91 results about "Radar reflectivity" patented technology

The invention relates to the technical field of radar reflectivity factor calibration, and discloses a terahertz cloud measurement radar calibration method based on channel perception, which comprises the following steps: S1, acquiring two types of input data; s2, constructing a multi-scale channel model; s3, constructing a distance error estimation model; s4, constructing a dynamic time correction model; and S5, constructing a calibration module. According to the method, a high-precision physical channel model is constructed to independently predict path attenuation of signals, the concepts of gain deviation and path attenuation factors are put forward, the influence of thermal noise attenuated by radar on radar waves in the atmospheric environment is reflected respectively, and a Kalman filtering algorithm is used to estimate the path attenuation of the signals. And filtering errors generated in the time direction, and finally, calibrating the radar reflectivity factor by using the channel data by means of a calibration formula.

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 discloses a convective cloud in-cloud temperature gradient joint inversion method based on a laser radar, and relates to the technical field of meteorological detection, and the method comprises the steps: synchronously collecting multi-modal meteorological remote sensing data through a multi-modal meteorological remote sensing device; constructing an adversarial unsupervised correction network, inputting the multi-modal meteorological remote sensing data into the adversarial unsupervised correction network, and generating corrected multi-source observation data; mapping the corrected multi-source observation data to a uniform space-time grid to form a space-time aligned multi-modal data set; and based on the vertical profile of the temperature gradient, in combination with the radar reflectivity factor and the fusion feature vector, calculating the vertical distribution of the liquid water content, the water vapor density and the particle effective radius in the convective cloud, and generating a quantitative analysis report of the macro and micro characteristics of the convective cloud. According to the invention, by constructing the antagonistic unsupervised correction network, errors in multi-source observation data can be effectively corrected, and a high-quality data basis is provided for fusion and inversion in meteorological service.

The invention provides a plateau multi-source rainfall data fusion method and device based on a deep bidirectional sequential network. The method comprises the following steps: acquiring historical multi-source rainfall data; constructing and training a bidirectional time sequence attention multi-source fusion network model, and performing feature extraction and fusion calculation training; and performing precipitation estimation on the target area by using the bidirectional time sequence attention multi-source fusion network model, and outputting fused precipitation grid point data. Through multi-branch feature fusion, the network shows good nonlinear expression ability when processing a multi-scale rainfall field, the estimation precision of complex terrains and extreme rainfall events is effectively improved, a lightweight design is adopted, the network greatly reduces the calculation complexity while guaranteeing the multi-source rainfall fusion precision, and the calculation efficiency is improved. The method improves the flexibility of practical application, improves the fusion precision of multi-channel information such as radar reflectivity, satellite brightness temperature and ground rainfall, and greatly improves the precision and calculation efficiency of multi-source rainfall fusion.

The invention relates to the technical field of rainfall prediction, in particular to a high-frequency second-length time sequence short-imminent forecasting method and system based on generative AI, and the method comprises the following steps: compressing an original radar reflectivity sequence into a compact hidden variable Token sequence through employing a pre-trained variational auto-encoder; in the compressed hidden space, with the hidden variable Token sequence as a condition, a prediction hidden variable Token sequence in a future time period is generated step by step in a probability generation mode; and reconstructing the predicted hidden variable Token sequence into a predicted radar reflectivity sequence in a future time period by using a decoder of the variational auto-encoder. According to the method, high-resolution and long-time-efficiency radar nowcasting is realized, the accuracy of rainfall forecasting in a 2-6-hour short-term and imminent forecasting grey area time window is improved, and a solid support is provided for constructing early warning and accurate decision-making of extreme weather.

The invention discloses an automatic driving risk assessment method and device, a vehicle, equipment and a medium, and belongs to the technical field of automatic driving. The method comprises the following steps: respectively acquiring a visual image and a radar reflectivity graph of a target road surface; after time synchronization and space calibration are carried out on the visual image and the radar reflectivity graph, visual features of an area where a lane line is located are extracted from the visual image by adopting a convolutional neural network, a target reflectivity area higher than a reflectivity threshold in the radar reflectivity graph is identified, and reflectivity distribution features of the target reflectivity area are extracted; fusing the visual features and the reflectivity distribution features according to time synchronization and space calibration results to obtain a lane line comprehensive feature set; generating a lane line sensing result according to the lane line comprehensive feature set; and matching is performed from the risk level relationship according to the aging degree of the lane line and the complexity of the road surface, and the target risk level corresponding to the lane line modeling information is obtained, so that the safety of automatic driving is improved.

This invention discloses a short-term precipitation prediction method based on big data. The method includes radar data acquisition, radar echo image interference suppression, spatiotemporal feature encoding construction, differentiated adversarial loss design, short-term precipitation prediction model construction, and short-term precipitation prediction. This invention belongs to the field of data processing, specifically referring to a short-term precipitation prediction method based on big data. This scheme specifically extracts near-surface precipitation-related echo information and removes interference from redundant upper-air elevation angle data; using AWS grid precipitation observations as ground truth anchors, it constructs a joint logical calibration rule for radar reflectivity and surface precipitation; it designs differentiated loss weights, introducing strong and weak scaling factors to impose a heavier loss penalty on heavy precipitation samples; it introduces interference suppression coefficients and interference tolerance boundaries to constrain the loss contribution of interference samples; it designs a loss adjustment function to smooth the loss curve and improve the learning sensitivity of heavy precipitation samples; thereby improving the final short-term precipitation prediction effect.

The present application relates to the technical field of precipitation forecast, solves the problem that the prior art is prone to underestimate the peak value of precipitation or produce false alarm under strong convective weather, and particularly relates to a short-impending precipitation forecast method fusing GNSS and water conservancy rain measurement radar, GNSS observation data and water conservancy rain measurement radar base data in a monitoring area are acquired, pretreated, corresponding atmospheric precipitable water distribution field and radar reflectivity factor distribution field are generated, based on a preset time sliding window, time sequence characteristics of the atmospheric precipitable water distribution field and the radar reflectivity factor distribution field are extracted, and the two are superimposed in channel dimension to construct a multi-channel spatio-temporal fusion input tensor. The present application overcomes the defect that the traditional extrapolation method rapidly decays with the prolongation of the forecast time, significantly improves the prediction accuracy of the rainstorm center intensity and the falling area, reduces the missed report and underestimation of the precipitation event, and solves the problem of low prediction accuracy of the traditional linear extrapolation algorithm under strong convective weather.

The application discloses a low-resolution radar reflectivity factor refinement method based on a phased array radar, relates to the radio technology field, and comprises the following steps: time alignment is performed on the cleaned phased array radar linear domain reflectivity factor and the cleaned low-resolution radar linear domain reflectivity factor, and simulation is performed based on an interpolation model; space geometry matching is performed on the cleaned phased array radar linear domain reflectivity factor and the cleaned low-resolution radar linear domain reflectivity factor, and simulation is performed on the phased array radar linear domain reflectivity factor dataset based on a multi-base radar detection data conversion model and a coarse beam simulation operator; a residual network model based on a self-attention mechanism is trained and iteratively optimized to obtain an optimized model, the cleaned low-resolution radar linear domain reflectivity factor data are input into the optimized model, and low-resolution radar spatial refined reflectivity factor data are output. The application can improve the authenticity, physical consistency and business availability of the refined reconstruction result.

A yarn structure (14) is provided for use in a camouflage net. It comprises first (3), second (4) and third (5) yarn elements comprising, respectively, polymer, metal and carbon- or graphene-based material. The yarn elements are combined to form the yarn structure. The second yarn element (4) is present in an amount sufficient to provide radar scattering capabilities. The third yarn element (5) is present in an amount sufficient to reduce radar reflectivity of the yarn structure, compared to a yarn structure without the third yarn element.

The invention relates to a process arrangement for simulating a radar target at a relative velocity to a radar sensor (3), comprising at least one test unit (1) with a rotor (7) rotatably mounted about a rotor axis, the rotor having a number of test elements (11, 29, 31) distributed in the circumferential direction of the rotor, which reflect an electromagnetic wave (10) emitted by the radar sensor (3) back towards the radar sensor (3). According to the invention, for optimized radar reflection (12) towards the radar sensor (3), the test elements (11, 29, 31) are configured such that the test unit (1) provides a constant radar reflectance surface (RCS) during rotor rotation over at least one rotor rotation angle range (α).

The embodiment of the invention provides a weather radar consistency evaluation method and device based on raindrop spectrum parameters, and is applied to the technical field of atmospheric detection. The method comprises the following steps: carrying out quality control processing on raindrop spectrometer data; performing radar reflectivity factor data space matching on the weather radar data and the quality-controlled raindrop spectrometer data based on a preset space matching rule to obtain a radar reflectivity factor value and scanning time of the weather radar corresponding to the raindrop spectrometer position; based on a preset time matching rule, according to the radar reflectivity factor value of the weather radar corresponding to the raindrop spectrometer position and the scanning time, carrying out radar reflectivity factor data time matching on the weather radar data and the raindrop spectrometer data after quality control to obtain matched raindrop spectrum parameters; and evaluating the matched raindrop spectrum parameters based on a preset evaluation rule. In conclusion, on the basis of ground raindrop spectrum parameters, verification and evaluation of weather radar reflectivity factor data can be carried out through space-time matching.

The invention relates to the technical field of weather radar networking, in particular to a multiband networking weather radar reflectivity factor puzzle fusion method and system, and the method comprises the following steps: extracting an observation overlapping region of an S-band weather radar network and an X-band weather radar network, and generating mask data containing geographic information; on the basis of the mask data, generating puzzle data on an equal-altitude surface with different S-band weather radar reflectivity factors; based on the mask data, generating jigsaw data on an equal-altitude surface with different X-band weather radar reflectivity factors; and based on a fusion algorithm, fusing the reflectivity factor puzzle data of the S wave band and the X wave band. According to the invention, the networking observation data of the S-band and X-band weather radars are subjected to jigsaw and fusion processing to be processed into a new data product, the collaborative observation result of the S-band and X-band networking weather radars and the low-altitude blind compensation effect can be visually presented, and a data product support is provided for a meteorological department to monitor and early warn weather with strong disastrous conditions.

An apparatus determines a lateral position of a land vehicle on a pathway on which one or more targets are distributed on a surface of the pathway in a direction of travel. The targets have different radar-reflectivity than the surface. The apparatus comprises a radar sensor and a processor. The radar sensor is mounted to the vehicle and configured to transmit a radar signal having a beam pattern center aimed downward from the vehicle toward the surface of the pathway and sideways from the vehicle generally perpendicular to the direction of travel. The radar sensor is configured to detect radar from the pathway and to detect a substantial absence of reflections from the targets. The processor is operatively connected to the radar sensor and configured to determine a lateral position of the vehicle from a boundary between the surface of the pathway and at least one of the targets.

The application discloses a radar data blind area filling method and system based on 3D-GANs, and the method comprises the following steps: based on the hierarchical characteristics of three-dimensional radar data, a simulated blind area mask is constructed and spliced with corresponding radar reflectivity data to form an input tensor; the input tensor is input into a generator network to output the completed radar volume data; the generator network adopts an improved three-dimensional U-Net structure based on a three-dimensional attention mechanism, and in the encoding layer downsampling, the whole is compressed first, then horizontally compressed to reserve the vertical structure; in the process of the skip connection, a three-dimensional attention module is introduced to weight and control the low-order features output by the encoder; the real radar data and the completion result are compared and discriminated through a three-dimensional discriminator network, and the generator network performance is optimized in combination with the adversarial loss and the reconstruction loss. The application can fill the blind area of three-dimensional radar observation data, keep the echo structure consistency, and improve the detail restoration ability of three-dimensional radar data.

The invention relates to the technical field of weather radar synthesis, in particular to a weather radar reflectivity synthesis method and system based on a synchronous stationary satellite. The method comprises the following steps: acquiring satellite multi-channel observation data and weather radar data; performing data preprocessing based on the acquired satellite multi-channel observation data and weather radar data; the method comprises the following steps of: constructing a weather radar synthesis model based on an improved Swin-Transform and a U-Net; performing model training on the constructed weather radar synthesis model; and performing forward reasoning by using the trained model and obtaining weather radar reflectivity synthesis. According to the method, high-quality weather observation data of the areas are provided, the problem of data vacancy of the weather radar is solved, the observation range of the weather radar is remarkably expanded, and particularly, the monitoring capability of a large-scale weather system and severe convective weather is improved.

Embodiments of the present application provide a laser radar reflectivity calibration method, a measurement method and related devices, wherein the calibration method constructs a curve equation between pulse width and light energy by using echo data under fixed gain, and constructs a curve equation between distance and dynamic gain by using echo data under fixed gain and echo data under dynamic gain, then uses the two curve equations to calculate the distance calibration value and light energy calculation value corresponding to one echo data under dynamic gain, then uses the relationship between reflectivity, distance and light energy to calculate the current reflectivity calculation value based on the distance calibration value and the light energy calculation value, that is, the reflectivity calculation value under dynamic gain is obtained, and finally the reflectivity calculation value and the actual reflectivity are calibrated to obtain the calibration relationship between the reflectivity calculation value and the actual reflectivity of the laser radar under dynamic gain.

This invention discloses a method, apparatus, device, and storage medium for intelligent rainfall correction. The method includes: spatiotemporal matching and normalization of X-band phased array radar reflectivity data and meteorological grid real-time data to construct sample pairs; using a dual-branch network to extract features from the two types of data respectively, and strengthening key areas by introducing an attention mechanism; using a downsampling attention module to extract multi-scale spatial features, and then using a cross-modal residual embedding module to achieve deep fusion of local texture features of radar data and global physical features of meteorological data; using an enhanced upsampling channel attention module to progressively upsample the fused features to restore resolution, and combining skip connections and feature alignment, using channel attention to adaptively adjust information at each scale to optimize detail reconstruction. Finally, the constructed samples are used to train the network to obtain an intelligent rainfall data correction model, thereby improving the accuracy and reliability of correcting meteorological grid real-time data.

The application discloses a radar reflectivity inversion method based on satellite infrared data and lightning observation data, and belongs to the technical field of meteorological monitoring. The three-channel input image formed by lightning observation data and two infrared images from different infrared channels is taken as the input of the scattering model. Since lightning activity is closely related to severe convective weather, the scattering model can help to invert radar reflectivity in areas where radar data is missing, such as the open sea, plateau and desert. The application not only makes up for the insufficient accurate modeling of severe weather events in the prior art, but also provides more comprehensive and more dimensional input information for radar data reconstruction, and significantly improves the accuracy of radar reflectivity inversion.

The invention discloses a thunderstorm splitting and merging process detection method and system based on a long-term memory algorithm, and relates to the technical field of meteorological monitoring and early warning, and the method comprises the steps: receiving multi-source meteorological data (Doppler radar reflectivity data and lightning positioning data), carrying out the collaborative preprocessing of the two types of data, and obtaining a multi-source data preprocessing result; obtaining space-time sequence sample effective data adaptive to thunderstorm detection; extracting a long-time sequence labeling sample in the effective data, inputting the long-time sequence labeling sample into a pre-constructed long-time memory network model for training, and outputting the trained long-time memory network model (the model comprises three core stages of feature extraction, feature fusion and feature decoding); and inputting effective data of the space-time sequence sample into the trained long-time memory network model, and outputting a detection result (including starting time, position and dynamic trend) of a thunderstorm splitting and merging process.

This invention discloses a rainstorm tracking and analysis method based on multi-source rainfall monitoring data collaboration. It constructs a dual-source input channel for rainfall in the cloud from a rain-measuring radar and for rainfall measured at ground rain gauges, collecting radar reflectivity, its inverted rainfall, and ground-measured rainfall data, and extracting key parameters. A time window of 1-2 hours is set to trace back from the current point, with the first appearance of the rainstorm center as the tracking starting point. The 1-hour window is iteratively updated to track the evolution of the rainstorm center's location, coverage area, and other dimensions. An adaptive weighted intelligent algorithm is used to predict the movement direction of the rainstorm center based on historical data. Three types of cloud physical characteristics—resident, mobile, and train-like multi-center mobile—are integrated to construct a complete analysis model and output results. This invention achieves full-link rainfall monitoring, improves the comprehensiveness of rainstorm tracking and prediction accuracy, and solves the problems of blind spots in monitoring from single data sources, poor collaboration among multi-source data, and insufficient accuracy in rainstorm tracking and prediction in existing technologies.

The embodiment of the invention provides a short and temporary rainfall forecasting method, device and equipment and a storage medium, and the method comprises the steps: obtaining a radar reflectivity factor and reanalysis data of a research region, segmenting the research region through the radar reflectivity factor and reanalysis data, obtaining a segmentation sample set, and obtaining a short and temporary rainfall forecasting result; training a four-classification model and a rainstorm classification network according to the segmented sample set, determining a predicted rainstorm type by using the trained four-classification model and the rainstorm classification network in combination with live data of a research area, matching a rainstorm prediction model of a corresponding type according to the predicted rainstorm type, and outputting a prediction result. Through parting prediction and physical mechanism decoupling, the prediction timeliness is prolonged, and the accuracy of rainstorm falling area and intensity prediction is improved in combination with numerical prediction and a physical mechanism.

The invention discloses a tropical rainfall identification method based on S and C wave band weather radars, and belongs to the technical field of tropical rainfall identification, and the method comprises the steps: forming a rainfall vertical reflectivity profile based on radar reflectivity data obtained by S and C wave band single polarization radars; acquiring 0 DEG C layer height data, and determining the initial height of tropical rainfall identification in combination with the rainfall vertical reflectivity profile; searching a local radar reflectivity maximum value Zmax in the rainfall vertical reflectivity profile from the initial height downwards; according to the method, a rainfall reflectivity vertical profile is taken as a core identification basis, a starting height is determined by combining 0 DEG C layer height data, a local reflectivity maximum value Zmax is searched through layer-by-layer traversal, and multi-dimensional judgment conditions such as melting layer bright band judgment and a difference threshold value of Zmax and Zmin are introduced, so that the accuracy of the rainfall reflectivity is improved. The method can accurately identify the core characteristic of tropical rainfall that the low-layer reflectivity is enhanced along with the reduction of the altitude, prevents the common convective or stratified cloud rainfall from being misjudged as tropical rainfall, and effectively improves the identification precision.

The invention discloses a position and influence-oriented precipitation echo near-real-time simulation and correction method, which comprises the following steps of: performing primary correction on near-real-time radar precipitation echo data by using near-real-time lightning data through a lightning-radar reflectivity inversion model, and performing secondary correction on the near-real-time radar precipitation echo data by using near-real-time automatic station precipitation data; performing secondary correction on the radar rainfall echo reflectivity data after the primary correction through an established rainfall rate model to obtain corrected real-time radar echo rainfall data; recognizing a storm object in the radar echo through a multi-threshold object recognition algorithm; performing radar echo object matching based on a Hungary solution, performing identification tracking on the identified storm object, and calculating the echo rainfall intensity, the influence range, the moving speed and the moving direction of the storm object; and the influence of the storm object is judged according to the position of the user, and real-time storm alarm service is carried out. According to the invention, position and influence-oriented rainfall professional meteorological services are provided for users through near real-time lightning, automatic station rainfall, radar and other multi-source data fusion technologies.

The invention discloses a gust front identification method based on Hough transform, and belongs to the technical field of radar detection and identification, and the method comprises the steps: carrying out the processing of a preprocessed elevation radar reflectivity factor based on multi-sample statistics through employing a preprocessed dual-polarization parameter, and obtaining a reflectivity non-meteorological echo map; carrying out binarization processing and filtering processing on the reflectivity non-meteorological echo map; traversing each pixel region in the filtered reflectivity non-meteorological echo map to carry out region shrinkage and edge detection to obtain a reflectivity non-meteorological echo map after edge detection; and carrying out identification by adopting Hough transform to obtain linear position features of the gust front, splicing a gust front region by adopting joint constraint of endpoint distance and direction consistency to obtain a gust front image pixel position, and sketching to obtain a gust front identification result image of the original image. The gust front identification method solves the problems that an existing gust front identification method is high in dependence on a template, poor in adaptability and insufficient in identification precision and stability under the complex radar echo condition.

The invention provides a cloud detection and radar clutter filtering method and system based on a sunflower satellite, and the system comprises a weighted dynamic cloud index module, a cloud detection discrimination module, a radar reflectivity data quality control module, a space-time matching module and a non-rainfall clutter filtering module. Constructing a cloud detection model, and generating a cloud mask based on the cloud detection model; performing preliminary quality control on the radar reflectivity data; and performing space-time matching on the satellite cloud mask and radar data, and filtering non-rainfall clutters based on the cloud mask. The method has the beneficial effects that the sunflower satellite multispectral observation capability and radar detection advantages are fused, and efficient filtering of non-rainfall clutters is realized by constructing a satellite cloud detection-radar quality control-space-time fusion processing chain.

The invention discloses a migratory fly insect population average body length estimation method based on a polarization weather radar. According to the method, migration insect individuals are equivalent to long ellipsoid scatterers under the Rayleigh scattering condition, statistical averaging is carried out on insect body axis orientations, migration number density is introduced to serve as constraint parameters, and the migration insect population average body length inversion method based on the weather radar differential phase shift rate is constructed. And the model is verified and calibrated by combining electromagnetic scattering numerical simulation with weather radar and insect radar combined observation data, so that stable estimation of the average body length of the migrated insect population is realized. Compared with a traditional method depending on radar reflectivity and other amplitude information, the method is not sensitive to factors such as insect number fluctuation and distance attenuation, and the stability and reliability of the migrated insect body length estimation result are fundamentally improved. According to the method, the body shape effect and the group scale effect are effectively distinguished, and migrant fly group length inversion with clear physical significance is realized.

The present application belongs to the technical field of meteorological radar detection, and particularly relates to a radar reflectivity inversion method based on multiple meteorological modes; comprising: step 1: obtaining FY4B-AGRI satellite infrared data, X / C band dual-polarization radar reflectivity data and ERA5 reanalysis meteorological data; step 2: performing median filtering and adaptive threshold denoising on the FY4B-AGRI satellite infrared data; step 3: uniformly projecting the FY4B-AGRI satellite infrared data, the X / C band dual-polarization radar reflectivity data and the ERA5 reanalysis meteorological data to the WGS84 coordinate system, and spatially matching the X / C band dual-polarization radar reflectivity data and the ERA5 reanalysis meteorological data to the grid of the FY4B-AGRI satellite infrared data through bilinear interpolation; realizing spatial grid alignment through bilinear interpolation, and simultaneously linearly interpolating the ERA5 data to improve the time resolution from 1 hour to 15 minutes, synchronizing with the time base of satellite data, so as to systematically solve the fusion deviation problem caused by the time and space inconsistency of multiple sources of data.

The invention provides a meteorological and hydrological data analysis method and system based on multi-source data fusion, and relates to the technical field of data analys.The method comprises the steps that a to-be-analyzed area is divided into grids, and radar grid point rainfall intensity is calculated in combination with meteorological radar reflectivity factors and rainfall station data; correcting the soil water content by using the gradient, and calculating a grid disaster potential index in combination with historical disaster situation data; predicting a future direct runoff depth based on an LSTM model, and calculating a disaster risk index in combination with the disaster potential index; and finally, fusing the elevation change rate and the people flow density, constructing a multi-layer early warning trigger mechanism, and dynamically issuing disaster early warnings of different levels. Prediction precision is optimized through multi-source data fusion and machine learning, early recognition and graded early warning of disaster risks are achieved, scientificity and timeliness of disaster prevention and reduction decisions are improved, and the method is suitable for refined monitoring and early warning of meteorological and hydrological disasters such as urban waterlogging and mountain torrent disasters.

The invention discloses a three-dimensional atmosphere live field fusion product generation method, device and equipment based on machine learning. The method comprises the steps of background field downscaling, observation data preprocessing, observation data correction, three-dimensional live analysis and product generation. According to the method, the space texture generation capability of the GAN and the long-distance dependence modeling of the Transform are fused, hectometer-level resolution reconstruction of three-dimensional meteorological elements is realized, and system errors of different observation systems are eliminated based on an MLP correction model of a sounding benchmark. According to the method, three-dimensional space lattice point visualization of multiple meteorological elements such as temperature, humidity, wind field and radar reflectivity factors is achieved in a breakthrough mode, comprehensive deployment is achieved, deep fusion with an existing service system is achieved, the meteorological data visualization capability is remarkably improved, emerging application scenes such as disaster prevention and reduction and low-altitude economy are emphatically expanded, and the method has a wide application prospect. Value potential of meteorological data in the fields of industry enabling, disaster early warning and the like is deeply mined, and innovative kinetic energy is continuously injected for high-quality development of new-quality productivity enabling meteorological undertakings.