzzy logic algorithm, performing confirmation by adopting multi-source cross validation, and generating forest fire segmentation labeling data of the monitoring points; s40, performing model training by using the mountain fire segmentation historical annotation data set to obtain a mountain fire monitoring model; and S50, inputting the mountain fire segmentation labeling data of the monitoring point into a mountain fire monitoring model to obtain mountain fire monitoring data.

The present application relates to the technical field of meteorological identification, more particularly, to a differential reflectivity factor column identification method, system, medium and equipment. The scheme comprises importing radar-based data, performing quality control, and generating basic radar data; performing three-dimensional grid point processing according to the basic radar data to generate non-uniform grid point data; performing Cartesian coordinate conversion according to the non-uniform grid point data to generate three-dimensional grid point data; performing Zdr section identification according to the three-dimensional grid point data to generate Zdr section characteristic quantities, performing two-dimensional component identification to generate Zdr two-dimensional components; and performing Zdr column synthesis at different altitudes starting from the zero-degree layer height according to the Zdr two-dimensional components, and generating the maximum value of Zdr in real time. The scheme is a differential reflectivity factor column identification method based on dual-polarization weather radar observation data, which realizes quantitative analysis of severe convective storms and provides a powerful means for monitoring and early warning of severe convective storms.

The present invention provides a real-time data quality assessment method for dual-polarization weather radars, comprising: S1. Obtaining a base data file for a dual-polarization weather radar; if the base data file is VOL data, executing S2 to S5 below; if the base data file is PPI data, executing S2 to S3 and S5 below; S2. Performing echo identification; S3. If the base data file is a meteorological echo, calculating a system deviation factor of differential reflectivity, a reflectivity sensitivity factor, a system deviation factor of horizontal reflectivity, and a phase correlation quality factor; S4. If the base data file is a meteorological echo, calculating an over-suppression factor for ground clutter; S5. If the base data file is not a meteorological echo, calculating an under-suppression factor for ground clutter; and outputting each calculation result as an assessment result. The present invention assesses the data quality of base data obtained by the radar in real time from multiple perspectives, utilizing the inherent relationships between parameters, and thus providing more accurate assessment results.