31 results about "Cloud top" patented technology

The invention relates to the technical field of weather forecast, discloses a method for classifying severe convection weather forecast, and aims to solve the problem that complexity of severe convection weather requires multi-dimensional data support, so that a three-dimensional data acquisition network covering'ground-air-sky 'needs to be constructed. Ground observation data need to include minute-level rainfall, hourly air temperature and humidity (emphatically paying attention to humidity difference between 850hPa and 500hPa and reflecting unstable stratification) and 10-minute average wind speed of a meteorological station, and high-altitude detection data need to extract temperature vertical profiles (calculating convective condensation height LCL) and wind speed vertical shear (shear values of 0-3km and 0-6km) at 08 o'clock and 20 o'clock every day. According to the method for classifying severe convection weather forecast, new signals (such as sudden cloud top brightness temperature drop) observed in real time are rapidly absorbed, and meanwhile, the method is adaptive to severe convection characteristic differences of different areas (such as mountainous areas and plains) and different seasons, so that the forecast precision is improved, and the requirements of refined disaster prevention for high-accuracy and high-timeliness forecast are met.

The invention belongs to the technical field of sea fog monitoring, and particularly relates to a sea fog monitoring method and system based on multi-source satellite remote sensing data, and the method comprises the steps: collecting satellite radiation and microwave observation data, discriminating the cloud top phase state in combination with infrared ground microwave channel response differences, dividing clear sky and low cloud shielding scenes, and outputting identification data. And sea fog is detected in combination with the single-time infrared brightness temperature difference and the empty pixel parameters, optical parameters are inverted, and a sub-scene sea fog physical parameter set is formed. Carrying out normalized coupling reconstruction on the parameter set, constructing a sea fog exclusive feature space, separating a fog region from a non-fog region through phase state constraint to obtain an initial judgment feature field, correcting the edge of the fog region through neighborhood correlation and brightness temperature gradient constraint, strengthening spatial continuity to generate a spatial distribution field, locking a credible region, and outputting a final sea fog detection result. According to the invention, through layer-by-layer progressive design of scene division, feature optimization and multi-source verification, the precision and integrity of sea fog identification are significantly improved.

The invention relates to the technical field of numerical weather forecast, in particular to a meteorological large model prediction method based on a data correction model, which comprises the following steps of: firstly acquiring multi-source atmospheric observation, screening observation by topology-optimal transmission quantum annealing, and constructing a weighted error covariance; applying mass, energy and earth rotation gradient, and generating a conservation assimilation field through diffusion implicit sampling; calculating a mutual information mask and coupling a cloud top optical flow fine tuning phase; cloud motion consistent field pulse codes are sent to the symplectic decomposition pulse neural network for neural form hardware reasoning, a pulse threshold is adjusted in a closed loop to control energy drift, and an uncertainty field is output through parallel disturbance reasoning. The method has the advantages of high resolution, low power consumption and probability prediction capability, and the extreme weather path and intensity prediction precision is obviously improved.

The invention discloses a pixel response inconsistency correction method and system. The method relates to the technical field of pixel response correction and comprises the following steps of pixel dynamic offset calculation, flat field coefficient acquisition, pixel-level comprehensive correction and dead pixel self-repairing secondary optimization. The method comprises the following steps: acquiring a dark frame image and real-time temperature data, and calculating a dynamic offset of a pixel by means of a temperature-time double-domain prediction model; a flat field coefficient is obtained by fusing a basic value calibrated by a laboratory miniature integrating sphere in a full spectrum band and an updated value obtained by in-orbit observation of a deep space and a cloud top and processing of dead pixel detection and the like; then combining a GPS, an attitude angle and a real-time temperature, and using an offset and a flat-field coefficient to carry out pixel-level correction; and finally, a gain compensation matrix is output by means of a deep reinforcement learning model containing a convolutional neural network and a long-short-term memory network, the image is secondarily optimized through a dead pixel self-repairing module, the pixel response inconsistency correction precision is improved, and the problem that in the prior art, the pixel response inconsistency correction error is large is solved.

The application belongs to the technical field of sea fog monitoring, and particularly relates to a sea fog monitoring method and system based on multi-source satellite remote sensing data, which comprises the following steps: collecting satellite radiation and microwave observation data, combining the response difference of infrared and microwave channels to distinguish the cloud top phase state, dividing clear sky and low cloud blocking scenes, and outputting identification data; combining single-time infrared brightness temperature difference and air pixel parameters to detect sea fog and retrieve optical parameters, forming a scene-specific sea fog physical parameter set; normalizing and coupling the parameter set for reconstruction, constructing a sea fog-specific feature space, separating the fog area and the non-fog area through phase state constraint to obtain an initial judgment feature field, correcting the fog area edge through neighborhood correlation and brightness temperature gradient constraint, strengthening the spatial continuity to generate a spatial distribution field, and locking a reliable area to output the final sea fog detection result. Through the progressive design of scene division, feature optimization and multi-source verification, the accuracy and integrity of sea fog identification are significantly improved.

The invention discloses a convection inception identification method and device based on an FY-4B stationary satellite. The method comprises the following steps: performing preliminary screening on convective cloud clusters according to FY-4B stationary satellite multi-channel brightness temperatures and channel difference values of two time points at an interval of preset time to obtain candidate cloud cluster sets of the two time points; calculating the maximum brightness temperature space gradient, the brightness temperature standard deviation and the maximum cloud top height of each candidate cloud cluster in the candidate cloud cluster set at the two time points by taking the brightness temperature of the channel of 10.8 microns in the multi-channel brightness temperature as the cloud top brightness temperature; selecting a final cloud cluster set of the two time points from the candidate cloud cluster set according to the maximum brightness temperature space gradient, the brightness temperature standard deviation and the maximum cloud top height of the two time points and a preset second criterion; and matching the final cloud cluster sets at the two time points to identify convection inception.

This invention relates to a cloud height identification method integrating cloud radar and laser wind radar, belonging to the field of radio. The identification method includes: S1, obtaining the cloud base height and cloud top height by inverting the reflectivity product from the cloud radar in top-view mode; S2, obtaining the cloud base height and cloud top height by inverting the signal-to-noise ratio product from the laser wind radar in DBS mode; S3, performing time matching on the output products of the cloud radar and the laser wind radar, and then fusing the results obtained from the cloud radar and the laser wind radar to obtain the final cloud height identification result. This invention complements the characteristics of the two devices. The laser wind radar can compensate for the detection limitations of millimeter-wave radar in thin clouds and distinguish between cloud layers and aerosol layers, while the cloud radar can supplement the inability of laser wind radar to penetrate thick cloud layers, achieving accurate detection of cloud height across the entire cloud area.

The invention discloses an overshoot cloud top identification and area calculation method and device based on geostationary satellite data. The method comprises the following steps: acquiring geostationary satellite cloud radiation brightness temperature data and troposphere top temperature data of a target area in a required time period; extracting cloud radiation brightness temperature of water vapor, ozone and infrared channels; after space-time matching, a multi-channel threshold discrimination method is used to identify potential overshoot cloud top pixels; based on the infrared brightness temperature data, utilizing an isotherm analysis method to identify a potential overshoot cloud top area and an anvil cloud area corresponding to each potential overshoot cloud top pixel; identifying an overshoot cloud top region and calculating the area of the overshoot cloud top region by comparing the average brightness temperature difference between the potential overshoot cloud top pixel and the anvil cloud region; the device comprises a data receiving and caching module, a data processing unit and a result output unit, and all the steps in the method are achieved. According to the method, satellite multi-channel brightness temperature data and troposphere top temperature data are combined, interference of cirrus cloud pixels is effectively eliminated, the overshoot cloud top is accurately recognized, and overshoot cloud top area calculation is achieved.

The cloud recognition method and computer program product of the microwave laser composite radar are based on transfer learning and R-FCN network, remove the ground clutter, and remove the noise caused by the ground clutter of the microwave radar; a deep learning model is constructed by using transfer learning and R-FCN algorithm, microwave radar and laser radar echo images are input, the attention mechanism is used in the model to realize adaptive feature extraction, a multi-scale feature pyramid is introduced on the basis of the RPN network, and the recognition and detection capability for different scale cloud areas is enhanced. The single result of the microwave radar and the laser radar cloud recognition is realized by using cloud boundary self-checking, cloud feature marking, initial matching and joint inversion of the cloud boundary, and the composite recognition cloud area is obtained. The cloud recognition function is possessed under complex atmospheric conditions such as multi-layer clouds and under the interference of equipment noise, the inversion result is accurate, the ground clutter and the daytime background noise are not sensitive, and the recognition advantages of the laser radar for the thin roll cloud area and the cloud bottom and the recognition advantage of the microwave radar for the cloud top are fully integrated.

The application relates to a cloud top height inspection method and system based on strict cloud phase state matching, and relates to the technical field of atmospheric detection and remote sensing. The method comprises the following steps: acquiring an inspection source and a detected source to be matched in time and space, and performing time and space matching; reading time and space data of the inspection source and the detected source, searching for cloud phase state consistent pixels in the inspection source and the detected source, and calculating distances between the pixels by using the time and space data; calculating a time difference between the inspection source and the detected source by using the time and space data, selecting the cloud phase state consistent pixels within a preset time difference range and a preset distance range as matching data set output; and using the matching data set to statistically process cloud top height products of the same phase state, and generating statistical indexes of cloud top height inspection. Compared with general quality inspection, the strict cloud phase state improves the quality inspection precision, reduces the matching time consumption, and can evaluate the precision of the cloud top height for different cloud phase states.

A system onboard an aircraft is configured to determine when an aircraft enters or detects a cloud top or cloud base, and record a corresponding altitude. The correlated altitude, location, and time are shared to establish a set of data points corresponding to cloud ceilings and tops. Onboard cameras may be used to identify when the aircraft enters and leaves a cloud. The system correlates the recorded data points to satellite data such as satellite-based cloud images and / or clout top elevations based on location. The correlated images and data points may establish a more complete map of clouds in a wide area. Data points from multiple aircraft may be correlated and combined over time. Data points from multiple aircraft may be shared between aircraft directly or via a network including one or more ground stations.

The present disclosure relates to a freezing rain prediction training dataset construction method, device, equipment and medium. The freezing rain prediction training dataset construction method comprises: performing resolution alignment and data standardization processing on a multi-source fusion data body constructed based on satellite data and reanalysis data; determining freezing rain events in the processed multi-source fusion data body as positive sample data, and extracting non-freezing rain events based on a preset proportion to determine negative sample data, to construct a first training data sample set; incorporating a cloud top phase indicator factor calculated based on satellite data and an inversion layer intensity calculated based on reanalysis data into the first training data sample set in the form of new data channels to obtain a second training data sample set, and adding label information to the second training data sample set to obtain a freezing rain prediction training data sample set. Thus, the problem of imbalance between positive and negative sample categories is solved, and the accuracy of the freezing rain prediction result is improved.

The application discloses a volcanic ash cloud multi-task monitoring method based on a Transformer and field adaptation, and relates to the technical field of volcanic ash cloud detection and parameter inversion. The application adopts an uncertain-based weighting mode combined with a multi-task loss function, and uses an Adam optimizer to minimize the loss function, so that the model can better capture the relationship between bands from a large amount of simulation data, and is transferred to a real scene, thereby realizing the simultaneous and accurate prediction of information such as the position of a volcanic ash cloud, the cloud top height of the volcanic ash cloud and the volcanic ash concentration.

The invention belongs to the technical field of meteorological satellite data assimilation, and discloses a brightness temperature and cloud top height dependent adaptive localization all-air satellite data assimilation method, system, equipment and medium, so as to solve the problems of consistency deviation and poor adaptability of fixed scale localization. The method comprises the following steps: calculating set correlation between satellite observation data and mode state variables; performing packet filtering processing on the set correlation to obtain a global packet filtering function value as a horizontal localization scale; the radiation brightness temperature and the cloud top height of the mode background field are used as double adaptive factors; based on the double adaptive factors, a gradient descent method is adopted to jointly determine an optimal vertical position and an optimal vertical localization scale; and assimilating all-sky satellite data by using the optimal vertical position, the optimal vertical localization scale and the optimal horizontal localization scale.

The invention discloses a cloud recognition method based on multiple observation devices, which is characterized in that a weather phenomenon video intelligent observation instrument, an aerosol laser radar, a millimeter wave cloud radar and other observation devices are used for comprehensive judgment to finally obtain a recognition result, and comprises the following steps: acquiring a sky image from the weather phenomenon video intelligent observation instrument, and judging whether cloud exists or not; the cloud base height measured by the laser radar is combined to judge whether the cloud is high cloud or low cloud; non-cloud echoes and noise echoes are filtered through a quality control algorithm, and a reliable cloud radar product is obtained; the boundary of the cloud is identified through the cloud radar product, so that the cloud information such as the cloud bottom height, the cloud top height, the cloud layer thickness and the cloud layer number is obtained. The method is small in calculation amount, high in recognition rate and wide in application range, and overcomes the defects that in the prior art, manual work is relied on, and the processing speed is low.

The application discloses a precipitation forecast correction method, and relates to the technical field of weather forecast, which comprises the following steps: acquiring cloud top brightness temperature data and cloud cluster information data in a previous adjacent time period of a target area to be forecast; dividing the cloud cluster into multiple observation areas; statistically counting temperature type information and quantity in each observation area based on the cloud top brightness temperature data; respectively calculating the proportion of the low-temperature type quantity in the observation area; calculating the proportion of the heavy precipitation area in the cloud cluster; judging whether the cloud cluster area is increased; judging the change trend of the observation area with the low-temperature type proportion exceeding a threshold value; and correcting the precipitation grade forecast at the current time. In addition, the application also discloses a precipitation forecast correction device, which comprises a collection module, an image processing module, a calculation module, a comparison module and a correction module. The precipitation forecast correction method and device adopt satellite infrared cloud images as a data source, are less affected by other limiting conditions, have little interference on the final result, can be corrected, and can obtain a precipitation forecast with high accuracy.

The present application belongs to the technical field of satellite cloud wind retrieval, and specifically relates to a double-layer cloud wind retrieval algorithm based on double-layer cloud top height information. The present application utilizes the double-layer cloud (upper ice cloud and lower water cloud) cloud top height information retrieved from the thermal infrared multi-channel observation of Japanese HImawari-8 satellite, and according to the pixel offset of the double-layer cloud at continuous time, the optical flow field at the corresponding time is estimated by using the dense optical flow method, and then the cloud wind vector is converted. The present application firstly obtains the double-layer cloud wind information, and the cloud wind retrieval test for the 7th tropical storm "Mulan" in 2022 shows that the spiral center and wind field difference of the hollow structure typhoon at different height layers can be accurately retrieved, which has important significance for the typhoon path and intensity prediction and the assimilation application of cloud wind data in numerical prediction.

The invention provides a cloud top rapid cooling rate-based convection inception detection method, which comprises the following steps of: preliminarily filtering gray images to be detected at adjacent moments by using data of an IR1 channel, an IR2 channel and a VIS channel to obtain preliminarily filtered images; filtering the preliminarily filtered image by using cloud pixel mean filtering, and subtracting the filtered image at the current moment from the filtered image at the previous moment to obtain a cloud top rapid cooling rate image; designing a CTC filter and testing the cloud top rapid cooling rate image, if an area in the cloud top rapid cooling rate image passes the test, determining that the area is a convection primary area, otherwise, determining that the area is not the convection primary area; and performing extended maximum transformation on the convection primary region to obtain a convection core region of the convection primary region. The method is high in detection speed and high in accuracy, and can meet the real-time requirement of the meteorological field; compared with other methods, the CI detected by the method is most accurate and complete, and the false alarm rate is low.

The invention relates to the technical field of spaceflight meteorology, and particularly discloses a FY-4 cloud wind guide height redesignation and inspection method fusing cloud top air pressure, which comprises the following steps: S1, acquiring cloud top air pressure data and cloud wind guide data, and matching the cloud top air pressure data and the cloud wind guide data according to time difference and spatial distance to obtain a matching result; obtaining a plurality of cloud top air pressure data in the tracking frame corresponding to the cloud air guide; s2, retrieving the cloud top air pressure data in the tracking frame to obtain a target window with the strongest cloud top air pressure data consistency in the tracking frame; s3, averaging the cloud top air pressure data of the target window at the first time and the cloud top air pressure data of the target window at the second time; s4, determining a new cloud air guide height according to a difference value between the cloud top air pressure data average value at the first time and the cloud top air pressure data average value at the second time; and S5, carrying out effect inspection on the new cloud wind guide height. The invention provides a new thought for the multi-source data fusion method in the cloud wind deflection deviation correction process.

The invention relates to the technical field of data processing, in particular to a typhoon intensity change prediction method and system introducing daily change fluctuation characteristics, and the method comprises the following steps: collecting infrared cloud top brightness temperature data, and recognizing a daily change fluctuation event based on the infrared cloud top brightness temperature data to obtain the daily change fluctuation characteristics; meteorological analysis data are acquired, and prediction factors reflecting typhoon multi-scale physical characteristics are calculated in combination with the meteorological analysis data and the infrared cloud top brightness temperature data; integrating the predictive factors into a time sequence according to the daily variation fluctuation characteristics; and constructing a multi-task learning model, and inputting the time sequence into the multi-task learning model to obtain a typhoon intensity change prediction result. According to the method, the daily variation fluctuation characteristics are introduced as physical constraints, a multi-task learning model is utilized, typhoon enhancement rate prediction and rapid enhancement probability prediction are carried out at the same time, collaborative optimization is carried out, and the prediction accuracy and reliability of tropical cyclone rapid enhancement and other violent intensity changes are improved.

The invention discloses a satellite-borne lightning data correction method and a related device, and relates to the technical field of improving satellite-borne lightning positioning precision, and the method comprises the steps: obtaining to-be-corrected satellite-borne lightning original data; on the basis of a cloud top height parallax correction model, cloud top height parallax correction is carried out on the satellite-borne lightning original data, and corrected satellite-borne lightning data are obtained; based on a thermal deformation correction model, performing thermal deformation systematic deviation correction on the corrected satellite-borne lightning data, and outputting a correction result of the satellite-borne lightning data; according to the method, the cloud top height parallax and thermal deformation correction models are fused, and further optimization of satellite-borne satellite lightning data can be realized.

1. The name of the design product: kettle base (cloud top intelligent kettle). 2. The use of the design product: for kettle base. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

1. Name of the product in this design: Trash Can (Cloud Top Round). 2. Purpose of this design: For use in collecting garbage. 3. The key design features of this product are its overall shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The present application relates to the technical field of data processing, and particularly relates to a typhoon intensity change prediction method and system introducing diurnal fluctuation characteristics, the method comprising the following steps: collecting infrared cloud top brightness temperature data, identifying diurnal fluctuation events based on the infrared cloud top brightness temperature data to obtain diurnal fluctuation characteristics; obtaining meteorological analysis data, combining the meteorological analysis data and the infrared cloud top brightness temperature data to calculate a prediction factor reflecting the multi-scale physical characteristics of a typhoon; integrating the prediction factor into a time series according to the diurnal fluctuation characteristics; constructing a multi-task learning model, and inputting the time series into the multi-task learning model to obtain a typhoon intensity change prediction result. The present application introduces diurnal fluctuation characteristics as a physical constraint, utilizes a multi-task learning model, simultaneously performs typhoon intensification rate prediction and rapid intensification probability prediction and cooperatively optimizes, and improves the prediction accuracy and reliability of tropical cyclone rapid intensification and other severe intensity changes.

The invention provides a cloud identification method based on a zero crossing point and a threshold value method, and relates to the technical field of cloud identification, and the method comprises the steps: obtaining and preprocessing a laser radar signal, and solving a zero crossing point of the preprocessed laser radar signal; based on the preprocessed laser radar signal, the zero crossing point position is identified and recorded as a suspicious cloud bottom or a suspicious cloud top or a suspicious cloud peak according to the slope of a sampling point adjacent to the zero crossing point; from a set cloud finding starting point, when the suspicious cloud bottom and the suspicious cloud peak meet a set first condition, identifying the suspicious cloud bottom as the cloud bottom, and identifying the corresponding suspicious cloud peak as the cloud peak corresponding to the cloud bottom; when the suspicious cloud top position meets a set second condition, identifying the suspicious cloud top position as a cloud top; and repeating the process of identifying the cloud bottom, the cloud peak and the cloud bottom until all cloud layers are found.

The present application belongs to the technical field of flood prevention early warning monitoring, and relates to a water conservancy hub flood prevention early warning monitoring method based on radar and satellite data. The present application identifies cloud cluster active areas by fusing satellite cloud top brightness temperature and radar echo data, generates multi-level initial early warning signals according to the change trend of the overlapping area, dynamically adjusts the collection frequency according to the early warning level, calculates the rain intensity cumulative amount and water level rising gradient, deduces the dam front water level prediction sequence and extracts the peak value, generates the updated early warning signal in combination with the predicted peak water level, the current water level and the flood control water level relationship, sets the level comparison to adjust the collection frequency, and takes the updated signal as the next round of input to realize closed-loop iteration. The present application solves the problems that the traditional method cannot lock the high-risk area in advance, the monitoring frequency is fixed, the future water level and the early warning level cannot be predicted, and the early warning level frequently switches, and realizes the proactive early warning before the flood, the dynamic optimization of monitoring resources, the advanced water level prediction and the stable update of early warning.

This invention discloses a dynamic identification method for the rain enhancement potential of stratified mixed clouds in complex terrain areas, relating to the field of electronic digital data processing technology. The method includes: determining whether high-rain enhancement rate cumulus cloud cells possess macroscopic rain enhancement conditions based on satellite data and preset cloud top brightness temperature and cloud top height thresholds, obtaining a judgment result; classifying the rain enhancement potential of stratified mixed cloud systems in the target area into levels based on microscopic parameter characteristics and the judgment result; calculating the comprehensive membership score of four radar parameters in the range database at each rain enhancement potential level based on the microscopic parameter characteristics and the level classification result, using a composite Gaussian membership function; selecting the level with the highest comprehensive membership score as the rain enhancement potential level of the range database; if multiple rain enhancement potential levels have the same comprehensive membership score, selecting the higher level as the rain enhancement potential level of the range database. This invention improves data reliability, making the judgment of rain enhancement potential more systematic and scientific.

This invention discloses a method and system for correcting pixel response inconsistencies. The method relates to the field of pixel response correction technology and includes the following steps: calculation of dynamic pixel offset, acquisition of flat field coefficients, pixel-level comprehensive correction, and secondary optimization for bad pixel self-repair. This invention calculates the dynamic pixel offset using a temperature-time dual-domain prediction model by acquiring dark frame images and real-time temperature data; then, it integrates the baseline values ​​calibrated across the entire spectrum by a laboratory micro-integrating sphere, and updated values ​​from in-orbit observations of deep space and cloud tops, processed by bad pixel detection, to obtain the flat field coefficients; next, it combines GPS, attitude angles, and real-time temperature to perform pixel-level correction using the offset and flat field coefficients; finally, it relies on a deep reinforcement learning model containing convolutional neural networks and long short-term memory networks to output a gain compensation matrix, and uses a bad pixel self-repair module to perform secondary optimization of the image, improving the accuracy of pixel response inconsistency correction and solving the problem of large errors in pixel response inconsistency correction in existing technologies.

The invention relates to the technical field of regional convection identification, and discloses an automatic identification method, device and product of an isolated deep convection trigger event, which eliminates the interference of large-range cloud systems such as frontal surface and typhoon on isolated deep convection identification by identifying the deep convection trigger day of a target research region. Furthermore, in combination with visible light reflectivity data, infrared brightness temperature data and a cloud top cooling rate dynamic threshold value, isolated deep convective clouds which vertically develop vigorously can be accurately screened out, and the identification precision of the isolated deep convective clouds is improved. Furthermore, a time overlapping method is utilized to perform continuous moment tracking identification on the plurality of first isolated deep convection clouds, cloud body continuation conditions can be eliminated, isolated deep convection triggering events can be accurately locked, the defect that a traditional method cannot obtain refined triggering positions and time is made up, and then regional convection forecasting can be improved.

The application discloses a kind of aircraft icing cloud macro-microstructure monitoring methods based on GPM satellite observation, wherein method is by processing the dual-frequency precipitation radar data of GPM satellite, the macro-microstructure characteristics of aircraft icing cloud system are comprehensively analyzed.Use the unique observation ability of GPM satellite to penetrate cloud, obtain the vertical distribution information of cloud phase, based on this, the vertical gradient change of microphysical parameter in cloud can be accurately captured, the fine monitoring of macro-microstructure of aircraft icing cloud system and particle spectrum characteristics in cloud is realized.The application breaks through the limitation that traditional satellite observation can only monitor cloud top characteristics, provides scientific basis for aircraft icing early warning and prevention by comprehensively monitoring the structure in icing cloud, and can significantly improve the aviation flight safety guarantee capability.