49 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 relates to the technical field of atmospheric remote sensing, in particular to a satellite-borne laser radar-based multi-feature attention cloud top height estimation algorithm, which comprises the following steps of: full-waveform laser radar data acquisition and preprocessing: acquiring an original echo signal through a satellite-borne full-waveform laser radar system; performing time domain alignment, noise suppression and saturated signal elimination on the signals; a median filtering and wavelet threshold denoising algorithm is combined to eliminate high-frequency noise, and background radiation interference is eliminated through dynamic baseline correction; according to the method, a multi-stage noise reduction strategy combining sliding window median filtering and self-adaptive wavelet denoising is introduced in the waveform feature extraction stage, pulse interference and signal oscillation are effectively restrained, meanwhile, through a terrain matching error compensation mechanism, the detection accuracy is improved, and the detection accuracy is improved. The physical consistency of the elevation residual error under the real terrain background is enhanced, and the authenticity and the checkability of the point elevation return are improved.

The invention discloses a microwave laser composite radar cloud identification method and a computer program product, and the method comprises the steps: removing ground clutters based on transfer learning and an R-FCN network, and eliminating the noise caused by the ground clutters of a microwave radar; a deep learning model is constructed by using transfer learning and R-FCN algorithms, microwave radar and laser radar echo images are input, an attention mechanism is optimized and used in the model to realize adaptive feature extraction, a multi-scale feature pyramid is introduced on the basis of an RPN network, and the recognition and detection capability of cloud regions with different scales is enhanced. Single-result joint inversion of microwave radar and laser radar cloud identification is realized by utilizing cloud boundary self-inspection, cloud feature marking, initial matching and joint inversion of cloud boundaries, and a composite identification cloud area is obtained. The method has a cloud identification function under complex atmospheric conditions such as multilayer clouds and equipment noise interference, is accurate in inversion result, is not sensitive to ground clutter and daytime background noise, and fully integrates the identification advantage of a laser radar on a thin-roll cloud area and a cloud bottom and the cloud top identification advantage of a microwave radar.

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 discloses a cloud top height quality inspection method and device, computing equipment and a storage medium, and belongs to the technical field of atmospheric detection and remote sensing, and the method comprises the steps: taking laser radar cloud profile data as a detection truth value, taking imaging type radiometer cloud product data as to-be-inspected data, and carrying out the quality inspection of the to-be-inspected data on the basis of the time-space matching of the two types of data; the method comprises the following steps: screening out unqualified data through a space-time uniformity test, and carrying out layer-by-layer and step-by-step analysis on qualified matching data to count a total sample size, correct detection, missing detection, false detection, a correct screening sample size and a proportion, a correct identification sample size of each cloud type including cirrus cloud and lower cloud, an error identification sample size and a corresponding cloud top height difference; therefore, complete and accurate cloud top height quality detection is realized.

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 present application provides an iterative inversion method and system for atmospheric stratification based on the Fernald method, which involves lidar technology. The method includes preprocessing the original echo signal and identifying cloud position information based on the slope method; initializing the aerosol extinction backscattering ratio of the cloud layer, iteratively adjusting the aerosol extinction backscattering ratio of the cloud layer based on the aerosol backscattering coefficient of the cloud base area until the set conditions are met, and recording the final aerosol extinction backscattering ratio of the cloud layer; inverting the aerosol backscattering coefficient sub-profiles of the area below the cloud base and above the cloud top based on the initialized backscattering ratio, inverting the cloud layer aerosol backscattering coefficient sub-profile based on the cloud backscattering ratio, and calculating the vertical profile of the aerosol extinction coefficient at the full height. By dynamically optimizing the aerosol extinction backscattering ratio in the cloud area, negative values ​​and over-suppression at the cloud base are eliminated, which helps to improve the inversion accuracy of the vertical profile of the aerosol extinction coefficient under complex cloud conditions.

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.

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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 invention relates to a cloud height identification method based on fusion of a cloud radar and a laser wind finding radar, and belongs to the field of radio, and the identification method comprises the steps: S1, obtaining the cloud bottom height and the cloud top height through the inversion of a reflectivity product of a cloud radar top-to-top mode; s2, performing inversion by using a signal-to-noise ratio product in a laser wind finding radar DBS mode to obtain a cloud bottom height and a cloud top height; and S3, performing time matching on output products of the cloud radar and the laser wind finding radar, and performing product fusion on results obtained by the cloud radar and the laser wind finding radar to obtain a final cloud height identification result. According to the invention, the characteristics of the two devices are complementary, the laser wind-finding radar can make up for the detection defect of the millimeter wave radar on the thin cloud and distinguish the cloud layer from the aerosol layer, the cloud radar can make up for the defect that the laser wind-finding radar cannot penetrate through the deep cloud layer, and the cloud height global precise detection is realized.

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 invention belongs to the field of image processing, and discloses a fast and efficient mesoscale convection system identification method and system for medium and low latitude areas, and the method comprises the steps: obtaining cloud top brightness temperature data; constructing a mesoscale convection system identification data set according to the cloud top brightness temperature data; constructing a mesoscale convection system identification model; training the model by using the constructed training set; and after a specified number of training rounds is reached, testing the model by using the test set to obtain a final mesoscale convection system identification result. According to the method, a fragment learning strategy is designed, so that the model can simultaneously learn the characteristics of the mesoscale convection system in different regions; a semi-residual multi-scale feature extractor is constructed to prevent the problem of small-scale feature loss in the feature extraction process; a semantic consistency regularization is designed to restrain feature asymmetry in redundant feature extraction and feature recovery processes. According to the method, the problem that the recognition speed is low when a traditional threshold value method is used in medium and low latitude areas is solved.

The present invention discloses a method and device for rainfall inversion assisted by GNSS PWV in meteorological satellite remote sensing. The method includes: obtaining PWV data, cloud top brightness temperature data, and brightness temperature difference data during the rainfall period; inputting the PWV data, cloud top brightness temperature data, and brightness temperature difference data during the rainfall period into a trained multi-layer neural network model to output the rainfall amount for the corresponding period. The present invention combines the atmospheric water vapor content information (PWV data) obtained by real-time calculation of GNSS observation data, supplements the deficiency of satellite remote sensing in cloud base water vapor observation, fuses GNSS observation data with meteorological satellite data, and improves the accuracy of precipitation inversion. At the same time, a trained multi-layer neural network model is introduced to automatically extract features from complex data, realizing high-time and high-precision rainfall inversion.

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 present invention discloses a sea fog detection method and system, which relates to the field of sea fog detection. By means of various cloud and fog simulation models, the reflectance and brightness temperature of each spectral channel included therein are obtained under various cloud and fog simulation environments, so as to design criterion conditions corresponding to each set cloud and fog type. After the cloud top phase state corresponding to the target ocean area is discriminated, according to the reflectance of the first channel, the second channel and the third channel in the primary data, the set cloud and fog type corresponding to the target ocean area is obtained by using the single-layer water cloud criterion condition, the single-layer sea fog criterion condition, the single-layer ice cloud criterion condition, the upper-layer ice cloud and lower-layer water cloud criterion condition and the upper-layer ice cloud and lower-layer sea fog criterion condition. It breaks through the limitation that it is impossible to effectively detect low-level sea fog or water cloud when blocked by ice clouds, and realizes the detection of low-level water cloud and sea fog under ice clouds.

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.