129 results about "Radar reflectivity" patented technology

The invention relates to a wind resource prediction method based on quality control assimilation of multi-source observation data, and the method comprises the steps: integrating the multi-source observation data, including a multi-band radar reflectivity factor, a radial speed, a ground observation station, and wind profile radar data; quality control processing such as mottle removal, data filling, speed folding removal and discontinuous data removal is carried out, so that the accuracy and continuity of the data are improved; then, through lattice point interpolation and three-dimensional variation assimilation technologies, the processed observation data are fused into an initial background field, and an analysis field closer to the actual atmospheric state is generated, so that the reliability of severe convective weather risk assessment is remarkably improved, and the accuracy of wind energy prediction is also enhanced; and more comprehensive, detailed and stable information support is provided for weather forecast and energy management.

The invention discloses a thunder and lightning positioning method and system based on multi-source data fusion, and belongs to the technical field of thunder and lightning monitoring and positioning. The method comprises the following steps: in a preset time window, collecting first and second arrival time of a lightning event received by each ground lightning detection device, and calculating the arrival time difference; and extracting a lightning detection distance, a capture time difference and an observation included angle in combination with the lightning area image, obtaining an electric field change rate, a radar reflectivity and an equipment distance, substituting into a combined lightning positioning formula, and calculating a space distance between a lightning striking point and the first receiving equipment. Through the position information of the equipment, the geographical coordinate of the lightning striking point is calculated. According to the scheme, the spatial resolution and accuracy of lightning positioning can be effectively improved, positioning errors caused by a traditional radar or single-point monitoring method are reduced, simultaneous positioning of a plurality of lightning striking points is supported, and accurate data support and spatial decision basis are provided for subsequent lightning risk early warning, power equipment protection and disaster traceability.

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 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 relates to the technical field of artificial influence weather, and discloses a multi-source data fused artificial precipitation enhancement operation control method, which comprises the following steps: acquiring cloud body data of a target cloud body, including cloud body height, radar reflectivity and temperature intervals and boundaries of a plurality of temperature layers, and executing cloud body type judgment. Firstly, whether a preset temperature layer exists or not is judged, and if the preset temperature layer does not exist or the height of the cloud body is lower than a preset threshold value, the cloud body is determined as a non-operation cloud body; if the cloud body exists and the height meets the condition, calculating the fluctuation amount of the height, the temperature and the reflectivity, and dividing the cloud body into a first target cloud body or a second target cloud body according to the fluctuation amount. If the target cloud body is the first target cloud body, directly issuing a rainfall enhancement instruction; and if the target cloud body is a second target cloud body, carrying out secondary data acquisition and judgment, and if the target cloud body is still the second target cloud body, giving up the operation. Therefore, the scientificity and the accuracy of the rainfall enhancement operation are guaranteed.

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 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 relates to a satellite-ground radar reflectivity factor consistency evaluation and deviation traceability method and system, and belongs to the field of radar calibration evaluation, and the method comprises the steps: generating reference ground radar scanning parameters according to geographical environment multi-source heterogeneous data; acquiring satellite-ground radar data according to the scanning parameters of the reference ground-based radar when the satellite-borne radar passes the scene, and performing preprocessing and space-time matching to obtain a satellite-ground radar data pair; based on the target correction information of each height level region, performing sectional frequency correction on the spaceborne radar data to obtain an optimal satellite-ground radar data pair; and carrying out reflectivity factor consistency evaluation on the optimal satellite-ground radar data pair, taking an obtained evaluation result as a tracing reference, and carrying out deviation tracing calibration estimation on the service ground-based radar in combination with a satellite-ground radar reflectivity factor consistency evaluation result of the service ground-based radar. According to the method, the technical problems of precise evaluation of systematic deviation of satellite-ground radar data and unified evaluation and calibration of multi-service satellite-ground radars are solved.

The invention relates to a verification method and system for ground-based millimeter wave cloud radar data, and belongs to the technical field of meteorological analysis. The method comprises the following steps: establishing a clear sky echo identification and filtering method, and generating ground-based millimeter wave cloud radar base data after quality control; classifying the ground-based millimeter wave cloud radar base data after quality control into cloud data and rainfall data; the Cloud Sat satellite and the ground-based millimeter wave cloud radar after quality control have reflectivity factor matching and evaluation in cloud data; and matching and evaluating reflectivity factors in GPM satellite and quality-controlled ground-based millimeter wave cloud radar rainfall data. According to the invention, a ground-based millimeter wave cloud radar quality control and classification method is established, a high-quality refined ground-based millimeter wave cloud radar database can be generated, and a good basis is provided for ground-based millimeter wave cloud radar data application; a matching and evaluation method of spaceborne radar and ground-based millimeter wave cloud radar reflectivity factors under different conditions is established, and reference is provided for ground-based millimeter wave cloud radar calibration correction and satellite-ground radar combined application.

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.

This invention discloses a radar echo extrapolation method that incorporates three-dimensional environmental field data. Radar baseline data is collected; each set of 21 consecutive radar reflectivity maps is converted into a sample, with the first 11 serving as input and the last 10 serving as labels. Based on the sample information, matching ERA5 global analysis data is collected. The radar reflectivity sequence and the corresponding environmental field are used as input to train a 3D convolutional neural network model. Experimental results show that the inclusion of the environmental field improves the extrapolation performance compared to a model without the environmental field, and is more consistent with meteorological interpretation of the extrapolation principle. By incorporating simultaneous three-dimensional environmental field data, the invention achieves accurate constraints and corrections on the extrapolation results.

The invention relates to the technical field of meteorology and hydrology, and discloses a quantitative rainfall estimation method and system based on multi-source data correction radar echoes, and the method comprises the steps: collecting the multi-source data, such as rainfall station actually measured rainfall data, elevation data, hydrometric station flow, radar reflectivity, soil humidity and environmental attributes, in a target region; a Z-R relation parameter of each radar sampling grid is dynamically calibrated by combining a collaborative Kriging interpolation and physical constraint hybrid neural network model, and a radar reflectivity correction parameter of each grid is calculated by using a difference between rainfall station observation data and radar data; in real-time application, based on the corrected radar reflectivity and the calibrated Z-R parameter, the rainfall intensity of each grid is inverted, and the precision of quantitative rainfall estimation is improved.

The invention relates to a phased array radar reflectivity factor simulation method based on sparse sounding data, belongs to the field of radio, designs a sparse reconstruction simulation scheme based on acquired multi-point multi-height-layer sparse sounding data, and realizes high-quality restoration and three-dimensional continuous field construction of original sparse observation data. A multivariate dictionary base matrix is constructed; through a sparse representation theory and a Bayesian optimization model, a sparse coefficient reflecting rainfall essential characteristics is obtained, and high-spatial-resolution reconstruction of a temperature and humidity field and a rainfall micro-physical parameter field is realized. Thereafter, a meteorological parameter-driven phase judgment and probability field calculation model is constructed, and three-dimensional simulation of liquid, solid and mixed rainfall echoes is realized. And finally, calculating a unit direction vector and an azimuth guidance coding matrix of a beam ray of the phased array radar, introducing a radar volume scanning data implicit sampling simulator, and realizing high-precision reflectivity factor simulation of the phased array radar in a three-dimensional scanning mode.

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.

The present application relates to a method and device for forecasting severe convective weather based on spatiotemporal cross-convergence mapping. The method comprises: first, pre-processing radar reflectivity data and meteorological element data at the onset of convection; then, calculating the correlation between the meteorological element data and the area of ​​strong echo in the radar reflectivity, screening candidate factors based on the significance level, and constructing a candidate factor set; finally, calculating the causal relationship between the candidate factors and the radar reflectivity through spatiotemporal cross-convergence mapping; if a causal relationship exists, further calculating the strength of the causal relationship, and using the candidate factor as a severe convection forecast factor. This method provides input for severe convection forecasting by analyzing the severe convection forecast factors, which helps to improve the accuracy and timeliness of severe convective weather forecasts.

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.

The invention provides a miniaturized in-situ detection probe for radar reflectivity of a wave-absorbing coating, and belongs to the technical field of wave-absorbing performance testing of coating materials. The probe is based on a coaxial transmission line structure, a traditional uniform-section coaxial line is optimized into a gradient structure, and a step and ridge structure is introduced, so that the fringe effect introduced when the probe is loaded on the surface of a wave-absorbing coating to be detected is effectively reduced, that is, a microwave near field at the end face of the probe is corrected into a free space plane wave field distribution form through a physical structure; therefore, the test result of the vertical reflectivity of the coating can be compared with the reflectivity of the coating under the vertical incidence of free space waves. For a 2-18GHz test frequency band, the diameter of a testable coating area of the test probe can be 20mm to the minimum extent.

The invention belongs to the technical field of electromagnetic wave-absorbing materials, and relates to wave-absorbing slurry, polyurethane-based radar wave-absorbing foam as well as a preparation method and application. By adjusting the content ratio of the purified water, the polyurethane resin, the conductive carbon black / acetylene carbon black powder, the silane coupling agent, the anti-adhesion additive and the defoaming agent, the movement of the absorption peak of the polyurethane-based radar wave-absorbing foam between high frequency and low frequency in the frequency range of 2-18 GHz can be adjusted; the optimal average radar reflectivity performance at 2-18 GHz can reach-20 dB, the optimal average radar reflectivity performance at 8-18 GHz can be smaller than or equal to-23 dB, and the optimal radar reflectivity wave-absorbing performance at 13.6 GHz can reach-35 dB. The low-density polyurethane foam is used as a base body, the wave-absorbing slurry is immersed into a grid framework of the base body, the wave-absorbing layer is formed after drying, and meanwhile, the overall weight gain of the sponge is small, so that the polyurethane-based radar wave-absorbing foam achieves the light wave-absorbing effect.

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 relates to the technical field of weather modification, and discloses a multi-source data fusion artificial rain enhancement operation control method, which comprises the following steps: obtaining cloud body data of a target cloud body, including cloud body height, radar reflectivity, and temperature intervals and boundaries of multiple temperature layers, and performing cloud body type judgment. Firstly, it is judged whether a preset temperature layer exists, if not or the cloud body height is lower than a preset threshold, the cloud body is determined as a non-operation cloud body; if the preset temperature layer exists and the height meets the condition, the fluctuation amount of the height, temperature and reflectivity is calculated, and according to the fluctuation amount, the cloud body is divided into a first target cloud body or a second target cloud body. If the cloud body is the first target cloud body, a rain enhancement instruction is directly issued; if the cloud body is the second target cloud body, secondary data acquisition and judgment are carried out, if the cloud body is still the second target cloud body, the operation is abandoned. Therefore, the scientific nature and precision of the rain enhancement operation are guaranteed.

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.