310 results about "Weather radar" patented technology

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 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 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 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 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 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 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 invention discloses an artificial intelligence rainstorm prediction model construction method based on multi-source heterogeneous data fusion, and relates to the technical field of rainstorm prediction, and the method comprises the steps: collecting multi-source meteorological data of a to-be-predicted region, the meteorological data comprising satellite remote sensing data, weather radar data, ground meteorological station data and numerical prediction mode data; dividing the to-be-predicted region into different dominant type regions based on a preset dominant factor discrimination rule, and constructing a virtual cloud cluster entity based on the dominant type regions; a motion model of a virtual cloud cluster entity is established, a life development track of the virtual cloud cluster entity is generated, and then a cloud cluster motion map reflecting dynamic evolution of the virtual cloud cluster entity is constructed; the system analyzes and generates an intervention instruction based on the cloud cluster motion map, projects the intervention instruction into the cloud cluster motion map, and finally outputs a rainstorm prediction report including deterministic early warning and potential risk assessment; according to the invention, a virtual cloud cluster entity system with dynamic perception and active intervention capabilities is constructed, and accurate prediction of the rainstorm formation process is realized.

The invention relates to the technical field of weather forecasting, in particular to an intelligent generation method for grid-level short and temporary forecasting for severe convective weather, which comprises the following steps: step 1, data acquisition: acquiring meteorological radar, satellite remote sensing, Internet of Things sensors, social media texts and urban infrastructure monitoring data in real time; step 2, model construction: taking a space-time diagram convolutional network as a backbone network, inputting a space-time feature tensor, and introducing a physical constraint term; step 3, generating a result: outputting a grid-level severe convection weather element distribution diagram in the next 0-2 hours by utilizing the prediction model; 4, feedback and optimization: dynamically calculating a correction factor based on the difference between the real-time monitoring data of the user side and the forecast result; and dynamically deploying the updated model to an edge computing node. According to the method, through physical constraint space-time diagram convolution modeling and user feedback driven edge adaptive optimization, an efficient, robust and landing intelligent solution is provided for severe convection weather short-term and imminent forecasting.

The invention discloses a dual-polarization radar multi-parameter fusion rainfall estimation system and method, and relates to the technical field of meteorological radars, and the system comprises a data collection module which is used for starting a dual-polarization radar to carry out long-term observation when an observation scene with uniformly distributed light rain weather appears, recording radar polarization parameters at different elevation angles, and carrying out the data collection module; transmitting to the deviation analysis module; the deviation analysis module is used for receiving the radar polarization parameters and calculating ZDR statistical characteristics according to the reference uniform scene; according to the invention, by constructing the antenna isolation elevation angle dependence model, dynamic reverse correction is carried out on ZDR and rhohv measurement values under different elevation angles, the influence of dynamic degradation of the antenna cross polarization isolation along with the scanning elevation angle on the measurement values is effectively eliminated, and real-time sensing and compensation of the isolation degradation along with the dynamic change of the elevation angle are realized; the radar can keep high measurement precision in the whole scanning space, and the conditions of precipitation phase state misjudgment and missing report are reduced, so that the accuracy of precipitation estimation is improved.

The invention discloses a three-dimensional radar echo reflectivity variational auto-encoder pre-training method, which belongs to the technical field of meteorological radar data analysis, and comprises the following steps: extracting advanced features from three-dimensional high-resolution radar data through a space encoder based on an attention mechanism; mapping the advanced features into determined probability distribution parameters using a probabilistic encoder; sampling according to the distribution parameters by using a re-parameterization technique to obtain continuous potential variables; mapping the potential variables back to a pixel space by using a probability decoder based on an attention mechanism to complete data reconstruction; and finally, constructing a mixed loss function consisting of a mean square error and KL divergence by utilizing a reconstruction result and a distribution parameter to train the model. According to the invention, by optimizing the network architecture and introducing the attention mechanism, high-quality reconstruction of high-resolution three-dimensional radar data is realized while extremely low video memory requirements and low parameter quantity are ensured, and the practical value of data synthesis and enhancement is remarkably improved.

The invention relates to the technical field of meteorological disaster monitoring and early warning and numerical forecasting fusion, in particular to an extreme gale weather forecasting method based on a weather forecasting large model, which comprises the following steps: acquiring a numerical weather forecasting three-dimensional physical field, a weather radar, a weather satellite and ground meteorological observation and assimilating the numerical weather forecasting three-dimensional physical field into a consensus field; time advances, sinking potential energy and cold pool diagnosis are obtained through a micro-downburst calculation program, coarse-resolution gust is formed through boundary layer similarity mapping and a hysteresis kernel, and a fine-scale base map is generated under cold pool frontal surface limitation through optimal transmission of advection constraint; the conditional diffusion probability generation model outputs pixel gust distribution and a threshold exceeding probability and extracts a wind damage polygon; radar, satellites, lightning and ground gust are fused, monotonous normalized flow calibration is used, parameters are updated through intersection-to-parallel ratio and shape-preserving coverage inspection and online amplitude limitation, and the credibility of spatial positioning, occurrence time and probability description is improved.

The invention discloses a dual-polarization one-dimensional scanning phased-array antenna and a weather radar system, the antenna comprises a radiation layer, a first feed layer, a first PCB layer, a second feed layer, a second PCB layer and a third feed layer which are stacked from top to bottom, and a horizontal polarization radiation unit and a vertical polarization radiation unit of the radiation layer are arranged in an orthogonally staggered manner; each first feeder is etched on the top surface of the first PCB layer and corresponds to a first metal groove formed in the bottom of the first feed layer and a second metal groove formed in the top of the second feed layer in position; and each second feed line etched on the top surface of the second PCB layer corresponds to the position of a third metal groove formed in the bottom of the second feed layer and the position of a fourth metal groove formed in the top of the third feed layer. According to the invention, high cross polarization isolation is realized, and the antenna has dual polarization beams with low side lobes and consistent beam width and pointing height in the whole working frequency band.

The invention discloses a meteorological radar grading identification method combined with a meteorological numerical model, and relates to the technical field of intelligent meteorological perception, and the method comprises the steps: generating a unified initial state vector in a preset calibration region through employing historical typhoon similar sample statistical information, and achieving the cold start calibration under the condition of no radar data; then distributing a radar extrapolation field within two hours and a numerical mode field weight after two hours based on a time gating function, respectively calculating an observation residual error covariance matrix and an observation error covariance matrix, and carrying out joint correction on an amplitude component and a deformation component by adopting Kalman updating; through mode CLDAS field large-scale extrapolation, spatial resolution refinement, terrain factor correction and radar extrapolation field high-precision fusion, a fusion precipitation field is obtained, a hierarchical graph is generated based on an adaptive quantile threshold value, and iterative updating is completed; according to the method, through unified initial state vector generation of historical typhoon similar sample statistics, the stability of water field initialization under a radar-free condition is realized.

The invention discloses a weather radar meteorological echo and non-meteorological echo identification system, and belongs to the technical field of meteorological radar signal processing. The method comprises the following steps: acquiring radar original data containing reflectivity factors, radial velocity and spectral width, and generating a three-dimensional feature matrix through format standardization, combined noise removal, neighborhood interpolation filling and parameter normalization; constructing a sample set through professional labeling, consistency verification and multi-dimensional data enhancement; a deep convolutional neural network containing a ResNet50 feature extraction module, a CBAM double attention module and a classification output module is constructed, and a binary cross entropy loss function, an Adam optimizer and an early stop regularization strategy are adopted for training optimization; to-be-recognized data is preprocessed and then input into the model, and a classification result and confidence are output. The method does not need manual feature design, significantly improves the recognition stability of echoes difficult to distinguish in a complex scene, adapts to different radars and regional environments, and meets the real-time processing requirements.

The invention discloses a multi-source rainfall measurement product data fusion method, system and device and a medium, and belongs to the technical field of meteorological data processing, and the method comprises the steps: obtaining meteorological satellite rainfall measurement data, weather radar rainfall measurement data and ground rain gauge rainfall measurement data, and constructing a multi-source rainfall data set; performing preprocessing through the multi-source rainfall data set to obtain satellite rainfall data with a unified coordinate system and resolution, radar rainfall data after noise reduction and rainfall gauge rainfall data after interpolation completion; and in combination with the preprocessed multi-source rainfall data, constructing a time-space consistency data set. According to the method, meteorological satellite, radar and rain gauge data are integrated, a multi-source rainfall data set is constructed, a coordinate system and resolution are unified, noise reduction interpolation ensures time-space consistency, a Kriging interpolation method is adopted to fuse data, resolution and accuracy are improved, an evaluation model is subjected to cross validation, and accurate support is provided for weather forecast and disaster prevention and reduction.

The invention discloses a weather radar metal ball calibration precision improving method, and belongs to the technical field of metal ball calibration, and the method comprises the following steps: carrying out the quantification of a background clutter error of a metal ball calibration position, and removing the background clutter error in the calibration process: constructing a metal ball stability model, and carrying out the calibration of the metal ball stability model; dynamic errors caused by swinging of the metal ball body are quantified and compensated; after the background clutter error is removed and the stability of the metal ball meets a certain condition, the deviation error of the metal ball from the beam center is quantized and compensated; and meanwhile, the cross-library phenomenon of the metal balls is identified, adjusted and compensated. According to the method, the calibration precision of the metal ball of the weather radar is improved.

The invention relates to the technical field of meteorological monitoring, and discloses a rainfall early warning method and system based on a weather radar, and the method comprises the steps: obtaining weather radar body sweeping arrival base data and rainfall station observation data, carrying out the alignment, carrying out the meshing of the radar base data, and obtaining the grid echo data; constructing a quasi-vertical profile to determine the height range and uncertainty of the melting layer, and carrying out weighted bright band correction on echoes in the melting layer to obtain corrected echo data; determining rainfall type partitions and performing inversion to obtain a radar rainfall intensity field; according to the rainfall station data and the radar rainfall intensity field, station residual data is obtained, a space deviation field is obtained, and finally a calibration rainfall intensity field is obtained; determining an accumulated precipitation field of each member according to the calibrated rainfall field; and obtaining an over-threshold probability published by early warning according to the accumulated precipitation field of each member, and outputting precipitation early warning information when a probability threshold is continuously met. According to the invention, the rainfall intensity overestimation and false alarm jitter are reduced, and the accuracy and stability of short temporary rainfall early warning are improved.

The invention relates to the technical field of microwave remote sensing, in particular to a Ku, Ka and W three-frequency-band meteorological radar multi-source joint detection system and method and a storage medium, and the system comprises an antenna feeder subsystem, a main control computer, an integrated digital processor, a Ku / Ka integrated radio frequency machine, a Ku-frequency-band power amplifier, a Ka-frequency-band power amplifier, a W-frequency-band transmitter, a W-frequency-band power amplifier, a W-frequency-band receiver and a servo mechanism. The system is designed according to a working mode and working parameters of a main control computer, realizes multiple functions such as Ku / Ka / W single-frequency-band independent working or multi-frequency-band simultaneous working and the like, and has co-polarization and cross-polarization echo signal receiving capability; by controlling the servo mechanism, two modes of fixed-point detection and conical scanning detection are realized, and detection requirements of different meteorological elements are met; the detection capabilities of three frequency bands of Ku, Ka and W are integrated in the system, the detection mode is flexibly configured according to weather conditions and detection requirements, the system has the multi-frequency-band, multi-polarization and multi-element integrated detection capability, and the detection efficiency of the meteorological radar is improved.

The invention relates to the technical field of radars, in particular to a weather radar distance resolution calibration method and system. The method comprises the following steps: collecting oversampling original baseband I / Q data of a point target in a plurality of continuous pulse periods; determining a pulse signal-to-noise ratio, and screening out effective pulses according to a threshold value; carrying out sub-sampling stage time alignment on the effective pulses based on cross-correlation analysis, and carrying out incoherent averaging on power module values of all the aligned pulses to generate an average pulse power envelope; converting the average pulse power envelope to a logarithm domain, and selecting discrete data points in a main lobe region; according to the discrete data points, a nonlinear least square method is adopted to carry out iterative fitting on the generalized Gaussian mathematical model, and a continuous analytic function describing the pulse envelope is obtained; according to the continuous analytic function, determining a pulse time width when the power is reduced to a peak value of-6dB; and determining the real distance resolution according to the pulse time width. The measurement robustness is improved, and sub-meter calibration precision is achieved.

The invention belongs to the technical field of meteorological early warning, and discloses a hail reduction forecasting and early warning method and system based on meteorological radar data in order to solve the problem that a traditional hail reduction early warning system is low in accuracy. Determining vertical accumulated liquid water between the adjacent target temperature layers, and calculating the content of supercooled water between the adjacent target temperature layers according to the vertical accumulated liquid water; then, according to the number of the aqueous product particle libraries and the content of the supercooled water, the interaction rate of the supercooled water and the aqueous product particles between the adjacent target temperature layers is determined; then, according to each interaction rate, a hail reduction index is calculated, and hail reduction early warning is carried out according to the hail reduction index; therefore, hail reduction early warning is carried out by analyzing the interaction between the supercooled water and the water particles in the hail violence process, the evolution dynamic state of the supercooled water and the water particles in the hail violence process is revealed, and on the basis, the accuracy of hail reduction early warning is improved.

The invention discloses a phased array radar adaptive cooperative scanning method for severe convective weather, and belongs to the technical field of meteorological radar detection. The method comprises a central cooperative control module, a data analysis module and a distributed phased array radar network. Basic scanning is executed through the phased array radars, and the boundary and the core of the severe convective region of interest are identified and drawn in real time through threshold segmentation and a region growing algorithm; the data analysis module extracts characteristic parameters and calculates a dynamic threat index; the central cooperative control module constructs a global state vector and generates a scanning task instruction for each radar by using a multi-agent depth deterministic strategy gradient algorithm; after scanning is executed, the central module fuses observation data to generate a high-temporal-spatial-resolution three-dimensional meteorological data body and calculates a reward value to update algorithm parameters; according to the invention, multi-radar adaptive cooperative scanning is realized, the monitoring precision and efficiency of severe convection weather are improved, and radar resource allocation is optimized.

The invention discloses a large hail identification method and system based on dual-polarization radar three-body scattering characteristics, and relates to the technical field of meteorological identification, and the method comprises the steps: obtaining dual-polarization radar observation data of a historical hail event, and extracting a characteristic threshold value of a hail region dual-polarization radar parameter; acquiring dual-polarization radar base data in real time, retrieving the dual-polarization radar base data according to the characteristic threshold value, and judging whether a hail area exists or not; if the hail region exists, TBSS dual-polarization radar feature recognition is carried out on the hail region of the dual-polarization radar base data, and whether TBSS dual-polarization radar features exist at the hail region is judged; and when TBSS dual-polarization radar characteristics exist, determining that large hail exists at the position. According to the invention, quantitative analysis is carried out on the large hail based on the observation data of the dual-polarization weather radar, and then the large hail is identified, so that the vacancy of dual-polarization radar products is solved, and a powerful means is provided for monitoring and early warning of severe convective storms.

The invention provides a meteorological radar image sequence prediction method based on self-adaptive physical equation embedding, and belongs to the technical field of meteorological prediction.The method comprises the steps that firstly, a historical meteorological radar image sequence is obtained and input into a depth sequential network model for spatio-temporal feature extraction; constructing a radar image sequence prediction framework based on an adaptive physical equation; training a constructed radar image sequence prediction framework through a composite loss function; and outputting a prediction result of the future radar image sequence. Physical equation guidance is added in the loss function, information is supplemented from the mechanism level, the problem that the information amount of a data-driven deep learning method in long sequence prediction is insufficient is solved, and the effect of a model in long sequence prediction is improved.

The invention relates to the technical field of meteorological prediction, and provides a Beidou rainfall prediction method based on a deep learning large model and a related device, and the method comprises the steps: obtaining the time-space reference data of a target region through a Beidou satellite navigation system, including the high-precision positioning data of a Beidou ground-based augmentation station and the meteorological element inversion data of a Beidou remote sensing load; collecting real-time rainfall observation data of a ground rainfall monitoring terminal, a meteorological radar and satellite remote sensing; performing space-time alignment processing on the space-time reference data and the real-time rainfall observation data, and constructing a standardized data set; and constructing a deep learning large model, training the deep learning large model by using a space-time sequence prediction task, and inputting the standardized data set to the trained deep learning large model to output a multi-scale rainfall prediction result of the target area in a future preset duration.

The invention discloses a bilinear polarization phased array weather radar calibration method and system based on a calibration platform, and relates to the technical field of radar calibration, and the method comprises the steps: obtaining a first transmitting parameter, a first receiving parameter, a second transmitting parameter and a second receiving parameter which are matched with a current mode of a target radar; and calibrating the target radar in the current mode according to the first sending parameter, the first receiving parameter, the second sending parameter and the second receiving parameter. According to the method, the radio frequency signal matched with the mode of the phased array radar is acquired, various calibration parameters of the phased array radar in different working modes are acquired, and the method is suitable for calibration of the phased array radar in all complex modes; and the phased array radar is calibrated through the first sending parameter, the first receiving parameter, the second sending parameter and the second receiving parameter, so that accurate separation and traceability of errors of a plurality of links in the radar system are realized, and the detection precision of radar related parameters is effectively improved.

By changing the operating modes of weather observation sensors, including weather radar, according to the weather phenomenon being predicted, weather phenomena can be predicted with higher accuracy than before. [Solution] The weather forecasting device 3 is a variable-mode weather observation sensor that observes precipitation data and wind direction and wind speed data in an observation range, which is a spatial range to be observed. It includes a weather forecasting unit 5 that receives weather observation data, including precipitation data and wind direction and wind speed data, observed by a weather observation sensor including a weather radar 1 that is positioned so that the observation range encompasses a predetermined weather forecast area, and predicts the weather in the weather forecast area for a predetermined future forecast time range and outputs weather forecast data including precipitation; a sensor data storage unit 4 that stores the installation position, observation range and observation accuracy of each weather observation sensor; and a sensor control unit 6 that receives weather forecast data and controls the weather observation sensor, including changing the operating mode of the variable-mode weather observation sensor.

The invention relates to the technical field of weather radar monitoring and early warning, and particularly provides a weather radar tornado identification false alarm suppression method, which comprises the steps of acquiring an event to be predicted; substituting the event to be predicted into a pre-constructed tornado prediction model to obtain a tornado prediction result; and selectively executing tornado early warning based on the tornado prediction result. Wherein the step of constructing the tornado prediction model comprises the steps of obtaining a plurality of historical tornado events, and preprocessing the plurality of historical tornado events to obtain a historical tornado event data set; determining and optimizing an influence radius based on the historical tornado event data set to obtain an optimized influence radius; and constructing a tornado prediction model based on the historical tornado event data set and the optimized influence radius. According to the method, secondary judgment on the weather radar tornado recognition result is achieved, the situation of tornado false alarm is relieved, the accuracy of tornado early warning is improved, the trust degree of the public to tornado early warning is improved, and waste of emergency resources is avoided.

The invention provides a two-dimensional servo turntable for a microminiature weather radar. The two-dimensional servo turntable comprises an azimuth box body, an azimuth transmission system, a pitching mounting frame, a pitching driving assembly, a pitching supporting frame, a direct-current power supply, a control box, an antenna support and an antenna body, the pitching mounting frame is mounted to the azimuth box body through the azimuth transmission system, the pitching mounting frame can rotate relative to the azimuth box body under the driving of the azimuth transmission system, and the pitching supporting frame is mounted to the pitching mounting frame through the pitching driving assembly, so that the pitching supporting frame rotates relative to the pitching mounting frame; the DC power supply and the control box are installed on the pitching support frame, and the antenna body is installed on the top of the pitching support frame through the antenna support. According to the photoelectric slip ring, the number of cables needing to penetrate through the photoelectric slip ring is reduced, the problem of cable winding caused by azimuth rotation is fundamentally avoided, the structure is simplified, and the long-term operation reliability and maintenance convenience of equipment are remarkably improved.