502 results about "Weather radar" patented technology

The invention discloses an intelligent discharge port dynamic regulation and control system and method based on multi-source time sequence fusion, and relates to the technical field of intelligent water affairs, and the system comprises a data collection module which is used for obtaining the multi-source data of a target area in real time; the space-time fusion module is used for mapping the meteorological radar data to the topological space of the drainage pipe network through a space-time alignment encoder, dynamically fusing the meteorological radar data, the surface runoff sensor data and the historical drainage data by adopting a multi-head attention mechanism, and generating a space-time fusion tensor; the prediction model module is used for constructing a TCN-LSTM hybrid model, taking the space-time fusion tensor as input and taking future drainage flow as output; and the dynamic regulation and control module is used for optimizing the gate opening degree and the pump station power control sequence in a rolling manner based on a model predictive control algorithm, and performing dual-target optimization by combining real-time electricity price data and overflow risks. According to the system, the prediction precision of the drainage flow can be improved, and double-target balance of the overflow risk and the energy consumption cost can be achieved.

The invention discloses a method and system for estimating irrigation water consumption and water utilization efficiency, and relates to the technical field of water resource management, and the method comprises the steps: collecting the plant stem flow rate, canopy temperature and soil profile water content data of a target area through a multi-source sensing device, and obtaining a gradient and permeability distribution diagram of a geographic information system; dividing the farmland of the target area into sub-areas with different irrigation priorities based on a soil conductivity mutation point detection algorithm; constructing a dynamic water demand model fused with the plant transpiration hysteresis effect, performing space-time correlation analysis on the stem flow rate data and the soil moisture content gradient, and calculating the real-time water demand intensity of each sub-region; the method comprises the following steps: accessing short-time rainfall prediction data of a meteorological radar, dynamically correcting a future irrigation decision by adopting a sliding time window mechanism, and triggering a gradient delay irrigation instruction when the predicted rainfall amount exceeds an irrigation threshold value; and calculating the actual moisture utilization efficiency based on the inversion data of the moisture content of the irrigated soil and the unmanned aerial vehicle multispectral image.

The invention discloses a statistical result-based X-band phased array weather radar multi-beam scanning mode observation error calibration method and system. The method comprises the following steps of: S1, acquiring observation base data of an S-band dual-polarization Doppler weather radar and an X-band dual-polarization phased array weather radar as initial data; s2, performing data quality control on the radar-based data to obtain preprocessed data; and S3, by adopting a different radar time and space sampling volume coordinate matching method and taking an observation value of the S-band radar as a reference, counting a matching difference of the preprocessed data to obtain an observation error of the X-band radar and correcting the observation error.

The invention discloses a multi-mode radar echo extrapolation method based on a Doppler dual-polarization weather radar, and belongs to the technical field of meteorology. The method specifically comprises the following steps: firstly, acquiring Doppler dual-polarization weather-based data, and converting the Doppler dual-polarization weather-based data into multi-modal physical parameter data; then, constructing a multi-modal radar echo extrapolation model comprising a multi-modal encoder, a depth feature extraction module and a decoder; the encoder is composed of a double-branch structure, performs preliminary feature extraction on original multi-modal information, inputs the extracted features to the depth feature extraction module, further extracts depth spatial-temporal features through a self-attention mechanism, and finally, completes layer-by-layer decoding of the depth spatial-temporal features through the decoder, and introduces preliminary features to strengthen transmission of shallow feature information; and inputting newly observed radar information into the trained echo extrapolation model, and predicting a radar echo image at a future moment. According to the invention, powerful technical support is provided for real-time meteorological monitoring, rainfall forecast and extreme weather early warning.

The invention discloses a multi-source data and physical information fused tornado space-time intelligent prediction method, which comprises the steps of collecting observation data of a Doppler weather radar and reanalysis data of a mid-term weather forecast center, performing projection conversion, space alignment, time interpolation and the like of space-time dimensions, and fusing key physical characteristics of the tornado; establishing a space-time convolution model network suitable for tornado multi-scale feature extraction, inputting fusion features and corresponding coordinate information, and outputting a prediction control quantity of tornado space-time evolution; a physical control equation of the atmospheric motion and thermodynamic process is converted into a penalty term, a space-time convolution model loss function is embedded, and physical information constraint is carried out on network parameters; and obtaining tornado prediction control quantities of different time windows in the future, and generating a probability graph of the potential development area of the tornado. Through fusion of multi-source data and a physical mechanism, future tornado event probability is quantified, and reliable data support and scientific basis are provided for meteorological prediction and emergency response.

The invention provides a large-scale insect and bird migratory flight monitoring system based on a weather radar net, and the system employs a data collection module to obtain S, C and X wave band weather radar monitoring data of the national weather radar net, and a quality control module carries out the quality control of the radar-based data in the monitoring data, and further provides the radar-based data to an echo recognition module to construct recognition features. A classification recognition result is obtained by adopting a recognition model corresponding to the wave band; the inversion quantification module inverts the insect and bird density, the insect and bird speed and the vertical distribution of the movement direction based on the classified and identified insect and bird echoes to obtain an inversion quantification result; and the classification identification result and the inversion quantification result of the single wave band form a local monitoring result. And the visualization module displays a single-band local monitoring result or a multi-band fusion monitoring result. According to the invention, multi-band data can be collected, monitoring results are fused, real-time and efficient monitoring of migration of insects and birds is realized, and the monitoring range is expanded.

The invention discloses an intelligent ship formation navigation control method, and relates to the technical field of ship navigation control, and the control method comprises the specific steps: S100, multi-source data collection: installing various sensors and communication equipment on a ship, according to the invention, a ship AIS sensor, a meteorological radar echo sensor, an underwater sonar detection sensor, a satellite communication device and a VHF communication device are installed on a ship, so that comprehensive acquisition of various environmental information and states of the ship in the ship navigation process is realized; the multi-source heterogeneous data is subjected to cleaning, conversion and normalization processing of a data center and then is efficiently fused by a deep hybrid network model H-Net, and deep feature extraction is performed by using a convolution attention module and an improved gating cycle unit iGRU, so that the efficiency and accuracy of data processing are greatly improved, and the data processing efficiency and accuracy are greatly improved. And the key information in the navigation environment can be captured more accurately.

The invention discloses a rainfall forecast correction method based on CNN-BiLSTM-At and ST-CFormer. The rainfall forecast correction method comprises the following steps: acquiring a meteorological radar echo sequence image and meteorological station observation data, and preprocessing the meteorological radar echo sequence image and the meteorological station observation data; constructing a CNN-BiLSTM-At rainfall forecast model structure, training the structure by using the preprocessed meteorological radar echo sequence image, optimizing hyper-parameters by using an adaptive genetic algorithm, and obtaining a trained rainfall forecast target model; the method comprises the following steps: constructing an ST-CFormer rainfall forecast deviation correction model structure, training the ST-CFormer rainfall forecast deviation correction model structure by using data obtained by fusing a rainfall forecast image and observation data of a meteorological station, and optimizing hyper-parameters by using an NSGA-II algorithm to obtain a trained rainfall forecast deviation correction model. According to the method, secondary correction is carried out on the forecast result by utilizing meteorological station observation data and the global modeling capability of the Swindow-transformer, so that the rainfall forecast precision is effectively improved.

The invention relates to a thunderstorm and gale identification method based on multi-dimensional characteristic parameter fusion, and belongs to the field of radio, and the method comprises the steps: employing a neighborhood value method to remove the electromagnetic interference of dual-line polarization Doppler weather radar data, carrying out the rollback fuzzy processing, and then carrying out the meshing; dual-polarization radar data, wind profile radar data, sounding data and ground automatic station data are respectively obtained, and respective characteristic parameters are extracted from the data; carrying out normalization processing on the characteristic parameters, and obtaining corresponding parameters; constructing a multi-parameter integrated early warning model based on the plurality of fusion parameters, and training and verifying the model; and inputting parameters into the trained model for operation, and outputting the thunderstorm gale probability P. According to the thunderstorm gale identification method, through combination of multi-source data fusion and multiple physical mechanism fusion and integration of innovation of a machine learning model, the technology of thunderstorm gale identification from single index dependence to multi-dimensional collaboration and from static threshold value to dynamic self-adaption is realized.

The invention discloses a phased array weather radar calibration method based on a generalized metal ball calibration theory. Comprising the following steps: in a microwave anechoic chamber, collecting two-dimensional antenna pattern data of a phased array weather radar in at least one working mode and pointing to a plurality of different beams; performing numerical integration on the two-dimensional antenna pattern data, calculating a combined beam main lobe solid angle of the transmitting antenna and the receiving antenna, and calculating an equivalent beam width product; calculating a calibration theoretical reflectivity factor of the metal ball based on the product of the theoretical radar scattering sectional area and the equivalent beam width of the metal ball; actual detection echo data of the phased array radar on the metal ball under different working modes and beam pointing is acquired, and an actual reflectivity factor is calculated; and comparing the actual reflectivity factor with the calibration theoretical reflectivity factor to complete calibration of the phased array weather radar. Aiming at the non-Gaussian working mode of the phased array weather radar, the reflectivity factor of the phased array weather radar can be quantitatively evaluated, and the radar detection performance is improved.

The invention discloses a microwave link-weather radar combined rainfall inversion method based on Z-R relation adaptive adjustment, and the method comprises the steps: defining a related parameter set of microwave link rainfall estimation and weather radar reflectivity measurement in observation time, and carrying out the matching and space-time averaging processing, and obtaining rainfall intensity-radar reflectivity characteristics; fitting a radar reflectivity-rainfall intensity relation within observation time by using a least square method based on the rainfall intensity-radar reflectivity characteristics; according to the radar reflectivity-rainfall intensity relation sample of the known position, calculating the spatial distribution of the radar reflectivity-rainfall intensity relation in the research area by using an inverse distance weighted interpolation method; and according to the spatial distribution of the radar reflectivity-rainfall intensity relationship within the observation time and the average reflectivity factor measured by the radar, calculating to obtain a spatial distribution result of the accumulated rainfall within the observation time. According to the invention, the accuracy of regional rainfall monitoring is effectively improved.

The invention relates to a weather radar beam shielding rate calculation method based on temporal-spatial resolution improvement, belongs to the field of radio, realizes adaptive precise temporal-spatial matching of meteorological unmanned aerial vehicle sounding data and ECMWF forecast data under different weather echo types, realizes calculation of spatial distance in grids in an expansion field, and improves the weather radar beam shielding rate. The method realizes preliminary extraction of spatial-temporal characteristics, constructs a self-attention mechanism model fused with physical perception, successfully generates a high-spatial-resolution atmospheric temperature, atmospheric relative humidity and atmospheric pressure data set, realizes generation of the high-spatial-temporal-resolution atmospheric temperature, atmospheric relative humidity and atmospheric pressure data set, and improves the spatial-temporal-resolution atmospheric temperature, atmospheric relative humidity and atmospheric pressure data set. Accurate calculation of the beam ray propagation path of the high-temporal-spatial-resolution radar is achieved, calculation of the shielding orientation and the maximum elevation angle of a shielding object in each orientation ray beam block is achieved, finally, a beam shielding power integral calculation model is introduced, and accurate calculation of the weather radar beam shielding rate is successfully achieved.

The invention relates to an improved weather radar echo short-time nowcasting method based on a diffusion model, and the method comprises the steps: obtaining a to-be-measured radar echo image, combining the to-be-measured radar echo image with time sequence information, inputting the to-be-measured radar echo image into a radar echo extrapolation model, and obtaining a denoised radar echo image; the radar echo extrapolation model is obtained by using a training set to train a diffusion model; the training set is a combined reflectivity data set; and processing the image blocks through a core denoising module in the radar echo extrapolation model, and predicting noise. According to the invention, the precision and anti-interference capability of short temporary rainfall prediction can be improved.

The invention provides a method for generating weather radar contour surface grid point data by considering beam width, and belongs to the technical field of weather radars, and the method comprises the following steps: obtaining radar base data, radar scanning range, target contour surface height, grid spacing and calculation precision, constructing a standardized grid, and obtaining a target contour surface grid point data set; the method comprises the following steps of: screening standard grid points meeting a radar scanning range, extracting data of which the current elevation angle meets a contour surface requirement, calculating a central azimuth angle of each layer of elevation angle and each radial wave beam, merging radar data of each layer under polar coordinates, and mapping radar data of the polar coordinates to a screened standard grid. The calculation complexity of the method is greatly optimized, and the calculation efficiency of the method is remarkably different from that of a traditional method.

The invention relates to the technical field of forest fire monitoring and power grid safety prevention and control, and discloses a fire scene spreading time sequence reconstruction method based on Doppler weather radar mountain fire echoes, which comprises the following steps: acquiring original data of the Doppler weather radar mountain fire echoes and carrying out quality control processing to obtain radar echo data; performing automatic segmentation of a smoke plume area on radar echo data by adopting an optimized Otsu-Unet network model, and extracting a centroid coordinate of a mountain fire echo area; a Kalman filtering algorithm is used and combined with external three-dimensional wind field data to predict a future motion trend of centroid coordinates of a forest fire echo region so as to reconstruct a continuous fire scene spreading time sequence voxel sequence; performing 3DTiles slicing processing on the fire scene spreading time sequence voxel sequence, and performing real-time rendering display on a power grid monitoring platform by using a WebGL technology; and obtaining an external expansion boundary of fire scene spreading based on the fire scene spreading time sequence voxel sequence, and calculating the distance between the external expansion boundary and the power transmission line GIS data in real time. The fire prevention and control capability of the power transmission line of the power grid is improved.

The invention belongs to the technical field of meteorological radars, and particularly relates to an airship-borne meteorological radar three-dimensional wind field inversion method based on multi-angle asymmetric coplanar analysis, and the method specifically comprises the steps: enabling an airship to fly linearly in a certain direction, and carrying out the volume scanning, doppler radial velocity and radar scanning information of a target observed at each parking position of the airship-borne meteorological radar are acquired, and the position and slant distance of the target are calculated; dividing every two parking position observation results into one group, and selecting a combination meeting performance optimization conditions based on the performance optimization conditions of the MA-ASCAT model; on the basis of an inversion formula of an MA-ASCAT model, first two dimensions under a cylindrical coordinate system are obtained through inversion by utilizing the screened combination, and a third dimension is solved according to the wind speeds of the first two dimensions on the basis of an incompressible mass continuity equation; and converting the three-dimensional wind speed under the cylindrical coordinate system into a Cartesian coordinate system, so that the three-dimensional wind field inversion method is completed.

The invention relates to the technical field of reflectivity factor testing, and discloses a weather radar reflectivity factor precision calibration testing system, which comprises a radar data acquisition module for acquiring reflectivity factor data and finishing time synchronization by adopting an IEEE1588 protocol; the reference data management module is used for generating an electronic reference data set by utilizing a block chain technology; the calibration processing module is used for performing multi-source joint correction on the reflectivity factor data, the rain gauge data, the standard meteorological target template and the environmental parameters, and outputting the corrected reflectivity data; the precision verification module is used for carrying out real-time precision test on the correction data based on a sliding window, continuously generating test results, forming a precision evaluation vector by adopting weighted average, extracting a quality index to obtain a comprehensive score, and judging whether the correction data is normal or abnormal; and the alarm and report module is used for triggering graded alarm in case of abnormity and generating a calibration certificate and a test report. According to the invention, the precision monitoring capability is improved, manual judgment errors are reduced, and the operation efficiency is improved.

The invention provides a radar rainfall real-time estimation system based on double-method fusion and ground verification, belongs to the technical field of meteorological radar rainfall estimation, and adopts double-method fusion of a physical model estimation module and a driving model estimation module to cooperate with a causal inference fusion engine to improve the precision and adaptability of rainfall estimation and improve the rainfall estimation accuracy. The physical model module adaptively selects a Z-R, ZDR-R or KDP-R relation according to the quality of radar-based data and rainfall scenes, drives the model module to capture space-time nonlinear evolution characteristics of rainfall echoes and predicts short-time rainfall dynamics, and the causal inference engine identifies differentiated characteristics of two estimation fields, inferes hail, vertical airflow and other physical hybrid factors, and performs real-time prediction on the hail, the vertical airflow and other physical hybrid factors. A comprehensive rainfall estimation field is generated, a continuous error correction field converted from ground station observation data is corrected through a space-time error atlas network, and finally rainfall estimation information is generated for real-time rainfall estimation, so that the adaptability to a complex weather field and the precision of the estimation field are improved.

The invention provides a severe convective weather early warning method and system based on monomer identification and lightning jump, and is applied to the technical field of data processing. The method comprises the following steps: processing high-precision lightning positioning data and meteorological radar data to generate convection monomer characteristic parameter information; processing the ground observation data to generate ground meteorological element associated information; processing the convective monomer characteristic parameter information and the ground meteorological element association information to generate severe convective monomer identification result information; processing the high-precision lightning positioning data based on the strong convection monomer identification result information to generate lightning jump judgment information; and processing the severe convection monomer identification result information and the lightning jump judgment information based on the target severe convection weather early warning model to generate severe convection weather early warning result information.

The invention provides a non-linear flight control method and system for an anti-wind disturbance unmanned aerial vehicle. The method comprises the following steps: acquiring wind field disturbance characteristics in real time by using a preset meteorological radar; inputting the wind field disturbance characteristics into a preset aerodynamic disturbance prediction model to generate an aerodynamic torque fluctuation prediction value; preset dynamic filtering processing is applied to the amplitude characteristic of the aerodynamic torque fluctuation predicted value so as to generate a control instruction correction signal; and the control instruction correction signal is coupled with a preset nonlinear control law of an unmanned aerial vehicle flight control system, so that unmanned aerial vehicle attitude offset caused by wind disturbance is inhibited in real time, and unmanned aerial vehicle nonlinear flight control is carried out. According to the method, the composite defect of hysteresis and filtering mismatch of traditional linear control in a strong wind sudden change scene is overcome, stable attitude control of the unmanned aerial vehicle in low-altitude wind shear and gust sudden attack is realized, the anti-interference response speed of the unmanned aerial vehicle is remarkably improved, and the out-of-control risk of the unmanned aerial vehicle is greatly reduced.

The invention discloses a recognition and trajectory tracking system for radar echoes with different intensities based on computer vision, and belongs to the technical field of meteorological data analysis and processing. The method comprises the following steps: firstly, acquiring meteorological chart data through a meteorological radar to obtain meteorological radar echo pictures of echoes with different intensities; secondly, preprocessing the meteorological radar echo images, constructing a radar echo image training data set, and then constructing and training to obtain a rainstorm detection model; and then identifying each frame of meteorological chart in the meteorological chart data by using a rainstorm detection model, outputting a corresponding rainstorm region bounding box, and calculating a dynamic moving path of the rainstorm region bounding box to realize real-time tracking of the rainstorm region. The method can automatically identify the rainstorm area and track the dynamic change of the rainstorm area, and provides important support for meteorological monitoring and disaster early warning.

The invention belongs to the technical field of photovoltaic power generation dispatching, and discloses an AI-based distributed photovoltaic power grid intelligent dispatching method and system, and the method comprises the steps: employing a double-layer architecture of an edge node and a federation center, enabling the edge node to carry out the preprocessing and encryption of local data, enabling the federation center to cooperatively train a global model through parameter exchange, and achieving the intelligent dispatching of a distributed photovoltaic power grid. Privacy risks caused by original data transmission are avoided, and multi-node data features are fused; model training samples are more comprehensive, and the load prediction precision is remarkably improved; a'meteorological field sensing-power mapping 'dual-channel model is constructed, a meteorological field sensing channel captures the relevance between adjacent photovoltaic power stations and meteorological stations by using a graph convolutional neural network, a power mapping channel processes multi-scale features through time sequence decomposition transformer, meteorological radar echo data is introduced to deal with sudden weather, and the meteorological field sensing-power mapping dual-channel model is established. The model can accurately grasp the illumination change in a complex meteorological scene, greatly reduces the prediction error of a multi-cloud rapid movement scene and the like, and improves the accuracy of power generation power prediction.

The invention discloses a rainfall prediction method based on multi-scale physical Transform, and the method comprises the steps: firstly collecting continuous historical radar observation data from a meteorological radar system, and carrying out the preprocessing of the data; secondly, according to the processed observation data, on the basis of a Transform network, capturing global semantics to obtain aggregation features; and then motion field and intensity residual field prediction is carried out based on the aggregation features to obtain a deterministic prediction sequence. And finally, constructing a generative network based on a multi-scale adapter, obtaining a rainfall probabilistic prediction map according to a deterministic prediction sequence, constructing a multi-scale space-time physical consistency loss function, and carrying out end-to-end training. According to the method, the problems of error accumulation, physical inconsistency, low calculation efficiency and the like in a traditional forecasting method are effectively solved, so that a brand new and accurate technical path is provided for real-time forecasting of the extreme rainfall event.

The invention discloses a mountain torrent disaster danger area dynamic demarcation method based on multi-source data coupling, and the method comprises the steps: obtaining meteorological radar rainfall forecast data, surface deformation monitoring data and high-precision DEM topographic data in real time, carrying out the data preprocessing, and generating a fused multi-source data set; constructing a distributed hydrological model, and calculating the production and convergence flow of the sub-basins; training a Seq2Seq-LSTM model on the basis of rainfall-water level-swelling speed time sequence data, predicting danger areas in a future set time period, and dynamically dividing high / medium / low risk areas by adopting an SOM-CLARA clustering algorithm and taking rainfall intensity, earth surface deformation rate and gradient as feature vectors; and determining the early warning level of each risk area in combination with the critical rainfall threshold and the spatial heterogeneity index. Through the multi-source data dynamic coupling and self-adaptive demarcation algorithm, the dangerous area range is dynamically adjusted, and the accuracy and timeliness of mountain torrent disaster risk prevention and control are remarkably improved.

The invention relates to the technical field of automatic detection and early warning of weather radar gust front, and discloses a weather radar gust front automatic identification system based on deep learning, which comprises a radar data acquisition and preprocessing module used for acquiring radar echo data from weather radar equipment, carrying out format conversion on the data, and sending the converted data to a server; the radar reflectivity is converted from a polar coordinate format to a Cartesian coordinate format, and meanwhile, normalization processing of the radar reflectivity value is carried out; and the feature extraction and region candidate generation module is used for performing feature extraction on the radar image by adopting a deep convolutional neural network and generating a target region candidate box through a region candidate network, and the region candidate network comprises a classification branch and a regression branch. According to the method, rapid and accurate gust front identification is realized through a deep learning target detection and mask segmentation technology, the stability and robustness of detection are remarkably improved, false alarms and missing alarms are effectively reduced through dynamic alarm threshold calculation and trend analysis, and the accuracy and timeliness of alarm are improved.

The invention discloses a weather radar data quality control method based on a dynamic clutter map and texture features, and relates to the technical field of weather radars, and the method comprises the following steps: inputting radar body scanning data; clutter frequency statistics is carried out; generating a mask matrix based on the clutter map and a reflection threshold, preliminarily determining clutter positions based on the mask matrix, and preliminarily filtering clutters; defining 9 * 9 window sliding capture scanning elevation angle data, extracting speed features and spectral width texture features of each layer of scanning elevation angle clutter region, and calculating a speed standard deviation and a spectral width texture standard deviation; performing secondary judgment on the preliminarily filtered clutters by combining the speed standard deviation and the spectral width texture standard deviation, filtering echoes which are secondarily judged to be clutters, and retaining other echoes which are not secondarily judged to be clutters; filling the holes; and outputting the processed radar data. According to the invention, the accuracy and reliability of weather radar data are improved, so that higher-quality data support is provided for meteorological monitoring and forecasting.

The invention discloses a lightweight short-time rainfall prediction method based on context attention fusion, and the method introduces multiple structural improvements on the basis of UNet, and comprises the steps: enabling a deformable convolution module to enhance the extraction capability of local complex features; the context expansion perception module improves the capability of capturing multi-scale features; the adjacent layer feature fusion module realizes information flow and detail retention capability among different levels of features; the SMamba module improves the long-range dependence modeling capability of the model, enhances the time sequence understanding of the model, introduces a depth separable convolution and jump connection mechanism in the encoding-decoding process, reduces the parameter quantity of the model, and improves the training stability of the model. According to the meteorological radar image sequence prediction method, the model parameter quantity and the calculation overhead are remarkably reduced on the premise of keeping the prediction precision, so that the model is more suitable for an application environment with higher requirements on the real-time performance and the resource consumption, and meanwhile, the model has good performance in a meteorological radar image sequence prediction task and has better application potential.

The present invention discloses a multi-source heterogeneous meteorological data assimilation and fusion method, which includes the following steps: synchronously acquiring meteorological data from multiple data sources through a distributed data interface and performing preprocessing, where the data sources include satellite remote sensing data, ground meteorological station data, and meteorological radar data; after completing the preprocessing of the meteorological data from multiple data sources, based on the weighted least squares method, respectively fusing the data from multiple data sources into meteorological element values of each meteorological element; assimilating the obtained meteorological element values with a numerical weather prediction model. The present invention has the following advantages and effects: The present invention can accurately identify abnormal data and perform targeted repair, and dynamically adjust the weights of data sources through weighting, effectively avoiding the interference of low-quality data on the fusion result, and then improving the final prediction accuracy by improving the numerical weather prediction model assimilation step.

The invention discloses a radar sounding-based hail grading early warning method and system for a convective monomer hazard index and jump increase, and relates to the technical field of severe convective weather early warning. According to the method, monomer segmentation is carried out and a severe convection monomer hazard model is constructed based on regional multi-weather radar data and air sounding data, a discrimination threshold is determined based on local air sounding data, and segmented convection monomers are screened for three times to obtain classification types of the convection monomers; and on the basis, tracking a hazard index track of a convective monomer core area in real time, and when a monomer hazard index shows a remarkable jump characteristic in a set time window, dynamically judging the occurrence probability and grade of hail, and pushing a digital alarm. The classification, digitization and progressive algorithm of the invention fuses multi-source data collaborative analysis, dynamic threshold determination and real-time evolution tracking, and significantly improves the timeliness and accuracy of the turning point of the heavy rainfall hail severe convection weather process and hail classification early warning identification.

The embodiment of the invention provides a method and device for generating a convective scale atmosphere reanalysis product in a target area, and is applied to the technical field of meteorology. Specifically, according to the embodiment of the invention, an hour-by-hour convective scale atmosphere reanalysis system adaptive to a target area can be constructed based on a high-resolution mesoscale numerical forecasting mode system adaptive to the target area, and then the hour-by-hour convective scale atmosphere reanalysis system is used for reanalyzing collected global hour-by-hour atmosphere products. Processing an hour-by-hour observation data set of a target area, a weather radar networking three-dimensional reflectivity data set, a total satellite cloud amount and an equivalent black body brightness temperature data set; and obtaining a three-dimensional atmosphere reanalysis product, a three-dimensional hydrogel reanalysis product and a three-dimensional reflectivity reanalysis product of the hourly convection scale of the target area and a rainfall forecast product of the target area in the future one hour. In this way, the precision and reliability of reanalysis products in the target area can be improved, and then meteorological refinement research and business requirements are met.